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The trion represents a localized group of neurons with three levels of firing activity. Networks of a small number of trions (with structured interactions and firing dependent on activity at two previous discrete time steps) support a repertoire of hundreds of quasistable, periodic firing patterns (which can be learned). We report striking phenomena with variation in temperature T: There exist a series of phase transitions at precise values T(n), giving new repertoires of periodic firing patterns, and the average time for any initial firing configuration to project onto a firing pattern shows a quite sharp change at each T(n). Near a phase transition, in a Monte Carlo simulation, the temporal evolution wanders back and forth between sets of these firing patterns in contrast to the more structured sequential evolutions far from a T(n). © 1991 The American Physical Society.

Motivated by V. B. Mountcastle's organizational principle for neocortical function, and by M. E. Fisher's model of physical spin systems, we introduce a cooperative model of the cortical column incorporating an idealized substructure, the trion, which represents a localized group of neurons. Computer studies reveal that typical networks composed of a small number of trions (with symmetric interactions) exhibit striking behavior--e.g., hundreds to thousands of quasistable, periodic firing patterns, any of which can be selected out and enhanced with only small changes in interaction strengths by using a Hebb-type algorithm.

We developed a cooperative model of the cortical column incorporating an idealized subunit, the trion (which represents a localized group of neurons), and a discrete time step for firing. We found that networks composed of a small number of trions (with symmetric interactions) supported up to thousands of quasi-stable, periodic firing patterns (MPs) which could be selected out with only small changes in interaction strengths using a Hebb-type algorithm. Here we report a study of the associative recall properties showing striking features: By considering all possible initial firing patterns (for a given set of network connections), we find 1) It takes on the average only 2-5 time steps to recall an MP. 2) Many of the MPs can be individually accessed by thousands of different initial patterns. The variety of examples presented illustrate the rich, general nature of the model.