TY - JOUR T1 - Efficient Sparse Coding in Early Sensory Processing: Lessons from Signal Recovery A1 - Lörincz, András A1 - Palotai, Zsolt A1 - Szirtes, Gábor Y1 - 2012/03/01 N2 - Author Summary Neural systems favor overcomplete sparse codes in which the number of potential output neurons may exceed the number of input neurons, but only a small subset of neurons become actually active. We argue that efficient use of such large dimensional overcomplete sparse codes requires structural sparsity by controlling the number of active synapses. Motivated by recent results in signal recovery, we introduce a particular signal decomposition as a pre-filtering stage prior to the actual sparse coding, which efficiently supports structural sparsity. In contrast to most models of sensory processing, we hypothesize that the observed transformations may actually realize parallel encoding of the stimuli into representations that describe typical and atypical parts. When trained on natural images, the resulting system can handle large, overcomplete representations and the learned transformations seem compatible with the various receptive fields characteristic to different stages of early vision. In particular, transformations realized by the prefiltering units can be approximated as ‘Difference-of-Gaussians’ filters, similar to the receptive fields of neurons in the retina and the LGN. In addition, sparse coding units have localized and oriented edge filters like the receptive fields of the simple cells in the primary visual cortex, V1. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 8 IS - 3 UR - https://doi.org/10.1371/journal.pcbi.1002372 SP - e1002372 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002372 ER -