Abstract
Sparse coding has been used to model the acquisition of V1 simple cell receptive fields. The Locally Competitive Algorithm (LCA) provides a biologically plausible implementation of sparse coding based on lateral inhibition. LCA can be reformulated to support dictionary learning 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.
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