A columnar primary visual cortex (V1) model emulation using a PS3 Cell-BE array

A model of portions of the cerebral cortex is being developed to explore neuromorphic computing strategies in the context of highly parallel platforms. The interest is driven by the value of applications which can make use of highly parallel architectures we expect to see surpassing one thousand cores per die in the next few years. A central question we seek to answer is what the architecture of hyper-parallel machines should be. We also seek to understand computational methods akin to how a brain deals with sensing, perception, memory, and cognition. The model is being developed incrementally, starting with the primary visual cortex (V1) field. It is based upon structures roughly corresponding to neocortical minicolumn and functional column structures. Gaps in neuroscience, such as inter-cell connectivity, are filled using estimates of functionality that are plausible given current understanding of the micro-anatomy. The success we encountered with achieving real-time performance is evidence validating the use of Cell-Be architecture in some classes of neuromorphic emulation. In this study we identified a particular gap-fill algorithm for lateral connections within V1 that is suggestive of a learning strategy whereby the lateral network subsumes expectation affect, reducing perception time and improving perception affect.