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

Margot Wagner,Terrence J Sejnowski,Thomas M Bartol,Gert Cauwenberghs

doi:10.3389/fnins.2021.698635

Abstract

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.

Highlights

It has been known since the pioneering of computer architecture by John von Neumann that brains are far more effective and efficient in processing sensory information than digital computers, owing to the massively parallel distributed organization of neural circuits in the brain that tightly couple synaptic memory and computing at a fine grain scale (von Neumann, 1958)
The fully biophysically complex system of synaptic transmission can be abstracted and sampled to create a Markov Chain Monte Carlo (MCMC) simulation which answers the same question of neurotransmitter release utilizing tunable biophysical parameters while providing scalability for implementation in neuromorphic architectures
At the maximum membrane potential, almost all voltage-dependent calcium channels (VDCCs) are in the open state

Summary

Introduction

It has been known since the pioneering of computer architecture by John von Neumann that brains are far more effective and efficient in processing sensory information than digital computers, owing to the massively parallel distributed organization of neural circuits in the brain that tightly couple synaptic memory and computing at a fine grain scale (von Neumann, 1958). Moore’s law’s relentless scaling of semiconductor technology, with a doubling of integration density every 2 years, has allowed the von Neumann architecture to remain fundamentally unchanged since its advent. As the shrinking dimensions of transistors supporting the progression of Moore’s law are approaching fundamental limits, it has become essential to consider alternative novel computing architectures to meet increasing computational needs in this age of the deep learning revolution, which itself is driven by advances rooted in a deeper understanding of brain function (Sejnowski, 2020). Neuromorphic engineering looks toward human brains as inspiration for hardware systems due to their highly efficient computational nature. The human brain is regarded as the pinnacle of efficient computing, operating at an estimated rate of 1016 complex

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