  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.
