Using Sinusoidally-Modulated Noise as a Surrogate for Slow-Wave Sleep to Accomplish Stable Unsupervised Dictionary Learning in a Spike-Based Sparse Coding Models

Published in IEEE Computer Vision and Pattern Recognition Workshops, CVPRW, 2020

Sparse coding algorithms have been used to model the acquisition of V1 simple cell receptive fields as well as to accomplish the unsupervised acquisition of features for a variety of machine learning applications. The Locally Com- petitive Algorithm (LCA) provides a biologically plausible implementation of sparse coding based on lateral inhibi- tion. LCA can be reformulated to support dictionary learn- ing via an online local Hebbian rule that reduces predictive coding error. Although originally formulated in terms of leaky integrator rate-coded neurons, LCA based on lateral inhibition between leaky integrate-and-fire (LIF) neurons has been implemented on spiking neuromorphic processors but such implementations preclude local online learning. We previously reported that spiking LCA can be expressed in terms of predictive coding error in a manner that allows for unsupervised dictionary learning via a local Hebbian rule but the issue of stability has not previously been ad- dressed.

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