An Efficient Spike-Based Communication Protocol for Neurally Inspired Feature Maps

We describe the implementation of an efficient and flexible protocol for communicating biologically inspired feature maps between processors in a multi-processor parallel hardware system. A feature map is a retinotopic array of neurons sharing the same feature selectivity, e.g. to spatial contrast changes. The retina and visual cortex are thought to compute many feature maps in interpreting the environment. Our spike-based encoding method exploits the sparsity of these maps. We also use contrast normalization to amplify responses in areas of small contrast, while maintaining selectivity in areas with large contrast. This communication architecture will enable us to easily expand our system to handle more complex models.