Trion Model of Cortical Organization: Toward a Theory of Information Processing and Memory

Abstract

In the spirit of Mountcastle’s [1] organizational principle for neocortical function, and strongly motivated by Fisher’s [2] model of physical spin systems, we have introduced [3] a new cooperative mathematical model of the cortical column. Our model incorporates an idealized substructure, the trion, which represents a localized group of neurons. The trion model allows for a completely new framework for information processing and associative memory storage and recall: Small networks of trions with highly symmetric interactions are found to yield hundreds to thousands of quasi-stable, periodic firing patterns, MP’s, which can evolve from one to another (see Fig. 1). Experience or learning would then modify the interactions (away from the symmetric values) and select out the desired MP’s (as in the selection principle of Edelman [4]). Remarkably, we have found that relatively small modifications in trion interaction strengths (away from the symmetric values) via a Hebb-type algorithm [5] will enhance and select out any desired MP. Conceptually this suggests a radically different approach from those information processing models which start at the opposite extreme of a randomly connected neural network with no periodic firing patterns, and then (via Hebb-type modifications [5] in the synaptic interactions) reinforce specific firing patterns. More recently [6], in studying the associative recall properties of the networks we find that, on the average, any of the initial firing configurations rapidly (in 2 to 4 time steps) projects onto an MP.