psycoloquy.92.3.68.categorization.1.murre (160 lines)

[Stevan Harnad]

                CALL FOR BOOK REVIEWERS

Below is the Precis of LEARNING AND CATEGORIZATION IN MODULAR NEURAL
NETWORKS by JMJ Murre. This book has been selected for multiple review
in PSYCOLOQUY. If you wish to submit a formal book review (see
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psycoloquy.92.3.68.categorization.1.murre    Thursday, 31 December 1992
ISSN 1055-0143                    (6 paragraphs, 1 reference, 83 lines)
PSYCOLOQUY is sponsored by the American Psychological Association (APA)
		Copyright 1992 Jacob MJ Murre

                        Precis of:
        LEARNING AND CATEGORIZATION IN MODULAR NEURAL NETWORKS
                JMJ Murre 1992, 244 pages
                Hemel Hempstead: Harvester Wheatsheaf
        (In Canada and the USA: Hillsdale, NJ: Lawrence Erlbaum)

                Jacob M.J. Murre
                MRC Applied Psychology Unit
                Cambridge, United Kingdom
                jaap.murre at mrc-applied-psychology.cambridge.ac.uk

1.0  MODULARITY AND MODULATION IN NEURAL NETWORKS

1.1  This book introduces a new neural network model, CALM, for
categorization and learning in neural networks. CALM is based on ideas
from neurobiology, psychology, and engineering. It defines a neural
network paradigm that is both modular and modulatory. CALM stands for
Categorizing And Learning Module and it may be viewed as a building
block for neural networks. The internal structure of the CALM module is
inspired by the neocortical minicolumn. Several of these modules are
connected to form an initial neural network architecture. Throughout
the book it is argued that modularity is important in overcoming many
of the problems and limitations of current neural networks. Another
pivotal concept in the CALM module is self-induced arousal, which may
modulate the local learning rate and noise level.

1.2  The concept of arousal has roots in both biology and psychology. In
CALM, this concept underlies two different modes of learning:
elaboration learning and activation learning. Mandler and coworkers
have conjectured that these two distinct modes of learning may cause
the dissociation of memory observed in explicit and implicit memory
tasks. A series of simulations of such experiments demonstrates that
arousal-modulated learning and categorization in modular neural
networks can account for experimental results with both normal and
amnesic patients. In the latter case, pathological but psychologically
accurate behavior is produced by "lesioning" the arousal system of the
model. The behavior obtained in this way is similar to that in patients
with hippocampal lesions, suggesting that the hippocampus may form
part of an arousal system in the brain.

1.3  Another application of CALM to psychological modelling shows how a
modular CALM network can learn the word superiority effect for letter
recognition. As an illustrative practical application, a small model is
described that learns to recognize handwritten digits.

2.0  MODULAR NEURAL ARCHITECTURES AND NEUROCOMPUTERS

2.1  The book contains a concise introduction to genetic algorithms, a
new computing method based on the metaphor of biological evolution that
can be used to design network architectures with superior performance.
In particular, it is shown how a genetic algorithm results in a better
architecture for the digit-recognition model.

2.2  In five appendices, the role of modularity in parallel hardware and
software implementations is discussed in some depth. Several hardware
implementations are considered, including a formal analysis of their
efficiency on transputer networks and an overview of a dedicated 400-
processor neurocomputer built by the developers of CALM in cooperation
with Delft Technical University. One of the appendices is dedicated to
a discussion of the requirements of simulators for modular neural
networks.

3.0  CATASTROPHIC INTERFERENCE AND OTHER ISSUES

3.1  The book ends with an evaluation of the psychological and
biological plausibility of CALM models and a discussion of
generalization, representational capacity of modular neural networks,
and catastrophic interference. A series of simulations and a detailed
analysis of Ratcliff's simulations of catastrophic interference show
that in almost all cases interference can be attributed to overlap of
hidden-layer representations across subsequent blocks of stimuli. It is
argued that introducing modularity, or some other form of
semidistributed representations, may reduce interference to a more
psychologically plausible level.

REFERENCE

Murre, J.M.J. (1992) Learning and Categorization in Modular Neural
Networks.  Harvester Wheatsheaf/Erlbaum
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