Neural Computation, Vol 9, 911-936, Copyright © 1997 by The MIT Press
Marr's theory of the neocortex as a self-organizing neural network
D Willshaw, J Hallam, S Gingell and SL Lau
Centre for Cognitive Science, University of Edinburgh, Scotland, UK.
Marr's proposal for the functioning of the neocortex (Marr, 1970) is the
least known of his various theories for specific neural circuitries. He
suggested that the neocortex learns by self- organization to extract the
structure from the patterns of activity incident upon it. He proposed a
feedforward neural network in which the connections to the output cells
(identified with the pyramidal cells of the neocortex) are modified by a
mechanism of competitive learning. It was intended that each output cell
comes to be selective for the input patterns from a different class and is
able to respond to new patterns from the same class that have not been seen
before. The learning rule that Marr proposed was underspecified, but a
logical extension of the basic idea results in a synaptic learning rule in
which the total amount of synaptic strength of the connections from each
input ("presynaptic") cell is kept at a constant level. In contrast,
conventional competitive learning involves rules of the "postsynaptic"
type. The network learns by exploiting the structure that Marr assumed to
exist within the ensemble of input patterns. For this case, analysis is
possible that extends that carried out by Marr, which was restricted to the
binary classification task. This analysis is presented here, together with
results from computer simulations of different types of competitive
learning mechanisms. The presynaptic mechanism is best known in the
computational neuroscience literature. In neural network applications, it
may be a more suitable mechanism of competitive learning than those
normally considered.
