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* Preprint title: "Hebbian Learning in Parallel and Modular Memories" *
* Authors: C.-S. Poon and J.V. Shah *
* Harvard-M.I.T. Division of Health Sci. & Technology *
* Journal: Biological Cybernetics, 1998 (in press) *
* Request to: cpoon@mit.edu *
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ABSTRACT
Many cognitive and sensorimotor functions in the brain involve parallel and
modular memory subsystems that are adapted by activity-dependent Hebbian
synaptic plasticity. This is in contrast to the multilayer perceptron (MLP)
model of supervised learning where sensory information is presumed to be
integrated by a common pool of hidden units through backpropagation learning.
Here we show that Hebbian learning in parallel and modular memories is more
advantageous than backpropagation learning in lumped memories in two respects:
it is computationally much more efficient and structurally much simpler to
implement with biological neurons. Accordingly, we propose a more biologically
relevant neural network model, called a tree-like perceptron (TLP), which is a
simple modification of the MLP model to account for the general neural
architecture, neuronal specificity, and synaptic learning rule in the brain.
The model features a parallel and modular architecture in which adaptation of
the input-to-hidden connection follows either a Hebbian or anti-Hebbian rule
depending on whether the hidden units are excitatory or inhibitory,
respectively. The proposed parallel and modular architecture and implicit
interplay between the types of synaptic plasticity and neuronal specificity
are exhibited by some neocortical and cerebellar systems.