Markov Chain Abstractions of Electrochemical Reaction-Diffusion in Synaptic Transmission for Neuromorphic Computing

Markov Chain Abstractions of Electrochemical Reaction-Diffusion in Synaptic Transmission for Neuromorphic Computing

Progress in computational neuroscience toward understanding brain function is challenged both by the complexity of molecular-scale electrochemical interactions at the level of individual neurons and synapses and the dimensionality of network dynamics across the brain covering a vast range of spatial...

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Bibliographic Details
Published in:Frontiers in neuroscience Vol. 15; p. 698635
Main Authors: Wagner, Margot, Bartol, Thomas M, Sejnowski, Terrence J, Cauwenberghs, Gert
Format: Journal Article
Language:English
Published: Switzerland Frontiers Research Foundation 29-11-2021
Frontiers Media S.A
Subjects:
Biophysics
Brain
computational efficiency
Computational neuroscience
Engineering
Markov chain
Markov chains
Memory
Monte Carlo
Nervous system
Neural networks
neuromorphic
Neuroscience
Noise
Sensors
synapse
Synaptic transmission
Transistors
Markov chain
computational efficiency
synaptic transmission
synapse
Monte Carlo
neuromorphic
Online Access:Get full text
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Summary:Progress in computational neuroscience toward understanding brain function is challenged both by the complexity of molecular-scale electrochemical interactions at the level of individual neurons and synapses and the dimensionality of network dynamics across the brain covering a vast range of spatial and temporal scales. Our work abstracts an existing highly detailed, biophysically realistic 3D reaction-diffusion model of a chemical synapse to a compact internal state space representation that maps onto parallel neuromorphic hardware for efficient emulation at a very large scale and offers near-equivalence in input-output dynamics while preserving biologically interpretable tunable parameters.
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This article was submitted to Neuromorphic Engineering, a section of the journal Frontiers in Neuroscience
Edited by: Erika Covi, NaMLab gGmbH, Germany
Reviewed by: Stanisław Woźniak, IBM Research-Zurich, Switzerland; Fabien Alibart, UMR8520 Institut d'électronique, de microélectronique et de nanotechnologie (IEMN), France
ISSN:1662-4548
1662-453X
1662-453X
DOI:10.3389/fnins.2021.698635