Toward tractable AGI: Challenges for system identification in neural circuitry

Abstract

Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the "connectome") as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry. © 2012 Springer-Verlag.