1
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Fietkiewicz C, McDougal RA, Corrales Marco D, Chiel HJ, Thomas PJ. Tutorial: using NEURON for neuromechanical simulations. Front Comput Neurosci 2023; 17:1143323. [PMID: 37583894 PMCID: PMC10424731 DOI: 10.3389/fncom.2023.1143323] [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: 01/12/2023] [Accepted: 06/20/2023] [Indexed: 08/17/2023] Open
Abstract
The dynamical properties of the brain and the dynamics of the body strongly influence one another. Their interaction generates complex adaptive behavior. While a wide variety of simulation tools exist for neural dynamics or biomechanics separately, there are few options for integrated brain-body modeling. Here, we provide a tutorial to demonstrate how the widely-used NEURON simulation platform can support integrated neuromechanical modeling. As a first step toward incorporating biomechanics into a NEURON simulation, we provide a framework for integrating inputs from a "periphery" and outputs to that periphery. In other words, "body" dynamics are driven in part by "brain" variables, such as voltages or firing rates, and "brain" dynamics are influenced by "body" variables through sensory feedback. To couple the "brain" and "body" components, we use NEURON's pointer construct to share information between "brain" and "body" modules. This approach allows separate specification of brain and body dynamics and code reuse. Though simple in concept, the use of pointers can be challenging due to a complicated syntax and several different programming options. In this paper, we present five different computational models, with increasing levels of complexity, to demonstrate the concepts of code modularity using pointers and the integration of neural and biomechanical modeling within NEURON. The models include: (1) a neuromuscular model of calcium dynamics and muscle force, (2) a neuromechanical, closed-loop model of a half-center oscillator coupled to a rudimentary motor system, (3) a closed-loop model of neural control for respiration, (4) a pedagogical model of a non-smooth "brain/body" system, and (5) a closed-loop model of feeding behavior in the sea hare Aplysia californica that incorporates biologically-motivated non-smooth dynamics. This tutorial illustrates how NEURON can be integrated with a broad range of neuromechanical models. Code available at https://github.com/fietkiewicz/PointerBuilder.
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Affiliation(s)
- Chris Fietkiewicz
- Department of Mathematics and Computer Science, Hobart and William Smith Colleges, Geneva, NY, United States
| | - Robert A. McDougal
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
- Wu Tsai Institute, Yale University, New Haven, CT, United States
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
- Section for Biomedical Informatics, Yale School of Medicine, New Haven, CT, United States
| | - David Corrales Marco
- Department of Mathematics and Computer Science, Hobart and William Smith Colleges, Geneva, NY, United States
| | - Hillel J. Chiel
- Department of Biology, Case Western Reserve University, Cleveland, OH, United States
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Peter J. Thomas
- Department of Biology, Case Western Reserve University, Cleveland, OH, United States
- Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, United States
- Department of Cognitive Science, Case Western Reserve University, Cleveland, OH, United States
- Department of Electrical, Control, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Data and Computer Science, Case Western Reserve University, Cleveland, OH, United States
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2
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Anwar H, Caby S, Dura-Bernal S, D’Onofrio D, Hasegan D, Deible M, Grunblatt S, Chadderdon GL, Kerr CC, Lakatos P, Lytton WW, Hazan H, Neymotin SA. Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning. PLoS One 2022; 17:e0265808. [PMID: 35544518 PMCID: PMC9094569 DOI: 10.1371/journal.pone.0265808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 03/08/2022] [Indexed: 11/18/2022] Open
Abstract
Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time. Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward. Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.
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Affiliation(s)
- Haroon Anwar
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Simon Caby
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Salvador Dura-Bernal
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
- Dept. Physiology & Pharmacology, State University of New York Downstate, Brooklyn, New York, United States of America
| | - David D’Onofrio
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Daniel Hasegan
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Matt Deible
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Sara Grunblatt
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - George L. Chadderdon
- Dept. Physiology & Pharmacology, State University of New York Downstate, Brooklyn, New York, United States of America
| | - Cliff C. Kerr
- Dept Physics, University of Sydney, Sydney, Australia
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America
| | - Peter Lakatos
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
- Dept. Psychiatry, NYU Grossman School of Medicine, New York, New York, United States of America
| | - William W. Lytton
- Dept. Physiology & Pharmacology, State University of New York Downstate, Brooklyn, New York, United States of America
- Dept Neurology, Kings County Hospital Center, Brooklyn, New York, United States of America
| | - Hananel Hazan
- Dept of Biology, Tufts University, Medford, Massachusetts, United States of America
| | - Samuel A. Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
- Dept. Psychiatry, NYU Grossman School of Medicine, New York, New York, United States of America
- * E-mail:
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3
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Fu Y, Wang F, Li Y, Gong A, Qian Q, Su L, Zhao L. Real-time recognition of different imagined actions on the same side of a single limb based on the fNIRS correlation coefficient. BIOMED ENG-BIOMED TE 2022; 67:173-183. [PMID: 35420003 DOI: 10.1515/bmt-2021-0422] [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: 12/22/2021] [Accepted: 03/25/2022] [Indexed: 11/15/2022]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a type of functional brain imaging. Brain-computer interfaces (BCIs) based on fNIRS have recently been implemented. Most existing fNIRS-BCI studies have involved off-line analyses, but few studies used online performance testing. Furthermore, existing online fNIRS-BCI experimental paradigms have not yet carried out studies using different imagined movements of the same side of a single limb. In the present study, a real-time fNIRS-BCI system was constructed to identify two imagined movements of the same side of a single limb (right forearm and right hand). Ten healthy subjects were recruited and fNIRS signal was collected and real-time analyzed with two imagined movements (leftward movement involving the right forearm and right-hand clenching). In addition to the mean and slope features of fNIRS signals, the correlation coefficient between fNIRS signals induced by different imagined actions was extracted. A support vector machine (SVM) was used to classify the imagined actions. The average accuracy of real-time classification of the two imagined movements was 72.25 ± 0.004%. The findings suggest that different imagined movements on the same side of a single limb can be recognized real-time based on fNIRS, which may help to further guide the practical application of online fNIRS-BCIs.
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Affiliation(s)
- Yunfa Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Fan Wang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Yu Li
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Anmin Gong
- School of Information Engineering, Chinese People's Armed Police Force Engineering University, Xian, China
| | - Qian Qian
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Lei Su
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Lei Zhao
- Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China.,Faculty of Science, Kunming University of Science and Technology, Kunming, China
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4
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Medlock L, Sekiguchi K, Hong S, Dura-Bernal S, Lytton WW, Prescott SA. Multiscale Computer Model of the Spinal Dorsal Horn Reveals Changes in Network Processing Associated with Chronic Pain. J Neurosci 2022; 42:3133-3149. [PMID: 35232767 PMCID: PMC8996343 DOI: 10.1523/jneurosci.1199-21.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 02/17/2022] [Accepted: 02/17/2022] [Indexed: 11/21/2022] Open
Abstract
Pain-related sensory input is processed in the spinal dorsal horn (SDH) before being relayed to the brain. That processing profoundly influences whether stimuli are correctly or incorrectly perceived as painful. Significant advances have been made in identifying the types of excitatory and inhibitory neurons that comprise the SDH, and there is some information about how neuron types are connected, but it remains unclear how the overall circuit processes sensory input or how that processing is disrupted under chronic pain conditions. To explore SDH function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based neuron models that reproduce the characteristic firing patterns of spinal neurons. Excitatory and inhibitory neuron populations, defined by their expression of genetic markers, spiking pattern, or morphology, were synaptically connected according to available qualitative data. Using a genetic algorithm, synaptic weights were tuned to reproduce projection neuron firing rates (model output) based on primary afferent firing rates (model input) across a range of mechanical stimulus intensities. Disparate synaptic weight combinations could produce equivalent circuit function, revealing degeneracy that may underlie heterogeneous responses of different circuits to perturbations or pathologic insults. To validate our model, we verified that it responded to the reduction of inhibition (i.e., disinhibition) and ablation of specific neuron types in a manner consistent with experiments. Thus validated, our model offers a valuable resource for interpreting experimental results and testing hypotheses in silico to plan experiments for examining normal and pathologic SDH circuit function.SIGNIFICANCE STATEMENT We developed a multiscale computer model of the posterior part of spinal cord gray matter (spinal dorsal horn), which is involved in perceiving touch and pain. The model reproduces several experimental observations and makes predictions about how specific types of spinal neurons and synapses influence projection neurons that send information to the brain. Misfiring of these projection neurons can produce anomalous sensations associated with chronic pain. Our computer model will not only assist in planning future experiments, but will also be useful for developing new pharmacotherapy for chronic pain disorders, connecting the effect of drugs acting at the molecular scale with emergent properties of neurons and circuits that shape the pain experience.
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Affiliation(s)
- Laura Medlock
- Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
| | - Kazutaka Sekiguchi
- Drug Developmental Research Laboratory, Shionogi Pharmaceutical Research Center, Toyonaka, Osaka 561-0825, Japan
- State University of New York Downstate Health Science University, Brooklyn, New York 11203
| | - Sungho Hong
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa, 904-0495, Japan
| | - Salvador Dura-Bernal
- State University of New York Downstate Health Science University, Brooklyn, New York 11203
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York 10962
| | - William W Lytton
- State University of New York Downstate Health Science University, Brooklyn, New York 11203
- Kings County Hospital, Brooklyn, New York 11207
| | - Steven A Prescott
- Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
- Department of Physiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
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5
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Moore B, Khang S, Francis JT. Noise-Correlation Is Modulated by Reward Expectation in the Primary Motor Cortex Bilaterally During Manual and Observational Tasks in Primates. Front Behav Neurosci 2020; 14:541920. [PMID: 33343308 PMCID: PMC7739882 DOI: 10.3389/fnbeh.2020.541920] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 09/30/2020] [Indexed: 11/17/2022] Open
Abstract
Reward modulation is represented in the motor cortex (M1) and could be used to implement more accurate decoding models to improve brain-computer interfaces (BCIs; Zhao et al., 2018). Analyzing trial-to-trial noise-correlations between neural units in the presence of rewarding (R) and non-rewarding (NR) stimuli adds to our understanding of cortical network dynamics. We utilized Pearson's correlation coefficient to measure shared variability between simultaneously recorded units (32-112) and found significantly higher noise-correlation and positive correlation between the populations' signal- and noise-correlation during NR trials as compared to R trials. This pattern is evident in data from two non-human primates (NHPs) during single-target center out reaching tasks, both manual and action observation versions. We conducted a mean matched noise-correlation analysis to decouple known interactions between event-triggered firing rate changes and neural correlations. Isolated reward discriminatory units demonstrated stronger correlational changes than units unresponsive to reward firing rate modulation, however, the qualitative response was similar, indicating correlational changes within the network as a whole can serve as another information channel to be exploited by BCIs that track the underlying cortical state, such as reward expectation, or attentional modulation. Reward expectation and attention in return can be utilized with reinforcement learning (RL) towards autonomous BCI updating.
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Affiliation(s)
- Brittany Moore
- Department of Biomedical Engineering, Cullen College of Engineering, The University of Houston, Houston, TX, United States
| | - Sheng Khang
- Department of Biomedical Engineering, Cullen College of Engineering, The University of Houston, Houston, TX, United States
| | - Joseph Thachil Francis
- Department of Biomedical Engineering, Cullen College of Engineering, The University of Houston, Houston, TX, United States
- Department of Electrical and Computer Engineering, Cullen College of Engineering, The University of Houston, Houston, TX, United States
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6
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Nguyen HD, Yoo SH, Bhutta MR, Hong KS. Adaptive filtering of physiological noises in fNIRS data. Biomed Eng Online 2018; 17:180. [PMID: 30514303 PMCID: PMC6278088 DOI: 10.1186/s12938-018-0613-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 11/27/2018] [Indexed: 11/10/2022] Open
Abstract
The study presents a recursive least-squares estimation method with an exponential forgetting factor for noise removal in functional near-infrared spectroscopy data and extraction of hemodynamic responses (HRs) from the measured data. The HR is modeled as a linear regression form in which the expected HR, the first and second derivatives of the expected HR, a short-separation measurement data, three physiological noises, and the baseline drift are included as components in the regression vector. The proposed method is applied to left-motor-cortex experiments on the right thumb and little finger movements in five healthy male participants. The algorithm is evaluated with respect to its performance improvement in terms of contrast-to-noise ratio in comparison with Kalman filter, low-pass filtering, and independent component method. The experimental results show that the proposed model achieves reductions of 77% and 99% in terms of the number of channels exhibiting higher contrast-to-noise ratios in oxy-hemoglobin and deoxy-hemoglobin, respectively. The approach is robust in obtaining consistent HR data. The proposed method is applied for both offline and online noise removal.
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Affiliation(s)
- Hoang-Dung Nguyen
- Department of Automation Technology, Can Tho University, Can Tho, 900000, Vietnam
| | - So-Hyeon Yoo
- School of Mechanical Engineering, Pusan National University, Busan, 46241, Republic of Korea
| | - M Raheel Bhutta
- Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Republic of Korea
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, 46241, Republic of Korea. .,Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, 46241, Republic of Korea.
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7
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Cantarelli M, Marin B, Quintana A, Earnshaw M, Court R, Gleeson P, Dura-Bernal S, Silver RA, Idili G. Geppetto: a reusable modular open platform for exploring neuroscience data and models. Philos Trans R Soc Lond B Biol Sci 2018; 373:rstb.2017.0380. [PMID: 30201843 PMCID: PMC6158222 DOI: 10.1098/rstb.2017.0380] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/18/2018] [Indexed: 11/25/2022] Open
Abstract
Geppetto is an open-source platform that provides generic middleware infrastructure for building both online and desktop tools for visualizing neuroscience models and data and managing simulations. Geppetto underpins a number of neuroscience applications, including Open Source Brain (OSB), Virtual Fly Brain (VFB), NEURON-UI and NetPyNE-UI. OSB is used by researchers to create and visualize computational neuroscience models described in NeuroML and simulate them through the browser. VFB is the reference hub for Drosophila melanogaster neural anatomy and imaging data including neuropil, segmented neurons, microscopy stacks and gene expression pattern data. Geppetto is also being used to build a new user interface for NEURON, a widely used neuronal simulation environment, and for NetPyNE, a Python package for network modelling using NEURON. Geppetto defines domain agnostic abstractions used by all these applications to represent their models and data and offers a set of modules and components to integrate, visualize and control simulations in a highly accessible way. The platform comprises a backend which can connect to external data sources, model repositories and simulators together with a highly customizable frontend. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.
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Affiliation(s)
- Matteo Cantarelli
- OpenWorm Foundation, USA .,MetaCell Limited, UK.,Department of Neuroscience, Physiology and Pharmacology, University College London, UK
| | - Boris Marin
- Department of Neuroscience, Physiology and Pharmacology, University College London, UK.,Departamento de Física, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Brazil
| | - Adrian Quintana
- MetaCell Limited, UK.,Department of Neuroscience, Physiology and Pharmacology, University College London, UK.,EyeSeeTea Limited, UK
| | - Matt Earnshaw
- Department of Neuroscience, Physiology and Pharmacology, University College London, UK
| | - Robert Court
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, UK
| | | | - R Angus Silver
- Department of Neuroscience, Physiology and Pharmacology, University College London, UK
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8
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Bing Z, Meschede C, Röhrbein F, Huang K, Knoll AC. A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks. Front Neurorobot 2018; 12:35. [PMID: 30034334 PMCID: PMC6043678 DOI: 10.3389/fnbot.2018.00035] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 06/14/2018] [Indexed: 11/30/2022] Open
Abstract
Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.
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Affiliation(s)
- Zhenshan Bing
- Chair of Robotics, Artificial Intelligence and Real-time Systems, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Claus Meschede
- Chair of Robotics, Artificial Intelligence and Real-time Systems, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Florian Röhrbein
- Chair of Robotics, Artificial Intelligence and Real-time Systems, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Kai Huang
- Department of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
| | - Alois C. Knoll
- Chair of Robotics, Artificial Intelligence and Real-time Systems, Department of Informatics, Technical University of Munich, Munich, Germany
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Dura-Bernal S, Neymotin SA, Kerr CC, Sivagnanam S, Majumdar A, Francis JT, Lytton WW. Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM JOURNAL OF RESEARCH AND DEVELOPMENT 2017; 61:6.1-6.14. [PMID: 29200477 PMCID: PMC5708558 DOI: 10.1147/jrd.2017.2656758] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics.
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10
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Nguyen HD, Hong KS, Shin YI. Bundled-Optode Method in Functional Near-Infrared Spectroscopy. PLoS One 2016; 11:e0165146. [PMID: 27788178 PMCID: PMC5082888 DOI: 10.1371/journal.pone.0165146] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 10/09/2016] [Indexed: 11/18/2022] Open
Abstract
In this paper, a theory for detection of the absolute concentrations of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) from hemodynamic responses using a bundled-optode configuration in functional near-infrared spectroscopy (fNIRS) is proposed. The proposed method is then applied to the identification of two fingers (i.e., little and thumb) during their flexion and extension. This experiment involves a continuous-wave-type dual-wavelength (760 and 830 nm) fNIRS and five healthy male subjects. The active brain locations of two finger movements are identified based on the analysis of the t- and p-values of the averaged HbOs, which are quite distinctive. Our experimental results, furthermore, revealed that the hemodynamic responses of two-finger movements are different: The mean, peak, and time-to-peak of little finger movements are higher than those of thumb movements. It is noteworthy that the developed method can be extended to 3-dimensional fNIRS imaging.
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Affiliation(s)
- Hoang-Dung Nguyen
- Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Keum-Shik Hong
- Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan, 46241, Republic of Korea
- School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan, 46241, Republic of Korea
- * E-mail:
| | - Yong-Il Shin
- Department of Rehabilitation Medicine, School of Medicine, Pusan National University & Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 20, Geumo-ro, Mulgeum-eup, Yangsan-si, Gyeongsangnam-do, 50612, Republic of Korea
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11
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Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW. Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm. Front Neurosci 2016; 10:28. [PMID: 26903796 PMCID: PMC4746359 DOI: 10.3389/fnins.2016.00028] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Accepted: 01/25/2016] [Indexed: 01/08/2023] Open
Abstract
Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA
| | - Kan Li
- Department of Electrical and Computer Engineering, University of Florida Gainesville, FL, USA
| | - Samuel A Neymotin
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA
| | - Joseph T Francis
- Department of Physiology and Pharmacology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; BME Cullen College of Engineering, University of HoustonHouston, TX, USA
| | - Jose C Principe
- Department of Electrical and Computer Engineering, University of Florida Gainesville, FL, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; Department of Neurology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; Department of Neurology, Kings County Hospital CenterBrooklyn, NY, USA
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