Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks

TL;DR

This paper introduces a framework for training spiking neural networks using neuromodulated synaptic plasticity, enabling online learning that bridges neuroscience models and machine learning performance.

Contribution

It presents a novel method to train neuroscience-inspired synaptic plasticity models in SNNs via gradient descent for online learning tasks.

Findings

Successful training of neuromodulated plasticity models in SNNs.

Enhanced online learning capabilities in brain-like models.

Framework bridges neuroscience and machine learning approaches.

Abstract

We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Although substantial progress has been made toward understanding the dynamics of learning in the brain, neuroscience-derived models of learning have yet to demonstrate the same performance capabilities as methods in deep learning such as gradient descent. Inspired by the successes of machine learning using gradient descent, we demonstrate that models of neuromodulated synaptic plasticity from neuroscience can be trained in Spiking Neural Networks (SNNs) with a framework of learning to learn through gradient descent to address challenging online learning problems. This framework opens a new path toward developing neuroscience inspired online learning algorithms.