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Haşegan D, Deible M, Earl C, D’Onofrio D, Hazan H, Anwar H, Neymotin SA. Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning. Front Comput Neurosci 2022; 16:1017284. [PMID: 36249482 PMCID: PMC9563231 DOI: 10.3389/fncom.2022.1017284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively suboptimal. In this work, we aimed to push the field of SNNs forward by exploring the potential of different learning mechanisms to achieve optimal performance. We trained SNNs to solve the CartPole reinforcement learning (RL) control problem using two learning mechanisms operating at different timescales: (1) spike-timing-dependent reinforcement learning (STDP-RL) and (2) evolutionary strategy (EVOL). Though the role of STDP-RL in biological systems is well established, several other mechanisms, though not fully understood, work in concert during learning in vivo. Recreating accurate models that capture the interaction of STDP-RL with these diverse learning mechanisms is extremely difficult. EVOL is an alternative method and has been successfully used in many studies to fit model neural responsiveness to electrophysiological recordings and, in some cases, for classification problems. One advantage of EVOL is that it may not need to capture all interacting components of synaptic plasticity and thus provides a better alternative to STDP-RL. Here, we compared the performance of each algorithm after training, which revealed EVOL as a powerful method for training SNNs to perform sensory-motor behaviors. Our modeling opens up new capabilities for SNNs in RL and could serve as a testbed for neurobiologists aiming to understand multi-timescale learning mechanisms and dynamics in neuronal circuits.
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Affiliation(s)
- Daniel Haşegan
- Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, United States
| | - Matt Deible
- Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, United States
| | - Christopher Earl
- Department of Computer Science, University of Massachusetts Amherst, Amherst, MA, United States
| | - David D’Onofrio
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Hananel Hazan
- Allen Discovery Center, Tufts University, Boston, MA, United States
| | - Haroon Anwar
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Samuel A. Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, United States
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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
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3
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Azevedo Carvalho N, Contassot-Vivier S, Buhry L, Martinez D. Simulation of Large Scale Neural Models With Event-Driven Connectivity Generation. Front Neuroinform 2020; 14:522000. [PMID: 33154719 PMCID: PMC7591773 DOI: 10.3389/fninf.2020.522000] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 08/31/2020] [Indexed: 11/15/2022] Open
Abstract
Accurate simulations of brain structures is a major problem in neuroscience. Many works are dedicated to design better models or to develop more efficient simulation schemes. In this paper, we propose a hybrid simulation scheme that combines time-stepping second-order integration of Hodgkin-Huxley (HH) type neurons with event-driven updating of the synaptic currents. As the HH model is a continuous model, there is no explicit spike events. Thus, in order to preserve the accuracy of the integration method, a spike detection algorithm is developed that accurately determines spike times. This approach allows us to regenerate the outgoing connections at each event, thereby avoiding the storage of the connectivity. Consequently, memory consumption is significantly reduced while preserving execution time and accuracy of the simulations, especially the spike times of detailed point neuron models. The efficiency of the method, implemented in the SiReNe software, is demonstrated by the simulation of a striatum model which consists of more than 106 neurons and 108 synapses (each neuron has a fan-out of 504 post-synaptic neurons), under normal and Parkinson's conditions.
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Affiliation(s)
| | | | - Laure Buhry
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, France
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Dura-Bernal S, Suter BA, Gleeson P, Cantarelli M, Quintana A, Rodriguez F, Kedziora DJ, Chadderdon GL, Kerr CC, Neymotin SA, McDougal RA, Hines M, Shepherd GMG, Lytton WW. NetPyNE, a tool for data-driven multiscale modeling of brain circuits. eLife 2019; 8:e44494. [PMID: 31025934 PMCID: PMC6534378 DOI: 10.7554/elife.44494] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 04/25/2019] [Indexed: 12/22/2022] Open
Abstract
Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
| | - Benjamin A Suter
- Department of PhysiologyNorthwestern UniversityChicagoUnited States
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and PharmacologyUniversity College LondonLondonUnited Kingdom
| | | | | | - Facundo Rodriguez
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- MetaCell LLCBostonUnited States
| | - David J Kedziora
- Complex Systems Group, School of PhysicsUniversity of SydneySydneyAustralia
| | - George L Chadderdon
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
| | - Cliff C Kerr
- Complex Systems Group, School of PhysicsUniversity of SydneySydneyAustralia
| | - Samuel A Neymotin
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- Nathan Kline Institute for Psychiatric ResearchOrangeburgUnited States
| | - Robert A McDougal
- Department of Neuroscience and School of MedicineYale UniversityNew HavenUnited States
- Center for Medical InformaticsYale UniversityNew HavenUnited States
| | - Michael Hines
- Department of Neuroscience and School of MedicineYale UniversityNew HavenUnited States
| | | | - William W Lytton
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- Department of NeurologyKings County HospitalBrooklynUnited States
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5
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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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6
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Lee G, Matsunaga A, Dura-Bernal S, Zhang W, Lytton WW, Francis JT, Fortes JA. Towards real-time communication between in vivo neurophysiological data sources and simulator-based brain biomimetic models. ACTA ACUST UNITED AC 2015; 3:1-23. [PMID: 26702394 PMCID: PMC4685709 DOI: 10.1186/s40244-014-0012-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Development of more sophisticated implantable brain-machine interface (BMI) will require both interpretation of the neurophysiological data being measured and subsequent determination of signals to be delivered back to the brain. Computational models are the heart of the machine of BMI and therefore an essential tool in both of these processes. One approach is to utilize brain biomimetic models (BMMs) to develop and instantiate these algorithms. These then must be connected as hybrid systems in order to interface the BMM with in vivo data acquisition devices and prosthetic devices. The combined system then provides a test bed for neuroprosthetic rehabilitative solutions and medical devices for the repair and enhancement of damaged brain. We propose here a computer network-based design for this purpose, detailing its internal modules and data flows. We describe a prototype implementation of the design, enabling interaction between the Plexon Multichannel Acquisition Processor (MAP) server, a commercial tool to collect signals from microelectrodes implanted in a live subject and a BMM, a NEURON-based model of sensorimotor cortex capable of controlling a virtual arm. The prototype implementation supports an online mode for real-time simulations, as well as an offline mode for data analysis and simulations without real-time constraints, and provides binning operations to discretize continuous input to the BMM and filtering operations for dealing with noise. Evaluation demonstrated that the implementation successfully delivered monkey spiking activity to the BMM through LAN environments, respecting real-time constraints.
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Affiliation(s)
- Giljae Lee
- Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116200, 216 Larsen Hall, Gainesville 32611, FL, USA
| | - Andréa Matsunaga
- Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116200, 216 Larsen Hall, Gainesville 32611, FL, USA
| | - Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn 11203, NY, USA
| | - Wenjie Zhang
- Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116200, 216 Larsen Hall, Gainesville 32611, FL, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Department of Neurology, State University New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Department of Neurology, Kings County Hospital, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Joint Program in Biomedical Engineering at Polytechnic Institute of New York University and State University of New York Downstate, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Program in Neural and Behavioral Science at State University of New York Downstate, 450 Clarkson Avenue, Brooklyn, NY, 11203, USA ; The Robert F. Furchgott Center for Neural & Behavioral Science, State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn, NY, 11203, USA
| | - Joseph T Francis
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Joint Program in Biomedical Engineering at Polytechnic Institute of New York University and State University of New York Downstate, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Program in Neural and Behavioral Science at State University of New York Downstate, 450 Clarkson Avenue, Brooklyn, NY, 11203, USA ; The Robert F. Furchgott Center for Neural & Behavioral Science, State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn, NY, 11203, USA
| | - José Ab Fortes
- Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116200, 216 Larsen Hall, Gainesville 32611, FL, USA ; Department of Computer and Information Science and Engineering, University of Florida, P.O. Box 116120, E301 CSE Building, Gainesville 32611, FL, USA
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Dura-Bernal S, Zhou X, Neymotin SA, Przekwas A, Francis JT, Lytton WW. Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm. Front Neurorobot 2015; 9:13. [PMID: 26635598 PMCID: PMC4658435 DOI: 10.3389/fnbot.2015.00013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 11/09/2015] [Indexed: 11/13/2022] Open
Abstract
Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of limb prosthetics.
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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
| | | | - 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 Center Brooklyn, NY, USA ; The Robert Furchgott Center for Neural and Behavioral Science, State University of New York Downstate Medical Center Brooklyn, NY, USA ; Joint Graduate Program in Biomedical Engineering, State University of New York Downstate and Polytechnic Institute of New York University Brooklyn, NY, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA ; The Robert Furchgott Center for Neural and Behavioral Science, State University of New York Downstate Medical Center Brooklyn, NY, USA ; Joint Graduate Program in Biomedical Engineering, State University of New York Downstate and Polytechnic Institute of New York University Brooklyn, NY, USA ; Department of Neurology, State University of New York Downstate Medical Center Brooklyn, NY, USA ; Department of Neurology, Kings County Hospital Center Brooklyn, NY, USA
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Chadderdon GL, Mohan A, Suter BA, Neymotin SA, Kerr CC, Francis JT, Shepherd GMG, Lytton WW. Motor cortex microcircuit simulation based on brain activity mapping. Neural Comput 2014; 26:1239-62. [PMID: 24708371 DOI: 10.1162/neco_a_00602] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The deceptively simple laminar structure of neocortex belies the complexity of intra- and interlaminar connectivity. We developed a computational model based primarily on a unified set of brain activity mapping studies of mouse M1. The simulation consisted of 775 spiking neurons of 10 cell types with detailed population-to-population connectivity. Static analysis of connectivity with graph-theoretic tools revealed that the corticostriatal population showed strong centrality, suggesting that would provide a network hub. Subsequent dynamical analysis confirmed this observation, in addition to revealing network dynamics that cannot be readily predicted through analysis of the wiring diagram alone. Activation thresholds depended on the stimulated layer. Low stimulation produced transient activation, while stronger activation produced sustained oscillations where the threshold for sustained responses varied by layer: 13% in layer 2/3, 54% in layer 5A, 25% in layer 5B, and 17% in layer 6. The frequency and phase of the resulting oscillation also depended on stimulation layer. By demonstrating the effectiveness of combined static and dynamic analysis, our results show how static brain maps can be related to the results of brain activity mapping.
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Affiliation(s)
- George L Chadderdon
- Department of Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY 11203
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9
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Rowan MS, Neymotin SA, Lytton WW. Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci 2014; 8:39. [PMID: 24765074 PMCID: PMC3982056 DOI: 10.3389/fncom.2014.00039] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2013] [Accepted: 03/18/2014] [Indexed: 01/23/2023] Open
Abstract
Cell death and synapse dysfunction are two likely causes of cognitive decline in AD. As cells die and synapses lose their drive, remaining cells suffer an initial decrease in activity. Neuronal homeostatic synaptic scaling then provides a feedback mechanism to restore activity. This homeostatic mechanism is believed to sense levels of activity-dependent cytosolic calcium within the cell and to adjust neuronal firing activity by increasing the density of AMPA synapses at remaining synapses to achieve balance. The scaling mechanism increases the firing rates of remaining cells in the network to compensate for decreases in network activity. However, this effect can itself become a pathology, as it produces increased imbalance between excitatory and inhibitory circuits, leading to greater susceptibility to further cell loss via calcium-mediated excitotoxicity. Here, we present a mechanistic explanation of how directed brain stimulation might be expected to slow AD progression based on computational simulations in a 470-neuron biomimetic model of a neocortical column. The simulations demonstrate that the addition of low-intensity electrostimulation (neuroprosthesis) to a network undergoing AD-like cell death can raise global activity and break this homeostatic-excitotoxic cascade. The increase in activity within the remaining cells in the column results in lower scaling-driven AMPAR upregulation, reduced imbalances in excitatory and inhibitory circuits, and lower susceptibility to ongoing damage.
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Affiliation(s)
- Mark S Rowan
- School of Computer Science, University of Birmingham Birmingham, UK
| | - Samuel A Neymotin
- Department Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA ; Department Neurobiology, Yale University School of Medicine New Haven, CT, USA
| | - William W Lytton
- Department Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA ; Department Neurology, State University of New York Downstate Medical Center Brooklyn, NY, USA ; Department Neurology, Kings County Hospital Center Brooklyn, NY, USA
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10
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Dura-Bernal S, Chadderdon GL, Neymotin SA, Francis JT, Lytton WW. Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm. Pattern Recognit Lett 2014; 36:204-212. [PMID: 26709323 DOI: 10.1016/j.patrec.2013.05.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Brain-machine interfaces can greatly improve the performance of prosthetics. Utilizing biomimetic neuronal modeling in brain machine interfaces (BMI) offers the possibility of providing naturalistic motor-control algorithms for control of a robotic limb. This will allow finer control of a robot, while also giving us new tools to better understand the brain's use of electrical signals. However, the biomimetic approach presents challenges in integrating technologies across multiple hardware and software platforms, so that the different components can communicate in real-time. We present the first steps in an ongoing effort to integrate a biomimetic spiking neuronal model of motor learning with a robotic arm. The biomimetic model (BMM) was used to drive a simple kinematic two-joint virtual arm in a motor task requiring trial-and-error convergence on a single target. We utilized the output of this model in real time to drive mirroring motion of a Barrett Technology WAM robotic arm through a user datagram protocol (UDP) interface. The robotic arm sent back information on its joint positions, which was then used by a visualization tool on the remote computer to display a realistic 3D virtual model of the moving robotic arm in real time. This work paves the way towards a full closed-loop biomimetic brain-effector system that can be incorporated in a neural decoder for prosthetic control, to be used as a platform for developing biomimetic learning algorithms for controlling real-time devices.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, USA
| | - George L Chadderdon
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, USA
| | - Samuel A Neymotin
- Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Joseph T Francis
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, USA ; Department of Neurology, State University New York Downstate, Brooklyn, New York, USA ; Department of Neurology, Kings County Hospital, Brooklyn, New York, USA
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11
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Neymotin SA, Chadderdon GL, Kerr CC, Francis JT, Lytton WW. Reinforcement learning of two-joint virtual arm reaching in a computer model of sensorimotor cortex. Neural Comput 2013; 25:3263-93. [PMID: 24047323 DOI: 10.1162/neco_a_00521] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Neocortical mechanisms of learning sensorimotor control involve a complex series of interactions at multiple levels, from synaptic mechanisms to cellular dynamics to network connectomics. We developed a model of sensory and motor neocortex consisting of 704 spiking model neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a two-joint virtual arm to reach to a fixed target. For each of 125 trained networks, we used 200 training sessions, each involving 15 s reaches to the target from 16 starting positions. Learning altered network dynamics, with enhancements to neuronal synchrony and behaviorally relevant information flow between neurons. After learning, networks demonstrated retention of behaviorally relevant memories by using proprioceptive information to perform reach-to-target from multiple starting positions. Networks dynamically controlled which joint rotations to use to reach a target, depending on current arm position. Learning-dependent network reorganization was evident in both sensory and motor populations: learned synaptic weights showed target-specific patterning optimized for particular reach movements. Our model embodies an integrative hypothesis of sensorimotor cortical learning that could be used to interpret future electrophysiological data recorded in vivo from sensorimotor learning experiments. We used our model to make the following predictions: learning enhances synchrony in neuronal populations and behaviorally relevant information flow across neuronal populations, enhanced sensory processing aids task-relevant motor performance and the relative ease of a particular movement in vivo depends on the amount of sensory information required to complete the movement.
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Affiliation(s)
- Samuel A Neymotin
- Department of Neurobiology, Yale University School of Medicine, New Haven, CT 06510, U.S.A., and Department of Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY 11203, U.S.A.
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Kerr CC, Neymotin SA, Chadderdon GL, Fietkiewicz CT, Francis JT, Lytton WW. Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex. IEEE Trans Neural Syst Rehabil Eng 2011; 20:153-60. [PMID: 22180517 DOI: 10.1109/tnsre.2011.2178614] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Damage to a cortical area reduces not only information transmitted to other cortical areas, but also activation of these areas. This phenomenon, whereby the dynamics of a follower area are dramatically altered, is typically manifested as a marked reduction in activity. Ideally, neuroprosthetic stimulation would replace both information and activation. However, replacement of activation alone may be valuable as a means of restoring dynamics and information processing of other signals in this multiplexing system. We used neuroprosthetic stimulation in a computer model of the cortex to repair activation dynamics, using a simple repetitive stimulation to replace the more complex, naturalistic stimulation that had been removed. We found that we were able to restore activity in terms of neuronal firing rates. Additionally, we were able to restore information processing, measured as a restoration of causality between an experimentally recorded signal fed into the in silico brain and a cortical output. These results indicate that even simple neuroprosthetics that do not restore lost information may nonetheless be effective in improving the functionality of surrounding areas of cortex.
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Affiliation(s)
- Cliff C Kerr
- Department of Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY11203, USA.
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Lytton WW, Omurtag A, Neymotin SA, Hines ML. Just-in-time connectivity for large spiking networks. Neural Comput 2008; 20:2745-56. [PMID: 18533821 DOI: 10.1162/neco.2008.10-07-622] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The scale of large neuronal network simulations is memory limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed: just in time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities, and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON's standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that added items to the queue only when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run.
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Affiliation(s)
- William W Lytton
- Department of Physiology and Pharmacology, Biomedical Engineering, and Neurology, SUNY Downstate, Brooklyn, NY 11203, USA.
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Lytton WW, Orman R, Stewart M. Broadening of activity with flow across neural structures. Perception 2008; 37:401-7. [PMID: 18491717 DOI: 10.1068/p5871] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Synfire chains have long been suggested as a substrate for perception and information processing in the nervous system. However, embedding activation chains in a densely connected nervous matrix risks spread of signal that will obscure or obliterate the message. We used computer modeling and physiological measurements in rat hippocampus to assess this problem of activity broadening. We simulated a series of neural modules with feedforward propagation and random connectivity within each module and from one module to the next. We found that activity broadened as it propagated from one module to the next. This occurred over a wide array of parameters with greater broadening seen with increasing excitatory-excitatory synaptic strength. Activity broadening correlated positively with propagation velocity. Multi-electrode measurements of activity propagation in disinhibited CA1 slice demonstrated broadening of about 50% over 1 mm. Such broadening is a problem for information transfer that must be dealt with in a fully functioning nervous system.
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Affiliation(s)
- William W Lytton
- Downstate Medical Center, State University of New York, 450 Clarkson Avenue, Brooklyn, New York, NY 11203, USA.
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Abstract
Epilepsy is a complex set of disorders that can involve many areas of the cortex, as well as underlying deep-brain systems. The myriad manifestations of seizures, which can be as varied as déjà vu and olfactory hallucination, can therefore give researchers insights into regional functions and relations. Epilepsy is also complex genetically and pathophysiologically: it involves microscopic (on the scale of ion channels and synaptic proteins), macroscopic (on the scale of brain trauma and rewiring) and intermediate changes in a complex interplay of causality. It has long been recognized that computer modelling will be required to disentangle causality, to better understand seizure spread and to understand and eventually predict treatment efficacy. Over the past few years, substantial progress has been made in modelling epilepsy at levels ranging from the molecular to the socioeconomic. We review these efforts and connect them to the medical goals of understanding and treating the disorder.
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Affiliation(s)
- William W Lytton
- Department of Physiology, State University of New York, Downstate Medical Center, Brooklyn, New York, USA.
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The virtual slice setup. J Neurosci Methods 2008; 171:309-15. [PMID: 18452996 DOI: 10.1016/j.jneumeth.2008.03.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2007] [Revised: 03/12/2008] [Accepted: 03/13/2008] [Indexed: 11/22/2022]
Abstract
In an effort to design a simulation environment that is more similar to that of neurophysiology, we introduce a virtual slice setup in the NEURON simulator. The virtual slice setup runs continuously and permits parameter changes, including changes to synaptic weights and time course and to intrinsic cell properties. The virtual slice setup permits shocks to be applied at chosen locations and activity to be sampled intra- or extracellularly from chosen locations. By default, a summed population display is shown during a run to indicate the level of activity and no states are saved. Simulations can run for hours of model time, therefore it is not practical to save all of the state variables. These, in any case, are primarily of interest at discrete times when experiments are being run: the simulation can be stopped momentarily at such times to save activity patterns. The virtual slice setup maintains an automated notebook showing shocks and parameter changes as well as user comments. We demonstrate how interaction with a continuously running simulation encourages experimental prototyping and can suggest additional dynamical features such as ligand wash-in and wash-out-alternatives to typical instantaneous parameter change. The virtual slice setup currently uses event-driven cells and runs at approximately 2 min/h on a laptop.
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Abstract
SUMMARY Network simulations can help identify underlying mechanisms of epileptic activity that are hard to isolate in biologic preparations. To be useful, simulations must be sufficiently realistic to make possible biologic and clinical prediction. This requirement for large networks of sufficiently detailed neurons raises challenges both with regard to computational load and the difficulty of obtaining insights with large numbers of free parameters and the large amounts of generated data. The authors have addressed these problems by simulating computationally manageable networks of moderate size consisting of 1,000 to 3,000 neurons with multiple intrinsic and synaptic properties. Experiments on these simulations demonstrated the presence of epileptiform behavior in the form of repetitive high-intensity population events (clonic behavior) or latch-up with near maximal activity (tonic behavior). Intrinsic neuronal excitability is not always a predictor of network epileptiform activity but may paradoxically produce antiepileptic effects, depending on the settings of other parameters. Several simulations revealed the importance of random coincident inputs to shift a network from a low-activation to a high-activation epileptiform state. Finally, a simulated anticonvulsant acting on excitability tended to preferentially decrease tonic activity.
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