1 research outputs found
Online computation of sparse representations of time varying stimuli using a biologically motivated neural network
Natural stimuli are highly redundant, possessing significant spatial and
temporal correlations. While sparse coding has been proposed as an efficient
strategy employed by neural systems to encode sensory stimuli, the underlying
mechanisms are still not well understood. Most previous approaches model the
neural dynamics by the sparse representation dictionary itself and compute the
representation coefficients offline. In reality, faced with the challenge of
constantly changing stimuli, neurons must compute the sparse representations
dynamically in an online fashion. Here, we describe a leaky linearized Bregman
iteration (LLBI) algorithm which computes the time varying sparse
representations using a biologically motivated network of leaky rectifying
neurons. Compared to previous attempt of dynamic sparse coding, LLBI exploits
the temporal correlation of stimuli and demonstrate better performance both in
representation error and the smoothness of temporal evolution of sparse
coefficients.Comment: 9 pages, 5 figure