Deep Unsupervised Learning Using Spike-Timing-Dependent Plasticity

arXiv:2307.04054v2 Announce Type: replace
Abstract: Spike-Timing-Dependent Plasticity (STDP) is an unsupervised learning mechanism for Spiking Neural Networks (SNNs) that has received significant attention from the neuromorphic hardware community. However, scaling such local learning techniques to deeper networks and large-scale tasks has remained elusive. In this work, we investigate a Deep-STDP framework where a rate-based convolutional network, that can be deployed in a neuromorphic setting, is trained in tandem with pseudo-labels generated by the STDP clustering process on the network outputs. …

abstract arxiv attention community cs.cv framework hardware however networks neural networks neuromorphic rate scale scaling spiking neural networks tasks type unsupervised unsupervised learning work