Belief Propagation and Wiring Length Optimization as Organizing Principles for Cortical Microcircuits

In this paper we explore how functional and anatomical constraints and resource optimization could be combined to obtain a canonical cortical micro-circuit and an explanation for its laminar organization. We start with the assumption that cortical regions are involved in Bayesian Belief Propagation. This imposes a set of constraints on the type of neurons and the connection patterns between neurons in that region. In addition there are anatomical constraints that a region has to adhere to. There are several different configurations of neurons consistent with both these constraints. Among all such configurations, it is reasonable to expect that Nature has chosen the configuration with the minimum wiring length. We cast the problem of finding the optimum configuration as a combinatorial optimization problem. A near-optimal solution to this problem matched anatomical and physiological data. As the result of this investigation, we propose a canonical cortical micro-circuit that will support Bayesian Belief Propagation computation and whose laminar organization is near optimal in its wiring length. We describe how the details of this circuit match many of the anatomical and physiological findings and discuss the implications of these results to experimenters and theorists.