Spike-based local synaptic plasticity: A survey of computational models
and neuromorphic circuits
- URL: http://arxiv.org/abs/2209.15536v1
- Date: Fri, 30 Sep 2022 15:35:04 GMT
- Title: Spike-based local synaptic plasticity: A survey of computational models
and neuromorphic circuits
- Authors: Lyes Khacef, Philipp Klein, Matteo Cartiglia, Arianna Rubino, Giacomo
Indiveri, Elisabetta Chicca
- Abstract summary: We review historical, bottom-up, and top-down approaches to modeling synaptic plasticity.
We identify computational primitives that can support low-latency and low-power hardware implementations of spike-based learning rules.
- Score: 1.8464222520424338
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Understanding how biological neural networks carry out learning using
spike-based local plasticity mechanisms can lead to the development of
powerful, energy-efficient, and adaptive neuromorphic processing systems. A
large number of spike-based learning models have recently been proposed
following different approaches. However, it is difficult to assess if and how
they could be mapped onto neuromorphic hardware, and to compare their features
and ease of implementation. To this end, in this survey, we provide a
comprehensive overview of representative brain-inspired synaptic plasticity
models and mixed-signal \acs{CMOS} neuromorphic circuits within a unified
framework. We review historical, bottom-up, and top-down approaches to modeling
synaptic plasticity, and we identify computational primitives that can support
low-latency and low-power hardware implementations of spike-based learning
rules. We provide a common definition of a locality principle based on pre- and
post-synaptic neuron information, which we propose as a fundamental requirement
for physical implementations of synaptic plasticity. Based on this principle,
we compare the properties of these models within the same framework, and
describe the mixed-signal electronic circuits that implement their computing
primitives, pointing out how these building blocks enable efficient on-chip and
online learning in neuromorphic processing systems.
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