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Abbott JR, Jeakle EN, Haghighi P, Usoro JO, Sturgill BS, Wu Y, Geramifard N, Radhakrishna R, Patnaik S, Nakajima S, Hess J, Mehmood Y, Devata V, Vijayakumar G, Sood A, Doan Thai TT, Dogra K, Hernandez-Reynoso AG, Pancrazio JJ, Cogan SF. Planar amorphous silicon carbide microelectrode arrays for chronic recording in rat motor cortex. Biomaterials 2024; 308:122543. [PMID: 38547834 PMCID: PMC11065583 DOI: 10.1016/j.biomaterials.2024.122543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 04/21/2024]
Abstract
Chronic implantation of intracortical microelectrode arrays (MEAs) capable of recording from individual neurons can be used for the development of brain-machine interfaces. However, these devices show reduced recording capabilities under chronic conditions due, at least in part, to the brain's foreign body response (FBR). This creates a need for MEAs that can minimize the FBR to possibly enable long-term recording. A potential approach to reduce the FBR is the use of MEAs with reduced cross-sectional geometries. Here, we fabricated 4-shank amorphous silicon carbide (a-SiC) MEAs and implanted them into the motor cortex of seven female Sprague-Dawley rats. Each a-SiC MEA shank was 8 μm thick by 20 μm wide and had sixteen sputtered iridium oxide film (SIROF) electrodes (4 per shank). A-SiC was chosen as the fabrication base for its high chemical stability, good electrical insulation properties, and amenability to thin film fabrication. Electrochemical analysis and neural recordings were performed weekly for 4 months. MEAs were characterized pre-implantation in buffered saline and in vivo using electrochemical impedance spectroscopy and cyclic voltammetry at 50 mV/s and 50,000 mV/s. Neural recordings were analyzed for single unit activity. At the end of the study, animals were sacrificed for immunohistochemical analysis. We observed statistically significant, but small, increases in 1 and 30 kHz impedance values and 50,000 mV/s charge storage capacity over the 16-week implantation period. Slow sweep 50 mV/s CV and 1 Hz impedance did not significantly change over time. Impedance values increased from 11.6 MΩ to 13.5 MΩ at 1 Hz, 1.2 MΩ-2.9 MΩ at 1 kHz, and 0.11 MΩ-0.13 MΩ at 30 kHz over 16 weeks. The median charge storage capacity of the implanted electrodes at 50 mV/s was 58.1 mC/cm2 on week 1 and 55.9 mC/cm2 on week 16, and at 50,000 mV/s, 4.27 mC/cm2 on week 1 and 5.93 mC/cm2 on week 16. Devices were able to record neural activity from 92% of all active channels at the beginning of the study, At the study endpoint, a-SiC devices were still recording single-unit activity on 51% of electrochemically active electrode channels. In addition, we observed that the signal-to-noise ratio experienced a small decline of -0.19 per week. We also classified observed units as fast and slow repolarizing based on the trough-to-peak time. Although the overall presence of single units declined, fast and slow repolarizing units declined at a similar rate. At recording electrode depth, immunohistochemistry showed minimal tissue response to the a-SiC devices, as indicated by statistically insignificant differences in activated glial cell response between implanted brains slices and contralateral sham slices at 150 μm away from the implant location, as evidenced by GFAP staining. NeuN staining revealed the presence of neuronal cell bodies close to the implantation site, again statistically not different from a contralateral sham slice. These results warrant further investigation of a-SiC MEAs for future long-term implantation neural recording studies.
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Affiliation(s)
- Justin R Abbott
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Eleanor N Jeakle
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Pegah Haghighi
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Joshua O Usoro
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Brandon S Sturgill
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Yupeng Wu
- Department of Materials Science and Engineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Negar Geramifard
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Rahul Radhakrishna
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Sourav Patnaik
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Shido Nakajima
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Jordan Hess
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Yusef Mehmood
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Veda Devata
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, TX, United States
| | - Gayathri Vijayakumar
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Armaan Sood
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Teresa Thuc Doan Thai
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Komal Dogra
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Ana G Hernandez-Reynoso
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Joseph J Pancrazio
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Stuart F Cogan
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States.
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Erden YJ, Brey P. Neurotechnology and ethics guidelines for human enhancement: The case of the hippocampal cognitive prosthesis. Artif Organs 2023; 47:1235-1241. [PMID: 37533179 DOI: 10.1111/aor.14615] [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] [Indexed: 08/04/2023]
Abstract
Neurotechnologies offer both therapeutic and enhancement potential. In this article, we demonstrate how ethics guidelines can help with critical reflection on their potential for enhancement. We do this through the case of the hippocampal cognitive prosthesis. This prothesis developed in the US, has primarily therapeutic ends, with scope for enhancement. This technology raises several ethical issues, including as related to identity and memory, autonomy and authenticity. In the first section, we outline what we mean by enhancement, and introduce neurotechnologies generally and the hippocampal cognitive prosthesis specifically, with an introduction to generally relevant ethical issues. In the second section, we outline ethical issues pertinent to the hippocampal cognitive prosthesis and explore how ethics guidelines can help to promote essential critical reflection on a technology like this. Through all this, our emphasis is to balance between technological optimism and caution, especially where technologies have enhancement potential.
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Affiliation(s)
- Yasemin J Erden
- Philosophy Section, Faculty of Behavioural, Management and Social Sciences, University of Twente, Enschede, The Netherlands
| | - Philip Brey
- Philosophy Section, Faculty of Behavioural, Management and Social Sciences, University of Twente, Enschede, The Netherlands
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3
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Jeon I, Kim T. Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network. Front Comput Neurosci 2023; 17:1092185. [PMID: 37449083 PMCID: PMC10336230 DOI: 10.3389/fncom.2023.1092185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.
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Affiliation(s)
| | - Taegon Kim
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
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4
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Chang S, Wang J, Zhu Y, Wei X, Deng B, Li H, Liu C. Nonlinear dynamical modeling of neural activity using volterra series with GA-enhanced particle swarm optimization algorithm. Cogn Neurodyn 2023; 17:467-476. [PMID: 37007203 PMCID: PMC10050660 DOI: 10.1007/s11571-022-09822-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 03/23/2022] [Accepted: 05/09/2022] [Indexed: 11/03/2022] Open
Abstract
In order to improve the modeling performance of Volterra sequence for nonlinear neural activity, in this paper, a new optimization algorithm is proposed to identify Volterra sequence parameters. Algorithm combines the advantages of particle swarm optimization (PSO) and genetic algorithm (GA) improve the performance of the identification of nonlinear model parameters from rapidity and accuracy. In the modeling experiments of neural signal data generated by the neural computing model and clinical neural data set in this paper, the proposed algorithm shows its excellent potential in nonlinear neural activity modeling. Compared with PSO and GA, the algorithm can achieve less identification error, and better balance the convergence speed and identification error. Further, we explore the influence of algorithm parameters on identification efficiency, which provides possible guiding significance for parameter setting in practical application of the algorithm.
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Affiliation(s)
- Siyuan Chang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China
| | - Yulin Zhu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China
| | - Xile Wei
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China
| | - Bin Deng
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China
| | - Huiyan Li
- School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Chen Liu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 30072 China
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Isagulyan ED, Mikhailova VA, Aslakhanova KS, Slavin KV. Prospects of neuromodulation for chronic pain. BRAIN DISORDERS 2022. [DOI: 10.1016/j.dscb.2021.100027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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6
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Accelerating Input-Output Model Estimation with Parallel Computing for Testing Hippocampal Memory Prostheses in Human. J Neurosci Methods 2022; 370:109492. [DOI: 10.1016/j.jneumeth.2022.109492] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/21/2022] [Accepted: 01/27/2022] [Indexed: 11/16/2022]
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She X, Berger TW, Song D. A Double-Layer Multi-Resolution Classification Model for Decoding Spatiotemporal Patterns of Spikes With Small Sample Size. Neural Comput 2021; 34:219-254. [PMID: 34758485 DOI: 10.1162/neco_a_01459] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 08/19/2021] [Indexed: 11/04/2022]
Abstract
We build a double-layer, multiple temporal-resolution classification model for decoding single-trial spatiotemporal patterns of spikes. The model takes spiking activities as input signals and binary behavioral or cognitive variables as output signals and represents the input-output mapping with a double-layer ensemble classifier. In the first layer, to solve the underdetermined problem caused by the small sample size and the very high dimensionality of input signals, B-spline functional expansion and L1-regularized logistic classifiers are used to reduce dimensionality and yield sparse model estimations. A wide range of temporal resolutions of neural features is included by using a large number of classifiers with different numbers of B-spline knots. Each classifier serves as a base learner to classify spatiotemporal patterns into the probability of the output label with a single temporal resolution. A bootstrap aggregating strategy is used to reduce the estimation variances of these classifiers. In the second layer, another L1-regularized logistic classifier takes outputs of first-layer classifiers as inputs to generate the final output predictions. This classifier serves as a meta-learner that fuses multiple temporal resolutions to classify spatiotemporal patterns of spikes into binary output labels. We test this decoding model with both synthetic and experimental data recorded from rats and human subjects performing memory-dependent behavioral tasks. Results show that this method can effectively avoid overfitting and yield accurate prediction of output labels with small sample size. The double-layer, multi-resolution classifier consistently outperforms the best single-layer, single-resolution classifier by extracting and utilizing multi-resolution spatiotemporal features of spike patterns in the classification.
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Affiliation(s)
- Xiwei She
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Theodore W Berger
- Department of Biomedical Engineering and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, U.S.A.
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8
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Yoo J, Shoaran M. Neural interface systems with on-device computing: machine learning and neuromorphic architectures. Curr Opin Biotechnol 2021; 72:95-101. [PMID: 34735990 DOI: 10.1016/j.copbio.2021.10.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/18/2021] [Accepted: 10/19/2021] [Indexed: 11/26/2022]
Abstract
Development of neural interface and brain-machine interface (BMI) systems enables the treatment of neurological disorders including cognitive, sensory, and motor dysfunctions. While neural interfaces have steadily decreased in form factor, recent developments target pervasive implantables. Along with advances in electrodes, neural recording, and neurostimulation circuits, integration of disease biomarkers and machine learning algorithms enables real-time and on-site processing of neural activity with no need for power-demanding telemetry. This recent trend on combining artificial intelligence and machine learning with modern neural interfaces will lead to a new generation of low-power, smart, and miniaturized therapeutic devices for a wide range of neurological and psychiatric disorders. This paper reviews the recent development of the 'on-chip' machine learning and neuromorphic architectures, which is one of the key puzzles in devising next-generation clinically viable neural interface systems.
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Affiliation(s)
- Jerald Yoo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117585, Singapore; The N.1 Institute for Health, Singapore, Singapore, 117456, Singapore
| | - Mahsa Shoaran
- Institute of Electrical Engineering, Center for Neuroprosthetics, École polytechnique federal de Lausanne (EPFL), 1202, Geneva, Switzerland.
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9
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A neuromimetic realization of hippocampal CA1 for theta wave generation. Neural Netw 2021; 142:548-563. [PMID: 34340189 DOI: 10.1016/j.neunet.2021.07.002] [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: 11/23/2020] [Revised: 04/29/2021] [Accepted: 07/02/2021] [Indexed: 11/20/2022]
Abstract
Recent advances in neural engineering allowed the development of neuroprostheses which facilitate functionality in people with neurological problems. In this research, a real-time neuromorphic system is proposed to artificially reproduce the theta wave and firing patterns of different neuronal populations in the CA1, a sub-region of the hippocampus. The hippocampal theta oscillations (4-12 Hz) are an important electrophysiological rhythm that contributes in various cognitive functions, including navigation, memory, and novelty detection. The proposed CA1 neuromimetic circuit includes 100 linearized Pinsky-Rinzel neurons and 668 excitatory and inhibitory synapses on a field programmable gate array (FPGA). The implemented spiking neural network of the CA1 includes the main neuronal populations for the theta rhythm generation: excitatory pyramidal cells, PV+ basket cells, and Oriens Lacunosum-Moleculare (OLM) cells which are inhibitory interneurons. Moreover, the main inputs to the CA1 region from the entorhinal cortex via the perforant pathway, the CA3 via Schaffer collaterals, and the medial septum via fimbria-fornix are also implemented on the FPGA using a bursting leaky-integrate and fire (LIF) neuron model. The results of hardware realization show that the proposed CA1 neuromimetic circuit successfully reconstructs the theta oscillations and functionally illustrates the phase relations between firing responses of the different neuronal populations. It is also evaluated the impact of medial septum elimination on the firing patterns of the CA1 neuronal population and the theta wave's characteristics. This neuromorphic system can be considered as a potential platform that opens opportunities for neuroprosthetic applications in future works.
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De Ridder D, Maciaczyk J, Vanneste S. The future of neuromodulation: smart neuromodulation. Expert Rev Med Devices 2021; 18:307-317. [PMID: 33764840 DOI: 10.1080/17434440.2021.1909470] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Introduction: The International Neuromodulation Society defines neuromodulation as the alteration of nerve activity through targeted delivery of a stimulus, such as electrical stimulation or chemical agents, to specific neurological sites in the body.Areas covered: In the near future (<5 years) increasingly complex implantable neuromodulation systems will enter the market. These devices are capable of closed-loop stimulation and the delivery of novel stimulation designs, pushing the need for upgradability. But what about the near-to-far future, meaning 5-10 years from now?Expert opinion: We propose that neuromodulation in the near to far future (5-10 years) will involve integration of adaptive network neuromodulation with predictive artificial intelligence, automatically adjusted by brain and external sensors, and controlled via cloud-based applications. The components will be introduced in a phased approach, culminating in a fully autonomous brain-stimulator-cloud interface. This may, in the long future (>10 years), lead to the brain of the future, a brain with integrated artificial intelligence.
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Affiliation(s)
- Dirk De Ridder
- Department of Surgical Sciences, Section of Neurosurgery, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Jarek Maciaczyk
- Stereotactic and Functional Neurosurgery, Department of Neurosurgery, University Hospital Bonn, Bonn, Germany
| | - Sven Vanneste
- Lab for Clinical & Integrative Neuroscience, Trinity College Institute for Neuroscience, Trinity College Dublin, Dublin, Ireland
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11
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Bennett C, Álvarez-Ciara A, Franklin M, Dietrich WD, Prasad A. The complement cascade at the Utah microelectrode-tissue interface. Biomaterials 2021; 268:120583. [PMID: 33310540 PMCID: PMC7856077 DOI: 10.1016/j.biomaterials.2020.120583] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/16/2020] [Accepted: 11/25/2020] [Indexed: 01/05/2023]
Abstract
Devices implanted within the central nervous system (CNS) are subjected to tissue reactivity due to the lack of biocompatibility between implanted material and the cells' microenvironment. Studies have attributed blood-brain barrier disruption, inflammation, and oxidative stress as main contributing factors that lead to electrode recording failure. The complement cascade is a part of the innate immunity that focuses on recognizing and targeting foreign objects; however, its role in the context of neural implants is substantially unknown. In this study, we implanted a non-functional 4x4 Utah microelectrode array (UEA) into the somatosensory cortex and studied the complement cascade via combined gene and immunohistochemistry quantification at acute (48-h), sub-acute (1-week), and early chronic (4-weeks) time points. The results of this study demonstrate the activation and continuation of the complement cascade at the electrode-tissue interface, illustrating the therapeutic potential of modulating the foreign body response via the complement cascade.
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Affiliation(s)
- Cassie Bennett
- Department of Biomedical Engineering, University of Miami, FL, USA
| | | | - Melissa Franklin
- Department of Biomedical Engineering, University of Miami, FL, USA
| | | | - Abhishek Prasad
- Department of Biomedical Engineering, University of Miami, FL, USA; The Miami Project to Cure Paralysis, University of Miami, FL, USA.
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12
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George R, Chiappalone M, Giugliano M, Levi T, Vassanelli S, Partzsch J, Mayr C. Plasticity and Adaptation in Neuromorphic Biohybrid Systems. iScience 2020; 23:101589. [PMID: 33083749 PMCID: PMC7554028 DOI: 10.1016/j.isci.2020.101589] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.
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Affiliation(s)
- Richard George
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | | | - Michele Giugliano
- Neuroscience Area, International School of Advanced Studies, Trieste, Italy
| | - Timothée Levi
- Laboratoire de l’Intégration du Matéeriau au Systéme, University of Bordeaux, Bordeaux, France
- LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Stefano Vassanelli
- Department of Biomedical Sciences and Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Johannes Partzsch
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | - Christian Mayr
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
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Medley JK, Teo J, Woo SS, Hellerstein J, Sarpeshkar R, Sauro HM. A compiler for biological networks on silicon chips. PLoS Comput Biol 2020; 16:e1008063. [PMID: 32966274 PMCID: PMC7535129 DOI: 10.1371/journal.pcbi.1008063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 10/05/2020] [Accepted: 06/16/2020] [Indexed: 12/18/2022] Open
Abstract
The explosive growth in semiconductor integrated circuits was made possible in large part by design automation software. The design and/or analysis of synthetic and natural circuits in living cells could be made more scalable using the same approach. We present a compiler which converts standard representations of chemical reaction networks and circuits into hardware configurations that can be used to simulate the network on specialized cytomorphic hardware. The compiler also creates circuit–level models of the target configuration, which enhances the versatility of the compiler and enables the validation of its functionality without physical experimentation with the hardware. We show that this compiler can translate networks comprised of mass–action kinetics, classic enzyme kinetics (Michaelis–Menten, Briggs–Haldane, and Botts–Morales formalisms), and genetic repressor kinetics, thereby allowing a large class of models to be transformed into a hardware representation. Rule–based models are particularly well–suited to this approach, as we demonstrate by compiling a MAP kinase model. Development of specialized hardware and software for simulating biological networks has the potential to enable the simulation of larger kinetic models than are currently feasible or allow the parallel simulation of many smaller networks with better performance than current simulation software. We present a “silicon compiler” that is capable of translating biochemical models encoded in the SBML standard into specialized analog cytomorphic hardware and transfer function–level simulations of such hardware. We show how the compiler and hardware address challenges in analog computing: 1) We ensure that the integration of errors due to the mismatch between analog circuit parameters does not become infinite over time but always remains finite via the use of total variables (the solution of the “divergence problem”); 2) We describe the compilation process through a series of examples using building blocks of biological networks, and show the results of compiling two SBML models from the literature: the Elowitz repressilator model and a rule–based model of a MAP kinase cascade. Source code for the compiler is available at https://doi.org/10.5281/zenodo.3948393.
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Affiliation(s)
- J. Kyle Medley
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
- * E-mail:
| | - Jonathan Teo
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Sung Sik Woo
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, United States of America
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Joseph Hellerstein
- eScience Institute, University of Washington, Seattle, Washington, United States of America
| | - Rahul Sarpeshkar
- Departments of Engineering, Microbiology & Immunology, Physics, and Molecular and Systems Biology, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
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14
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Kumaravelu K, Tomlinson T, Callier T, Sombeck J, Bensmaia SJ, Miller LE, Grill WM. A comprehensive model-based framework for optimal design of biomimetic patterns of electrical stimulation for prosthetic sensation. J Neural Eng 2020; 17:046045. [PMID: 32759488 PMCID: PMC8559728 DOI: 10.1088/1741-2552/abacd8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Touch and proprioception are essential to motor function as shown by the movement deficits that result from the loss of these senses, e.g. due to neuropathy of sensory nerves. To achieve a high-performance brain-controlled prosthetic arm/hand thus requires the restoration of somatosensation, perhaps through intracortical microstimulation (ICMS) of somatosensory cortex (S1). The challenge is to generate patterns of neuronal activation that evoke interpretable percepts. We present a framework to design optimal spatiotemporal patterns of ICMS (STIM) that evoke naturalistic patterns of neuronal activity and demonstrate performance superior to four previous approaches. APPROACH We recorded multiunit activity from S1 during a center-out reach task (from proprioceptive neurons in Brodmann's area 2) and during application of skin indentations (from cutaneous neurons in Brodmann's area 1). We implemented a computational model of a cortical hypercolumn and used a genetic algorithm to design STIM that evoked patterns of model neuron activity that mimicked their experimentally-measured counterparts. Finally, from the ICMS patterns, the evoked neuronal activity, and the stimulus parameters that gave rise to it, we trained a recurrent neural network (RNN) to learn the mapping function between the physical stimulus and the biomimetic stimulation pattern, i.e. the sensory encoder to be integrated into a neuroprosthetic device. MAIN RESULTS We identified ICMS patterns that evoked simulated responses that closely approximated the measured responses for neurons within 50 µm of the electrode tip. The RNN-based sensory encoder generalized well to untrained limb movements or skin indentations. STIM designed using the model-based optimization approach outperformed STIM designed using existing linear and nonlinear mappings. SIGNIFICANCE The proposed framework produces an encoder that converts limb state or patterns of pressure exerted onto the prosthetic hand into STIM that evoke naturalistic patterns of neuronal activation.
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Affiliation(s)
| | | | - Thierri Callier
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Joseph Sombeck
- Department of Biomedical Engineering, Northwestern University, Chicago, IL
| | - Sliman J. Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Lee E. Miller
- Department of Biomedical Engineering, Northwestern University, Chicago, IL
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL
- Deptartment of Physiology, Northwestern University, Chicago, IL
| | - Warren M. Grill
- Department of Biomedical Engineering, Duke University, Durham, NC
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
- Department of Neurobiology, Duke University, Durham, NC
- Department of Neurosurgery, Duke University, Durham, NC
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Chang S, Wei X, Su F, Liu C, Yi G, Wang J, Han C, Che Y. Model Predictive Control for Seizure Suppression Based on Nonlinear Auto-Regressive Moving-Average Volterra Model. IEEE Trans Neural Syst Rehabil Eng 2020; 28:2173-2183. [PMID: 32763855 DOI: 10.1109/tnsre.2020.3014927] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This article investigates a closed-loop brain stimulation method based on model predictive control strategy to suppress epileptic seizures. A neural mass model (NMM), exhibiting the normal and various epileptic seizures by changing physiologically meaningful parameters, is used as a black-box model of the brain. Based on system identification, an auto-regressive moving-average Volterra model is established to reveal the relationship between stimulation and neuronal responses. Then, the model predictive control strategy is implemented based the Volterra model, which can generate an optimal stimulation waveform to eliminate epileptiform waves. The computational simulation results indicate the proposed closed-loop control strategy can optimize the stimulation waveform without particular knowledge of the physiological properties of the system. The robustness of the proposed control strategy to system disturbances makes it more appropriate for future clinical application.
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Wicks RT, Witcher MR, Couture DE, Laxton AW, Popli G, Whitlow CT, Fetterhoff D, Dakos AS, Roeder BM, Deadwyler SA, Hampson RE. Hippocampal CA1 and CA3 neural recording in the human brain: validation of depth electrode placement through high-resolution imaging and electrophysiology. Neurosurg Focus 2020; 49:E5. [PMID: 32610296 DOI: 10.3171/2020.4.focus20164] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 04/16/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Intracranial human brain recordings typically utilize recording systems that do not distinguish individual neuron action potentials. In such cases, individual neurons are not identified by location within functional circuits. In this paper, verified localization of singly recorded hippocampal neurons within the CA3 and CA1 cell fields is demonstrated. METHODS Macro-micro depth electrodes were implanted in 23 human patients undergoing invasive monitoring for identification of epileptic seizure foci. Individual neurons were isolated and identified via extracellular action potential waveforms recorded via macro-micro depth electrodes localized within the hippocampus. A morphometric survey was performed using 3T MRI scans of hippocampi from the 23 implanted patients, as well as 46 normal (i.e., nonepileptic) patients and 26 patients with a history of epilepsy but no history of depth electrode placement, which provided average dimensions of the hippocampus along typical implantation tracks. Localization within CA3 and CA1 cell fields was tentatively assigned on the basis of recording electrode site, stereotactic positioning of the depth electrode in comparison with the morphometric survey, and postsurgical MRI. Cells were selected as candidate CA3 and CA1 principal neurons on the basis of waveform and firing rate characteristics and confirmed within the CA3-to-CA1 neural projection pathways via measures of functional connectivity. RESULTS Cross-correlation analysis confirmed that nearly 80% of putative CA3-to-CA1 cell pairs exhibited positive correlations compatible with feed-forward connection between the cells, while only 2.6% exhibited feedback (inverse) connectivity. Even though synchronous and long-latency correlations were excluded, feed-forward correlation between CA3-CA1 pairs was identified in 1071 (26%) of 4070 total pairs, which favorably compares to reports of 20%-25% feed-forward CA3-CA1 correlation noted in published animal studies. CONCLUSIONS This study demonstrates the ability to record neurons in vivo from specified regions and subfields of the human brain. As brain-machine interface and neural prosthetic research continues to expand, it is necessary to be able to identify recording and stimulation sites within neural circuits of interest.
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Affiliation(s)
| | | | | | | | | | | | - Dustin Fetterhoff
- 6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Alexander S Dakos
- 6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Brent M Roeder
- 6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Sam A Deadwyler
- 5Physiology and Pharmacology, and.,6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Robert E Hampson
- 2Neurology.,5Physiology and Pharmacology, and.,6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
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She X, Robinson BS, Berger TW, Song D. Accelerating Estimation of a Multi-Input Multi-Output Model of the Hippocampus with a Parallel Computing Strategy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2479-2482. [PMID: 33018509 DOI: 10.1109/embc44109.2020.9175490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
To build hippocampal memory prosthesis for restoring memory functions, we previously developed and implemented a multi-input multi-output (MIMO) nonlinear dynamic model of the hippocampus. This model can successfully predict hippocampal output spike activities based on input spike activities, and thus be used to drive microstimulation to bypass the damaged hippocampal region. Building such a MIMO model involves estimations of a large number of model coefficients, which typically takes hundreds of hours using a single personal computer. In practice, however, due to the requirement of medical care and clinical trials, the modeling processes must be completed within 72 hours after the recording, so that models can be used to drive stimulations. To solve this problem, we utilized a parallelization strategy to divide the whole MIMO model computation involving iterative estimation and optimization into independent computing tasks that can be performed simultaneously in multiple computer nodes. Such a strategy was implemented on the high-performance computing cluster at the University of Southern California. It reduced the model estimation time to tens of hours and thus allowed us to complete the modeling process within the required time frame to further test model-driven electrical stimulation for the hippocampal memory prosthesis.
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Kia B, Mendes A, Parnami A, George R, Mobley K, Ditto WL. Nonlinear dynamics based machine learning: Utilizing dynamics-based flexibility of nonlinear circuits to implement different functions. PLoS One 2020; 15:e0228534. [PMID: 32126089 PMCID: PMC7053732 DOI: 10.1371/journal.pone.0228534] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Accepted: 01/17/2020] [Indexed: 11/25/2022] Open
Abstract
The core element of machine learning is a flexible, universal function approximator that can be trained and fit into the data. One of the main challenges in modern machine learning is to understand the role of nonlinearity and complexity in these universal function approximators. In this research, we focus on nonlinear complex systems, and show their capability in representation and learning of different functions. Complex nonlinear dynamics and chaos naturally yield an almost infinite diversity of dynamical behaviors and functions. Physical, biological and engineered systems can utilize this diversity to implement adaptive, robust behaviors and operations. A nonlinear dynamical system can be considered as an embodiment of a collection of different possible behaviors or functions, from which different behaviors or functions can be chosen as a response to different conditions or problems. This process of selection can be manual in the sense that one can manually pick and choose the right function through directly setting parameters. Alternatively, we can automate the process and allow the system itself learn how to do it. This creates an approach to machine learning, wherein the nonlinear dynamics represents and embodies different possible functions, and it learns through training how to pick the right function from this function space. We report on how we utilized nonlinear dynamics and chaos to design and fabricate nonlinear dynamics based, morphable hardware in silicon as a physical embodiment for different possible functions. We demonstrate how this flexible, morphable hardware learns through learning and searching algorithms such as genetic algorithm to implement different desired functions. In this approach, we combine two powerful natural and biological phenomenon, Darwinian evolution and nonlinear dynamics and chaos, as a dynamics-oriented approach to designing intelligent, adaptive systems with applications. Nonlinear dynamics embodies different functions at the hardware level, while an evolutionary method is utilized in order to find the parameters to implement the right function.
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Affiliation(s)
- Behnam Kia
- Nonlinear Artificial Intelligence Laboratory, Department of Physics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Allen Mendes
- Nonlinear Artificial Intelligence Laboratory, Department of Physics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Akshay Parnami
- Nonlinear Artificial Intelligence Laboratory, Department of Physics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Robin George
- Nonlinear Artificial Intelligence Laboratory, Department of Physics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Kenneth Mobley
- First Pass Engineering, Castle Rock, Colorado, United States of America
| | - William L. Ditto
- Nonlinear Artificial Intelligence Laboratory, Department of Physics, North Carolina State University, Raleigh, North Carolina, United States of America
- * E-mail:
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Schneider F, Horowitz A, Lesch KP, Dandekar T. Delaying memory decline: different options and emerging solutions. Transl Psychiatry 2020; 10:13. [PMID: 32066684 PMCID: PMC7026464 DOI: 10.1038/s41398-020-0697-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 11/28/2019] [Accepted: 12/08/2019] [Indexed: 12/13/2022] Open
Abstract
Memory decline can be a devastating disease and increases in aging Western populations. Memory enhancement technologies hold promise for this and other conditions. Approaches include stem cell transplantation, which improved memory in several animal studies as well as vaccination against Alzheimer´s disease (AD) by β-amyloid antibodies. For a positive clinical effect, the vaccine should probably be administered over a long period of time and before amyloid pathologies manifest in the brain. Different drugs, such as erythropoietin or antiplatelet therapy, improve memory in neuropsychiatric diseases or AD or at least in animal studies. Omega-3 polyunsaturated fatty acid-rich diets improve memory through the gut-brain axis by altering the gut flora through probiotics. Sports, dancing, and memory techniques (e.g., Method of Loci) utilize behavioral approaches for memory enhancement, and were effective in several studies. Augmented reality (AR) is an auspicious way for enhancing memory in real time. Future approaches may include memory prosthesis for head-injured patients and light therapy for restoring memory in AD. Memory enhancement in humans in health and disease holds big promises for the future. Memory training helps only in mild or no impairment. Clinical application requires further investigation.
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Affiliation(s)
- Felicitas Schneider
- grid.8379.50000 0001 1958 8658Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Alan Horowitz
- grid.8379.50000 0001 1958 8658Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Klaus-Peter Lesch
- grid.8379.50000 0001 1958 8658Division of Molecular Psychiatry, Laboratory of Translational Neuroscience, Center of Mental Health, University of Würzburg, Würzburg, Germany ,grid.448878.f0000 0001 2288 8774Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia ,grid.5012.60000 0001 0481 6099Department of Psychiatry and Psychology, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074, Würzburg, Germany. .,EMBL, Computational Biology and Structures Program, 69117, Heidelberg, Germany.
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Qian C, Sun X, Yang Z, Pan G, Wang Y. A K-Medoids based Point-Process Modeling on Neural Spike Transformation using Binless Kernel. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4387-4390. [PMID: 31946839 DOI: 10.1109/embc.2019.8856479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A neural prosthesis is designed to compensate for cognitive functional losses by modeling the information transmission among cortical areas. Existing methods generally build a generalized linear model to approximate the nonlinear transformation among two areas, and use the temporal information of the neural spike with low efficiency. It is essential to efficiently model the nonlinearity embedded in spike generation and transmission for the real-time. This paper proposes a nonlinear point-process model to describe spike-in and spike-out transformation using the theory of reproducing kernel Hilbert space (RKHS) and the binless kernel on spike trains. The binless kernel efficiently maps exact spike timing information to the RKHS to describe nonlinear transformations with global minimum regardless of the weight initialization. A streaming K-medoids algorithm is introduced to select typical and important features in this nonlinear binless kernel for further modeling. We test our model on the nonlinearly generated synthetic neural spike trains, and compare with the existing spike transformation methods, such as Volterra model and staged point-process model. The results show that our model has higher goodness-of-fit evaluated by Kolmogorov-Smirnov test and less variance on the prediction results, which indicates the potential better modeling approach for neural prosthesis application.
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Machine Learning: From Expert Systems to Deep Learning. Cogn Sci 2019. [DOI: 10.1017/9781108339216.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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22
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The Prehistory of Cognitive Science. Cogn Sci 2019. [DOI: 10.1017/9781108339216.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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23
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Preface. Cogn Sci 2019. [DOI: 10.1017/9781108339216.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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24
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Bibliography. Cogn Sci 2019. [DOI: 10.1017/9781108339216.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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25
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Bayesianism in Cognitive Science. Cogn Sci 2019. [DOI: 10.1017/9781108339216.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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26
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Object Perception and Folk Physics. Cogn Sci 2019. [DOI: 10.1017/9781108339216.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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27
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Glossary. Cogn Sci 2019. [DOI: 10.1017/9781108339216.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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28
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Strategies for Brain Mapping. Cogn Sci 2019. [DOI: 10.1017/9781108339216.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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29
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Modules and Architectures. Cogn Sci 2019. [DOI: 10.1017/9781108339216.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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30
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Introduction. Cogn Sci 2019. [DOI: 10.1017/9781108339216.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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31
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The Discipline Matures: Three Milestones. Cogn Sci 2019. [DOI: 10.1017/9781108339216.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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32
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Models of Language Learning. Cogn Sci 2019. [DOI: 10.1017/9781108339216.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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33
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Applying Dynamical Systems Theory to Model the Mind. Cogn Sci 2019. [DOI: 10.1017/9781108339216.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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34
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Exploring Mindreading. Cogn Sci 2019. [DOI: 10.1017/9781108339216.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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35
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Robotics: From GOFAI to Situated Cognition and Behavior-Based Robotics. Cogn Sci 2019. [DOI: 10.1017/9781108339216.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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36
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The Cognitive Science of Consciousness. Cogn Sci 2019. [DOI: 10.1017/9781108339216.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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37
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The Turn to the Brain. Cogn Sci 2019. [DOI: 10.1017/9781108339216.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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38
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Index for Cognitive Science (3rd edition). Cogn Sci 2019. [DOI: 10.1017/9781108339216.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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39
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Mindreading: Advanced Topics. Cogn Sci 2019. [DOI: 10.1017/9781108339216.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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40
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Physical Symbol Systems and the Language of Thought. Cogn Sci 2019. [DOI: 10.1017/9781108339216.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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41
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Looking Ahead: Challenges and Opportunities. Cogn Sci 2019. [DOI: 10.1017/9781108339216.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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42
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Neural Networks and Distributed Information Processing. Cogn Sci 2019. [DOI: 10.1017/9781108339216.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Xu T, Xiao N, Zhai X, Kwan Chan P, Tin C. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning. J Neural Eng 2019; 15:016021. [PMID: 29115280 DOI: 10.1088/1741-2552/aa98e9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). APPROACH The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. MAIN RESULTS This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. SIGNIFICANCE This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.
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Affiliation(s)
- Tao Xu
- Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong, SAR, People's Republic of China
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Buccelli S, Bornat Y, Colombi I, Ambroise M, Martines L, Pasquale V, Bisio M, Tessadori J, Nowak P, Grassia F, Averna A, Tedesco M, Bonifazi P, Difato F, Massobrio P, Levi T, Chiappalone M. A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks. iScience 2019; 19:402-414. [PMID: 31421595 PMCID: PMC6706626 DOI: 10.1016/j.isci.2019.07.046] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/18/2019] [Accepted: 07/29/2019] [Indexed: 12/20/2022] Open
Abstract
Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care.
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Affiliation(s)
- Stefano Buccelli
- Rehab Technologies IIT-INAIL Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child science (DINOGMI), University of Genova, L.go P. Daneo 3, 16132 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Yannick Bornat
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France
| | - Ilaria Colombi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child science (DINOGMI), University of Genova, L.go P. Daneo 3, 16132 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Matthieu Ambroise
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France
| | - Laura Martines
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy
| | - Valentina Pasquale
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Marta Bisio
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neurosciences, University of Padova, Via Nicolò Giustiniani 5, 35128 Padova, Italy
| | - Jacopo Tessadori
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Przemysław Nowak
- Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy; Institute of Information Technology, Lodz University of Technology, ul. Wolczanska 215, 90-924 Lodz, Poland
| | - Filippo Grassia
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France; University of Picardie Jules Verne, Laboratory of Innovative Technologies (LTI, EA 3899), Avenue des Facultés, Le Bailly, 80025 Amiens, France
| | - Alberto Averna
- Rehab Technologies IIT-INAIL Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child science (DINOGMI), University of Genova, L.go P. Daneo 3, 16132 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Mariateresa Tedesco
- Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy
| | - Paolo Bonifazi
- School of Physics and Astronomy, Tel Aviv University, 69978 Tel Aviv, Israel; Computational Neuroimaging Laboratory, Biocruces Health Research Institute, Hospital Universitario Cruces, Baracaldo, Vizcaya 48903, Spain; Ikerbasque: The Basque Foundation for Science, Bilbao, Bizkaia 48013, Spain
| | - Francesco Difato
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy
| | - Timothée Levi
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France; LIMMS CNRS-IIS, The University of Tokyo, 153-8505 Tokyo, Japan.
| | - Michela Chiappalone
- Rehab Technologies IIT-INAIL Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy.
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Mirzaee MS, Moghimi S. Detection of reaching intention using EEG signals and nonlinear dynamic system identification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 175:151-161. [PMID: 31104704 DOI: 10.1016/j.cmpb.2019.04.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 04/19/2019] [Accepted: 04/21/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVES Low frequency electroencephalography (EEG) signals are associated with preparation of movement and thus provide valuable information for brain-machine interface applications. The purpose of this study was to detect movement intention from EEG signals before execution of self-paced arm reaching movements. METHODS Ten healthy individuals were recruited. Movement onset was determined from surface electromyography recordings time-locked with EEG signals. Unlike previous studies, which employed feature extraction and classification for decoding, a nonlinear dynamic multiple-input/single output (MISO) model was developed. The MISO model consisted of a cascade of Volterra structures and a threshold block, generating the binary output corresponding to intention/no-intention. The modeling process included input selection from a pool of different EEG channels. The predictive performance of the model was evaluated using the receiver operating characteristics curve, from which the optimum threshold was also selected. The Mann-Whitney statistics was employed to select the significant EEG channels for the output by examining the statistical significance of improvement in the predictive capability of the model when the respective channels were included. RESULTS With the proposed approach, movement intention was detected approximately 500 ms before the movement onset and on average, with an accuracy of 96.37 ± 0.94%, a sensitivity of 77.93 ± 4.40% and a specificity of 98.52 ± 1.19%. CONCLUSIONS The model output can be converted to motion commands for neuroprosthetic devices and exoskeletons in future applications.
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Affiliation(s)
| | - Sahar Moghimi
- Electrical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran; Rayan Center for Neuroscience and Behavior, Ferdowsi University of Mashhad, Mashhad, Iran.
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Geng K, Shin DC, Song D, Hampson RE, Deadwyler SA, Berger TW, Marmarelis VZ. Multi-Input, Multi-Output Neuronal Mode Network Approach to Modeling the Encoding Dynamics and Functional Connectivity of Neural Systems. Neural Comput 2019; 31:1327-1355. [PMID: 31113305 DOI: 10.1162/neco_a_01204] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
This letter proposes a novel method, multi-input, multi-output neuronal mode network (MIMO-NMN), for modeling encoding dynamics and functional connectivity in neural ensembles such as the hippocampus. Compared with conventional approaches such as the Volterra-Wiener model, linear-nonlinear-cascade (LNC) model, and generalized linear model (GLM), the NMN has several advantages in terms of estimation accuracy, model interpretation, and functional connectivity analysis. We point out the limitations of current neural spike modeling methods, especially the estimation biases caused by the imbalanced class problem when the number of zeros is significantly larger than ones in the spike data. We use synthetic data to test the performance of NMN with a comparison of the traditional methods, and the results indicate the NMN approach could reduce the imbalanced class problem and achieve better predictions. Subsequently, we apply the MIMO-NMN method to analyze data from the human hippocampus. The results indicate that the MIMO-NMN method is a promising approach to modeling neural dynamics and analyzing functional connectivity of multi-neuronal data.
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Affiliation(s)
- Kunling Geng
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dae C Shin
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, U.S.A.
| | - Samuel A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, U.S.A.
| | - Theodore W Berger
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
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Keren H, Partzsch J, Marom S, Mayr CG. A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks. Front Neurosci 2019; 13:432. [PMID: 31133779 PMCID: PMC6517490 DOI: 10.3389/fnins.2019.00432] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 04/15/2019] [Indexed: 12/30/2022] Open
Abstract
Developing technologies for coupling neural activity and artificial neural components, is key for advancing neural interfaces and neuroprosthetics. We present a biohybrid experimental setting, where the activity of a biological neural network is coupled to a biomimetic hardware network. The implementation of the hardware network (denoted NeuroSoC) exhibits complex dynamics with a multiplicity of time-scales, emulating 2880 neurons and 12.7 M synapses, designed on a VLSI chip. This network is coupled to a neural network in vitro, where the activities of both the biological and the hardware networks can be recorded, processed, and integrated bidirectionally in real-time. This experimental setup enables an adjustable and well-monitored coupling, while providing access to key functional features of neural networks. We demonstrate the feasibility to functionally couple the two networks and to implement control circuits to modify the biohybrid activity. Overall, we provide an experimental model for neuromorphic-neural interfaces, hopefully to advance the capability to interface with neural activity, and with its irregularities in pathology.
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Affiliation(s)
- Hanna Keren
- Department of Physiology, Biophysics and Systems Biology, Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
- Network Biology Research Laboratory, Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
- Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, School of Engineering Sciences, Dresden University of Technology, Dresden, Germany
| | - Johannes Partzsch
- Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, School of Engineering Sciences, Dresden University of Technology, Dresden, Germany
| | - Shimon Marom
- Department of Physiology, Biophysics and Systems Biology, Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
- Network Biology Research Laboratory, Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Christian G Mayr
- Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, School of Engineering Sciences, Dresden University of Technology, Dresden, Germany
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Wirdatmadja S, Johari P, Desai A, Bae Y, Stachowiak EK, Stachowiak MK, Jornet JM, Balasubramaniam S. Analysis of Light Propagation on Physiological Properties of Neurons for Nanoscale Optogenetics. IEEE Trans Neural Syst Rehabil Eng 2019; 27:108-117. [PMID: 30624220 DOI: 10.1109/tnsre.2019.2891271] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Miniaturization of implantable devices is an important challenge for future brain-computer interface applications, and in particular for achieving precise neuron stimulation. For stimulation that utilizes light, i.e., optogenetics, the light propagation behavior and interaction at the nanoscale with elements within the neuron is an important factor that needs to be considered when designing the device. This paper analyzes the effect of light behavior for a single neuron stimulation and focuses on the impact from different cell shapes. Based on the Mie scattering theory, the paper analyzes how the shape of the soma and the nucleus contributes to the focusing effect resulting in an intensity increase, which ensures that neurons can assist in transferring light through the tissue toward the target cells. At the same time, this intensity increase can in turn also stimulate neighboring cells leading to interference within the neural circuits. This paper also analyzes the ideal placements of the device with respect to the angle and position within the cortex that can enable axonal biophoton communications, which can contain light within the cell to avoid the interference.
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Xu H, Han Y, Han X, Xu J, Lin S, Cheung RCC. Unsupervised and real-time spike sorting chip for neural signal processing in hippocampal prosthesis. J Neurosci Methods 2018; 311:111-121. [PMID: 30339881 DOI: 10.1016/j.jneumeth.2018.10.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 10/10/2018] [Accepted: 10/14/2018] [Indexed: 10/28/2022]
Abstract
BACKGROUND Damage to the hippocampus will result in the loss of ability to form new long-term memories and cognitive disorders. At present, there is no effective medical treatment for this issue. Hippocampal cognitive prosthesis is proposed to replace damaged regions of the hippocampus to mimic the function of original biological tissue. This prosthesis requires a spike sorter to detect and classify spikes in the recorded neural signal. NEW METHOD A 16-channel spike sorting processor is presented in this paper, where all channels are considered as independent. An automatic threshold estimation method suitable for hardware implementation is proposed for the Osort clustering algorithm. A new distance metric is also introduced to facilitate clustering. Bayes optimal template matching classification algorithm is optimized to reduce computational complexity by introducing a preselection mechanism. RESULTS The chip was fabricated in 40-nm CMOS process with a core area of 0.0175 mm2/ch and power consumption of 19.0 μW/ch. Synthetic and realistic test data are used to evaluate the chip. The test result shows that it has high performance on both data. COMPARISON WITH EXISTING METHOD(S) Compared with the other three spike sorting processors, the proposed chip achieves the highest detection and classification accuracy. It also has the ability to deal with partially overlapping spikes, which is not reported in the other work. CONCLUSIONS We have developed a 16-channel spike sorting chip used in hippocampal prosthesis, which provides unsupervised clustering and real-time detection and classification. It also has the ability to deal with partially overlapping spikes.
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Affiliation(s)
- Hao Xu
- Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, Hangzhou 310027, China; Institute of Microelectronics and Nanoelectronics, Zhejiang University, Hangzhou 310027, China
| | - Yan Han
- Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, Hangzhou 310027, China; Institute of Microelectronics and Nanoelectronics, Zhejiang University, Hangzhou 310027, China.
| | - Xiaoxia Han
- Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, Hangzhou 310027, China; Institute of Microelectronics and Nanoelectronics, Zhejiang University, Hangzhou 310027, China
| | - Junyu Xu
- Department of Neurobiology, Key Laboratory of Medical Neurobiology of Ministry of Health, Zhejiang Province Key Laboratory of Neurobiology, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Shen Lin
- Department of Neurobiology, Key Laboratory of Medical Neurobiology of Ministry of Health, Zhejiang Province Key Laboratory of Neurobiology, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Ray C C Cheung
- Department of Electronic Engineering, City University of Hong Kong, 999077, Hong Kong, China
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Qian C, Sun X, Zhang S, Xing D, Li H, Zheng X, Pan G, Wang Y. Nonlinear Modeling of Neural Interaction for Spike Prediction Using the Staged Point-Process Model. Neural Comput 2018; 30:3189-3226. [PMID: 30314427 DOI: 10.1162/neco_a_01137] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Neurons communicate nonlinearly through spike activities. Generalized linear models (GLMs) describe spike activities with a cascade of a linear combination across inputs, a static nonlinear function, and an inhomogeneous Bernoulli or Poisson process, or Cox process if a self-history term is considered. This structure considers the output nonlinearity in spike generation but excludes the nonlinear interaction among input neurons. Recent studies extend GLMs by modeling the interaction among input neurons with a quadratic function, which considers the interaction between every pair of input spikes. However, quadratic effects may not fully capture the nonlinear nature of input interaction. We therefore propose a staged point-process model to describe the nonlinear interaction among inputs using a few hidden units, which follows the idea of artificial neural networks. The output firing probability conditioned on inputs is formed as a cascade of two linear-nonlinear (a linear combination plus a static nonlinear function) stages and an inhomogeneous Bernoulli process. Parameters of this model are estimated by maximizing the log likelihood on output spike trains. Unlike the iterative reweighted least squares algorithm used in GLMs, where the performance is guaranteed by the concave condition, we propose a modified Levenberg-Marquardt (L-M) algorithm, which directly calculates the Hessian matrix of the log likelihood, for the nonlinear optimization in our model. The proposed model is tested on both synthetic data and real spike train data recorded from the dorsal premotor cortex and primary motor cortex of a monkey performing a center-out task. Performances are evaluated by discrete-time rescaled Kolmogorov-Smirnov tests, where our model statistically outperforms a GLM and its quadratic extension, with a higher goodness-of-fit in the prediction results. In addition, the staged point-process model describes nonlinear interaction among input neurons with fewer parameters than quadratic models, and the modified L-M algorithm also demonstrates fast convergence.
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Affiliation(s)
- Cunle Qian
- College of Computer Science, Zhejiang University, Hangzhou, 310027, China
| | - Xuyun Sun
- College of Computer Science, Zhejiang University, Hangzhou, 310027, China
| | - Shaomin Zhang
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China
| | - Dong Xing
- College of Computer Science, Zhejiang University, Hangzhou, 310027, China
| | - Hongbao Li
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China
| | - Xiaoxiang Zheng
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China
| | - Gang Pan
- State Key Lab of CAD&CG, and College of Computer Science, Zhejiang University, Hangzhou, 310027, China
| | - Yiwen Wang
- Department of Electronic and Computer Engineering and Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, 999077, China
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