SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition.

ABSTRACT: In this work, we propose a Spiking Neural Network (SNN) consisting of input neurons sparsely connected by plastic synapses to a randomly interlinked liquid, referred to as Liquid-SNN, for unsupervised speech and image recognition. We adapt the strength of the synapses interconnecting the input and liquid using Spike Timing Dependent Plasticity (STDP), which enables the neurons to self-learn a general representation of unique classes of input patterns. The presented unsupervised learning methodology makes it possible to infer the class of a test input directly using the liquid neuronal spiking activity. This is in contrast to standard Liquid State Machines (LSMs) that have fixed synaptic connections between the input and liquid followed by a readout layer (trained in a supervised manner) to

SUBMITTER: Srinivasan G 

PROVIDER: S-EPMC6116788 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature