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Castro D, Grayden DB, Meffin H, Spencer M. Neural activity shaping in visual prostheses with deep learning. J Neural Eng 2024; 21:046025. [PMID: 38986450 DOI: 10.1088/1741-2552/ad6186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 07/10/2024] [Indexed: 07/12/2024]
Abstract
Objective.The visual perception provided by retinal prostheses is limited by the overlapping current spread of adjacent electrodes. This reduces the spatial resolution attainable with unipolar stimulation. Conversely, simultaneous multipolar stimulation guided by the measured neural responses-neural activity shaping (NAS)-can attenuate excessive spread of excitation allowing for more precise control over the pattern of neural activation. However, defining effective multipolar stimulus patterns is a challenging task. Previous attempts focused on analytical solutions based on an assumed linear nonlinear model of retinal response; an analytical model inversion (AMI) approach. Here, we propose a model-free solution for NAS, using artificial neural networks (ANNs) that could be trained with data acquired from the implant.Approach.Our method consists of two ANNs trained sequentially. The measurement predictor network (MPN) is trained on data from the implant and is used to predict how the retina responds to multipolar stimulation. The stimulus generator network is trained on a large dataset of natural images and uses the trained MPN to determine efficient multipolar stimulus patterns by learning its inverse model. We validate our methodin silicousing a realistic model of retinal response to multipolar stimulation.Main results.We show that our ANN-based NAS approach produces sharper retinal activations than the conventional unipolar stimulation strategy. As a theoretical bench-mark of optimal NAS results, we implemented AMI stimulation by inverting the model used to simulate the retina. Our ANN strategy produced equivalent results to AMI, while not being restricted to any specific type of retina model and being three orders of magnitude more computationally efficient.Significance.Our novel protocol provides a method for efficient and personalized retinal stimulation, which may improve the visual experience and quality of life of retinal prosthesis users.
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Affiliation(s)
- Domingos Castro
- Neuroengineering and Computational Neuroscience Lab, i3S-Institute for Research and Innovation in Health, University of Porto, Porto, Portugal
- Faculty of Engineering of the University of Porto, Porto, Portugal
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Graeme 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
- Graeme Clark Institute of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - Martin Spencer
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Graeme Clark Institute of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
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Madugula SS, Vilkhu R, Shah NP, Grosberg LE, Kling A, Gogliettino AR, Nguyen H, Hottowy P, Sher A, Litke AM, Chichilnisky EJ. Inference of Electrical Stimulation Sensitivity from Recorded Activity of Primate Retinal Ganglion Cells. J Neurosci 2023; 43:4808-4820. [PMID: 37268418 PMCID: PMC10312054 DOI: 10.1523/jneurosci.1023-22.2023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 06/04/2023] Open
Abstract
High-fidelity electronic implants can in principle restore the function of neural circuits by precisely activating neurons via extracellular stimulation. However, direct characterization of the individual electrical sensitivity of a large population of target neurons, to precisely control their activity, can be difficult or impossible. A potential solution is to leverage biophysical principles to infer sensitivity to electrical stimulation from features of spontaneous electrical activity, which can be recorded relatively easily. Here, this approach is developed and its potential value for vision restoration is tested quantitatively using large-scale multielectrode stimulation and recording from retinal ganglion cells (RGCs) of male and female macaque monkeys ex vivo Electrodes recording larger spikes from a given cell exhibited lower stimulation thresholds across cell types, retinas, and eccentricities, with systematic and distinct trends for somas and axons. Thresholds for somatic stimulation increased with distance from the axon initial segment. The dependence of spike probability on injected current was inversely related to threshold, and was substantially steeper for axonal than somatic compartments, which could be identified by their recorded electrical signatures. Dendritic stimulation was largely ineffective for eliciting spikes. These trends were quantitatively reproduced with biophysical simulations. Results from human RGCs were broadly similar. The inference of stimulation sensitivity from recorded electrical features was tested in a data-driven simulation of visual reconstruction, revealing that the approach could significantly improve the function of future high-fidelity retinal implants.SIGNIFICANCE STATEMENT This study demonstrates that individual in situ primate retinal ganglion cells of different types respond to artificially generated, external electrical fields in a systematic manner, in accordance with theoretical predictions, that allows for prediction of electrical stimulus sensitivity from recorded spontaneous activity. It also provides evidence that such an approach could be immensely helpful in the calibration of clinical retinal implants.
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Affiliation(s)
- Sasidhar S Madugula
- Neurosciences PhD Program, Stanford University, Stanford, California 94305
- School of Medicine, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
| | - Ramandeep Vilkhu
- Department of Electrical Engineering, Stanford University, Stanford, California 94305
| | - Nishal P Shah
- Department of Neurosurgery, Stanford University, Stanford, California 94305
- Department of Electrical Engineering, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
| | - Lauren E Grosberg
- Department of Neurosurgery, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
- Facebook Reality Labs, Facebook, Mountain View, California 94040
| | - Alexandra Kling
- Department of Neurosurgery, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
| | - Alex R Gogliettino
- Neurosciences PhD Program, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
| | - Huy Nguyen
- Department of Neurosurgery, Stanford University, Stanford, California 94305
| | - Paweł Hottowy
- Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland 30-059
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
| | - Alan M Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
| | - E J Chichilnisky
- Department of Neurosurgery, Stanford University, Stanford, California 94305
- Department of Ophthalmology, Stanford University, Stanford, California 94305
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California 94305
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Kish KE, Lempka SF, Weiland JD. Modeling extracellular stimulation of retinal ganglion cells: theoretical and practical aspects. J Neural Eng 2023; 20:026011. [PMID: 36848677 PMCID: PMC10010067 DOI: 10.1088/1741-2552/acbf79] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/15/2023] [Accepted: 02/27/2023] [Indexed: 03/01/2023]
Abstract
Objective.Retinal prostheses use electric current to activate inner retinal neurons, providing artificial vision for blind people. Epiretinal stimulation primarily targets retinal ganglion cells (RGCs), which can be modeled with cable equations. Computational models provide a tool to investigate the mechanisms of retinal activation, and improve stimulation paradigms. However, documentation of RGC model structure and parameters is limited, and model implementation can influence model predictions.Approach.We created a functional guide for building a mammalian RGC multi-compartment cable model and applying extracellular stimuli. Next, we investigated how the neuron's three-dimensional shape will influence model predictions. Finally, we tested several strategies to maximize computational efficiency.Main results.We conducted sensitivity analyses to examine how dendrite representation, axon trajectory, and axon diameter influence membrane dynamics and corresponding activation thresholds. We optimized the spatial and temporal discretization of our multi-compartment cable model. We also implemented several simplified threshold prediction theories based on activating function, but these did not match the prediction accuracy achieved by the cable equations.Significance.Through this work, we provide practical guidance for modeling the extracellular stimulation of RGCs to produce reliable and meaningful predictions. Robust computational models lay the groundwork for improving the performance of retinal prostheses.
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Affiliation(s)
- Kathleen E Kish
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
- BioInterfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - Scott F Lempka
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States of America
- BioInterfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - James D Weiland
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
- Department of Ophthalmology and Visual Science, University of Michigan, Ann Arbor, MI, United States of America
- BioInterfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
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Wang C, Fang C, Zou Y, Yang J, Sawan M. Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction. J Neural Eng 2023; 20. [PMID: 36634357 DOI: 10.1088/1741-2552/acb295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 01/12/2023] [Indexed: 01/14/2023]
Abstract
Objective. Retinal prostheses are promising devices to restore vision for patients with severe age-related macular degeneration or retinitis pigmentosa disease. The visual processing mechanism embodied in retinal prostheses play an important role in the restoration effect. Its performance depends on our understanding of the retina's working mechanism and the evolvement of computer vision models. Recently, remarkable progress has been made in the field of processing algorithm for retinal prostheses where the new discovery of the retina's working principle and state-of-the-arts computer vision models are combined together.Approach. We investigated the related research on artificial intelligence techniques for retinal prostheses. The processing algorithm in these studies could be attributed to three types: computer vision-related methods, biophysical models, and deep learning models.Main results. In this review, we first illustrate the structure and function of the normal and degenerated retina, then demonstrate the vision rehabilitation mechanism of three representative retinal prostheses. It is necessary to summarize the computational frameworks abstracted from the normal retina. In addition, the development and feature of three types of different processing algorithms are summarized. Finally, we analyze the bottleneck in existing algorithms and propose our prospect about the future directions to improve the restoration effect.Significance. This review systematically summarizes existing processing models for predicting the response of the retina to external stimuli. What's more, the suggestions for future direction may inspire researchers in this field to design better algorithms for retinal prostheses.
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Affiliation(s)
- Chuanqing Wang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou 310030, People's Republic of China
| | - Chaoming Fang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou 310030, People's Republic of China
| | - Yong Zou
- Beijing Institute of Radiation Medicine, Beijing, People's Republic of China
| | - Jie Yang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou 310030, People's Republic of China
| | - Mohamad Sawan
- Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies, School of Engineering, Westlake University, Hangzhou 310030, People's Republic of China
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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] [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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Shah NP, Chichilnisky EJ. Computational challenges and opportunities for a bi-directional artificial retina. J Neural Eng 2020; 17:055002. [PMID: 33089827 DOI: 10.1088/1741-2552/aba8b1] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A future artificial retina that can restore high acuity vision in blind people will rely on the capability to both read (observe) and write (control) the spiking activity of neurons using an adaptive, bi-directional and high-resolution device. Although current research is focused on overcoming the technical challenges of building and implanting such a device, exploiting its capabilities to achieve more acute visual perception will also require substantial computational advances. Using high-density large-scale recording and stimulation in the primate retina with an ex vivo multi-electrode array lab prototype, we frame several of the major computational problems, and describe current progress and future opportunities in solving them. First, we identify cell types and locations from spontaneous activity in the blind retina, and then efficiently estimate their visual response properties by using a low-dimensional manifold of inter-retina variability learned from a large experimental dataset. Second, we estimate retinal responses to a large collection of relevant electrical stimuli by passing current patterns through an electrode array, spike sorting the resulting recordings and using the results to develop a model of evoked responses. Third, we reproduce the desired responses for a given visual target by temporally dithering a diverse collection of electrical stimuli within the integration time of the visual system. Together, these novel approaches may substantially enhance artificial vision in a next-generation device.
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Affiliation(s)
- Nishal P Shah
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America. Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA, United States of America. Department of Neurosurgery, Stanford University, Stanford, CA, United States of America. Author to whom any correspondence should be addressed
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Haji Ghaffari D, Finn KE, Jeganathan VSE, Patel U, Wuyyuru V, Roy A, Weiland JD. The effect of waveform asymmetry on perception with epiretinal prostheses. J Neural Eng 2020; 17:045009. [PMID: 32590371 DOI: 10.1088/1741-2552/aba07e] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Objective Retinal prosthetic implants have helped improve vision in patients blinded by photoreceptor degeneration. Retinal implant users report improvements in light perception and performing visual tasks, but their ability to perceive shapes and letters is limited due to the low precision of retinal activation, which is exacerbated by axonal stimulation and high perceptual thresholds. A previous in vitro study in our lab used calcium imaging to measure the spatial activity of mouse retinal ganglion cells (RGCs) in response to electrical stimulation. Based on this study, symmetric anodic-first (SA) stimulation effectively avoided axonal activation and asymmetric anodic-first stimulation (AA) with duration ratios (ratio of the anodic to cathodic phase) greater than 10 reduced RGC activation thresholds significantly. Applying these novel stimulation strategies in clinic may increase perception precision and improve the overall patient outcomes. Approach We combined human subject testing and computational modeling to further examine the effect of SA and AA stimuli on perception shapes and thresholds for epiretinal stimulation of RGCs. Main results Threshold measurement in three Argus II participants indicated that AA stimulation could increase perception probabilities compared to a standard symmetric cathodic-first (SC) pulse, and this effect can be intensified by addition of an interphae gap (IPG). Our in silico RGC model predicts lower thresholds with AA and asymmetric cathodic-first (AC) stimuli compared to a SC pulse. This effect was more pronounced at shorter pulse widths. The most effective pulse for threshold reduction with short pulse durations (≤0.12 ms) was AA stimulation with small duration ratios (≤5) and long IPGs (≥2 ms). For the 0.5 ms pulse duration, SC stimulation with IPGs longer than 0.5 ms, or asymmetric stimuli with large duration ratios (≥20) were most effective in threshold reduction. Phosphene shape analysis did not reveal a significant change in percept elongation with SA stimulation. However, there was a significant increase in percept size (P < 0.01) with AA stimulation compared to the standard pulse in one participant. Significane Including asymmetric waveform capability will provide more flexible options for optimization and personalized fitting of retinal implants.
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Affiliation(s)
- Dorsa Haji Ghaffari
- Department of Biomedical Engineering, Michigan Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America. Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
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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] [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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Ahn J, Rueckauer B, Yoo Y, Goo YS. New Features of Receptive Fields in Mouse Retina through Spike-triggered Covariance. Exp Neurobiol 2020; 29:38-49. [PMID: 32122107 PMCID: PMC7075653 DOI: 10.5607/en.2020.29.1.38] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 02/19/2020] [Accepted: 02/19/2020] [Indexed: 12/31/2022] Open
Abstract
Retinal ganglion cells (RGCs) encode various spatiotemporal features of visual information into spiking patterns. The receptive field (RF) of each RGC is usually calculated by spike-triggered average (STA), which is fast and easy to understand, but limited to simple and unimodal RFs. As an alternative, spike-triggered covariance (STC) has been proposed to characterize more complex patterns in RFs. This study compares STA and STC for the characterization of RFs and demonstrates that STC has an advantage over STA for identifying novel spatiotemporal features of RFs in mouse RGCs. We first classified mouse RGCs into ON, OFF, and ON/OFF cells according to their response to full-field light stimulus, and then investigated the spatiotemporal patterns of RFs with random checkerboard stimulation, using both STA and STC analysis. We propose five sub-types (T1–T5) in the STC of mouse RGCs together with their physiological implications. In particular, the relatively slow biphasic pattern (T1) could be related to excitatory inputs from bipolar cells. The transient biphasic pattern (T2) allows one to characterize complex patterns in RFs of ON/OFF cells. The other patterns (T3–T5), which are contrasting, alternating, and monophasic patterns, could be related to inhibitory inputs from amacrine cells. Thus, combining STA and STC and considering the proposed sub-types unveil novel characteristics of RFs in the mouse retina and offer a more holistic understanding of the neural coding mechanisms of mouse RGCs.
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Affiliation(s)
- Jungryul Ahn
- Department of Physiology, Chungbuk National University School of Medicine, Cheongju 28644, Korea
| | - Bodo Rueckauer
- Institute of Neuroinformatics, ETH Zurich and University of Zurich, Zurich 8057, Switzerland
| | - Yongseok Yoo
- Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea
| | - Yong Sook Goo
- Department of Physiology, Chungbuk National University School of Medicine, Cheongju 28644, Korea
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Spencer MJ, Kameneva T, Grayden DB, Meffin H, Burkitt AN. Global activity shaping strategies for a retinal implant. J Neural Eng 2019; 16:026008. [DOI: 10.1088/1741-2552/aaf071] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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11
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Rathbun DL, Ghorbani N, Shabani H, Zrenner E, Hosseinzadeh Z. Spike-triggered average electrical stimuli as input filters for bionic vision—a perspective. J Neural Eng 2018; 15:063002. [DOI: 10.1088/1741-2552/aae493] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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