Neuron Perspective The Scientific Case for Brain Simulations

Gaute T. Einevoll,1,2,* Alain Destexhe,3,4 Markus Diesmann,5,6,7 Sonja Gr€ un,5,8 Viktor Jirsa,9 Marc de Kamps,10 Michele Migliore,11 Torbjørn V. Ness,1 Hans E. Plesser,1,5 and Felix Sch€ urmann12 1Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway 2Department of Physics, University of Oslo, 0316 Oslo, Norway 3Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique, 91198 Gif-sur-Yvette, France 4European Institute for Theoretical Neuroscience, 75012 Paris, France 5Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), and JARA-Institut Brain Structure-Function Relationships (INM-10), J€ ulich Research Centre, 52425 J€ ulich, Germany 6Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany 7Department of Physics, RWTH Aachen University, 52074 Aachen, Germany 8Theoretical Systems Neurobiology, RWTH Aachen University, 52074 Aachen, Germany 9Institut de Neurosciences des Systèmes (INS), INSERM, Aix Marseille Université, 13005 Marseille, France 10Institute for Artificial and Biological Intelligence, School of Computing, Leeds LS2 9JT, UK 11Institute of Biophysics, National Research Council, 90146 Palermo, Italy 12Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland *Correspondence: gaute.einevoll@nmbu.no https://doi.org/10.1016/j.neuron.2019.03.027

[1]  James G. King,et al.  CoreNEURON : An Optimized Compute Engine for the NEURON Simulator , 2019, Front. Neuroinform..

[2]  Alexander Peyser,et al.  Arbor — A Morphologically-Detailed Neural Network Simulation Library for Contemporary High-Performance Computing Architectures , 2019, 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP).

[3]  Sonja Grün,et al.  Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data , 2018, Front. Neuroinform..

[4]  Gaute T. Einevoll,et al.  Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0 , 2018, bioRxiv.

[5]  Markus Diesmann,et al.  A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas , 2018, PLoS Comput. Biol..

[6]  Sonja Grün,et al.  1st INCF Workshop on Validation of Analysis Methods , 2018 .

[7]  Christof Koch,et al.  BioNet: A Python interface to NEURON for modeling large-scale networks , 2018, PloS one.

[8]  Steve B. Furber,et al.  Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model , 2018, Front. Neurosci..

[9]  Bijan Pesaran,et al.  Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation , 2018, Nature Neuroscience.

[10]  Brian R. Lee,et al.  Visual physiology of the layer 4 cortical circuit in silico , 2018, bioRxiv.

[11]  Gaute T. Einevoll,et al.  Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience , 2018, bioRxiv.

[12]  Gaute T. Einevoll,et al.  A Kirchhoff-Nernst-Planck framework for modeling large scale extracellular electrodiffusion surrounding morphologically detailed neurons , 2018, bioRxiv.

[13]  Cecilia Romaro,et al.  Reimplementation of the Potjans-Diesmann cortical microcircuit model: from NEST to Brian , 2018, bioRxiv.

[14]  Sven Rahmann,et al.  Genome analysis , 2022 .

[15]  Markus Diesmann,et al.  Multi-scale account of the network structure of macaque visual cortex , 2017, Brain Structure and Function.

[16]  W. Bosking,et al.  Saturation in Phosphene Size with Increasing Current Levels Delivered to Human Visual Cortex , 2017, The Journal of Neuroscience.

[17]  Alain Destexhe,et al.  Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons , 2017, bioRxiv.

[18]  Gustavo Deco,et al.  Inferring multi-scale neural mechanisms with brain network modelling , 2017, bioRxiv.

[19]  Wulfram Gerstner,et al.  Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[20]  Svenn-Arne Dragly,et al.  Neuronify: An Educational Simulator for Neural Circuits , 2017, eNeuro.

[21]  M. Breakspear Dynamic models of large-scale brain activity , 2017, Nature Neuroscience.

[22]  Wulfram Gerstner,et al.  Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size , 2016, PLoS Comput. Biol..

[23]  D. Kleinfeld,et al.  The roadmap for estimation of cell-type-specific neuronal activity from non-invasive measurements , 2016, Philosophical Transactions of the Royal Society B: Biological Sciences.

[24]  Vishnu B. Sridhar,et al.  Cell type specificity of neurovascular coupling in cerebral cortex , 2016, eLife.

[25]  Moritz Helias,et al.  Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit , 2015, PLoS Comput. Biol..

[26]  Sonja Grün,et al.  Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks , 2015, BMC Neuroscience.

[27]  Christof Koch,et al.  A Biological Imitation Game , 2015, Cell.

[28]  James G. King,et al.  Reconstruction and Simulation of Neocortical Microcircuitry , 2015, Cell.

[29]  Peter Bauer,et al.  The quiet revolution of numerical weather prediction , 2015, Nature.

[30]  G. Shepherd,et al.  Synaptic clusters function as odor operators in the olfactory bulb , 2015, Proceedings of the National Academy of Sciences.

[31]  Wulfram Gerstner,et al.  Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models , 2015, PLoS Comput. Biol..

[32]  Gaute T. Einevoll,et al.  ViSAPy: A Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms , 2015, Journal of Neuroscience Methods.

[33]  Reisa A. Sperling,et al.  Alzheimer's disease , 2015, Nature Reviews Disease Primers.

[34]  Nicholas Cain,et al.  The computational properties of a simplified cortical column model , 2014, BMC Neuroscience.

[35]  Xing Chen,et al.  Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue , 2014, Brain Structure and Function.

[36]  Gaute T. Einevoll,et al.  LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons , 2014, Front. Neuroinform..

[37]  Tobias C. Potjans,et al.  The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model , 2012, Cerebral cortex.

[38]  Stefano Panzeri,et al.  Modelling and analysis of local field potentials for studying the function of cortical circuits , 2013, Nature Reviews Neuroscience.

[39]  Anders M. Dale,et al.  The Challenge of Connecting the Dots in the B.R.A.I.N. , 2013, Neuron.

[40]  E. Kandel,et al.  Neuroscience thinks big (and collaboratively) , 2013, Nature Reviews Neuroscience.

[41]  Michael W. Reimann,et al.  A Biophysically Detailed Model of Neocortical Local Field Potentials Predicts the Critical Role of Active Membrane Currents , 2013, Neuron.

[42]  Hans E Plesser,et al.  Firing-rate models for neurons with a broad repertoire of spiking behaviors , 2013, BMC Neuroscience.

[43]  Viktor K. Jirsa,et al.  The Virtual Brain: a simulator of primate brain network dynamics , 2013, Front. Neuroinform..

[44]  Viktor K. Jirsa,et al.  The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging , 2013, Brain Connect..

[45]  Steven E. Hyman,et al.  Revolution Stalled , 2012, Science Translational Medicine.

[46]  Sven Rahmann,et al.  Snakemake--a scalable bioinformatics workflow engine. , 2012, Bioinformatics.

[47]  C. Koch,et al.  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.

[48]  David Moratal Pérez,et al.  Principles of Computational Modelling in Neuroscience , 2012 .

[49]  Henry Markram,et al.  Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties , 2011, PLoS Comput. Biol..

[50]  Nicolas Brunel,et al.  From Spiking Neuron Models to Linear-Nonlinear Models , 2011, PLoS Comput. Biol..

[51]  U. Bhalla,et al.  Reaction-Diffusion Modeling , 2009 .

[52]  Anders M. Dale,et al.  Estimation of Thalamocortical and Intracortical Network Models from Joint Thalamic Single-Electrode and Cortical Laminar-Electrode Recordings in the Rat Barrel System , 2009, PLoS Comput. Biol..

[53]  Pierre Yger,et al.  PyNN: A Common Interface for Neuronal Network Simulators , 2008, Front. Neuroinform..

[54]  C. Agner Oxford Handbook of Transcranial Stimulation, 1st Edition , 2008 .

[55]  Wulfram Gerstner,et al.  The quantitative single-neuron modeling competition , 2008, Biological Cybernetics.

[56]  Sonja Grün,et al.  Detecting synfire chain activity using massively parallel spike train recording. , 2008, Journal of neurophysiology.

[57]  Lorenz Mösenlechner,et al.  The state of MIIND , 2008 .

[58]  Karl J. Friston,et al.  The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields , 2008, PLoS Comput. Biol..

[59]  G. Edelman,et al.  Large-scale model of mammalian thalamocortical systems , 2008, Proceedings of the National Academy of Sciences.

[60]  Gaute T. Einevoll,et al.  Estimation of population firing rates and current source densities from laminar electrode recordings , 2008, Journal of Computational Neuroscience.

[61]  Walter Senn,et al.  Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics , 2007, Neural Computation.

[62]  J. Gold,et al.  The neural basis of decision making. , 2007, Annual review of neuroscience.

[63]  Tim Gollisch,et al.  Modeling Single-Neuron Dynamics and Computations: A Balance of Detail and Abstraction , 2006, Science.

[64]  E. Marder,et al.  Variability, compensation and homeostasis in neuron and network function , 2006, Nature Reviews Neuroscience.

[65]  J. Mink,et al.  Deep brain stimulation. , 2006, Annual review of neuroscience.

[66]  F. van der Velde,et al.  Neural blackboard architectures of combinatorial structures in cognition , 2006, Behavioral and Brain Sciences.

[67]  Fiona E. N. LeBeau,et al.  Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. , 2005, Journal of neurophysiology.

[68]  E. Rolls,et al.  A Neurodynamical cortical model of visual attention and invariant object recognition , 2004, Vision Research.

[69]  S. Nelson,et al.  Homeostatic plasticity in the developing nervous system , 2004, Nature Reviews Neuroscience.

[70]  Nicolas Brunel,et al.  Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons , 2000, Journal of Computational Neuroscience.

[71]  Ben H. Jansen,et al.  Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns , 1995, Biological Cybernetics.

[72]  R. Desimone,et al.  The Role of Neural Mechanisms of Attention in Solving the Binding Problem , 1999, Neuron.

[73]  A. Borst Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.

[74]  G. Edelman,et al.  Neural dynamics in a model of the thalamocortical system. I. Layers, loops and the emergence of fast synchronous rhythms. , 1997, Cerebral cortex.

[75]  M Migliore,et al.  Computer simulations of morphologically reconstructed CA3 hippocampal neurons. , 1995, Journal of neurophysiology.

[76]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[77]  D. McCormick,et al.  A model of the electrophysiological properties of thalamocortical relay neurons. , 1992, Journal of neurophysiology.

[78]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990, Bulletin of mathematical biology.

[79]  A. Young,et al.  A polymorphic DNA marker genetically linked to Huntington's disease , 1983, Nature.

[80]  J. Fermaglich Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[81]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[82]  A. Hodgkin,et al.  Chance and design in electrophysiology: an informal account of certain experiments on nerve carried out between 1934 and 1952. , 1976, The Journal of physiology.

[83]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.