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Li J, Feng P, Zhao L, Chen J, Du M, Song J, Wu Y. Transition behavior of the seizure dynamics modulated by the astrocyte inositol triphosphate noise. CHAOS (WOODBURY, N.Y.) 2022; 32:113121. [PMID: 36456345 DOI: 10.1063/5.0124123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 10/17/2022] [Indexed: 06/17/2023]
Abstract
Epilepsy is a neurological disorder with recurrent seizures, which convey complex dynamical characteristics including chaos and randomness. Until now, the underlying mechanism has not been fully elucidated, especially the bistable property beneath the epileptic random induction phenomena in certain conditions. Inspired by the recent finding that astrocyte GTPase-activating protein (G-protein)-coupled receptors could be involved in stochastic epileptic seizures, we proposed a neuron-astrocyte network model, incorporating the noise of the astrocytic second messenger, inositol triphosphate (IP3) that is modulated by G-protein-coupled receptor activation. Based on this model, we have statistically analyzed the transitions of epileptic seizures by performing repeatable simulation trials. Our simulation results show that the increase in the IP3 noise intensity induces depolarization-block epileptic seizures together with an increase in neuronal firing frequency, consistent with corresponding experiments. Meanwhile, the bistable states of the seizure dynamics were present under certain noise intensities, during which the neuronal firing pattern switches between regular sparse spiking and epileptic seizure states. This random presence of epileptic seizures is absent when the noise intensity continues to increase, accompanying with an increase in the epileptic depolarization block duration. The simulation results also shed light on the fact that calcium signals in astrocytes play significant roles in the pattern formations of the epileptic seizure. Our results provide a potential pathway for understanding the epileptic randomness in certain conditions.
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Affiliation(s)
- Jiajia Li
- College of Information and Control Engineering, Xi'an University of Architecture and Technology, Shaanxi, Xi'an 710055, China
| | - Peihua Feng
- State Key Laboratory for Strength and Vibration of Mechanical Structures, National Demonstration Center for Experimental Mechanics Education, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Liang Zhao
- College of Information and Control Engineering, Xi'an University of Architecture and Technology, Shaanxi, Xi'an 710055, China
| | - Junying Chen
- College of Information and Control Engineering, Xi'an University of Architecture and Technology, Shaanxi, Xi'an 710055, China
| | - Mengmeng Du
- School of Mathematics and Data Science, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Jian Song
- Department of Neurosurgery, Wuhan General Hospital of PLA, Wuhan 430070, China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures, National Demonstration Center for Experimental Mechanics Education, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
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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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3
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Zou X, Zhu Z, Guo Y, Zhang H, Liu Y, Cui Z, Ke Z, Jiang S, Tong Y, Wu Z, Mao Y, Chen L, Wang D. Neural excitatory rebound induced by valproic acid may predict its inadequate control of seizures. EBioMedicine 2022; 83:104218. [PMID: 35970021 PMCID: PMC9399967 DOI: 10.1016/j.ebiom.2022.104218] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 07/21/2022] [Accepted: 07/29/2022] [Indexed: 01/02/2023] Open
Abstract
Background Valproic acid (VPA) represents one of the most efficient antiseizure medications (ASMs) for both general and focal seizures, but some patients may have inadequate control by VPA monotherapy. In this study, we aimed to verify the hypothesis that excitatory dynamic rebound induced by inhibitory power may contribute to the ineffectiveness of VPA therapy and become a predictor of post-operative inadequate control of seizures. Methods Awake craniotomy surgeries were performed in 16 patients with intro-operative high-density electrocorticogram (ECoG) recording. The relationship between seizure control and the excitatory rebound was further determined by diagnostic test and univariate analysis. Thereafter, kanic acid (KA)-induced epileptic mouse model was used to confirm that its behavior and neural activity would be controlled by VPA. Finally, a computational simulation model was established to verify the hypothesis. Findings Inadequate control of seizures by VPA monotherapy and post-operative status epilepticus are closely related to a significant excitatory rebound after VPA injection (rebound electrodes≧5/64, p = 0.008), together with increased synchronization of the local field potential (LFP). In addition, the neural activity in the model mice showed a significant rebound on spike firing (53/77 units, 68.83%). The LFP increased the power spectral density in multiple wavebands after VPA injection in animal experiments (p < 0.001). Computational simulation experiments revealed that inhibitory power-induced excitatory rebound is an intrinsic feature in the neural network. Interpretation Despite the limitations, we provide evidence that inadequate control of seizures by VPA monotherapy could be associated with neural excitatory rebounds, which were predicted by intraoperative ECoG analysis. Combined with the evidence from computational models and animal experiments, our findings suggested that ineffective ASMs may be because of the excitatory rebound, which is mediated by increased inhibitory power. Funding This work was supported by National Natural Science Foundation of China (62127810, 81970418), Shanghai Municipal Science and Technology Major Project (2018SHZDZX03) and ZJLab; Science and Technology Commission of Shanghai Municipality (18JC1410403, 19411969000, 19ZR1477700, 20Z11900100); MOE Frontiers Center for Brain Science; Shanghai Key Laboratory of Health Identification and Assessment (21DZ2271000); Shanghai Shenkang (SHDC2020CR3073B).
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Gandolfi D, Boiani GM, Bigiani A, Mapelli J. Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders. Int J Mol Sci 2021; 22:4565. [PMID: 33925434 PMCID: PMC8123833 DOI: 10.3390/ijms22094565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/22/2021] [Accepted: 04/25/2021] [Indexed: 02/06/2023] Open
Abstract
The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.
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Affiliation(s)
- Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
| | - Giulia Maria Boiani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
| | - Albertino Bigiani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy
| | - Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy
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Latorre A, Rocchi L, Magrinelli F, Mulroy E, Berardelli A, Rothwell JC, Bhatia KP. Unravelling the enigma of cortical tremor and other forms of cortical myoclonus. Brain 2021; 143:2653-2663. [PMID: 32417917 DOI: 10.1093/brain/awaa129] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 02/11/2020] [Accepted: 02/27/2020] [Indexed: 12/21/2022] Open
Abstract
Cortical tremor is a fine rhythmic oscillation involving distal upper limbs, linked to increased sensorimotor cortex excitability, as seen in cortical myoclonus. Cortical tremor is the hallmark feature of autosomal dominant familial cortical myoclonic tremor and epilepsy (FCMTE), a syndrome not yet officially recognized and characterized by clinical and genetic heterogeneity. Non-coding repeat expansions in different genes have been recently recognized to play an essential role in its pathogenesis. Cortical tremor is considered a rhythmic variant of cortical myoclonus and is part of the 'spectrum of cortical myoclonus', i.e. a wide range of clinical motor phenomena, from reflex myoclonus to myoclonic epilepsy, caused by abnormal sensorimotor cortical discharges. The aim of this update is to provide a detailed analysis of the mechanisms defining cortical tremor, as seen in FCMTE. After reviewing the clinical and genetic features of FCMTE, we discuss the possible mechanisms generating the distinct elements of the cortical myoclonus spectrum, and how cortical tremor fits into it. We propose that the spectrum is due to the evolution from a spatially limited focus of excitability to recruitment of more complex mechanisms capable of sustaining repetitive activity, overcoming inhibitory mechanisms that restrict excitatory bursts, and engaging wide areas of cortex. Finally, we provide evidence for a possible common denominator of the elements of the spectrum, i.e. the cerebellum, and discuss its role in FCMTE, according to recent genetic findings.
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Affiliation(s)
- Anna Latorre
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- Department of Human Neurosciences, Sapienza University of Rome, Italy
| | - Lorenzo Rocchi
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
| | - Francesca Magrinelli
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Eoin Mulroy
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
| | - Alfredo Berardelli
- Department of Human Neurosciences, Sapienza University of Rome, Italy
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Neuromed, Pozzilli, IS, Italy
| | - John C Rothwell
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
| | - Kailash P Bhatia
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
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Schevon CA, Tobochnik S, Eissa T, Merricks E, Gill B, Parrish RR, Bateman LM, McKhann GM, Emerson RG, Trevelyan AJ. Multiscale recordings reveal the dynamic spatial structure of human seizures. Neurobiol Dis 2019; 127:303-311. [PMID: 30898669 PMCID: PMC6588430 DOI: 10.1016/j.nbd.2019.03.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 03/11/2019] [Accepted: 03/15/2019] [Indexed: 02/07/2023] Open
Abstract
The cellular activity underlying human focal seizures, and its relationship to key signatures in the EEG recordings used for therapeutic purposes, has not been well characterized despite many years of investigation both in laboratory and clinical settings. The increasing use of microelectrodes in epilepsy surgery patients has made it possible to apply principles derived from laboratory research to the problem of mapping the spatiotemporal structure of human focal seizures, and characterizing the corresponding EEG signatures. In this review, we describe results from human microelectrode studies, discuss some data interpretation pitfalls, and explain the current understanding of the key mechanisms of ictogenesis and seizure spread.
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Affiliation(s)
- Catherine A Schevon
- Department of Neurology, Columbia University Medical Center, New York, NY, USA.
| | - Steven Tobochnik
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Tahra Eissa
- Department of Applied Mathematics, University of Colorado at Boulder, Boulder, CO, USA
| | - Edward Merricks
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Brian Gill
- Department of Neurological Surgery, Columbia University Medical Center, New York, NY, USA
| | - R Ryley Parrish
- Institute for Aging, Newcastle University, Newcastle-Upon-Tyne, UK
| | - Lisa M Bateman
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Guy M McKhann
- Department of Neurological Surgery, Columbia University Medical Center, New York, NY, USA
| | - Ronald G Emerson
- Department of Neurology, Weill Cornell Medical Center, New York, NY, USA
| | - Andrew J Trevelyan
- Department of Neurology, Columbia University Medical Center, New York, NY, USA; Institute for Aging, Newcastle University, Newcastle-Upon-Tyne, UK
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Neymotin SA, Dura-Bernal S, Lakatos P, Sanger TD, Lytton WW. Multitarget Multiscale Simulation for Pharmacological Treatment of Dystonia in Motor Cortex. Front Pharmacol 2016; 7:157. [PMID: 27378922 PMCID: PMC4906029 DOI: 10.3389/fphar.2016.00157] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 05/30/2016] [Indexed: 12/20/2022] Open
Abstract
A large number of physiomic pathologies can produce hyperexcitability in cortex. Depending on severity, cortical hyperexcitability may manifest clinically as a hyperkinetic movement disorder or as epilpesy. We focus here on dystonia, a movement disorder that produces involuntary muscle contractions and involves pathology in multiple brain areas including basal ganglia, thalamus, cerebellum, and sensory and motor cortices. Most research in dystonia has focused on basal ganglia, while much pharmacological treatment is provided directly at muscles to prevent contraction. Motor cortex is another potential target for therapy that exhibits pathological dynamics in dystonia, including heightened activity and altered beta oscillations. We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. These models demonstrated degeneracy, meaning that there were many ways of obtaining the pathological syndrome. There was no single parameter alteration which would consistently distinguish pathological from physiological dynamics. At higher dimensions in parameter space, we were able to use support vector machines to distinguish the two patterns in different regions of space and thereby trace multitarget routes from dystonic to physiological dynamics. These results suggest the use of in silico models for discovery of multitarget drug cocktails.
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Affiliation(s)
- Samuel A Neymotin
- Department Physiology and Pharmacology, SUNY Downstate Medical Center, State University of New YorkBrooklyn, NY, USA; Department Neuroscience, Yale University School of MedicineNew Haven, CT, USA
| | - Salvador Dura-Bernal
- Department Physiology and Pharmacology, SUNY Downstate Medical Center, State University of New York Brooklyn, NY, USA
| | - Peter Lakatos
- Nathan S. Kline Institute for Psychiatric Research Orangeburg, NY, USA
| | - Terence D Sanger
- Department Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA; Division Neurology, Child Neurology and Movement Disorders, Children's Hospital Los AngelesLos Angeles, CA, USA
| | - William W Lytton
- Department Physiology and Pharmacology, SUNY Downstate Medical Center, State University of New YorkBrooklyn, NY, USA; Department Neurology, SUNY Downstate Medical CenterBrooklyn, NY, USA; Department Neurology, Kings County Hospital CenterBrooklyn, NY, USA; The Robert F. Furchgott Center for Neural and Behavioral ScienceBrooklyn, NY, US
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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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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, Van Albada SJ, Neymotin SA, Chadderdon GL, Robinson PA, Lytton WW. Cortical information flow in Parkinson's disease: a composite network/field model. Front Comput Neurosci 2013; 7:39. [PMID: 23630492 PMCID: PMC3635017 DOI: 10.3389/fncom.2013.00039] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 04/02/2013] [Indexed: 11/30/2022] Open
Abstract
The basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany Parkinson's disease (PD). Since motor commands originate in the cortex, an important question is how the basal ganglia influence cortical information flow, and how this influence becomes pathological in PD. To explore this, we developed a composite neuronal network/neural field model. The network model consisted of 4950 spiking neurons, divided into 15 excitatory and inhibitory cell populations in the thalamus and cortex. The field model consisted of the cortex, thalamus, striatum, subthalamic nucleus, and globus pallidus. Both models have been separately validated in previous work. Three field models were used: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. Spikes generated by these field models were then used to drive the network model. Compared to the network driven by the healthy model, the PD-driven network had lower firing rates, a shift in spectral power toward lower frequencies, and higher probability of bursting; each of these findings is consistent with empirical data on PD. In the healthy model, we found strong Granger causality between cortical layers in the beta and low gamma frequency bands, but this causality was largely absent in the PD model. In particular, the reduction in Granger causality from the main “input” layer of the cortex (layer 4) to the main “output” layer (layer 5) was pronounced. This may account for symptoms of PD that seem to reflect deficits in information flow, such as bradykinesia. In general, these results demonstrate that the brain's large-scale oscillatory environment, represented here by the field model, strongly influences the information processing that occurs within its subnetworks. Hence, it may be preferable to drive spiking network models with physiologically realistic inputs rather than pure white noise.
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Affiliation(s)
- Cliff C Kerr
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA ; School of Physics, University of Sydney NSW, Australia ; Brain Dynamics Centre, Westmead Millennium Institute Westmead, NSW, Australia
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13
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Ritter P, Schirner M, McIntosh AR, Jirsa VK. The virtual brain integrates computational modeling and multimodal neuroimaging. Brain Connect 2013; 3:121-45. [PMID: 23442172 PMCID: PMC3696923 DOI: 10.1089/brain.2012.0120] [Citation(s) in RCA: 143] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Brain function is thought to emerge from the interactions among neuronal populations. Apart from traditional efforts to reproduce brain dynamics from the micro- to macroscopic scales, complementary approaches develop phenomenological models of lower complexity. Such macroscopic models typically generate only a few selected-ideally functionally relevant-aspects of the brain dynamics. Importantly, they often allow an understanding of the underlying mechanisms beyond computational reproduction. Adding detail to these models will widen their ability to reproduce a broader range of dynamic features of the brain. For instance, such models allow for the exploration of consequences of focal and distributed pathological changes in the system, enabling us to identify and develop approaches to counteract those unfavorable processes. Toward this end, The Virtual Brain (TVB) ( www.thevirtualbrain.org ), a neuroinformatics platform with a brain simulator that incorporates a range of neuronal models and dynamics at its core, has been developed. This integrated framework allows the model-based simulation, analysis, and inference of neurophysiological mechanisms over several brain scales that underlie the generation of macroscopic neuroimaging signals. In this article, we describe how TVB works, and we present the first proof of concept.
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Affiliation(s)
- Petra Ritter
- Minerva Research Group Brain Modes, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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14
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Chadderdon GL, Neymotin SA, Kerr CC, Lytton WW. Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex. PLoS One 2012; 7:e47251. [PMID: 23094042 PMCID: PMC3477154 DOI: 10.1371/journal.pone.0047251] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2012] [Accepted: 09/12/2012] [Indexed: 11/19/2022] Open
Abstract
Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint “forearm” to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1), no learning (0), or punishment (−1), corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.
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Affiliation(s)
- George L Chadderdon
- Department of Physiology and Pharmacology, State University New York Downstate, Brooklyn, New York, United States of America.
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15
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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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16
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Neymotin SA, Lee H, Park E, Fenton AA, Lytton WW. Emergence of physiological oscillation frequencies in a computer model of neocortex. Front Comput Neurosci 2011; 5:19. [PMID: 21541305 PMCID: PMC3082765 DOI: 10.3389/fncom.2011.00019] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2010] [Accepted: 04/01/2011] [Indexed: 01/23/2023] Open
Abstract
Coordination of neocortical oscillations has been hypothesized to underlie the "binding" essential to cognitive function. However, the mechanisms that generate neocortical oscillations in physiological frequency bands remain unknown. We hypothesized that interlaminar relations in neocortex would provide multiple intermediate loops that would play particular roles in generating oscillations, adding different dynamics to the network. We simulated networks from sensory neocortex using nine columns of event-driven rule-based neurons wired according to anatomical data and driven with random white-noise synaptic inputs. We tuned the network to achieve realistic cell firing rates and to avoid population spikes. A physiological frequency spectrum appeared as an emergent property, displaying dominant frequencies that were not present in the inputs or in the intrinsic or activated frequencies of any of the cell groups. We monitored spectral changes while using minimal dynamical perturbation as a methodology through gradual introduction of hubs into individual layers. We found that hubs in layer 2/3 excitatory cells had the greatest influence on overall network activity, suggesting that this subpopulation was a primary generator of theta/beta strength in the network. Similarly, layer 2/3 interneurons appeared largely responsible for gamma activation through preferential attenuation of the rest of the spectrum. The network showed evidence of frequency homeostasis: increased activation of supragranular layers increased firing rates in the network without altering the spectral profile, and alteration in synaptic delays did not significantly shift spectral peaks. Direct comparison of the power spectra with experimentally recorded local field potentials from prefrontal cortex of awake rat showed substantial similarities, including comparable patterns of cross-frequency coupling.
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Affiliation(s)
- Samuel A. Neymotin
- SUNY Downstate/NYU-Poly Joint Biomedical Engineering ProgramBrooklyn, NY, USA
| | - Heekyung Lee
- Neural and Behavioral Science Program, SUNY DownstateBrooklyn, NY, USA
| | - Eunhye Park
- Center for Neural Science, New York UniversityNew York, NY, USA
| | - André A. Fenton
- SUNY Downstate/NYU-Poly Joint Biomedical Engineering ProgramBrooklyn, NY, USA
- Neural and Behavioral Science Program, SUNY DownstateBrooklyn, NY, USA
- Center for Neural Science, New York UniversityNew York, NY, USA
- Department of Physiology and Pharmacology, SUNY DownstateBrooklyn, NY, USA
| | - William W. Lytton
- SUNY Downstate/NYU-Poly Joint Biomedical Engineering ProgramBrooklyn, NY, USA
- Neural and Behavioral Science Program, SUNY DownstateBrooklyn, NY, USA
- Department of Physiology and Pharmacology, SUNY DownstateBrooklyn, NY, USA
- Department of Neurology, SUNY DownstateBrooklyn, NY, USA
- Kings County HospitalBrooklyn, NY, USA
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17
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Synaptic information transfer in computer models of neocortical columns. J Comput Neurosci 2010; 30:69-84. [PMID: 20556639 DOI: 10.1007/s10827-010-0253-4] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2009] [Revised: 05/24/2010] [Accepted: 05/31/2010] [Indexed: 10/19/2022]
Abstract
Understanding the direction and quantity of information flowing in neuronal networks is a fundamental problem in neuroscience. Brains and neuronal networks must at the same time store information about the world and react to information in the world. We sought to measure how the activity of the network alters information flow from inputs to output patterns. Using neocortical column neuronal network simulations, we demonstrated that networks with greater internal connectivity reduced input/output correlations from excitatory synapses and decreased negative correlations from inhibitory synapses, measured by Kendall's τ correlation. Both of these changes were associated with reduction in information flow, measured by normalized transfer entropy (nTE). Information handling by the network reflected the degree of internal connectivity. With no internal connectivity, the feedforward network transformed inputs through nonlinear summation and thresholding. With greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. This dynamic contribution amounts to added information drawn from that stored in the network. At still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through. The association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing.
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Easter A, Bell ME, Damewood JR, Redfern WS, Valentin JP, Winter MJ, Fonck C, Bialecki RA. Approaches to seizure risk assessment in preclinical drug discovery. Drug Discov Today 2009; 14:876-84. [PMID: 19545644 DOI: 10.1016/j.drudis.2009.06.003] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2009] [Revised: 06/09/2009] [Accepted: 06/10/2009] [Indexed: 01/01/2023]
Abstract
Assessment of seizure risk traditionally occurs late in the drug discovery process using low-throughput, resource intensive in vivo assays. Such approaches do not allow sufficient time to mitigate risk by influencing chemical design. Early identification using cheaper, higher throughput assays with lower animal and compound requirements would be preferable. Here we review the current techniques available to assess this issue and describe how they may be combined in a rational step-wise cascade allowing more effective assessment of seizure risk.
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Affiliation(s)
- Alison Easter
- Safety Assessment US, AstraZeneca R&D Wilmington, DE 19850, 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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20
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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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Wendling F. Computational models of epileptic activity: a bridge between observation and pathophysiological interpretation. Expert Rev Neurother 2008; 8:889-96. [PMID: 18505354 DOI: 10.1586/14737175.8.6.889] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Epilepsy is a neurological disorder characterized by the recurrence of seizures. It affects 50 million people worldwide. Although a considerable number of new antiepileptic drugs with reduced side effects and toxicity have been introduced since the 1950s, 30% of patients remain pharmacoresistant. Although epilepsy research is making progress, advances in understanding drug resistance have been hampered by the complexity of the underlying neuronal systems responsible for epileptic activity. In such systems where short- or long-term plasticity plays a role, pathophysiological alterations may take place at subcellular (i.e., membrane ion channels and neurotransmitter receptors), cellular (neurons), tissular (networks of neurons) and regional (networks of networks of neurons) scales. In such a context, the demand for integrative approaches is high and neurocomputational models become recognized tools for tackling the complexity of epileptic phenomena. The purpose of this report is to provide an overview on computational modeling as a way of structuring and interpreting multimodal data recorded from the epileptic brain. Some examples are briefly described, which illustrate how computational models closely related with either experimental or clinical data can markedly advance our understanding of essential issues in epilepsy such as the transition from background to seizure activity. A commentary is also made on the potential use of such models in the study of therapeutic strategies such as rational drug design or electrical stimulations.
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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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