Towards Cortex Sized Artificial Nervous Systems

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.

• Type: Conference
• Year: 2004
• Where: KES
• Authors: Christopher Johansson, Anders Lansner
• Keywords: Bayesian Confidence, Cortical Structure, KES 2004, Mammalian Cortices