Spatial propagation of associations in a cortex-like neural network model.

A neural network model is studied, having associative memory properties and allowing retrieved associations to propagate within the network. It is intended as a tentative description of the cerebral cortex consisting of "pyramidal cells" with modifiable synapses and "stellate cells" providing feedback through excitatory and inhibitory recurrent pathways. The model is based on some general assumptions: Learning occurs through facilitation of synapses which depends on simultaneous pre- and postsynaptic activity (two-conditional facilitation). Connections within the network are realizations of a random process, implying that nearby cells are more likely to be connected than distant one. The two-conditional facilitation makes it possible for an output signal pattern which occurred in conjunction with a certain input pattern to be retrieved later by reapplying the particular input, the model working as an associative memory. The random connections and the operation of the stellate cell models as linear threshold units give rise to pattern separation in the feedback link. This, in addition to the fact that patterns form associations with themselves, is of importance during the associative recall enabling the network to attain alternative stable modes of activity each corresponding to a learned association. It is shown that a learned pattern of activity which is retrieved, ie, a stable mode, can propagate across the surface of the network. The mode of activity evoked through a certain association may get into contact with modes originating from different associations, forming a stable or slowly moving boundary between the interacting modes. The model is discussed in relation to some properties of the visual system.