On looking for neural networks and 'cell assemblies' that underlie behavior: II. Neural realization of the mathematical model

Part I (P. H. Greene,Bull. Math. Biophysics,24, 247–275, 1962) discussed a number of formal properties of animal behavior, and presented evidence that these properties would follow naturally from a model in which patterns of neural activity in perception or motor action constituted the resonant responses of linear neural networks. Equations were derived for parameters characterizing networks which would possess desired resonant responses. These equations expressed purely mathematical requirements. The present paper shows that a simple neural model would be entirely adequate to meet these requirements. According to this model, an input locus may become functionally connected to a particular resonant response mode by firing at a frequency which comes to approach the resonant frequency of that mode. The information in a complicated “cell assembly” of the type considered could be transmitted through a nerve tract by a very simple frequency code. One neurological guess is that frequency-coded inputs excite the transients in dendritic networks. If the amplitude of the pattern becomes large, as it would near resonance, the all-or-none axonal response would become excited. This axonal response would tend to augment resonant patterns and disrupt other patterns, for a reason inherent in any linear network. Since resonant responses are automatically present in any linear network, unless special processes suppress them, they must have led to overt behavior in animals first possessing such networks. Evolution either suppressed this feature or exploited it. Since its properties resemble those of animal behavior, the latter might be suspected. Some implications are presented regarding what a physiologist might have to look for when he studies a neural system.