Towards Cortex Sized Artificial Nervous Systems

Abstract

We characterize the size and complexity of the mammalian cortices of human, macaque, cat, rat, and mouse. We map the cortical structure onto a Bayesian confidence propagating neural network (BCPNN). An architectural structure for the implementation of the BCPNN based on hypercolumnar modules is suggested. The bandwidth, memory, and computational demands for real-time operation of the system are calculated and simulated. It is concluded that the limiting factor is the computational and not the communication requirements. © Springer-Verlag 2004.