Attention allocation using interacting neural populations

It is hypothesized that the various kinds of attentional mechanisms seen in cognitive processing are all related to the modularization that results from the interaction of activity patterns generated by different neural populations. The author considers four basic types of neural populations that differ in their distribution of axon terminations and the effect of their neurotransmitters. Nodes with terminations restricted to a single region and circularly distributed around the node center are called short-range (ShR), while nodes with axons going between regions are called long-range columnar (LRC). The terminations of LRC units form a column in their target region which is several times larger than the circle formed by the terminations of an ShR node. The ShR correspond to interneurons, while the LRC correspond to the relay neurons found in the brain. Both the ShR and the LRC units consist of an excitatory and an inhibitory population. The author discusses how a neural network with these populations can do allocation of attention to sensory, memory, and cognitive channels