title: A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields.
abstract: Sparse coding algorithms trained on natural images can accurately predict the features that excite visual cortical neurons, but it is not known whether such codes can be learned using biologically realistic plasticity rules. We have developed a biophysically motivated spiking network, relying solely on synaptically local information, that can predict the full diversity of V1 simple cell receptive field shapes when trained on natural images. This represents the first demonstration that sparse coding principles, operating within the constraints imposed by cortical architecture, can successfully reproduce these receptive fields. We further prove, mathematically, that sparseness and decorrelation are the key ingredients that allow for synaptically local plasticity rules to optimize a cooperative, linear generative image model formed by the neural representation. Finally, we discuss several interesting emergent properties of our network, with the intent of bridging the gap between theoretical and experimental studies of visual cortex.
authors: Department of Physics, University of California, Berkeley, California, United States of America. joelz@berkeley.edu
authors: Joel
authors: J
authors: Zylberberg
authors: Jason Timothy
authors: JT
authors: Murphy
authors: Michael Robert
authors: MR
authors: DeWeese
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D008251:Macaca; D008959:Models, Neurological; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D051381:Rats; D014793:Visual Cortex / Q000166:cytology / Q000502:physiology*