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Jung YJ, Sun SH, Almasi A, Yunzab M, Meffin H, Ibbotson MR. Characterization of extracellular spike waveforms recorded in wallaby primary visual cortex. Front Neurosci 2023; 17:1244952. [PMID: 37746137 PMCID: PMC10517629 DOI: 10.3389/fnins.2023.1244952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023] Open
Abstract
Extracellular recordings were made from 642 units in the primary visual cortex (V1) of a highly visual marsupial, the Tammar wallaby. The receptive field (RF) characteristics of the cells were objectively estimated using the non-linear input model (NIM), and these were correlated with spike shapes. We found that wallaby cortical units had 68% regular spiking (RS), 12% fast spiking (FS), 4% triphasic spiking (TS), 5% compound spiking (CS) and 11% positive spiking (PS). RS waveforms are most often associated with recordings from pyramidal or spiny stellate cell bodies, suggesting that recordings from these cell types dominate in the wallaby cortex. In wallaby, 70-80% of FS and RS cells had orientation selective RFs and had evenly distributed linear and nonlinear RFs. We found that 47% of wallaby PS units were non-orientation selective and they were dominated by linear RFs. Previous studies suggest that the PS units represent recordings from the axon terminals of non-orientation selective cells originating in the lateral geniculate nucleus (LGN). If this is also true in wallaby, as strongly suggested by their low response latencies and bursty spiking properties, the results suggest that significantly more neurons in wallaby LGN are already orientation selective. In wallaby, less than 10% of recorded spikes had triphasic (TS) or sluggish compound spiking (CS) waveforms. These units had a mixture of orientation selective and non-oriented properties, and their cellular origins remain difficult to classify.
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Affiliation(s)
- Young Jun Jung
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Shi H. Sun
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
| | - Ali Almasi
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
| | - Molis Yunzab
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
| | - Hamish Meffin
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Michael R. Ibbotson
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, VIC, Australia
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Spencer M, Kameneva T, Grayden DB, Burkitt AN, Meffin H. Quantifying visual acuity for pre-clinical testing of visual prostheses. J Neural Eng 2023; 20. [PMID: 36270430 DOI: 10.1088/1741-2552/ac9c95] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 10/21/2022] [Indexed: 01/31/2023]
Abstract
Objective.Visual prostheses currently restore only limited vision. More research and pre-clinical work are required to improve the devices and stimulation strategies that are used to induce neural activity that results in visual perception. Evaluation of candidate strategies and devices requires an objective way to convert measured and modelled patterns of neural activity into a quantitative measure of visual acuity.Approach.This study presents an approach that compares evoked patterns of neural activation with target and reference patterns. A d-prime measure of discriminability determines whether the evoked neural activation pattern is sufficient to discriminate between the target and reference patterns and thus provides a quantified level of visual perception in the clinical Snellen and MAR scales. The magnitude of the resulting value was demonstrated using scaled standardized 'C' and 'E' optotypes.Main results.The approach was used to assess the visual acuity provided by two alternative stimulation strategies applied to simulated retinal implants with different electrode pitch configurations and differently sized spreads of neural activity. It was found that when there is substantial overlap in neural activity generated by different electrodes, an estimate of acuity based only upon electrode pitch is incorrect; our proposed method gives an accurate result in both circumstances.Significance.Quantification of visual acuity using this approach in pre-clinical development will allow for more rapid and accurate prototyping of improved devices and neural stimulation strategies.
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Affiliation(s)
- Martin Spencer
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.,Greame Clark Institute of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - Tatiana Kameneva
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.,School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.,Greame Clark Institute of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - Anthony N Burkitt
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.,Greame Clark Institute of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - Hamish Meffin
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.,Greame Clark Institute of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.,National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia
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3
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Jung YJ, Almasi A, Sun SH, Yunzab M, Cloherty SL, Bauquier SH, Renfree M, Meffin H, Ibbotson MR. Orientation pinwheels in primary visual cortex of a highly visual marsupial. Sci Adv 2022; 8:eabn0954. [PMID: 36179020 PMCID: PMC9524828 DOI: 10.1126/sciadv.abn0954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 08/12/2022] [Indexed: 06/16/2023]
Abstract
Primary visual cortices in many mammalian species exhibit modular and periodic orientation preference maps arranged in pinwheel-like layouts. The role of inherited traits as opposed to environmental influences in determining this organization remains unclear. Here, we characterize the cortical organization of an Australian marsupial, revealing pinwheel organization resembling that of eutherian carnivores and primates but distinctly different from the simpler salt-and-pepper arrangement of eutherian rodents and rabbits. The divergence of marsupials from eutherians 160 million years ago and the later emergence of rodents and rabbits suggest that the salt-and-pepper structure is not the primitive ancestral form. Rather, the genetic code that enables complex pinwheel formation is likely widespread, perhaps extending back to the common therian ancestors of modern mammals.
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Affiliation(s)
- Young Jun Jung
- National Vision Research Institute, Melbourne, VIC, Australia
| | - Ali Almasi
- Optalert Limited, Melbourne, VIC, Australia
| | - Shi H. Sun
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Molis Yunzab
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | | | - Sebastien H. Bauquier
- Veterinary Hospital, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Marilyn Renfree
- School of BioSciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Hamish Meffin
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Michael R. Ibbotson
- National Vision Research Institute, Melbourne, VIC, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, VIC, Australia
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4
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Yunzab M, Soto-Breceda A, Maturana M, Kirkby S, Slattery M, Newgreen A, Meffin H, Kameneva T, Burkitt AN, Ibbotson M, Tong W. Preferential modulation of individual retinal ganglion cells by electrical stimulation. J Neural Eng 2022; 19. [PMID: 35917811 DOI: 10.1088/1741-2552/ac861f] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 08/01/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Retinal prostheses have been able to recover partial vision in blind patients with retinal degeneration by electrically stimulating surviving cells in the retina, such as retinal ganglion cells (RGCs), but the restored vision is limited. This is partly due to non-preferential stimulation of all RGCs near a single stimulating electrode, which include cells that conflict in their response properties and their contribution to the vision process. Our study proposes a stimulation strategy to preferentially stimulate individual RGCs based on their temporal electrical receptive fields (tERFs). APPROACH We recorded the responses of RGCs using whole-cell current-clamp and demonstrated the stimulation strategy, first using intracellular stimulation, then via extracellular stimulation. MAIN RESULTS We successfully reconstructed the tERFs according to the RGC response to Gaussian white noise current stimulation. The characteristics of the tERFs were extracted and compared according to the morphological and light response types of the cells. By re-delivering stimulation trains that are composed of the tERFs obtained from different cells, we could target individual RGCs as the cells showed lower activation thresholds to their own tERFs. SIGNIFICANCE This proposed stimulation strategy implemented in the next generation of recording and stimulating retinal prostheses may improve the quality of artificial vision.
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Affiliation(s)
- Molis Yunzab
- National Vision Research Institute, Australian College of Optometry, Corner of Keppel and Cardigan Streets, Carlton, Victoria, 3053, AUSTRALIA
| | - Artemio Soto-Breceda
- National Vision Research Institute, Australian College of Optometry, Corner of Keppel and Cardigan Streets, Carlton, Victoria, 3053, AUSTRALIA
| | - Matias Maturana
- National Vision Research Institute, Australian College of Optometry, Corner of Keppel and Cardigan Streets, Carlton, Victoria, 3053, AUSTRALIA
| | - Stephanie Kirkby
- National Vision Research Institute, Australian College of Optometry, Corner of Keppel and Cardigan Streets, Carlton, Victoria, 3053, AUSTRALIA
| | - Maximilian Slattery
- National Vision Research Institute, Australian College of Optometry, Corner of Keppel and Cardigan Streets, Carlton, Victoria, 3053, AUSTRALIA
| | - Anton Newgreen
- National Vision Research Institute, Australian College of Optometry, Corner of Keppel and Cardigan Streets, Carlton, Victoria, 3053, AUSTRALIA
| | - Hamish Meffin
- Biomedical Engineering, The University of Melbourne, Grattan Street, Melbourne, Victoria, 3010, AUSTRALIA
| | - Tatiana Kameneva
- School of Science, Engineering, and Computing Technologies, Swinburne University of Technology, School of Science, Engineering, and Computing Technologies, Swinburne University of Technology, Hawthorn, Victoria, 3122, AUSTRALIA
| | - Anthony N Burkitt
- Department of Biomedical Engineering, University of Melbourne, University of Melbourne, Parkville, Victoria, 3010, AUSTRALIA
| | - Michael Ibbotson
- National Vision Research Institute, Australian College of Optometry, Corner of Keppel and Cardigan Streets, Carlton, Victoria, 3053, AUSTRALIA
| | - Wei Tong
- University of Melbourne, School of Physics, University of Melbourne, Parkville, Melbourne, Victoria, 3010, AUSTRALIA
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Almasi A, Sun SH, Yunzab M, Jung YJ, Meffin H, Ibbotson MR. How Stimulus Statistics Affect the Receptive Fields of Cells in Primary Visual Cortex. J Neurosci 2022; 42:5198-5211. [PMID: 35610048 PMCID: PMC9236288 DOI: 10.1523/jneurosci.0664-21.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/08/2022] [Accepted: 05/11/2022] [Indexed: 12/31/2022] Open
Abstract
We studied the changes that neuronal receptive field (RF) models undergo when the statistics of the stimulus are changed from those of white Gaussian noise (WGN) to those of natural scenes (NSs), by fitting the models to multielectrode data recorded from primary visual cortex (V1) of female cats. This allowed the estimation of both a cascade of linear filters on the stimulus, as well as the static nonlinearities that map the output of the filters to the neuronal spike rates. We found that cells respond differently to these two classes of stimuli, with mostly higher spike rates and shorter response latencies to NSs than to WGN. The most striking finding was that NSs resulted in RFs that had additional uncovered filters compared with WGN. This finding was not an artifact of the higher spike rates observed for NSs relative to WGN, but rather was related to a change in coding. Our results reveal a greater extent of nonlinear processing in V1 neurons when stimulated using NSs compared with WGN. Our findings indicate the existence of nonlinear mechanisms that endow V1 neurons with context-dependent transmission of visual information.SIGNIFICANCE STATEMENT This study addresses a fundamental question about the concept of the receptive field (RF): does the encoding of information depend on the context or statistical regularities of the stimulus type? We applied state-of-the-art RF modeling techniques to data collected from multielectrode recordings from cat visual cortex in response to two statistically distinct stimulus types: white Gaussian noise and natural scenes. We find significant differences between the RFs that emerge from our data-driven modeling. Natural scenes result in far more complex RFs that combine multiple features in the visual input. Our findings reveal that different regimes or modes of operation are at work in visual cortical processing depending on the information present in the visual input. The complexity of V1 neural coding appears to be dependent on the complexity of the stimulus. We believe this new finding will have interesting implications for our understanding of the efficient transmission of information in sensory systems, which is an integral assumption of many computational theories (e.g., efficient and predictive coding of sensory processing in the brain).
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Affiliation(s)
- Ali Almasi
- National Vision Research Institute, Carlton, Victoria 3053, Australia
| | - Shi Hai Sun
- National Vision Research Institute, Carlton, Victoria 3053, Australia
| | - Molis Yunzab
- National Vision Research Institute, Carlton, Victoria 3053, Australia
| | - Young Jun Jung
- National Vision Research Institute, Carlton, Victoria 3053, Australia
| | - Hamish Meffin
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Michael R Ibbotson
- National Vision Research Institute, Carlton, Victoria 3053, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, Victoria 3010, Australia
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Hadjinicolaou AE, Meffin H, Maturana MI, Cloherty SL, Ibbotson MR. Prosthetic vision: devices, patient outcomes and retinal research. Clin Exp Optom 2021; 98:395-410. [DOI: 10.1111/cxo.12342] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Revised: 07/06/2015] [Accepted: 08/04/2015] [Indexed: 12/11/2022] Open
Affiliation(s)
- Alex E Hadjinicolaou
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia,
- ARC Centre of Excellence for Integrative Brain Function and Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia,
| | - Hamish Meffin
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia,
- ARC Centre of Excellence for Integrative Brain Function and Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia,
| | - Matias I Maturana
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia,
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria, Australia,
| | - Shaun L Cloherty
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia,
- ARC Centre of Excellence for Integrative Brain Function and Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia,
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria, Australia,
| | - Michael R Ibbotson
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia,
- ARC Centre of Excellence for Integrative Brain Function and Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia,
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Spencer MJ, Kameneva T, Grayden DB, Burkitt AN, Meffin H. Neural activity shaping utilizing a partitioned target pattern. J Neural Eng 2021; 18. [PMID: 33684894 DOI: 10.1088/1741-2552/abecc4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 03/08/2021] [Indexed: 11/11/2022]
Abstract
Electrical stimulation of neural tissue is used in both clinical and experimental devices to evoke a desired spatiotemporal pattern of neural activity. These devices induce a local field that drives neural activation, referred to as an activating function or generator signal. In visual prostheses, the spread of generator signal from each electrode within the neural tissue results in a spread of visual perception, referred to as a phosphene. In cases where neighboring phosphenes overlap, it is desirable to use current steering or neural activity shaping strategies to manipulate the generator signal between the electrodes to provide greater control over the total pattern of neural activity. Applying opposite generator signal polarities in neighboring regions of the retina forces the generator signal to pass through zero at an intermediate point, thus inducing low neural activity that may be perceived as a high-contrast line. This approach provides a form of high contrast visual perception, but it requires partitioning of the target pattern into those regions that use positive or negative generator signals. This discrete optimization is an NP-hard problem that is subject to being trapped in detrimental local minima. This investigation proposes a new partitioning method using image segmentation to determine the most beneficial positive and negative generator signal regions. Utilizing a database of 1000 natural images, the method is compared to alternative approaches based upon the mean squared error of the outcome. Under nominal conditions and with a set computation limit, partitioning provided improvement for 32% of these images. This percentage increased to 89% when utilizing image pre-processing to emphasize perceptual features of the images. The percentage of images that were dealt with most effectively with image segmentation increased as lower computation limits were imposed on the algorithms.
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Affiliation(s)
- Martin J Spencer
- Department of Biomedical Engineering, The University of Melbourne - Parkville Campus, Parkville, Melbourne, Victoria, 3010, AUSTRALIA
| | - Tatiana Kameneva
- Telecommunication, Electrical, Robotics and Biomedical Engineering, Swinburne University of Technology, Hawthorn, Hawthorn, Victoria, 3122, AUSTRALIA
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne - Parkville Campus, Parkville, Melbourne, Victoria, 3010, AUSTRALIA
| | - Anthony N Burkitt
- Department of Biomedical Engineering, The University of Melbourne - Parkville Campus, Parkville, Melbourne, Victoria, 3010, AUSTRALIA
| | - Hamish Meffin
- Australian College of Optometry, Parkville, Carlton, Victoria, 3010, AUSTRALIA
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Sun SH, Almasi A, Yunzab M, Zehra S, Hicks DG, Kameneva T, Ibbotson MR, Meffin H. Analysis of extracellular spike waveforms and associated receptive fields of neurons in cat primary visual cortex. J Physiol 2021; 599:2211-2238. [PMID: 33501669 DOI: 10.1113/jp280844] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/12/2021] [Indexed: 12/11/2022] Open
Abstract
KEY POINTS Extracellular spikes recorded in the visual cortex (Area 17/18, V1) are commonly classified into either regular-spiking (RS) or fast-spiking (FS). Using multi-electrode arrays positioned in cat V1 and a broadband stimulus, we show that there is also a distinct class with positive-spiking (PS) waveforms. PS units were associated mainly with non-oriented receptive fields while RS and FS units had orientation-selective receptive fields. We suggest that PS units are recordings of axons originating from the thalamus. This conclusion was reinforced by our finding that we could record PS units after cortical silencing, but not record RS and FS units. The importance of our findings is that we were able to correlate spike shapes with receptive field characteristics with high precision using multi-electrode extracellular recording techniques. This allows considerable increases in the amount of information that can be extracted from future cortical experiments. ABSTRACT Extracellular spike waveforms from recordings in the visual cortex have been classified into either regular-spiking (RS) or fast-spiking (FS) units. While both these types of spike waveforms are negative-dominant, we show that there are also distinct classes of spike waveforms in visual Area 17/18 (V1) of anaesthetised cats with positive-dominant waveforms, which are not regularly reported. The spatial receptive fields (RFs) of these different spike waveform types were estimated, which objectively revealed the existence of oriented and non-oriented RFs. We found that units with positive-dominant spikes, which have been associated with recordings from axons in the literature, had mostly non-oriented RFs (84%), which are similar to the centre-surround RFs observed in the dorsal lateral geniculate nucleus (dLGN). Thus, we hypothesise that these positive-dominant waveforms may be recordings from dLGN afferents. We recorded from V1 before and after the application of muscimol (a cortical silencer) and found that the positive-dominant spikes (PS) remained while the RS and FS cells did not. We also noted that the PS units had spiking characteristics normally associated with dLGN units (i.e. higher response spike rates, lower response latencies and higher proportion of burst spikes). Our findings show quantitatively that it is possible to correlate the RF properties of cortical neurons with particular spike waveforms. This has implications for how extracellular recordings should be interpreted and complex experiments can now be contemplated that would have been very challenging previously, such as assessing the feedforward connectivity between brain areas in the same location of cortical tissue.
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Affiliation(s)
- Shi H Sun
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, 3053, Australia
| | - Ali Almasi
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, 3053, Australia
| | - Molis Yunzab
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, 3053, Australia
| | - Syeda Zehra
- Faculty of Science, Engineering and Technology, Swinburne University, Hawthorn, Victoria, 3122, Australia.,Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Damien G Hicks
- Faculty of Science, Engineering and Technology, Swinburne University, Hawthorn, Victoria, 3122, Australia.,Optical Sciences Centre, Swinburne University, Hawthorn, Victoria, 3122, Australia
| | - Tatiana Kameneva
- Faculty of Science, Engineering and Technology, Swinburne University, Hawthorn, Victoria, 3122, Australia.,Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Michael R Ibbotson
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, 3053, Australia.,Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Hamish Meffin
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, 3053, Australia.,Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, 3010, Australia
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Lian Y, Almasi A, Grayden DB, Kameneva T, Burkitt AN, Meffin H. Learning receptive field properties of complex cells in V1. PLoS Comput Biol 2021; 17:e1007957. [PMID: 33651790 PMCID: PMC7954310 DOI: 10.1371/journal.pcbi.1007957] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 03/12/2021] [Accepted: 02/09/2021] [Indexed: 11/24/2022] Open
Abstract
There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally. Many cortical functions originate from the learning ability of the brain. How the properties of cortical cells are learned is vital for understanding how the brain works. There are many models that explain how V1 simple cells can be learned. However, how V1 complex cells are learned still remains unclear. In this paper, we propose a model of learning in complex cells based on the Bienenstock, Cooper, and Munro (BCM) rule. We demonstrate that properties of receptive fields of complex cells can be learned using this biologically plausible learning rule. Quantitative comparisons between the model and experimental data are performed. Results show that model complex cells can account for the diversity of complex cells found in experimental studies. In summary, this study provides a plausible explanation for how complex cells can be learned using biologically plausible plasticity mechanisms. Our findings help us to better understand biological vision processing and provide us with insights into the general signal processing principles that the visual cortex employs to process visual information.
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Affiliation(s)
- Yanbo Lian
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
- * E-mail:
| | - Ali Almasi
- National Vision Research Institute, The Australian College of Optometry, Melbourne, Australia
| | - David B. Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Tatiana Kameneva
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
- Faculty of Science, Engineering and Technology, Swinburne University, Melbourne, Australia
| | - Anthony N. Burkitt
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Hamish Meffin
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
- National Vision Research Institute, The Australian College of Optometry, Melbourne, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, Australia
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10
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Shivdasani MN, Evans M, Burns O, Yeoh J, Allen PJ, Nayagam DAX, Villalobos J, Abbott CJ, Luu CD, Opie NL, Sabu A, Saunders AL, McPhedran M, Cardamone L, McGowan C, Maxim V, Williams RA, Fox KE, Cicione R, Garrett DJ, Ahnood A, Ganesan K, Meffin H, Burkitt AN, Prawer S, Williams CE, Shepherd RK. In vivo feasibility of epiretinal stimulation using ultrananocrystalline diamond electrodes. J Neural Eng 2020; 17:045014. [PMID: 32659750 DOI: 10.1088/1741-2552/aba560] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
OBJECTIVE Due to their increased proximity to retinal ganglion cells (RGCs), epiretinal visual prostheses present the opportunity for eliciting phosphenes with low thresholds through direct RGC activation. This study characterised the in vivo performance of a novel prototype monolithic epiretinal prosthesis, containing Nitrogen incorporated ultrananocrystalline (N-UNCD) diamond electrodes. APPROACH A prototype implant containing up to twenty-five 120 × 120 µm N-UNCD electrodes was implanted into 16 anaesthetised cats and attached to the retina either using a single tack or via magnetic coupling with a suprachoroidally placed magnet. Multiunit responses to retinal stimulation using charge-balanced biphasic current pulses were recorded acutely in the visual cortex using a multichannel planar array. Several stimulus parameters were varied including; the stimulating electrode, stimulus polarity, phase duration, return configuration and the number of electrodes stimulated simultaneously. MAIN RESULTS The rigid nature of the device and its form factor necessitated complex surgical procedures. Surgeries were considered successful in 10/16 animals and cortical responses to single electrode stimulation obtained in eight animals. Clinical imaging and histological outcomes showed severe retinal trauma caused by the device in situ in many instances. Cortical measures were found to significantly depend on the surgical outcomes of individual experiments, phase duration, return configuration and the number of electrodes stimulated simultaneously, but not stimulus polarity. Cortical thresholds were also found to increase over time within an experiment. SIGNIFICANCE The study successfully demonstrated that an epiretinal prosthesis containing diamond electrodes could produce cortical activity with high precision, albeit only in a small number of cases. Both surgical approaches were highly challenging in terms of reliable and consistent attachment to and stabilisation against the retina, and often resulted in severe retinal trauma. There are key challenges (device form factor and attachment technique) to be resolved for such a device to progress towards clinical application, as current surgical techniques are unable to address these issues.
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Affiliation(s)
- Mohit N Shivdasani
- Graduate School of Biomedical Engineering, University of New South Wales, Kensington, NSW 2033, Australia. The Bionics Institute of Australia, East Melbourne, VIC 3002, Australia
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11
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Tong W, Hejazi M, Garrett DJ, Esler T, Prawer S, Meffin H, Ibbotson MR. Minimizing axon bundle activation of retinal ganglion cells with oriented rectangular electrodes. J Neural Eng 2020; 17:036016. [PMID: 32375131 DOI: 10.1088/1741-2552/ab909e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Retinal prostheses aim to restore vision in patients with retinal degenerative diseases, such as age-related macular degeneration and retinitis pigmentosa. By implanting an array of microelectrodes, such a device creates percepts in patients through electrical stimulation of surviving retinal neurons. A challenge for retinal prostheses when trying to return high quality vision is the unintended activation of retinal ganglion cells through the stimulation of passing axon bundles, which leads to patients reporting large, elongated patches of light instead of focal spots. APPROACH In this work, we used calcium imaging to record the responses of retinal ganglion cells to electrical stimulation in explanted retina using rectangular electrodes placed with different orientations relative to the axon bundles. MAIN RESULTS We showed that narrow, rectangular electrodes oriented parallel to the axon bundles can achieve focal stimulation. To further improve the strategy, we studied the impact of different stimulation waveforms and electrode configurations. We found the selectivity for focal stimulation to be higher when using short (33 μs), anodic-first biphasic pulses, with long electrode lengths and at least 50 μm electrode-to-retinal separation. Focal stimulation was, in fact, less selective when the electrodes made direct contact with the retinal surface due to unwanted preferential stimulation of the proximal axon bundles. SIGNIFICANCE When employed in retinal prostheses, the proposed stimulation strategy is expected to provide improved quality of vision to the blind.
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Affiliation(s)
- Wei Tong
- National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia. School of Physics, The University of Melbourne, Parkville, VIC, Australia. Department of Vision Science and Optometry, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC, Australia
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12
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Almasi A, Meffin H, Cloherty SL, Wong Y, Yunzab M, Ibbotson MR. Mechanisms of Feature Selectivity and Invariance in Primary Visual Cortex. Cereb Cortex 2020; 30:5067-5087. [PMID: 32368778 DOI: 10.1093/cercor/bhaa102] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 03/26/2020] [Accepted: 03/26/2020] [Indexed: 11/14/2022] Open
Abstract
Visual object identification requires both selectivity for specific visual features that are important to the object's identity and invariance to feature manipulations. For example, a hand can be shifted in position, rotated, or contracted but still be recognized as a hand. How are the competing requirements of selectivity and invariance built into the early stages of visual processing? Typically, cells in the primary visual cortex are classified as either simple or complex. They both show selectivity for edge-orientation but complex cells develop invariance to edge position within the receptive field (spatial phase). Using a data-driven model that extracts the spatial structures and nonlinearities associated with neuronal computation, we quantitatively describe the balance between selectivity and invariance in complex cells. Phase invariance is frequently partial, while invariance to orientation and spatial frequency are more extensive than expected. The invariance arises due to two independent factors: (1) the structure and number of filters and (2) the form of nonlinearities that act upon the filter outputs. Both vary more than previously considered, so primary visual cortex forms an elaborate set of generic feature sensitivities, providing the foundation for more sophisticated object processing.
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Affiliation(s)
- Ali Almasi
- National Vision Research Institute, Australian College of Optometry, Carlton VIC 3053, Australia
| | - Hamish Meffin
- National Vision Research Institute, Australian College of Optometry, Carlton VIC 3053, Australia.,Department of Biomedical Engineering, The University of Melbourne, Parkville VIC 3010, Australia
| | - Shaun L Cloherty
- School of Engineering, RMIT University, Melbourne VIC 3001, Australia
| | - Yan Wong
- Department of Electrical and Computer Systems Engineering and Department of Physiology, Monash University, Clayton VIC 3800, Australia
| | - Molis Yunzab
- National Vision Research Institute, Australian College of Optometry, Carlton VIC 3053, Australia
| | - Michael R Ibbotson
- National Vision Research Institute, Australian College of Optometry, Carlton VIC 3053, Australia.,Department of Optometry and Vision Sciences, The University of Melbourne, Parkville VIC 3010, Australia
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13
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Tong W, Meffin H, Garrett DJ, Ibbotson MR. Stimulation Strategies for Improving the Resolution of Retinal Prostheses. Front Neurosci 2020; 14:262. [PMID: 32292328 PMCID: PMC7135883 DOI: 10.3389/fnins.2020.00262] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 03/09/2020] [Indexed: 12/17/2022] Open
Abstract
Electrical stimulation using implantable devices with arrays of stimulating electrodes is an emerging therapy for neurological diseases. The performance of these devices depends greatly on their ability to activate populations of neurons with high spatiotemporal resolution. To study electrical stimulation of populations of neurons, retina serves as a useful model because the neural network is arranged in a planar array that is easy to access. Moreover, retinal prostheses are under development to restore vision by replacing the function of damaged light sensitive photoreceptors, which makes retinal research directly relevant for curing blindness. Here we provide a progress review on stimulation strategies developed in recent years to improve the resolution of electrical stimulation in retinal prostheses. We focus on studies performed with explanted retinas, in which electrophysiological techniques are the most advanced. We summarize achievements in improving the spatial and temporal resolution of electrical stimulation of the retina and methods to selectively stimulate neurons with different visual functions. Future directions for retinal prostheses development are also discussed, which could provide insights for other types of neuromodulatory devices in which high-resolution electrical stimulation is required.
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Affiliation(s)
- Wei Tong
- National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia
- Department of Optometry and Vision Sciences, Melbourne School of Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
- School of Physics, The University of Melbourne, Melbourne, VIC, Australia
| | - Hamish Meffin
- National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia
- Department of Optometry and Vision Sciences, Melbourne School of Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - David J. Garrett
- School of Physics, The University of Melbourne, Melbourne, VIC, Australia
| | - Michael R. Ibbotson
- National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia
- Department of Optometry and Vision Sciences, Melbourne School of Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
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14
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Monfared O, Tahayori B, Freestone D, Nešić D, Grayden DB, Meffin H. Determination of the electrical impedance of neural tissue from its microscopic cellular constituents. J Neural Eng 2020; 17:016037. [PMID: 31711052 DOI: 10.1088/1741-2552/ab560a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The electrical properties of neural tissue are important in a range of different applications in biomedical engineering and basic science. These properties are characterized by the electrical admittivity of the tissue, which is the inverse of the specific tissue impedance. OBJECTIVE Here we derived analytical expressions for the admittivity of various models of neural tissue from the underlying electrical and morphological properties of the constituent cells. APPROACH Three models are considered: parallel bundles of fibers, fibers contained in stacked laminae and fibers crossing each other randomly in all three-dimensional directions. MAIN RESULTS An important and novel aspect that emerges from considering the underlying cellular composition of the tissue is that the resulting admittivity has both spatial and temporal frequency dependence, a property not shared with conventional conductivity-based descriptions. The frequency dependence of the admittivity results in non-trivial spatiotemporal filtering of electrical signals in the tissue models. These effects are illustrated by considering the example of pulsatile stimulation with a point source electrode. It is shown how changing temporal parameters of a current pulse, such as pulse duration, alters the spatial profile of the extracellular potential. In a second example, it is shown how the degree of electrical anisotropy can change as a function of the distance from the electrode, despite the underlying structurally homogeneity of the tissue. These effects are discussed in terms of different current pathways through the intra- and extra-cellular spaces, and how these relate to near- and far-field limits for the admittivity (which reduce to descriptions in terms of a simple conductivity). SIGNIFICANCE The results highlight the complexity of the electrical properties of neural tissue and provide mathematical methods to model this complexity.
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Affiliation(s)
- Omid Monfared
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria, Australia. Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
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15
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Tong W, Stamp M, Apollo NV, Ganesan K, Meffin H, Prawer S, Garrett DJ, Ibbotson MR. Improved visual acuity using a retinal implant and an optimized stimulation strategy. J Neural Eng 2019; 17:016018. [DOI: 10.1088/1741-2552/ab5299] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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16
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Spencer M, Kameneva T, Grayden DB, Meffin H, Burkitt AN. Erratum: Global activity shaping strategies for a retinal implant (2019 J. Neural Eng. 16 026008). J Neural Eng 2019. [DOI: 10.1088/1741-2552/ab2541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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17
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Lian Y, Grayden DB, Kameneva T, Meffin H, Burkitt AN. Toward a Biologically Plausible Model of LGN-V1 Pathways Based on Efficient Coding. Front Neural Circuits 2019; 13:13. [PMID: 30930752 PMCID: PMC6427952 DOI: 10.3389/fncir.2019.00013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 02/19/2019] [Indexed: 11/13/2022] Open
Abstract
Increasing evidence supports the hypothesis that the visual system employs a sparse code to represent visual stimuli, where information is encoded in an efficient way by a small population of cells that respond to sensory input at a given time. This includes simple cells in primary visual cortex (V1), which are defined by their linear spatial integration of visual stimuli. Various models of sparse coding have been proposed to explain physiological phenomena observed in simple cells. However, these models have usually made the simplifying assumption that inputs to simple cells already incorporate linear spatial summation. This overlooks the fact that these inputs are known to have strong non-linearities such the separation of ON and OFF pathways, or separation of excitatory and inhibitory neurons. Consequently these models ignore a range of important experimental phenomena that are related to the emergence of linear spatial summation from non-linear inputs, such as segregation of ON and OFF sub-regions of simple cell receptive fields, the push-pull effect of excitation and inhibition, and phase-reversed cortico-thalamic feedback. Here, we demonstrate that a two-layer model of the visual pathway from the lateral geniculate nucleus to V1 that incorporates these biological constraints on the neural circuits and is based on sparse coding can account for the emergence of these experimental phenomena, diverse shapes of receptive fields and contrast invariance of orientation tuning of simple cells when the model is trained on natural images. The model suggests that sparse coding can be implemented by the V1 simple cells using neural circuits with a simple biologically plausible architecture.
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Affiliation(s)
- Yanbo Lian
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.,Centre for Neural Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Tatiana Kameneva
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.,Faculty of Science, Engineering and Technology, Swinburne University, Melbourne, VIC, Australia
| | - Hamish Meffin
- Department of Optometry and Visual Science, The University of Melbourne, Melbourne, VIC, Australia.,National Vision Research Institute, The Australian College of Optometry, Melbourne, VIC, Australia
| | - Anthony N Burkitt
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
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19
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Abstract
We study how initial conditions of the Hodgkin-Huxley model affect the dynamics of simulated neurons. We systematically vary the amplitudes of depolarization currents in order to bring neuron dynamics to stable equilibrium. Our results demonstrate that simulated neurons can have spontaneous spiking or a silent state, depending on the initial conditions. We propose the methodology to study the circumstances under which Purkinje cells transit between hyperpolarized quiescent state (down state) and a depolarized spiking state (up state). We show that results derived using the Hodgkin-Huxley methodology should be carefully analyzed before suggesting a direct relevance to neuroprosthetic implants.
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20
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Mengl K, Meffin H, Michael Ibbotson R, Kameneva T. Neuroprostheses: method to evaluate the information content of stimulation strategies. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2018:4724-4727. [PMID: 30441404 DOI: 10.1109/embc.2018.8513122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We propose a framework to evaluate the information content of different stimulation strategies used in neuroprosthetic implants. We analyze the responses of retinal ganglion cells to electrical stimulation using an information theory framework. This methodology allows us to calculate the information content by looking at the consistency of neural responses generated across multiple repetitions of the same stimulation protocol.
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21
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Soto-Breceda A, Kameneva T, Meffin H, Maturana M, Ibbotson MR. Irregularly timed electrical pulses reduce adaptation of retinal ganglion cells. J Neural Eng 2018; 15:056017. [PMID: 30021932 DOI: 10.1088/1741-2552/aad46e] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Retinal prostheses aim to provide visual percepts to blind people affected by diseases caused by photoreceptor degeneration. One of the main challenges presented by current devices is neural adaptation in the retina, which is believed to be the cause of fading-an effect where artificially produced percepts disappear over a short period of time, despite continuous stimulation of the retina. We aim to understand the neural adaptation generated in retinal ganglion cells (RGCs) during electrical stimulation. APPROACH Current visual prostheses use electrical pulses with fixed frequencies and amplitudes modulated over hundreds of milliseconds to stimulate the retina. However, in nature, neuronal spiking occurs with stochastic timing, hence the information received naturally from other neurons by RGCs is irregularly timed. We used a single epiretinal electrode to stimulate and compare rat RGC responses to stimulus trains of biphasic pulses delivered at regular and random inter-pulse intervals (IPI), the latter taken from an exponential distribution. MAIN RESULTS Our observations suggest that stimulation with random IPIs result in lower adaptation rates than stimulation with constant IPIs at frequencies of 50 Hz and 200 Hz. We also found a high proportion of lower amplitude action potentials, or spikelets. The spikelets were more prominent at high stimulation frequencies (50 Hz and 200 Hz) and were less susceptible to adaptation, but it was not clear if they propagated along the axon. SIGNIFICANCE Using random IPI stimulation in retinal prostheses reduces the decay of RGCs and this could potentially reduce fading of electrically induced visual perception.
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Affiliation(s)
- A Soto-Breceda
- National Vision Research Institute, Australian College of Optometry, Melbourne, Australia. Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia. CSIRO, Data 61, Melbourne, Australia
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22
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Wong YT, Ahnood A, Maturana MI, Kentler W, Ganesan K, Grayden DB, Meffin H, Prawer S, Ibbotson MR, Burkitt AN. Feasibility of Nitrogen Doped Ultrananocrystalline Diamond Microelectrodes for Electrophysiological Recording From Neural Tissue. Front Bioeng Biotechnol 2018; 6:85. [PMID: 29988378 PMCID: PMC6024013 DOI: 10.3389/fbioe.2018.00085] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 06/05/2018] [Indexed: 01/19/2023] Open
Abstract
Neural prostheses that can monitor the physiological state of a subject are becoming clinically viable through improvements in the capacity to record from neural tissue. However, a significant limitation of current devices is that it is difficult to fabricate electrode arrays that have both high channel counts and the appropriate electrical properties required for neural recordings. In earlier work, we demonstrated nitrogen doped ultrananocrystalline diamond (N-UNCD) can provide efficacious electrical stimulation of neural tissue, with high charge injection capacity, surface stability and biocompatibility. In this work, we expand on this functionality to show that N-UNCD electrodes can also record from neural tissue owing to its low electrochemical impedance. We show that N-UNCD electrodes are highly flexible in their application, with successful recordings of action potentials from single neurons in an in vitro retina preparation, as well as local field potential responses from in vivo visual cortex tissue. Key properties of N-UNCD films, combined with scalability of electrode array fabrication with custom sizes for recording or stimulation along with integration through vertical interconnects to silicon based integrated circuits, may in future form the basis for the fabrication of versatile closed-loop neural prostheses that can both record and stimulate.
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Affiliation(s)
- Yan T. Wong
- Department of Physiology and Department of Electrical and Computer Systems Engineering, Biomedicine Discovery Institute, Monash University, Melbourne, VIC, Australia
| | - Arman Ahnood
- School of Physics, University of Melbourne, Melbourne, VIC, Australia
| | - Matias I. Maturana
- Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia
- National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia
| | - William Kentler
- Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia
| | | | - David B. Grayden
- Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia
| | - Hamish Meffin
- National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia
- Department of Optometry and Vision Science University of Melbourne, Melbourne, VIC, Australia
| | - Steven Prawer
- School of Physics, University of Melbourne, Melbourne, VIC, Australia
| | - Michael R. Ibbotson
- National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia
- Department of Optometry and Vision Science University of Melbourne, Melbourne, VIC, Australia
| | - Anthony N. Burkitt
- Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia
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23
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Spencer MJ, Meffin H, Burkitt AN, Grayden DB. Compensation for Traveling Wave Delay Through Selection of Dendritic Delays Using Spike-Timing-Dependent Plasticity in a Model of the Auditory Brainstem. Front Comput Neurosci 2018; 12:36. [PMID: 29922141 PMCID: PMC5996126 DOI: 10.3389/fncom.2018.00036] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 05/14/2018] [Indexed: 12/03/2022] Open
Abstract
Asynchrony among synaptic inputs may prevent a neuron from responding to behaviorally relevant sensory stimuli. For example, “octopus cells” are monaural neurons in the auditory brainstem of mammals that receive input from auditory nerve fibers (ANFs) representing a broad band of sound frequencies. Octopus cells are known to respond with finely timed action potentials at the onset of sounds despite the fact that due to the traveling wave delay in the cochlea, synaptic input from the auditory nerve is temporally diffuse. This paper provides a proof of principle that the octopus cells' dendritic delay may provide compensation for this input asynchrony, and that synaptic weights may be adjusted by a spike-timing dependent plasticity (STDP) learning rule. This paper used a leaky integrate and fire model of an octopus cell modified to include a “rate threshold,” a property that is known to create the appropriate onset response in octopus cells. Repeated audio click stimuli were passed to a realistic auditory nerve model which provided the synaptic input to the octopus cell model. A genetic algorithm was used to find the parameters of the STDP learning rule that reproduced the microscopically observed synaptic connectivity. With these selected parameter values it was shown that the STDP learning rule was capable of adjusting the values of a large number of input synaptic weights, creating a configuration that compensated the traveling wave delay of the cochlea.
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Affiliation(s)
- Martin J Spencer
- NeuroEngineering Laboratory, Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia.,Centre for Neural Engineering, University of Melbourne, Melbourne, VIC, Australia
| | - Hamish Meffin
- NeuroEngineering Laboratory, Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia.,Centre for Neural Engineering, University of Melbourne, Melbourne, VIC, Australia.,Victorian Research Laboratory, National ICT Australia, Sydney, NSW, Australia.,National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia.,Department of Optometry and Vision Sciences, ARC Centre of Excellence for Integrative Brain Function, University of Melbourne, Melbourne, VIC, Australia
| | - Anthony N Burkitt
- NeuroEngineering Laboratory, Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia.,Centre for Neural Engineering, University of Melbourne, Melbourne, VIC, Australia
| | - David B Grayden
- NeuroEngineering Laboratory, Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia.,Centre for Neural Engineering, University of Melbourne, Melbourne, VIC, Australia
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24
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Esler TB, Maturana MI, Kerr RR, Grayden DB, Burkitt AN, Meffin H. Biophysical basis of the linear electrical receptive fields of retinal ganglion cells. J Neural Eng 2018; 15:055001. [PMID: 29889051 DOI: 10.1088/1741-2552/aacbaa] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Responses of retinal ganglion cells to direct electrical stimulation have been shown experimentally to be well described by linear-nonlinear models. These models rely on the simplifying assumption that retinal ganglion cell responses to stimulation with an array of electrodes are driven by a simple linear weighted sum of stimulus current amplitudes from each electrode, known as the 'electrical receptive field'. OBJECTIVE This paper aims to demonstrate the biophysical basis of the linear-nonlinear model and the electrical receptive field to facilitate the development of improved stimulation strategies for retinal implants. APPROACH We compare the linear-nonlinear model of subretinal electrical stimulation with a multi-layered, biophysical, volume conductor model of retinal stimulation. MAIN RESULTS Our results show that the linear electrical receptive field of the linear-nonlinear model matches the transmembrane currents induced by electrodes (the activating function) at the site of the high-density sodium channel band with only minor discrepancies. The discrepancies are mostly eliminated by including axial current flow originating from adjacent cell compartments. Furthermore, for cells where a single linear electrical receptive field is insufficient, we show that cell responses are likely driven by multiple sites of action potential initiation with multiple distinct receptive fields, each of which can be accurately described by the activating function. SIGNIFICANCE This result establishes that the biophysical basis of the electrical receptive field of the linear-nonlinear model is the superposition of transmembrane currents induced by different electrodes at and near the site of action potential initiation. Together with existing experimental support for linear-nonlinear models of electrical stimulation, this provides a firm basis for using this much simplified model to generate more optimal stimulation patterns for retinal implants.
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Affiliation(s)
- Timothy B Esler
- NeuroEngineering Laboratory, Department of Biomedical Engineering, The University of Melbourne, Australia
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25
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Kotsakidis R, Meffin H, Ibbotson MR, Kameneva T. In vitro assessment of the differences in retinal ganglion cell responses to intra- and extracellular electrical stimulation. J Neural Eng 2018; 15:046022. [DOI: 10.1088/1741-2552/aac2f7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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26
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Meng K, Fellner A, Rattay F, Ghezzi D, Meffin H, Ibbotson MR, Kameneva T. Upper stimulation threshold for retinal ganglion cell activation. J Neural Eng 2018; 15:046012. [PMID: 29616983 DOI: 10.1088/1741-2552/aabb7d] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The existence of an upper threshold in electrically stimulated retinal ganglion cells (RGCs) is of interest because of its relevance to the development of visual prosthetic devices, which are designed to restore partial sight to blind patients. The upper threshold is defined as the stimulation level above which no action potentials (direct spikes) can be elicited in electrically stimulated retina. APPROACH We collected and analyzed in vitro recordings from rat RGCs in response to extracellular biphasic (anodic-cathodic) pulse stimulation of varying amplitudes and pulse durations. Such responses were also simulated using a multicompartment model. MAIN RESULTS We identified the individual cell variability in response to stimulation and the phenomenon known as upper threshold in all but one of the recorded cells (n = 20/21). We found that the latencies of spike responses relative to stimulus amplitude had a characteristic U-shape. In silico, we showed that the upper threshold phenomenon was observed only in the soma. For all tested biphasic pulse durations, electrode positions, and pulse amplitudes above lower threshold, a propagating action potential was observed in the distal axon. For amplitudes above the somatic upper threshold, the axonal action potential back-propagated in the direction of the soma, but the soma's low level of hyperpolarization prevented action potential generation in the soma itself. SIGNIFICANCE An upper threshold observed in the soma does not prevent spike conductance in the axon.
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Affiliation(s)
- Kevin Meng
- National Vision Research Institute, Australian College of Optometry, Australia. Department of Biomedical Engineering, The University of Melbourne, Australia
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27
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Esler TB, Kerr RR, Tahayori B, Grayden DB, Meffin H, Burkitt AN. Minimizing activation of overlying axons with epiretinal stimulation: The role of fiber orientation and electrode configuration. PLoS One 2018; 13:e0193598. [PMID: 29494655 PMCID: PMC5833203 DOI: 10.1371/journal.pone.0193598] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Accepted: 02/14/2018] [Indexed: 12/19/2022] Open
Abstract
Currently, a challenge in electrical stimulation of the retina with a visual prosthesis (bionic eye) is to excite only the cells lying directly under the electrode in the ganglion cell layer, while avoiding excitation of axon bundles that pass over the surface of the retina in the nerve fiber layer. Stimulation of overlying axons results in irregular visual percepts, limiting perceptual efficacy. This research explores how differences in fiber orientation between the nerve fiber layer and ganglion cell layer leads to differences in the electrical activation of the axon initial segment and axons of passage. Approach. Axons of passage of retinal ganglion cells in the nerve fiber layer are characterized by a narrow distribution of fiber orientations, causing highly anisotropic spread of applied current. In contrast, proximal axons in the ganglion cell layer have a wider distribution of orientations. A four-layer computational model of epiretinal extracellular stimulation that captures the effect of neurite orientation in anisotropic tissue has been developed using a volume conductor model known as the cellular composite model. Simulations are conducted to investigate the interaction of neural tissue orientation, stimulating electrode configuration, and stimulation pulse duration and amplitude. Main results. Our model shows that simultaneous stimulation with multiple electrodes aligned with the nerve fiber layer can be used to achieve selective activation of axon initial segments rather than passing fibers. This result can be achieved while reducing required stimulus charge density and with only modest increases in the spread of activation in the ganglion cell layer, and is shown to extend to the general case of arbitrary electrode array positioning and arbitrary target volume. Significance. These results elucidate a strategy for more targeted stimulation of retinal ganglion cells with experimentally-relevant multi-electrode geometries and achievable stimulation requirements.
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Affiliation(s)
- Timothy B. Esler
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
- * E-mail:
| | - Robert R. Kerr
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Seer Medical, Melbourne, Victoria, Australia
| | - Bahman Tahayori
- Monash Institute of Medical Engineering, Monash University, Clayton, Victoria, Australia
| | - David B. Grayden
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Neural Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - Hamish Meffin
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia
- ARC Centre of Excellence for Integrative Brain Function, Optometry & Vision Science, The University of Melbourne, Parkville, Victoria, Australia
| | - Anthony N. Burkitt
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
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Ibbotson MR, Hung YS, Meffin H, Boeddeker N, Srinivasan MV. Neural basis of forward flight control and landing in honeybees. Sci Rep 2017; 7:14591. [PMID: 29109404 PMCID: PMC5673959 DOI: 10.1038/s41598-017-14954-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Accepted: 10/18/2017] [Indexed: 01/04/2023] Open
Abstract
The impressive repertoire of honeybee visually guided behaviors, and their ability to learn has made them an important tool for elucidating the visual basis of behavior. Like other insects, bees perform optomotor course correction to optic flow, a response that is dependent on the spatial structure of the visual environment. However, bees can also distinguish the speed of image motion during forward flight and landing, as well as estimate flight distances (odometry), irrespective of the visual scene. The neural pathways underlying these abilities are unknown. Here we report on a cluster of descending neurons (DNIIIs) that are shown to have the directional tuning properties necessary for detecting image motion during forward flight and landing on vertical surfaces. They have stable firing rates during prolonged periods of stimulation and respond to a wide range of image speeds, making them suitable to detect image flow during flight behaviors. While their responses are not strictly speed tuned, the shape and amplitudes of their speed tuning functions are resistant to large changes in spatial frequency. These cells are prime candidates not only for the control of flight speed and landing, but also the basis of a neural 'front end' of the honeybee's visual odometer.
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Affiliation(s)
- M R Ibbotson
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia.
- Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia.
| | - Y-S Hung
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia
- Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia
- National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH), 9000, Rockville Pike, Bldg 35 A, Bethesda, MD, USA
| | - H Meffin
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia
- Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia
| | - N Boeddeker
- Department of Cognitive Neuroscience, Bielefeld University, 33615, Bielefeld, Germany
| | - M V Srinivasan
- Queensland Brain Institute, University of Queensland, St Lucia, QLD 4072, Australia
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Rubchinsky LL, Ahn S, Klijn W, Cumming B, Yates S, Karakasis V, Peyser A, Woodman M, Diaz-Pier S, Deraeve J, Vassena E, Alexander W, Beeman D, Kudela P, Boatman-Reich D, Anderson WS, Luque NR, Naveros F, Carrillo RR, Ros E, Arleo A, Huth J, Ichinose K, Park J, Kawai Y, Suzuki J, Mori H, Asada M, Oprisan SA, Dave AI, Babaie T, Robinson P, Tabas A, Andermann M, Rupp A, Balaguer-Ballester E, Lindén H, Christensen RK, Nakamura M, Barkat TR, Tosi Z, Beggs J, Lonardoni D, Boi F, Di Marco S, Maccione A, Berdondini L, Jędrzejewska-Szmek J, Dorman DB, Blackwell KT, Bauermeister C, Keren H, Braun J, Dornas JV, Mavritsaki E, Aldrovandi S, Bridger E, Lim S, Brunel N, Buchin A, Kerr CC, Chizhov A, Huberfeld G, Miles R, Gutkin B, Spencer MJ, Meffin H, Grayden DB, Burkitt AN, Davey CE, Tao L, Tiruvadi V, Ali R, Mayberg H, Butera R, Gunay C, Lamb D, Calabrese RL, Doloc-Mihu A, López-Madrona VJ, Matias FS, Pereda E, Mirasso CR, Canals S, Geminiani A, Pedrocchi A, D’Angelo E, Casellato C, Chauhan A, Soman K, Srinivasa Chakravarthy V, Muddapu VR, Chuang CC, Chen NY, Bayati M, Melchior J, Wiskott L, Azizi AH, Diba K, Cheng S, Smirnova EY, Yakimova EG, Chizhov AV, Chen NY, Shih CT, Florescu D, Coca D, Courtiol J, Jirsa VK, Covolan RJM, Teleńczuk B, Kempter R, Curio G, Destexhe A, Parker J, Klishko AN, Prilutsky BI, Cymbalyuk G, Franke F, Hierlemann A, da Silveira RA, Casali S, Masoli S, Rizza M, Rizza MF, Masoli S, Sun Y, Wong W, Farzan F, Blumberger DM, Daskalakis ZJ, Popovych S, Viswanathan S, Rosjat N, Grefkes C, Daun S, Gentiletti D, Suffczynski P, Gnatkovski V, De Curtis M, Lee H, Paik SB, Choi W, Jang J, Park Y, Song JH, Song M, Pallarés V, Gilson M, Kühn S, Insabato A, Deco G, Glomb K, Ponce-Alvarez A, Ritter P, Gilson M, Campo AT, Thiele A, Deeba F, Robinson PA, van Albada SJ, Rowley A, Hopkins M, Schmidt M, Stokes AB, Lester DR, Furber S, Diesmann M, Barri A, Wiechert MT, DiGregorio DA, Dimitrov AG, Vich C, Berg RW, Guillamon A, Ditlevsen S, Cazé RD, Girard B, Doncieux S, Doyon N, Boahen F, Desrosiers P, Laurence E, Doyon N, Dubé LJ, Eleonora R, Durstewitz D, Schmidt D, Mäki-Marttunen T, Krull F, Bettella F, Metzner C, Devor A, Djurovic S, Dale AM, Andreassen OA, Einevoll GT, Næss S, Ness TV, Halnes G, Halgren E, Halnes G, Mäki-Marttunen T, Pettersen KH, Andreassen OA, Sætra MJ, Hagen E, Schiffer A, Grzymisch A, Persike M, Ernst U, Harnack D, Ernst UA, Tomen N, Zucca S, Pasquale V, Pica G, Molano-Mazón M, Chiappalone M, Panzeri S, Fellin T, Oie KS, Boothe DL, Crone JC, Yu AB, Felton MA, Zulfiqar I, Moerel M, De Weerd P, Formisano E, Boothe DL, Crone JC, Felton MA, Oie K, Franaszczuk P, Diggelmann R, Fiscella M, Hierlemann A, Franke F, Guarino D, Antolík J, Davison AP, Frègnac Y, Etienne BX, Frohlich F, Lefebvre J, Marcos E, Mattia M, Genovesio A, Fedorov LA, Dijkstra TM, Sting L, Hock H, Giese MA, Buhry L, Langlet C, Giovannini F, Verbist C, Salvadé S, Giugliano M, Henderson JA, Wernecke H, Sándor B, Gros C, Voges N, Dabrovska P, Riehle A, Brochier T, Grün S, Gu Y, Gong P, Dumont G, Novikov NA, Gutkin BS, Tewatia P, Eriksson O, Kramer A, Santos J, Jauhiainen A, Kotaleski JH, Belić JJ, Kumar A, Kotaleski JH, Shimono M, Hatano N, Ahmad S, Cui Y, Hawkins J, Senk J, Korvasová K, Tetzlaff T, Helias M, Kühn T, Denker M, Mana P, Grün S, Dahmen D, Schuecker J, Goedeke S, Keup C, Goedeke S, Heuer K, Bakker R, Tiesinga P, Toro R, Qin W, Hadjinicolaou A, Grayden DB, Ibbotson MR, Kameneva T, Lytton WW, Mulugeta L, Drach A, Myers JG, Horner M, Vadigepalli R, Morrison T, Walton M, Steele M, Anthony Hunt C, Tam N, Amaducci R, Muñiz C, Reyes-Sánchez M, Rodríguez FB, Varona P, Cronin JT, Hennig MH, Iavarone E, Yi J, Shi Y, Zandt BJ, Van Geit W, Rössert C, Markram H, Hill S, O’Reilly C, Iavarone E, Shi Y, Perin R, Lu H, Zandt BJ, Bryson A, Rössert C, Hadrava M, Hlinka J, Hosaka R, Olenik M, Houghton C, Iannella N, Launey T, Kameneva T, Kotsakidis R, Meffin H, Soriano J, Kubo T, Inoue T, Kida H, Yamakawa T, Suzuki M, Ikeda K, Abbasi S, Hudson AE, Heck DH, Jaeger D, Lee J, Abbasi S, Janušonis S, Saggio ML, Spiegler A, Stacey WC, Bernard C, Lillo D, Bernard C, Petkoski S, Spiegler A, Drakesmith M, Jones DK, Zadeh AS, Kambhampati C, Karbowski J, Kaya ZG, Lakretz Y, Treves A, Li LW, Lizier J, Kerr CC, Masquelier T, Kheradpisheh SR, Kim H, Kim CS, Marakshina JA, Vartanov AV, Neklyudova AA, Kozlovskiy SA, Kiselnikov AA, Taniguchi K, Kitano K, Schmitt O, Lessmann F, Schwanke S, Eipert P, Meinhardt J, Beier J, Kadir K, Karnitzki A, Sellner L, Klünker AC, Kuch L, Ruß F, Jenssen J, Wree A, Sanz-Leon P, Knock SA, Chien SC, Maess B, Knösche TR, Cohen CC, Popovic MA, Klooster J, Kole MH, Roberts EA, Kopell NJ, Kepple D, Giaffar H, Rinberg D, Koulakov A, Forlim CG, Klock L, Bächle J, Stoll L, Giemsa P, Fuchs M, Schoofs N, Montag C, Gallinat J, Lee RX, Stephens GJ, Kuhn B, Tauffer L, Isope P, Inoue K, Ohmura Y, Yonekura S, Kuniyoshi Y, Jang HJ, Kwag J, de Kamps M, Lai YM, dos Santos F, Lam KP, Andras P, Imperatore J, Helms J, Tompa T, Lavin A, Inkpen FH, Ashby MC, Lepora NF, Shifman AR, Lewis JE, Zhang Z, Feng Y, Tetzlaff C, Kulvicius T, Li Y, Pena RFO, Bernardi D, Roque AC, Lindner B, Bernardi D, Vellmer S, Saudargiene A, Maninen T, Havela R, Linne ML, Powanwe A, Longtin A, Naveros F, Garrido JA, Graham JW, Dura-Bernal S, Angulo SL, Neymotin SA, Antic SD. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2. BMC Neurosci 2017. [PMCID: PMC5592442 DOI: 10.1186/s12868-017-0371-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Newton AJH, Seidenstein AH, McDougal RA, Pérez-Cervera A, Huguet G, M-Seara T, Haimerl C, Angulo-Garcia D, Torcini A, Cossart R, Malvache A, Skiker K, Maouene M, Ragognetti G, Lorusso L, Viggiano A, Marcelli A, Senatore R, Parziale A, Stramaglia S, Pellicoro M, Angelini L, Amico E, Aerts H, Cortés J, Laureys S, Marinazzo D, Stramaglia S, Bassez I, Faes L, Almgren H, Razi A, Van de Steen F, Krebs R, Aerts H, Kanari L, Dlotko P, Scolamiero M, Levi R, Shillcock J, de Kock CP, Hess K, Markram H, Ly C, Marsat G, Gillespie T, Sandström M, Abrams M, Grethe JS, Martone M, De Gernier R, Solinas S, Rössert C, Haelterman M, Massar S, Pasquale V, Pastore VP, Martinoia S, Massobrio P, Capone C, Tort-Colet N, Sanchez-Vives MV, Mattia M, Almasi A, Cloherty SL, Grayden DB, Wong YT, Ibbotson MR, Meffin H, Prince LY, Tsaneva-Atanasova K, Mellor JR, Mazzoni A, Rosa M, Carpaneto J, Romito LM, Priori A, Micera S, Migliore R, Lupascu CA, Franchina F, Bologna LL, Romani A, Saray S, Van Geit W, Káli S, Thomson A, Mercer A, Lange S, Falck J, Muller E, Schürmann F, Todorov D, Capps R, Barnett W, Molkov Y, Devalle F, Pazó D, Montbrió E, Mochol G, Azab H, Hayden BY, Moreno-Bote R, Balasubramani PP, Chakravarthy SV, Muddapu VR, Gheorghiu MD, Mimica B, Withlock J, Mureșan RC, Zick JL, Schultz K, Blackman RK, Chafee MV, Netoff TI, Roberts N, Nagaraj V, Lamperski A, Netoff TI, Grado LL, Johnson MD, Darrow DP, Lonardoni D, Amin H, Di Marco S, Maccione A, Berdondini L, Nieus T, Stimberg M, Goodman DFM, Nowotny T, Koren V, Dragoi V, Obermayer K, Castro S, Fernandez M, El-Deredy W, Xu K, Maidana JP, Orio P, Chen W, Hepburn I, Casalegno F, Devresse A, Ovcharenko A, Pereira F, Delalondre F, De Schutter E, Bratby P, Gallimore AR, Klingbeil G, Zamora C, Zang Y, Crotty P, Palmerduca E, Antonietti A, Casellato C, Erö C, D’Angelo E, Gewaltig MO, Pedrocchi A, Bytschok I, Dold D, Schemmel J, Meier K, Petrovici MA, Shen HA, Surace SC, Pfister JP, Lefebvre B, Marre O, Yger P, Papoutsi A, Park J, Ash R, Smirnakis S, Poirazi P, Felix RA, Dimitrov AG, Portfors C, Daun S, Toth TI, Jędrzejewska-Szmek J, Kabbani N, Blackwel KT, Moezzi B, Schaworonkow N, Plogmacher L, Goldsworthy MR, Hordacre B, McDonnell MD, Iannella N, Ridding MC, Triesch J, Maex R, Safaryan K, Steuber V, Tang R, Tang YY, Verveyko DV, Brazhe AR, Verisokin AY, Postnov DE, Günay C, Panuccio G, Giugliano M, Prinz AA, Varona P, Rabinovich MI, Denham J, Ranner T, Cohen N, Reva M, Rebola N, Kirizs T, Nusser Z, DiGregorio D, Mavritsaki E, Rentzelas P, Ukani NH, Tomkins A, Yeh CH, Bruning W, Fenichel AL, Zhou Y, Huang YC, Florescu D, Ortiz CL, Richmond P, Lo CC, Coca D, Chiang AS, Lazar AA, Moezzi B, Creaser JL, Lin C, Ashwin P, Brown JT, Ridler T, Levenstein D, Watson BO, Buzsáki G, Rinzel J, Curtu R, Nguyen A, Assadzadeh S, Robinson PA, Sanz-Leon P, Forlim CG, de Almeida LOB, Pinto RD, Rodríguez FB, Lareo Á, Forlim CG, Rodríguez FB, Montero A, Mosqueiro T, Huerta R, Rodriguez FB, Changoluisa V, Rodriguez FB, Cordeiro VL, Ceballos CC, Kamiji NL, Roque AC, Lytton WW, Knox A, Rosenthal JJC, Daun S, Popovych S, Liu L, Wang BA, Tóth TI, Grefkes C, Fink GR, Rosjat N, Perez-Trujillo A, Espinal A, Sotelo-Figueroa MA, Cruz-Aceves I, Rostro-Gonzalez H, Zapotocky M, Hoskovcová M, Kopecká J, Ulmanová O, Růžička E, Gärtner M, Duvarci S, Roeper J, Schneider G, Albert S, Schmack K, Remme M, Schreiber S, Migliore M, Lupascu CA, Bologna LL, Antonel SM, Courcol JD, Schürmann F, Çelikok SU, Navarro-López EM, Şengör NS, Elibol R, Sengor NS, Özdemir MY, Li T, Arleo A, Sheynikhovich D, Nakamura A, Shimono M, Song Y, Park S, Choi I, Jeong J, Shin HS, Sadeh S, Gleeson P, Angus Silver R, Chatzikalymniou AP, Skinner FK, Sanchez-Rodriguez LM, Sotero RC, Hertäg L, Mackwood O, Sprekeler H, Puhlmann S, Weber SN, Higgins D, Naumann LB, Weber SN, Iyer R, Mihalas S, Ticcinelli V, Stankovski T, McClintock PVE, Stefanovska A, Janjić P, Solev D, Seifert G, Kocarev L, Steinhäuser C, Salmasi M, Glasauer S, Stemmler M, Zhang D, Zhang C, Stepanyants A, Goncharenko J, Kros L, Davey N, de Zeeuw C, Hoebeek F, Sinha A, Adams R, Schmuker M, Psarrou M, Schilstra M, Torben-Nielsen B, Metzner C, Schweikard A, Mäki-Marttunen T, Zurowski B, Marinazzo D, Faes L, Stramaglia S, Jordan HOC, Stringer SM, Gajewska-Dendek E, Suffczyński P, Tam N, Zouridakis G, Pollonini L, Tang YY, Asl MM, Valizadeh A, Tass PA, Nold A, Fan W, Konrad S, Endle H, Vogt J, Tchumatchenko T, Herpich J, Tetzlaff C, Luboeinski J, Nachstedt T, Ciba M, Bahmer A, Thielemann C, Kuebler ES, Tauskela JS, Thivierge JP, Bakker R, García-Amado M, Evangelio M, Clascá F, Tiesinga P, Buckley CL, Toyoizumi T, Dubreuil AM, Monasson R, Treves A, Spalla D, Rosay S, Kleberg FI, Wong W, de Oliveira Floriano B, Matsuo T, Uchida T, Dibenedetto D, Uludağ K, Goodarzinick A, Schmidt M, Hilgetag CC, Diesmann M, van Albada SJ, Fauth M, van Rossum M, Reyes-Sánchez M, Amaducci R, Muñiz C, Varona P, Elices I, Arroyo D, Levi R, Cohen B, Chow C, Vattikuti S, Bertolotti E, Burioni R, di Volo M, Vezzani A, Menzat B, Vogels TP, Wagatsuma N, Saha S, Kapoor R, Kerr R, Wagner J, del Molino LCG, Yang GR, Mejias JF, Wang XJ, Song H, Goodliffe J, Luebke J, Weaver CM, Thomas J, Sinha N, Shaju N, Maszczyk T, Jin J, Cash SS, Dauwels J, Brandon Westover M, Karimian M, Moerel M, De Weerd P, Burwick T, Westra RL, Abeysuriya R, Hadida J, Sotiropoulos S, Jbabdi S, Woolrich M, Bensmail C, Wrobel B, Zhou X, Ji Z, Liu X, Xia Y, Wu S, Wang X, Zhang M, Wu S, Ofer N, Shefi O, Yaari G, Carnevale T, Majumdar A, Sivagnanam S, Yoshimoto K, Smirnova EY, Amakhin DV, Malkin SL, Zaitsev AV, Chizhov AV, Zaleshina M, Zaleshin A, Barranca VJ, Zhu G, Skilling QM, Maruyama D, Ognjanovski N, Aton SJ, Zochowski M, Wu J, Aton S, Rich S, Booth V, Budak M, Dura-Bernal S, Neymotin SA, Suter BA, Shepherd GMG, Felton MA, Yu AB, Boothe DL, Oie KS, Franaszczuk PJ, Shuvaev SA, Başerdem B, Zador A, Koulakov AA, López-Madrona VJ, Pereda E, Mirasso CR, Canals S, Masoli S, Rongala UB, Mazzoni A, Spanne A, Jorntell H, Oddo CM, Vartanov AV, Neklyudova AK, Kozlovskiy SA, Kiselnikov AA, Marakshina JA, Teleńczuk M, Teleńczuk B, Destexhe A, Kuokkanen PT, Kraemer A, McColgan T, Carr CE, Kempter R. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3. BMC Neurosci 2017. [PMCID: PMC5592441 DOI: 10.1186/s12868-017-0372-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Halupka KJ, Abbott CJ, Wong YT, Cloherty SL, Grayden DB, Burkitt AN, Sergeev EN, Luu CD, Brandli A, Allen PJ, Meffin H, Shivdasani MN. Neural Responses to Multielectrode Stimulation of Healthy and Degenerate Retina. Invest Ophthalmol Vis Sci 2017; 58:3770-3784. [PMID: 28744551 DOI: 10.1167/iovs.16-21290] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose Simultaneous stimulation of multiple retinal electrodes in normally sighted animals shows promise in improving the resolution of retinal prostheses. However, the effects of simultaneous stimulation on degenerate retinae remain unknown. Therefore, we investigated the characteristics of cortical responses to multielectrode stimulation of the degenerate retina. Methods Four adult cats were bilaterally implanted with retinal electrode arrays in the suprachoroidal space after unilateral adenosine triphosphate (ATP)-induced retinal photoreceptor degeneration. Functional and structural changes were characterized by using electroretinogram a-wave amplitude and optical coherence tomography. Multiunit activity was recorded from both hemispheres of the visual cortex. Responses to single- and multielectrode stimulation of the ATP-injected and fellow control eyes were characterized and compared. Results The retinae of ATP-injected eyes displayed structural and functional changes consistent with mid- to late-stage photoreceptor degeneration and remodeling. Responses to multielectrode stimulation of the ATP-injected eyes exhibited shortened latencies, lower saturated spike counts, and higher thresholds, compared to stimulation of the fellow control eyes. Electrical receptive field sizes were significantly larger in the ATP-injected eye than in the control eye, and positively correlated with the extent of degeneration. Conclusions Significant differences exist between cortical responses to stimulation of healthy and degenerate retinae. Our results highlight the importance of using a retinal degeneration model when evaluating the efficacy of novel stimulation paradigms.
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Affiliation(s)
- Kerry J Halupka
- NeuroEngineering Laboratory, Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia 2Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), New South Wales, Australia 3Bionics Institute, East Melbourne, Victoria, Australia
| | - Carla J Abbott
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital; Department of Surgery (Ophthalmology), The University of Melbourne, Victoria, Australia
| | - Yan T Wong
- Department of Physiology, Monash University, Victoria, Australia 6Department of Electrical and Computer Systems Engineering, Monash University, Victoria, Australia
| | - Shaun L Cloherty
- Department of Physiology, Monash University, Victoria, Australia 7National Vision Research Institute, Australian College of Optometry, Victoria, Australia
| | - David B Grayden
- NeuroEngineering Laboratory, Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia 3Bionics Institute, East Melbourne, Victoria, Australia 8Centre for Neural Engineering, The University of Melbourne, Victoria, Australia
| | - Anthony N Burkitt
- NeuroEngineering Laboratory, Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia 3Bionics Institute, East Melbourne, Victoria, Australia
| | - Evgeni N Sergeev
- NeuroEngineering Laboratory, Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - Chi D Luu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital; Department of Surgery (Ophthalmology), The University of Melbourne, Victoria, Australia
| | - Alice Brandli
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital; Department of Surgery (Ophthalmology), The University of Melbourne, Victoria, Australia
| | - Penelope J Allen
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital; Department of Surgery (Ophthalmology), The University of Melbourne, Victoria, Australia
| | - Hamish Meffin
- National Vision Research Institute, Australian College of Optometry, Victoria, Australia 9Australian Research Council Centre of Excellence for Integrative Brain Function, Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mohit N Shivdasani
- Bionics Institute, East Melbourne, Victoria, Australia 10Department of Medical Bionics, The University of Melbourne, Victoria, Australia
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Esler T, Burkitt AN, Grayden DB, Kerr RR, Tahayori B, Meffin H. A computational model of orientation-dependent activation of retinal ganglion cells. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2016:5447-5450. [PMID: 28269490 DOI: 10.1109/embc.2016.7591959] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Currently, a challenge in electrical stimulation for epiretinal prostheses is the avoidance of stimulation of axons of passage in the nerve fiber layer that originate from distant regions of the ganglion cell layer. A computational model of extracellular stimulation that captures the effect of neurite orientation in anisotropic tissue is developed using a modified version of the standard volume conductor model, known as the cellular composite model, embedded in a four layer model of the retina. Simulations are conducted to investigate the interaction of neural tissue orientation, electrode placement, and stimulation pulse duration and amplitude. Using appropriate multiple electrode configurations and higher frequency stimulation, preferential activation of the axon initial segment is shown to be possible for a range of realistic electrode-retina separation distances. These results establish a quantitative relationship between the time-course of stimulation and physical properties of the tissue, such as fiber orientation.
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Qin W, Hadjinicolaou A, Grayden DB, Meffin H, Burkitt AN, Ibbotson MR, Kameneva T. Single-compartment models of retinal ganglion cells with different electrophysiologies. Network 2017; 28:74-93. [PMID: 29649919 DOI: 10.1080/0954898x.2018.1455993] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
There are more than 15 different types of retinal ganglion cells (RGCs) in the mammalian retina. To model responses of RGCs to electrical stimulation, we use single-compartment Hodgkin-Huxley-type models and run simulations in the Neuron environment. We use our recently published in vitro data of different morphological cell types to constrain the model, and study the effects of electrophysiology on the cell responses separately from the effects of morphology. We find simple models that can match the spike patterns of different types of RGCs. These models, with different input-output properties, may be used in a network to study retinal network dynamics and interactions.
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Affiliation(s)
- Wei Qin
- a Department of Biomedical Engineering , The University of Melbourne , Melbourne , Australia
| | - Alex Hadjinicolaou
- b Department of Neurology, Massachusetts General Hospital , Harvard Medical School , Boston , USA
| | - David B Grayden
- a Department of Biomedical Engineering , The University of Melbourne , Melbourne , Australia
| | - Hamish Meffin
- c National Vision Research Institute , Australian College of Optometry , Melbourne , Australia
- d Department of Optometry and Vision Sciences , University of Melbourne , Melbourne , Australia
| | - Anthony N Burkitt
- a Department of Biomedical Engineering , The University of Melbourne , Melbourne , Australia
| | - Michael R Ibbotson
- c National Vision Research Institute , Australian College of Optometry , Melbourne , Australia
- d Department of Optometry and Vision Sciences , University of Melbourne , Melbourne , Australia
| | - Tatiana Kameneva
- a Department of Biomedical Engineering , The University of Melbourne , Melbourne , Australia
- e Engineering and Technology , Swinburne University of Technology , Melbourne , Australia
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Ahnood A, Meffin H, Garrett DJ, Fox K, Ganesan K, Stacey A, Apollo NV, Wong YT, Lichter SG, Kentler W, Kavehei O, Greferath U, Vessey KA, Ibbotson MR, Fletcher EL, Burkitt AN, Prawer S. Diamond Devices for High Acuity Prosthetic Vision. ACTA ACUST UNITED AC 2016; 1:e1600003. [DOI: 10.1002/adbi.201600003] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 10/27/2016] [Indexed: 12/17/2022]
Affiliation(s)
- Arman Ahnood
- School of Physics University of Melbourne Victoria 3010 Australia
| | - Hamish Meffin
- National Vision Research Institute Australian College of Optometry Victoria 3053 Australia
- ARC Centre of Excellence for Integrative Brain Function Department of Optometry and Vision Science University of Melbourne Victoria 3010 Australia
| | - David J. Garrett
- School of Physics University of Melbourne Victoria 3010 Australia
| | - Kate Fox
- School of Physics University of Melbourne Victoria 3010 Australia
- School of Engineering RMIT University Melbourne 3000 Australia
| | | | - Alastair Stacey
- School of Physics University of Melbourne Victoria 3010 Australia
| | | | - Yan T. Wong
- National Vision Research Institute Australian College of Optometry Victoria 3053 Australia
- Department of Electrical & Electronic Engineering The University of Melbourne Victoria 3010 Australia
| | | | - William Kentler
- Department of Electrical & Electronic Engineering The University of Melbourne Victoria 3010 Australia
| | - Omid Kavehei
- School of Engineering RMIT University Melbourne 3000 Australia
| | - Ursula Greferath
- Department of Anatomy and Neuroscience University of Melbourne Victoria 3010 Australia
| | - Kirstan A. Vessey
- Department of Anatomy and Neuroscience University of Melbourne Victoria 3010 Australia
| | - Michael R. Ibbotson
- National Vision Research Institute Australian College of Optometry Victoria 3053 Australia
- ARC Centre of Excellence for Integrative Brain Function Department of Optometry and Vision Science University of Melbourne Victoria 3010 Australia
| | - Erica L. Fletcher
- Department of Anatomy and Neuroscience University of Melbourne Victoria 3010 Australia
| | - Anthony N. Burkitt
- Department of Electrical & Electronic Engineering The University of Melbourne Victoria 3010 Australia
| | - Steven Prawer
- School of Physics University of Melbourne Victoria 3010 Australia
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Halupka KJ, Shivdasani MN, Cloherty SL, Grayden DB, Wong YT, Burkitt AN, Meffin H. Prediction of cortical responses to simultaneous electrical stimulation of the retina. J Neural Eng 2016; 14:016006. [DOI: 10.1088/1741-2560/14/1/016006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Maturana MI, Apollo NV, Garrett DJ, Kameneva T, Meffin H, Ibbotson MR, Cloherty SL, Grayden DB. The effects of temperature changes on retinal ganglion cell responses to electrical stimulation. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2015:7506-9. [PMID: 26738028 DOI: 10.1109/embc.2015.7320128] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Little is known about how the retina's response to electrical stimulation is modified by temperatures. In vitro experiments are often used to inform in vivo studies, hence it is important to understand what changes occur at physiological temperature. To investigate this, we recorded from eight RGCs in vitro at three temperatures; room temperature (24°C), 30°C and 34°C. Results show that response latencies and thresholds are reduced, bursting spike rates in response to stimulation increases, and the spiking becomes more consistently locked to the stimulus at higher temperatures.
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Sharpee TO, Destexhe A, Kawato M, Sekulić V, Skinner FK, Wójcik DK, Chintaluri C, Cserpán D, Somogyvári Z, Kim JK, Kilpatrick ZP, Bennett MR, Josić K, Elices I, Arroyo D, Levi R, Rodriguez FB, Varona P, Hwang E, Kim B, Han HB, Kim T, McKenna JT, Brown RE, McCarley RW, Choi JH, Rankin J, Popp PO, Rinzel J, Tabas A, Rupp A, Balaguer-Ballester E, Maturana MI, Grayden DB, Cloherty SL, Kameneva T, Ibbotson MR, Meffin H, Koren V, Lochmann T, Dragoi V, Obermayer K, Psarrou M, Schilstra M, Davey N, Torben-Nielsen B, Steuber V, Ju H, Yu J, Hines ML, Chen L, Yu Y, Kim J, Leahy W, Shlizerman E, Birgiolas J, Gerkin RC, Crook SM, Viriyopase A, Memmesheimer RM, Gielen S, Dabaghian Y, DeVito J, Perotti L, Kim AJ, Fenk LM, Cheng C, Maimon G, Zhao C, Widmer Y, Sprecher S, Senn W, Halnes G, Mäki-Marttunen T, Keller D, Pettersen KH, Andreassen OA, Einevoll GT, Yamada Y, Steyn-Ross ML, Alistair Steyn-Ross D, Mejias JF, Murray JD, Kennedy H, Wang XJ, Kruscha A, Grewe J, Benda J, Lindner B, Badel L, Ohta K, Tsuchimoto Y, Kazama H, Kahng B, Tam ND, Pollonini L, Zouridakis G, Soh J, Kim D, Yoo M, Palmer SE, Culmone V, Bojak I, Ferrario A, Merrison-Hort R, Borisyuk R, Kim CS, Tezuka T, Joo P, Rho YA, Burton SD, Bard Ermentrout G, Jeong J, Urban NN, Marsalek P, Kim HH, Moon SH, Lee DW, Lee SB, Lee JY, Molkov YI, Hamade K, Teka W, Barnett WH, Kim T, Markin S, Rybak IA, Forro C, Dermutz H, Demkó L, Vörös J, Babichev A, Huang H, Verduzco-Flores S, Dos Santos F, Andras P, Metzner C, Schweikard A, Zurowski B, Roach JP, Sander LM, Zochowski MR, Skilling QM, Ognjanovski N, Aton SJ, Zochowski M, Wang SJ, Ouyang G, Guang J, Zhang M, Michael Wong KY, Zhou C, Robinson PA, Sanz-Leon P, Drysdale PM, Fung F, Abeysuriya RG, Rennie CJ, Zhao X, Choe Y, Yang HF, Mi Y, Lin X, Wu S, Liedtke J, Schottdorf M, Wolf F, Yamamura Y, Wickens JR, Rumbell T, Ramsey J, Reyes A, Draguljić D, Hof PR, Luebke J, Weaver CM, He H, Yang X, Ma H, Xu Z, Wang Y, Baek K, Morris LS, Kundu P, Voon V, Agnes EJ, Vogels TP, Podlaski WF, Giese M, Kuravi P, Vogels R, Seeholzer A, Podlaski W, Ranjan R, Vogels T, Torres JJ, Baroni F, Latorre R, Gips B, Lowet E, Roberts MJ, de Weerd P, Jensen O, van der Eerden J, Goodarzinick A, Niry MD, Valizadeh A, Pariz A, Parsi SS, Warburton JM, Marucci L, Tamagnini F, Brown J, Tsaneva-Atanasova K, Kleberg FI, Triesch J, Moezzi B, Iannella N, Schaworonkow N, Plogmacher L, Goldsworthy MR, Hordacre B, McDonnell MD, Ridding MC, Zapotocky M, Smit D, Fouquet C, Trembleau A, Dasgupta S, Nishikawa I, Aihara K, Toyoizumi T, Robb DT, Mellen N, Toporikova N, Tang R, Tang YY, Liang G, Kiser SA, Howard JH, Goncharenko J, Voronenko SO, Ahamed T, Stephens G, Yger P, Lefebvre B, Spampinato GLB, Esposito E, et Olivier Marre MS, Choi H, Song MH, Chung S, Lee DD, Sompolinsky H, Phillips RS, Smith J, Chatzikalymniou AP, Ferguson K, Alex Cayco Gajic N, Clopath C, Angus Silver R, Gleeson P, Marin B, Sadeh S, Quintana A, Cantarelli M, Dura-Bernal S, Lytton WW, Davison A, Li L, Zhang W, Wang D, Song Y, Park S, Choi I, Shin HS, Choi H, Pasupathy A, Shea-Brown E, Huh D, Sejnowski TJ, Vogt SM, Kumar A, Schmidt R, Van Wert S, Schiff SJ, Veale R, Scheutz M, Lee SW, Gallinaro J, Rotter S, Rubchinsky LL, Cheung CC, Ratnadurai-Giridharan S, Shomali SR, Ahmadabadi MN, Shimazaki H, Nader Rasuli S, Zhao X, Rasch MJ, Wilting J, Priesemann V, Levina A, Rudelt L, Lizier JT, Spinney RE, Rubinov M, Wibral M, Bak JH, Pillow J, Zaho Y, Park IM, Kang J, Park HJ, Jang J, Paik SB, Choi W, Lee C, Song M, Lee H, Park Y, Yilmaz E, Baysal V, Ozer M, Saska D, Nowotny T, Chan HK, Diamond A, Herrmann CS, Murray MM, Ionta S, Hutt A, Lefebvre J, Weidel P, Duarte R, Morrison A, Lee JH, Iyer R, Mihalas S, Koch C, Petrovici MA, Leng L, Breitwieser O, Stöckel D, Bytschok I, Martel R, Bill J, Schemmel J, Meier K, Esler TB, Burkitt AN, Kerr RR, Tahayori B, Nolte M, Reimann MW, Muller E, Markram H, Parziale A, Senatore R, Marcelli A, Skiker K, Maouene M, Neymotin SA, Seidenstein A, Lakatos P, Sanger TD, Menzies RJ, McLauchlan C, van Albada SJ, Kedziora DJ, Neymotin S, Kerr CC, Suter BA, Shepherd GMG, Ryu J, Lee SH, Lee J, Lee HJ, Lim D, Wang J, Lee H, Jung N, Anh Quang L, Maeng SE, Lee TH, Lee JW, Park CH, Ahn S, Moon J, Choi YS, Kim J, Jun SB, Lee S, Lee HW, Jo S, Jun E, Yu S, Goetze F, Lai PY, Kim S, Kwag J, Jang HJ, Filipović M, Reig R, Aertsen A, Silberberg G, Bachmann C, Buttler S, Jacobs H, Dillen K, Fink GR, Kukolja J, Kepple D, Giaffar H, Rinberg D, Shea S, Koulakov A, Bahuguna J, Tetzlaff T, Kotaleski JH, Kunze T, Peterson A, Knösche T, Kim M, Kim H, Park JS, Yeon JW, Kim SP, Kang JH, Lee C, Spiegler A, Petkoski S, Palva MJ, Jirsa VK, Saggio ML, Siep SF, Stacey WC, Bernar C, Choung OH, Jeong Y, Lee YI, Kim SH, Jeong M, Lee J, Kwon J, Kralik JD, Jahng J, Hwang DU, Kwon JH, Park SM, Kim S, Kim H, Kim PS, Yoon S, Lim S, Park C, Miller T, Clements K, Ahn S, Ji EH, Issa FA, Baek J, Oba S, Yoshimoto J, Doya K, Ishii S, Mosqueiro TS, Strube-Bloss MF, Smith B, Huerta R, Hadrava M, Hlinka J, Bos H, Helias M, Welzig CM, Harper ZJ, Kim WS, Shin IS, Baek HM, Han SK, Richter R, Vitay J, Beuth F, Hamker FH, Toppin K, Guo Y, Graham BP, Kale PJ, Gollo LL, Stern M, Abbott LF, Fedorov LA, Giese MA, Ardestani MH, Faraji MJ, Preuschoff K, Gerstner W, van Gendt MJ, Briaire JJ, Kalkman RK, Frijns JHM, Lee WH, Frangou S, Fulcher BD, Tran PHP, Fornito A, Gliske SV, Lim E, Holman KA, Fink CG, Kim JS, Mu S, Briggman KL, Sebastian Seung H, Wegener D, Bohnenkamp L, Ernst UA, Devor A, Dale AM, Lines GT, Edwards A, Tveito A, Hagen E, Senk J, Diesmann M, Schmidt M, Bakker R, Shen K, Bezgin G, Hilgetag CC, van Albada SJ, Sun H, Sourina O, Huang GB, Klanner F, Denk C, Glomb K, Ponce-Alvarez A, Gilson M, Ritter P, Deco G, Witek MAG, Clarke EF, Hansen M, Wallentin M, Kringelbach ML, Vuust P, Klingbeil G, De Schutter E, Chen W, Zang Y, Hong S, Takashima A, Zamora C, Gallimore AR, Goldschmidt D, Manoonpong P, Karoly PJ, Freestone DR, Soundry D, Kuhlmann L, Paninski L, Cook M, Lee J, Fishman YI, Cohen YE, Roberts JA, Cocchi L, Sweeney Y, Lee S, Jung WS, Kim Y, Jung Y, Song YK, Chavane F, Soman K, Muralidharan V, Srinivasa Chakravarthy V, Shivkumar S, Mandali A, Pragathi Priyadharsini B, Mehta H, Davey CE, Brinkman BAW, Kekona T, Rieke F, Buice M, De Pittà M, Berry H, Brunel N, Breakspear M, Marsat G, Drew J, Chapman PD, Daly KC, Bradle SP, Seo SB, Su J, Kavalali ET, Blackwell J, Shiau L, Buhry L, Basnayake K, Lee SH, Levy BA, Baker CI, Leleu T, Philips RT, Chhabria K. 25th Annual Computational Neuroscience Meeting: CNS-2016. BMC Neurosci 2016; 17 Suppl 1:54. [PMID: 27534393 PMCID: PMC5001212 DOI: 10.1186/s12868-016-0283-6] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber O8 Analytic solution of cable energy function for cortical axons and dendrites Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network Jimin Kim, Will Leahy, Eli Shlizerman O10 Is the model any good? Objective criteria for computational neuroscience model selection Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook O11 Cooperation and competition of gamma oscillation mechanisms Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen O12 A discrete structure of the brain waves Yuri Dabaghian, Justin DeVito, Luca Perotti O13 Direction-specific silencing of the Drosophila gaze stabilization system Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon O14 What does the fruit fly think about values? A model of olfactory associative learning Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn O15 Effects of ionic diffusion on power spectra of local field potentials (LFP) Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits Yasunori Yamada O17 Spatial coarse-graining the brain: origin of minicolumns Moira L. Steyn-Ross, D. Alistair Steyn-Ross O18 Modeling large-scale cortical networks with laminar structure Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang O19 Information filtering by partial synchronous spikes in a neural population Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner O20 Decoding context-dependent olfactory valence in Drosophila Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama P1 Neural network as a scale-free network: the role of a hub B. Kahng P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging Nicoladie D. Tam P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique Nicoladie D.Tam, Luca Pollonini, George Zouridakis P4 Modeling jamming avoidance of weakly electric fish Jaehyun Soh, DaeEun Kim P5 Synergy and redundancy of retinal ganglion cells in prediction Minsu Yoo, S. E. Palmer P6 A neural field model with a third dimension representing cortical depth Viviana Culmone, Ingo Bojak P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk P8 The recognition dynamics in the brain Chang Sub Kim P9 Multivariate spike train analysis using a positive definite kernel Taro Tezuka P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia Pangyu Joo P11 The ionic basis of heterogeneity affects stochastic synchrony Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban P12 Circular statistics of noise in spike trains with a periodic component Petr Marsalek P14 Representations of directions in EEG-BCI using Gaussian readouts Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong P15 Action selection and reinforcement learning in basal ganglia during reaching movements Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak P17 Axon guidance: modeling axonal growth in T-Junction assay Csaba Forro, Harald Dermutz, László Demkó, János Vörös P19 Transient cell assembly networks encode persistent spatial memories Yuri Dabaghian, Andrey Babichev P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons Haiping Huang P21 Design of biologically-realistic simulations for motor control Sergio Verduzco-Flores P22 Towards understanding the functional impact of the behavioural variability of neurons Filipa Dos Santos, Peter Andras P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia Christoph Metzner, Achim Schweikard, Bartosz Zurowski P24 Memory recall and spike frequency adaptation James P. Roach, Leonard M. Sander, Michal R. Zochowski P25 Stability of neural networks and memory consolidation preferentially occur near criticality Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou P27 Neurofield: a C++ library for fast simulation of 2D neural field models Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao P28 Action-based grounding: Beyond encoding/decoding in neural code Yoonsuck Choe, Huei-Fang Yang P29 Neural computation in a dynamical system with multiple time scales Yuanyuan Mi, Xiaohan Lin, Si Wu P30 Maximum entropy models for 3D layouts of orientation selectivity Joscha Liedtke, Manuel Schottdorf, Fred Wolf P31 A behavioral assay for probing computations underlying curiosity in rodents Yoriko Yamamura, Jeffery R. Wickens P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon P35 Dynamics of cooperative excitatory and inhibitory plasticity Everton J. Agnes, Tim P. Vogels P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons William F. Podlaski, Tim P. Vogels P37 Phenomenological neural model for adaptation of neurons in area IT Martin Giese, Pradeep Kuravi, Rufin Vogels P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona P40 Different roles for transient and sustained activity during active visual processing Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh P42 High frequency neuron can facilitate propagation of signal in neural networks Aref Pariz, Shervin S. Parsi, Alireza Valizadeh P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP Florence I. Kleberg, Jochen Triesch P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. Ridding, Jochen Triesch P46 Structure and dynamics of axon network formed in primary cell culture Martin Zapotocky, Daniel Smit, Coralie Fouquet, Alain Trembleau P47 Efficient signal processing and sampling in random networks that generate variability Sakyasingha Dasgupta, Isao Nishikawa, Kazuyuki Aihara, Taro Toyoizumi P48 Modeling the effect of riluzole on bursting in respiratory neural networks Daniel T. Robb, Nick Mellen, Natalia Toporikova P49 Mapping relaxation training using effective connectivity analysis Rongxiang Tang, Yi-Yuan Tang P50 Modeling neuron oscillation of implicit sequence learning Guangsheng Liang, Seth A. Kiser, James H. Howard, Jr., Yi-Yuan Tang P51 The role of cerebellar short-term synaptic plasticity in the pathology and medication of downbeat nystagmus Julia Goncharenko, Neil Davey, Maria Schilstra, Volker Steuber P52 Nonlinear response of noisy neurons Sergej O. Voronenko, Benjamin Lindner P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion Tosif Ahamed, Greg Stephens P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre P55 Sufficient sampling rates for fast hand motion tracking Hansol Choi, Min-Ho Song P56 Linear readout of object manifolds SueYeon Chung, Dan D. Lee, Haim Sompolinsky P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves Ryan S. Phillips, Jeffrey Smith P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver P60 A set of curated cortical models at multiple scales on Open Source Brain Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver P61 A synaptic story of dynamical information encoding in neural adaptation Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu P62 Physical modeling of rule-observant rodent behavior Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes Hannah Choi, Anitha Pasupathy, Eric Shea-Brown P65 Stability of FORCE learning on spiking and rate-based networks Dongsung Huh, Terrence J. Sejnowski P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments Simon M. Vogt, Arvind Kumar, Robert Schmidt P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation Stephen Van Wert, Steven J. Schiff P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo Richard Veale, Matthias Scheutz P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions Sang Wan Lee P70 Maturation of sensory networks through homeostatic structural plasticity Júlia Gallinaro, Stefan Rotter P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings Paula Sanz-Leon, Peter A. Robinson P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan P73 Exact spike-timing distribution reveals higher-order interactions of neurons Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli P74 Neural mechanism of visual perceptual learning using a multi-layered neural network Xiaochen Zhao, Malte J. Rasch P75 Inferring collective spiking dynamics from mostly unobserved systems Jens Wilting, Viola Priesemann P76 How to infer distributions in the brain from subsampled observations Anna Levina, Viola Priesemann P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons Lucas Rudelt, Joseph T. Lizier, Viola Priesemann P78 A nearest-neighbours based estimator for transfer entropy between spike trains Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann P79 Active learning of psychometric functions with multinomial logistic models Ji Hyun Bak, Jonathan Pillow P81 Inferring low-dimensional network dynamics with variational latent Gaussian process Yuan Zaho, Il Memming Park P82 Computational investigation of energy landscapes in the resting state subcortical brain network Jiyoung Kang, Hae-Jeong Park P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map Jaeson Jang, Se-Bum Paik P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition Woochul Choi, Se-Bum Paik P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map Changju Lee, Jaeson Jang, Se-Bum Paik P86 Computational method classifying neural network activity patterns for imaging data Min Song, Hyeonsu Lee, Se-Bum Paik P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory Youngjin Park, Woochul Choi, Se-Bum Paik P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons Ergin Yilmaz, Veli Baysal, Mahmut Ozer P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance Veronika Koren, Klaus Obermayer P90 Methods for building accurate models of individual neurons Daniel Saska, Thomas Nowotny P91 A full size mathematical model of the early olfactory system of honeybees Ho Ka Chan, Alan Diamond, Thomas Nowotny P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks Christoph S. Herrmann, Micah M. Murray, Silvio Ionta, Axel Hutt, Jérémie Lefebvre P93 Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input Philipp Weidel, Renato Duarte, Abigail Morrison P94 Modulation of tuning induced by abrupt reduction of SST cell activity Jung H. Lee, Ramakrishnan Iyer, Stefan Mihalas P95 The functional role of VIP cell activation during locomotion Jung H. Lee, Ramakrishnan Iyer, Christof Koch, Stefan Mihalas P96 Stochastic inference with spiking neural networks Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier P97 Modeling orientation-selective electrical stimulation with retinal prostheses Timothy B. Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin P98 Ion channel noise can explain firing correlation in auditory nerves Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram P100 On the representation of arm reaching movements: a computational model Antonio Parziale, Rosa Senatore, Angelo Marcelli P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior Rosa Senatore, Antonio Parziale, Angelo Marcelli P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge K. Skiker, M. Maouene P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton P104 Effect of network size on computational capacity Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas Juhyoung Ryu, Sang-Hun Lee P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception Joonwon Lee, Sang-Hun Lee P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making Hyang Jung Lee, Sang-Hun Lee P110 A Bayesian algorithm for phoneme Perception and its neural implementation Daeseob Lim, Sang-Hun Lee P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol Jisung Wang, Heonsoo Lee P112 Self-organized criticality of neural avalanche in a neural model on complex networks Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee P114 Computational model to replicate seizure suppression effect by electrical stimulation Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy Felix Goetze, Pik-Yin Lai P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell Seonghyun Kim, Jeehyun Kwag P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model Hyun Jae Jang, Jeehyun Kwag P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison P120 Learning sparse representations in the olfactory bulb Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov P121 Functional classification of homologous basal-ganglia networks Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski P122 Short term memory based on multistability Tim Kunze, Andre Peterson, Thomas Knösche P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units Minjung Kim, Hojeong Kim P125 Decoding laser-induced somatosensory information from EEG Ji Sung Park, Ji Won Yeon, Sung-Phil Kim P126 Phase synchronization of alpha activity for EEG-based personal authentication Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa P130 Epileptic seizures in the unfolding of a codimension-3 singularity Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa P131 Incremental dimensional exploratory reasoning under multi-dimensional environment Oh-hyeon Choung, Yong Jeong P132 A low-cost model of eye movements and memory in personal visual cognition Yong-il Lee, Jaeseung Jeong P133 Complex network analysis of structural connectome of autism spectrum disorder patients Su Hyun Kim, Mir Jeong, Jaeseung Jeong P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong P136 Detecting purchase decision based on hyperfrontality of the EEG Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it? Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta P146 Swinging networks Michal Hadrava, Jaroslav Hlinka P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions Hannah Bos, Moritz Helias P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning Charles M. Welzig, Zachary J. Harper P149 Multiscale complexity analysis for the segmentation of MRI images Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han P150 A neuro-computational model of emotional attention René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker P151 Multi-site delayed feedback stimulation in parkinsonian networks Kelly Toppin, Yixin Guo P152 Bistability in Hodgkin–Huxley-type equations Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency Mark D. McDonnell, Bruce P. Graham P154 Quantifying resilience patterns in brain networks: the importance of directionality Penelope J. Kale, Leonardo L. Gollo P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations Merav Stern, L. F. Abbott P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues Leonid A. Fedorov, Martin A. Giese P157 Spiking model for the interaction between action recognition and action execution Mohammad Hovaidi Ardestani, Martin Giese P158 Surprise-modulated belief update: how to learn within changing environments? Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks Won Hee Lee, Sophia Frangou P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. Andreassen P167 Local field potentials in a 4 × 4 mm2 multi-layered network model Espen Hagen, Johanna Senk, Sacha J. van Albada, Markus Diesmann P168 A spiking network model explains multi-scale properties of cortical dynamics Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Claus-Christian Hilgetag, Markus Diesmann, Sacha Jennifer van Albada P169 Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups Haoqi Sun, Olga Sourina, Guang-Bin Huang, Felix Klanner, Cornelia Denk P170 Tensor decomposition reveals RSNs in simulated resting state fMRI Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco P171 Getting in the groove: testing a new model-based method for comparing task-evoked vs resting-state activity in fMRI data on music listening Matthieu Gilson, Maria AG Witek, Eric F. Clarke, Mads Hansen, Mikkel Wallentin, Gustavo Deco, Morten L. Kringelbach, Peter Vuust P172 STochastic engine for pathway simulation (STEPS) on massively parallel processors Guido Klingbeil, Erik De Schutter P173 Toolkit support for complex parallel spatial stochastic reaction–diffusion simulation in STEPS Weiliang Chen, Erik De Schutter P174 Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input Yunliang Zang, Erik De Schutter P175 Dendritic morphology determines how dendrites are organized into functional subunits Sungho Hong, Akira Takashima, Erik De Schutter P176 A model of Ca2+/calmodulin-dependent protein kinase II activity in long term depression at Purkinje cells Criseida Zamora, Andrew R. Gallimore, Erik De Schutter P177 Reward-modulated learning of population-encoded vectors for insect-like navigation in embodied agents Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta P178 Data-driven neural models part II: connectivity patterns of human seizures Philippa J. Karoly, Dean R. Freestone, Daniel Soundry, Levin Kuhlmann, Liam Paninski, Mark Cook P179 Data-driven neural models part I: state and parameter estimation Dean R. Freestone, Philippa J. Karoly, Daniel Soundry, Levin Kuhlmann, Mark Cook P180 Spectral and spatial information processing in human auditory streaming Jaejin Lee, Yonatan I. Fishman, Yale E. Cohen P181 A tuning curve for the global effects of local perturbations in neural activity: Mapping the systems-level susceptibility of the brain Leonardo L. Gollo, James A. Roberts, Luca Cocchi P182 Diverse homeostatic responses to visual deprivation mediated by neural ensembles Yann Sweeney, Claudia Clopath P183 Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography Soohyun Lee, Woo-Sung Jung, Jee Hyun Choi P184 Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep Bowon Kim, Youngsoo Kim, Eunjin Hwang, Jee Hyun Choi P185 Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response Younginha Jung, Eunjin Hwang, Yoon-Kyu Song, Jee Hyun Choi P186 Neural field model of localized orientation selective activation in V1 James Rankin, Frédéric Chavane P187 An oscillatory network model of Head direction and Grid cells using locomotor inputs Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy P188 A computational model of hippocampus inspired by the functional architecture of basal ganglia Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy P189 A computational architecture to model the microanatomy of the striatum and its functional properties Sabyasachi Shivkumar, Vignesh Muralidharan, V. Srinivasa Chakravarthy P190 A scalable cortico-basal ganglia model to understand the neural dynamics of targeted reaching Vignesh Muralidharan, Alekhya Mandali, B. Pragathi Priyadharsini, Hima Mehta, V. Srinivasa Chakravarthy P191 Emergence of radial orientation selectivity from synaptic plasticity Catherine E. Davey, David B. Grayden, Anthony N. Burkitt P192 How do hidden units shape effective connections between neurons? Braden A. W. Brinkman, Tyler Kekona, Fred Rieke, Eric Shea-Brown, Michael Buice P193 Characterization of neural firing in the presence of astrocyte-synapse signaling Maurizio De Pittà, Hugues Berry, Nicolas Brunel P194 Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics James A. Roberts, Leonardo L. Gollo, Michael Breakspear P195 Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings Gary Marsat, Jordan Drew, Phillip D. Chapman, Kevin C. Daly, Samual P. Bradley P196 Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses Sat Byul Seo, Jianzhong Su, Ege T. Kavalali, Justin Blackwell P199 Gamma oscillation via adaptive exponential integrate-and-fire neurons LieJune Shiau, Laure Buhry, Kanishka Basnayake P200 Visual face representations during memory retrieval compared to perception Sue-Hyun Lee, Brandon A. Levy, Chris I. Baker P201 Top-down modulation of sequential activity within packets modeled using avalanche dynamics Timothée Leleu, Kazuyuki Aihara Q28 An auto-encoder network realizes sparse features under the influence of desynchronized vascular dynamics Ryan T. Philips, Karishma Chhabria, V. Srinivasa Chakravarthy
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Tong W, Fox K, Zamani A, Turnley AM, Ganesan K, Ahnood A, Cicione R, Meffin H, Prawer S, Stacey A, Garrett DJ. Optimizing growth and post treatment of diamond for high capacitance neural interfaces. Biomaterials 2016; 104:32-42. [PMID: 27424214 DOI: 10.1016/j.biomaterials.2016.07.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Revised: 06/23/2016] [Accepted: 07/04/2016] [Indexed: 01/03/2023]
Abstract
Electrochemical and biological properties are two crucial criteria in the selection of the materials to be used as electrodes for neural interfaces. For neural stimulation, materials are required to exhibit high capacitance and to form intimate contact with neurons for eliciting effective neural responses at acceptably low voltages. Here we report on a new high capacitance material fabricated using nitrogen included ultrananocrystalline diamond (N-UNCD). After exposure to oxygen plasma for 3 h, the activated N-UNCD exhibited extremely high electrochemical capacitance greater than 1 mF/cm(2), which originates from the special hybrid sp(2)/sp(3) structure of N-UNCD. The in vitro biocompatibility of the activated N-UNCD was then assessed using rat cortical neurons and surface roughness was found to be critical for healthy neuron growth, with best results observed on surfaces with a roughness of approximately 20 nm. Therefore, by using oxygen plasma activated N-UNCD with appropriate surface roughness, and considering the chemical and mechanical stability of diamond, the fabricated neural interfaces are expected to exhibit high efficacy, long-term stability and a healthy neuron/electrode interface.
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Affiliation(s)
- Wei Tong
- School of Physics, University of Melbourne, Victoria 3010, Australia
| | - Kate Fox
- Centre for Additive Manufacturing, School of Engineering, RMIT University, Victoria 3001, Australia
| | - Akram Zamani
- Department of Anatomy and Neuroscience, University of Melbourne, Victoria 3010, Australia
| | - Ann M Turnley
- Department of Anatomy and Neuroscience, University of Melbourne, Victoria 3010, Australia
| | | | - Arman Ahnood
- School of Physics, University of Melbourne, Victoria 3010, Australia
| | - Rosemary Cicione
- School of Physics, University of Melbourne, Victoria 3010, Australia
| | - Hamish Meffin
- National Vision Research Institute, Department of Optometry and Vision Science University of Melbourne, Victoria 3010, Australia
| | - Steven Prawer
- School of Physics, University of Melbourne, Victoria 3010, Australia
| | - Alastair Stacey
- School of Physics, University of Melbourne, Victoria 3010, Australia
| | - David J Garrett
- School of Physics, University of Melbourne, Victoria 3010, Australia.
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Maturana MI, Apollo NV, Hadjinicolaou AE, Garrett DJ, Cloherty SL, Kameneva T, Grayden DB, Ibbotson MR, Meffin H. A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina. PLoS Comput Biol 2016; 12:e1004849. [PMID: 27035143 PMCID: PMC4818105 DOI: 10.1371/journal.pcbi.1004849] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 03/04/2016] [Indexed: 11/19/2022] Open
Abstract
Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron’s electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy. Implantable multi-electrode arrays (MEAs) are used to record neurological signals and stimulate the nervous system to restore lost function (e.g. cochlear implants). MEAs that can combine both sensing and stimulation will revolutionize the development of the next generation of devices. Simple models that can accurately characterize neural responses to electrical stimulation are necessary for the development of future neuroprostheses controlled by neural feedback. We demonstrate a model that accurately predicts neural responses to concurrent stimulation across multiple electrodes. The model is simple to evaluate, making it an appropriate model for use with neural feedback. The methods described are applicable to a wide range of neural prostheses, thus greatly assisting future device development.
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Affiliation(s)
- Matias I. Maturana
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia
- Department of Electrical and Electronic Engineering, NeuroEngineering Laboratory, University of Melbourne, Parkville, Victoria, Australia
| | - Nicholas V. Apollo
- Department of Physics, University of Melbourne, Parkville, Victoria, Australia
| | - Alex E. Hadjinicolaou
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia
| | - David J. Garrett
- Department of Physics, University of Melbourne, Parkville, Victoria, Australia
| | - Shaun L. Cloherty
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia
- Department of Electrical and Electronic Engineering, NeuroEngineering Laboratory, University of Melbourne, Parkville, Victoria, Australia
- Department of Optometry and Vision Sciences, ARC Centre of Excellence for Integrative Brain Function, University of Melbourne, Parkville, Victoria, Australia
| | - Tatiana Kameneva
- Department of Electrical and Electronic Engineering, NeuroEngineering Laboratory, University of Melbourne, Parkville, Victoria, Australia
| | - David B. Grayden
- Department of Electrical and Electronic Engineering, NeuroEngineering Laboratory, University of Melbourne, Parkville, Victoria, Australia
- Centre for Neural Engineering, University of Melbourne, Parkville, Victoria, Australia
| | - Michael R. Ibbotson
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia
- Department of Optometry and Vision Sciences, ARC Centre of Excellence for Integrative Brain Function, University of Melbourne, Parkville, Victoria, Australia
| | - Hamish Meffin
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia
- Department of Optometry and Vision Sciences, ARC Centre of Excellence for Integrative Brain Function, University of Melbourne, Parkville, Victoria, Australia
- * E-mail:
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Wong YT, Halupka K, Kameneva T, Cloherty SL, Grayden DB, Burkitt AN, Meffin H, Shivdasani MN. Spectral distribution of local field potential responses to electrical stimulation of the retina. J Neural Eng 2016; 13:036003. [DOI: 10.1088/1741-2560/13/3/036003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Kameneva T, Maturana MI, Hadjinicolaou AE, Cloherty SL, Ibbotson MR, Grayden DB, Burkitt AN, Meffin H. Retinal ganglion cells: mechanisms underlying depolarization block and differential responses to high frequency electrical stimulation of ON and OFF cells. J Neural Eng 2016; 13:016017. [DOI: 10.1088/1741-2560/13/1/016017] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Peterson ADH, Mareels IMY, Meffin H, Grayden DB, Cook MJ, Burkitt AN. The neurodynamics of epilepsy: a homotopy analysis between current-based and conductance-based synapses in a neural field model of epilepsy. BMC Neurosci 2015. [PMCID: PMC4697564 DOI: 10.1186/1471-2202-16-s1-p23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Fox K, Meffin H, Burns O, Abbott CJ, Allen PJ, Opie NL, McGowan C, Yeoh J, Ahnood A, Luu CD, Cicione R, Saunders AL, McPhedran M, Cardamone L, Villalobos J, Garrett DJ, Nayagam DAX, Apollo NV, Ganesan K, Shivdasani MN, Stacey A, Escudie M, Lichter S, Shepherd RK, Prawer S. Development of a Magnetic Attachment Method for Bionic Eye Applications. Artif Organs 2015; 40:E12-24. [DOI: 10.1111/aor.12582] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Kate Fox
- School of Physics; University of Melbourne; Melbourne Victoria Australia
- School of Aerospace, Mechanical and Manufacturing Engineering; RMIT University; Melbourne Victoria Australia
| | - Hamish Meffin
- Department of Electrical and Electronic Engineering; University of Melbourne; Melbourne Victoria Australia
- National Vision Research Institute; Australian College of Optometry; Melbourne Victoria Australia
| | - Owen Burns
- The Bionics Institute; Melbourne Victoria Australia
| | - Carla J. Abbott
- Centre for Eye Research Australia (CERA) Royal Victorian Eye and Ear Hospital; Melbourne Victoria Australia
| | - Penelope J. Allen
- Centre for Eye Research Australia (CERA) Royal Victorian Eye and Ear Hospital; Melbourne Victoria Australia
| | - Nicholas L. Opie
- Centre for Eye Research Australia (CERA) Royal Victorian Eye and Ear Hospital; Melbourne Victoria Australia
| | | | - Jonathan Yeoh
- Centre for Eye Research Australia (CERA) Royal Victorian Eye and Ear Hospital; Melbourne Victoria Australia
| | - Arman Ahnood
- School of Physics; University of Melbourne; Melbourne Victoria Australia
| | - Chi D. Luu
- Centre for Eye Research Australia (CERA) Royal Victorian Eye and Ear Hospital; Melbourne Victoria Australia
| | - Rosemary Cicione
- School of Physics; University of Melbourne; Melbourne Victoria Australia
| | | | | | | | | | - David J. Garrett
- School of Physics; University of Melbourne; Melbourne Victoria Australia
- The Bionics Institute; Melbourne Victoria Australia
| | | | - Nicholas V. Apollo
- School of Physics; University of Melbourne; Melbourne Victoria Australia
- The Bionics Institute; Melbourne Victoria Australia
| | - Kumaravelu Ganesan
- School of Physics; University of Melbourne; Melbourne Victoria Australia
| | | | - Alastair Stacey
- School of Physics; University of Melbourne; Melbourne Victoria Australia
| | - Mathilde Escudie
- School of Physics; University of Melbourne; Melbourne Victoria Australia
| | - Samantha Lichter
- School of Physics; University of Melbourne; Melbourne Victoria Australia
| | | | - Steven Prawer
- School of Physics; University of Melbourne; Melbourne Victoria Australia
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Apollo N, Grayden DB, Burkitt AN, Meffin H, Kameneva T. Modeling intrinsic electrophysiology of AII amacrine cells: preliminary results. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2013:6551-4. [PMID: 24111243 DOI: 10.1109/embc.2013.6611056] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In patients who have lost their photoreceptors due to retinal degenerative diseases, it is possible to restore rudimentary vision by electrically stimulating surviving neurons. AII amacrine cells, which reside in the inner plexiform layer, split the signal from rod bipolar cells into ON and OFF cone pathways. As a result, it is of interest to develop a computational model to aid in the understanding of how these cells respond to the electrical stimulation delivered by a prosthetic implant. The aim of this work is to develop and constrain parameters in a single-compartment model of an AII amacrine cell using data from whole-cell patch clamp recordings. This model will be used to explore responses of AII amacrine cells to electrical stimulation. Single-compartment Hodgkin-Huxley-type neural models are simulated in the NEURON environment. Simulations showed successful reproduction of the potassium currentvoltage relationship and some of the spiking properties observed in vitro.
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Meffin H, Hietanen MA, Cloherty SL, Ibbotson MR. Spatial phase sensitivity of complex cells in primary visual cortex depends on stimulus contrast. J Neurophysiol 2015; 114:3326-38. [PMID: 26378205 DOI: 10.1152/jn.00431.2015] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Accepted: 09/10/2015] [Indexed: 11/22/2022] Open
Abstract
Neurons in primary visual cortex are classified as simple, which are phase sensitive, or complex, which are significantly less phase sensitive. Previously, we have used drifting gratings to show that the phase sensitivity of complex cells increases at low contrast and after contrast adaptation while that of simple cells remains the same at all contrasts (Cloherty SL, Ibbotson MR. J Neurophysiol 113: 434-444, 2015; Crowder NA, van Kleef J, Dreher B, Ibbotson MR. J Neurophysiol 98: 1155-1166, 2007; van Kleef JP, Cloherty SL, Ibbotson MR. J Physiol 588: 3457-3470, 2010). However, drifting gratings confound the influence of spatial and temporal summation, so here we have stimulated complex cells with gratings that are spatially stationary but continuously reverse the polarity of the contrast over time (contrast-reversing gratings). By varying the spatial phase and contrast of the gratings we aimed to establish whether the contrast-dependent phase sensitivity of complex cells results from changes in spatial or temporal processing or both. We found that most of the increase in phase sensitivity at low contrasts could be attributed to changes in the spatial phase sensitivities of complex cells. However, at low contrasts the complex cells did not develop the spatiotemporal response characteristics of simple cells, in which paired response peaks occur 180° out of phase in time and space. Complex cells that increased their spatial phase sensitivity at low contrasts were significantly overrepresented in the supragranular layers of cortex. We conclude that complex cells in supragranular layers of cat cortex have dynamic spatial summation properties and that the mechanisms underlying complex cell receptive fields differ between cortical layers.
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Affiliation(s)
- H Meffin
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia; ARC Centre of Excellence for Integrative Brain Function, Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia; and
| | - M A Hietanen
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia; ARC Centre of Excellence for Integrative Brain Function, Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia; and
| | - S L Cloherty
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia; ARC Centre of Excellence for Integrative Brain Function, Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia; and Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Victoria, Australia
| | - M R Ibbotson
- National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia; ARC Centre of Excellence for Integrative Brain Function, Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia; and
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Spencer MJ, Nayagam DAX, Clarey JC, Paolini AG, Meffin H, Burkitt AN, Grayden DB. Broadband onset inhibition can suppress spectral splatter in the auditory brainstem. PLoS One 2015; 10:e0126500. [PMID: 25978772 PMCID: PMC4433210 DOI: 10.1371/journal.pone.0126500] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Accepted: 04/02/2015] [Indexed: 12/02/2022] Open
Abstract
In vivo intracellular responses to auditory stimuli revealed that, in a particular population of cells of the ventral nucleus of the lateral lemniscus (VNLL) of rats, fast inhibition occurred before the first action potential. These experimental data were used to constrain a leaky integrate-and-fire (LIF) model of the neurons in this circuit. The post-synaptic potentials of the VNLL cell population were characterized using a method of triggered averaging. Analysis suggested that these inhibited VNLL cells produce action potentials in response to a particular magnitude of the rate of change of their membrane potential. The LIF model was modified to incorporate the VNLL cells’ distinctive action potential production mechanism. The model was used to explore the response of the population of VNLL cells to simple speech-like sounds. These sounds consisted of a simple tone modulated by a saw tooth with exponential decays, similar to glottal pulses that are the repeated impulses seen in vocalizations. It was found that the harmonic component of the sound was enhanced in the VNLL cell population when compared to a population of auditory nerve fibers. This was because the broadband onset noise, also termed spectral splatter, was suppressed by the fast onset inhibition. This mechanism has the potential to greatly improve the clarity of the representation of the harmonic content of certain kinds of natural sounds.
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Affiliation(s)
- Martin J. Spencer
- NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
- National ICT Australia, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
- Centre for Neural Engineering, University of Melbourne, Melbourne, Australia
- * E-mail:
| | - David A. X. Nayagam
- Bionics Institute, Melbourne, Australia
- Department of Pathology, University of Melbourne, Melbourne, Australia
| | | | - Antonio G. Paolini
- Health Innovations Research Institute, RMIT University, Melbourne, Australia
| | - Hamish Meffin
- NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
- National ICT Australia, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
- Centre for Neural Engineering, University of Melbourne, Melbourne, Australia
| | - Anthony N. Burkitt
- NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
- National ICT Australia, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
- Centre for Neural Engineering, University of Melbourne, Melbourne, Australia
- Bionics Institute, Melbourne, Australia
| | - David B. Grayden
- NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
- National ICT Australia, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
- Centre for Neural Engineering, University of Melbourne, Melbourne, Australia
- Bionics Institute, Melbourne, Australia
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47
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Kameneva T, Abramian M, Zarelli D, Nĕsić D, Burkitt AN, Meffin H, Grayden DB. Spike history neural response model. J Comput Neurosci 2015; 38:463-81. [PMID: 25862472 DOI: 10.1007/s10827-015-0549-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Revised: 02/08/2015] [Accepted: 02/13/2015] [Indexed: 11/25/2022]
Abstract
There is a potential for improved efficacy of neural stimulation if stimulation levels can be modified dynamically based on the responses of neural tissue in real time. A neural model is developed that describes the response of neurons to electrical stimulation and that is suitable for feedback control neuroprosthetic stimulation. Experimental data from NZ white rabbit retinae is used with a data-driven technique to model neural dynamics. The linear-nonlinear approach is adapted to incorporate spike history and to predict the neural response of ganglion cells to electrical stimulation. To validate the fitness of the model, the penalty term is calculated based on the time difference between each simulated spike and the closest spike in time in the experimentally recorded train. The proposed model is able to robustly predict experimentally observed spike trains.
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Affiliation(s)
- Tatiana Kameneva
- Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia,
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48
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Lichter SG, Escudié MC, Stacey AD, Ganesan K, Fox K, Ahnood A, Apollo NV, Kua DC, Lee AZ, McGowan C, Saunders AL, Burns O, Nayagam DAX, Williams RA, Garrett DJ, Meffin H, Prawer S. Hermetic diamond capsules for biomedical implants enabled by gold active braze alloys. Biomaterials 2015; 53:464-74. [PMID: 25890743 DOI: 10.1016/j.biomaterials.2015.02.103] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 02/23/2015] [Accepted: 02/24/2015] [Indexed: 11/19/2022]
Abstract
As the field of biomedical implants matures the functionality of implants is rapidly increasing. In the field of neural prostheses this is particularly apparent as researchers strive to build devices that interact with highly complex neural systems such as vision, hearing, touch and movement. A retinal implant, for example, is a highly complex device and the surgery, training and rehabilitation requirements involved in deploying such devices are extensive. Ideally, such devices will be implanted only once and will continue to function effectively for the lifetime of the patient. The first and most pivotal factor that determines device longevity is the encapsulation that separates the sensitive electronics of the device from the biological environment. This paper describes the realisation of a free standing device encapsulation made from diamond, the most impervious, long lasting and biochemically inert material known. A process of laser micro-machining and brazing is described detailing the fabrication of hermetic electrical feedthroughs and laser weldable seams using a 96.4% gold active braze alloy, another material renowned for biochemical longevity. Accelerated ageing of the braze alloy, feedthroughs and hermetic capsules yielded no evidence of corrosion and no loss of hermeticity. Samples of the gold braze implanted for 15 weeks, in vivo, caused minimal histopathological reaction and results were comparable to those obtained from medical grade silicone controls. The work described represents a first account of a free standing, fully functional hermetic diamond encapsulation for biomedical implants, enabled by gold active alloy brazing and laser micro-machining.
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Affiliation(s)
- Samantha G Lichter
- School of Physics, The University of Melbourne, Victoria 3010, Australia
| | - Mathilde C Escudié
- School of Physics, The University of Melbourne, Victoria 3010, Australia
| | - Alastair D Stacey
- School of Physics, The University of Melbourne, Victoria 3010, Australia
| | - Kumaravelu Ganesan
- School of Physics, The University of Melbourne, Victoria 3010, Australia
| | - Kate Fox
- School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Victoria 3001, Australia
| | - Arman Ahnood
- School of Physics, The University of Melbourne, Victoria 3010, Australia
| | - Nicholas V Apollo
- School of Physics, The University of Melbourne, Victoria 3010, Australia
| | - Dunstan C Kua
- School of Physics, The University of Melbourne, Victoria 3010, Australia; Department of Materials Engineering, Faculty of Engineering, Monash University, Victoria 3800, Australia
| | - Aaron Z Lee
- School of Physics, The University of Melbourne, Victoria 3010, Australia; Department of Materials Engineering, Faculty of Engineering, Monash University, Victoria 3800, Australia
| | - Ceara McGowan
- The Bionics Institute, 384-388 Albert Street, East Melbourne, Victoria 3002, Australia
| | - Alexia L Saunders
- The Bionics Institute, 384-388 Albert Street, East Melbourne, Victoria 3002, Australia
| | - Owen Burns
- The Bionics Institute, 384-388 Albert Street, East Melbourne, Victoria 3002, Australia
| | - David A X Nayagam
- The Bionics Institute, 384-388 Albert Street, East Melbourne, Victoria 3002, Australia; Department of Pathology, The University of Melbourne, Victoria 3010, Australia
| | - Richard A Williams
- National Vision Research Institute, Department of Optometry and Vision Sciences, University of Melbourne, Victoria 3010, Australia; Department of Anatomical Pathology, St Vincent's Hospital, Fitzroy, Victoria 3065, Australia
| | - David J Garrett
- School of Physics, The University of Melbourne, Victoria 3010, Australia; The Bionics Institute, 384-388 Albert Street, East Melbourne, Victoria 3002, Australia.
| | - Hamish Meffin
- National Vision Research Institute, Department of Optometry and Vision Sciences, University of Melbourne, Victoria 3010, Australia
| | - Steven Prawer
- School of Physics, The University of Melbourne, Victoria 3010, Australia
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Garrett DJ, Saunders AL, McGowan C, Specks J, Ganesan K, Meffin H, Williams RA, Nayagam DA. In vivo
biocompatibility of boron doped and nitrogen included conductive‐diamond for use in medical implants. J Biomed Mater Res B Appl Biomater 2015; 104:19-26. [DOI: 10.1002/jbm.b.33331] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Revised: 10/12/2014] [Accepted: 11/04/2014] [Indexed: 11/10/2022]
Affiliation(s)
- David J. Garrett
- The Bionics Institute384‐388 Albert StreetEast Melbourne Victoria3002 Australia
- School of Physics, The University of Melbourne Victoria3010 Australia
| | - Alexia L. Saunders
- The Bionics Institute384‐388 Albert StreetEast Melbourne Victoria3002 Australia
| | - Ceara McGowan
- The Bionics Institute384‐388 Albert StreetEast Melbourne Victoria3002 Australia
| | - Joscha Specks
- Strategy Engineers GmbH & Co. KGPrinz‐Ludwig‐Straβe 780333Munich Germany
| | | | - Hamish Meffin
- NICTA, Department of Electrical and Electronic Engineering, The University of Melbourne Victoria3010 Australia
| | - Richard A. Williams
- Department of PathologyThe University of Melbourne Victoria3010 Australia
- Department of Anatomical PathologySt Vincent's HospitalFitzroy Victoria3065 Australia
| | - David A.X. Nayagam
- The Bionics Institute384‐388 Albert StreetEast Melbourne Victoria3002 Australia
- Department of PathologyThe University of Melbourne Victoria3010 Australia
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50
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Kameneva T, Grayden DB, Meffin H, Burkitt AN. Feedback stimulation strategy: control of retinal ganglion cells activation. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2014:1703-6. [PMID: 25570303 DOI: 10.1109/embc.2014.6943935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
It is possible to cause a sensation of light in patients who have lost photoreceptors due to degenerative eye diseases by targeting surviving neurons with electrical stimulation by means of visual prosthetic devices. All stimulation strategies in currently used visual prostheses are open-loop, that is, the stimulation parameters do not depend on the level of activation of neurons surrounding stimulating electrodes. In this paper, we investigate a closed-loop stimulation strategy using computer simulations of previously constrained models of ON and OFF retinal ganglion cells. Using a proportional-integral-type controller we show that it is possible to control activation level of both types of retinal ganglion cells. We also demonstrate that the controller tuned for a particular combination of synaptic currents continues to work during retina degeneration when excitatory currents are reduced by 20%.
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