THE THORNY PATH TO AN ARTIFICIAL BRAIN: How to Build a Bridge between Neurophysiology and Network Modeling

摘要

Humanoid robots are created to imitate some of the tasks that humans undergo, but no current robot can emulate the cognitive capabilities of even the simplest mammals. One approach to developing computing platforms for cognitive robotics is to make use of experimental characterizations of the neurobiological substrate for action and perception systems and simulate brain functions designing real-time spiking neural networks. Biologically detailed network models are a powerful tool to understand how molecular and cellular mechanisms determine high level network processing. Recent advances in experimental and theoretical studies of the dynamic organization of neuronal populations suggest that our further success in creation of higher intelligence robots will depend on the ability to incorporate such basic principles of brain functioning as (i) stochastic dynamics and intrinsic nonlinearities in input-output transformation of neurons, (ii) structural and functional plasticity, (iii) signaling through neuromodulator networks.