Abstract
Neuromorphic computing is emerging to be a disruptive computational paradigm that attempts to emulate various facets of the underlying structure and functionalities of the brain in the algorithm and hardware design of next-generation machine learning platforms. This work goes beyond the focus of current neuromorphic computing architectures on computational models for neuron and synapse to examine other computational units of the biological brain that might contribute to cognition and especially self-repair. We draw inspiration and insights from computational neuroscience regarding functionalities of glial cells and explore their role in the fault-tolerant capacity of Spiking Neural Networks (SNNs) trained in an unsupervised fashion using Spike-Timing Dependent Plasticity (STDP). We characterize the degree of self-repair that can be enabled in such networks with varying degree of faults ranging from 50 to 90% and evaluate our proposal on the MNIST and Fashion-MNIST datasets.
Highlights
Neuromorphic computing has made significant strides over the past few years—both from hardware (Merolla et al, 2014; Sengupta and Roy, 2017; Davies et al, 2018; Singh et al, 2020) and algorithmic perspective (Diehl and Cook, 2015; Neftci et al, 2019; Sengupta et al, 2019; Lu and Sengupta, 2020)
We explore the aspects of astrocyte functionality that would be relevant to self-repair in the context of Spiking Neural Networks (SNNs) based machine learning platforms and evaluate the degree of bio-fidelity required. (iii) While Refs. (Hazan et al, 2019; Saunders et al, 2019b) discusses impact of faults in unsupervised Spike-Timing Dependent Plasticity (STDP) enabled SNNs, self-repair functionality in such networks have not been studied previously
We evaluated our proposal in the context of unsupervised SNN training on standard image recognition benchmarks under two settings: scaling in network size and scaling in network complexity
Summary
Neuromorphic computing has made significant strides over the past few years—both from hardware (Merolla et al, 2014; Sengupta and Roy, 2017; Davies et al, 2018; Singh et al, 2020) and algorithmic perspective (Diehl and Cook, 2015; Neftci et al, 2019; Sengupta et al, 2019; Lu and Sengupta, 2020). Astrocytes are strategically positioned to ensheath tens of thousands of synapses, axons and dendrites among others, thereby having the capability to serve as a communication channel between multiple components and behave as a sensing medium for ongoing brain activity (Chung et al, 2015). This has led neuroscientists to conclude that astrocytes play a major role in higher order brain functions like learning and memory, in addition to neurons and synapses.
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