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Sparse coding algorithms trained on natural images can accurately predict thefeatures that excite visual cortical neurons, but it is not known whether suchcodes can be learned using biologically realistic plasticity rules. We havedeveloped a biophysically motivated spiking network, relying solely onsynaptically local information, that can predict the full diversity of V1simple cell receptive field shapes when trained on natural images. Thisrepresents the first demonstration that sparse coding principles, operatingwithin the constraints imposed by cortical architecture, can successfullyreproduce these receptive fields. We further prove, mathematically, thatsparseness and decorrelation are the key ingredients that allow forsynaptically local plasticity rules to optimize a cooperative, lineargenerative image model formed by the neural representation. Finally, we discussseveral interesting emergent properties of our network, with the intent ofbridging the gap between theoretical and experimental studies of visual cortex.

A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields