Higher mental functions require the prior
abitity to categorize objects and events
according to sensory signals reaching the
brain. The neuronal group selection theory
postulates that this ability arises from a
kind of Darwinian selection operating in
somatic time on groups of interconnected
neurons. These groups develop with varied
and overlapping abilities to respond to
patterns of input at their synapses. Groups
that contribute to responses having adap
tive value for the organism undergo modi
fications in the efficacies of their synaptic
connections that enhance their future re
sponses to similar stimuli. Computer
models of automata based on these prin
ciples can carry out simple tasks involving
recognition, categorization, generalization,
and visual tracking. A general program for
implementing such models is presented.
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