FUNDAMENTALS OF WHOLE BRAIN EMULATION: STATE, TRANSITION AND UPDATE REPRESENTATIONS

Whole brain emulation aims to re-implement functions of a mind in another computational substrate with the precision needed to predict the natural development of active states in as much as the influence of random processes allows. Furthermore, brain emulation does not present a possible model of a function, but rather presents the actual implementation of that function, based on the details of the circuitry of a specific brain. We introduce a notation for the representations of mind state, mind transition functions and transition update functions, for which elements and their relations must be quantified in accordance with measurements in the biological substrate. To discover the limits of significance in terms of the temporal and spatial resolution of measurements, we point out the importance of brain region and task specific constraints, as well as the importance of in-vivo measurements. We summarize further problems that need to be addressed.

[1]  Yong Yao,et al.  Model of biological pattern recognition with spatially chaotic dynamics , 1990, Neural Networks.

[2]  Wulfram Gerstner,et al.  How Good Are Neuron Models? , 2009, Science.

[3]  G. Buzsáki.  Large-scale recording of neuronal ensembles , 2004, Nature Neuroscience.

[4]  Richard Brown,et al.  What Is a Brain State? , 2006 .

[5]  A. Church.  An Unsolvable Problem of Elementary Number Theory , 1936 .

[6]  Allan R. Jones,et al.  Genome-wide atlas of gene expression in the adult mouse brain , 2007, Nature.

[7]  Chris Eliasmith,et al.  How we ought to describe computation in the brain. , 2010, Studies in history and philosophy of science.

[8]  Allan R. Jones,et al.  The Allen Brain Atlas: 5 years and beyond , 2009, Nature Reviews Neuroscience.

[9]  A. Turing.  On Computable Numbers, with an Application to the Entscheidungsproblem. , 1937 .

[10]  Kevin L. Briggman,et al.  Wiring specificity in the direction-selectivity circuit of the retina , 2011, Nature.

[11]  B H Gähwiler,et al.  Characterization of a Calcium‐dependent Current Generating a Slow Afterdepolarization of CA3 Pyramidal Cells in Rat Hippocampal Slice Cultures , 1993, The European journal of neuroscience.

[12]  J. Kurths,et al.  Structure–function relationship in complex brain networks expressed by hierarchical synchronization , 2007 .

[13]  K Amarnath,et al.  Photonic logic NOR gate based on two symmetric microring resonators. , 2004, Optics letters.

[14]  H. Sompolinsky,et al.  Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity , 1996, Science.

[15]  A. Turing,et al.  On Computable Numbers, with an Application to the Entscheidungsproblem. A Correction , 1938 .

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

[17]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[18]  J. Csicsvari,et al.  Massively parallel recording of unit and local field potentials with silicon-based electrodes. , 2003, Journal of neurophysiology.

[19]  H. Markram.  The Blue Brain Project , 2006, Nature Reviews Neuroscience.

[20]  A. Church.  A Set of Postulates for the Foundation of Logic , 1932 .

[21]  K. Harris.  Calcium from internal stores modifies dendritic spine shape. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Theodore W. Berger,et al.  Using an open-loop inverse control strategy to regulate CA1 nonlinear dynamics for an in vitro hippocampal prosthesis model , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  William Bialek,et al.  Reading a Neural Code , 1991, NIPS.

[24]  S. Koslow,et al.  Databasing the brain : from data to knowledge (neuroinformatics) , 2005 .

[25]  Randal A. Koene,et al.  NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies , 2009, Neuroinformatics.

[26]  W. Freeman.  The physiology of perception. , 1991, Scientific American.

[27]  Rajesh P. N. Rao,et al.  Bayesian brain : probabilistic approaches to neural coding , 2006 .

[28]  Bruce W. Knight,et al.  Frontiers in Computational Neuroscience Computational Neuroscience Materials and Methods Surgical Preparation , 2022 .

[29]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[30]  A. Church.  The calculi of lambda-conversion , 1941 .

[31]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[32]  E. Melhem,et al.  Diffusion-tensor MR imaging and tractography: exploring brain microstructure and connectivity. , 2007, Radiology.

[33]  Rolf Kötter,et al.  Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac Database , 2007, Neuroinformatics.

[34]  Fabio Rigat,et al.  Bayesian modelling and analysis of spatio-temporal neuronal networks , 2006 .

[35]  O. Sporns,et al.  Identification and Classification of Hubs in Brain Networks , 2007, PloS one.

[36]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[37]  Randal A. Koene,et al.  EXPERIMENTAL RESEARCH IN WHOLE BRAIN EMULATION: THE NEED FOR INNOVATIVE IN VIVO MEASUREMENT TECHNIQUES , 2012 .

[38]  1/ƒ noise and chaos in analog neural networks , 1994 .

[39]  M. F. Huerta,et al.  Neuroinformatics : An Overview of the Human Brain Project , 2013 .

[40]  Alonzo Church,et al.  A note on the Entscheidungsproblem , 1936, Journal of Symbolic Logic.

[41]  Christopher J. Bishop,et al.  Pulsed Neural Networks , 1998 .