Expandable hardware for computing cortical feature maps

Abstract

We describe expandable hardware architecture for the rapid simulation of feature maps inspired by the visual cortex. Feature maps are retinotopically organized arrays of neurons selective to different combinations of visual features. The responses of these maps are believed to be important for the brain to merge information from different visual cues. This architecture is based around a custom designed board containing DSP and FPGA chips. It is modular in the sense that additional boards can be integrated into the system to accommodate cortical models of increasing complexity.