Chapter 2. Prehension

Appendix C. Computational Neural Modelling
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This appendix gives some simple background material for
understanding the computational models presented in this book. An
excellent reference for more background is Rumelhart et al. (1986b)
and McClelland et al. (1986). Other review papers include Arbib
(1987) and Lippmann (1987). In addition, there are commercially
available software products that allow models to be implemented and
developed on personal computers and distributed workstations.
C.l Introduction
The power of a computational model using a neural network
architecture is in generalizing from a few sample points. Using
supervised learning, it would be similar to a child learning to grasp:
the first time, some posture is chosen, and the object drops from her
hand. Failure in the task tells the brain that parameters were chosen
incorrectly, and adjustments are made so that success is more likely
the next time. Mdfication can be made to either the parameter values
or to the selection of the parameters; e.g., using more fingers to
provide more force, realizing that the texture of the object surface is
important in choosing a grasp. Repeated trials allow the resetting of
parameters, until a successful grasp is selected. Generalizations are
made. This allows the child to eventually start picking up objects that
she has never interacted with before.
A model, according to Webster’s dictionary, is a representation
that shows the construction or serves as a copy of something.
Churchland and Sejnowski (1988) compare the usefulness of models
being developed in the emerging field of cognitive neuroscience.
Realistic models can be used as predictive tools for some aspect of
nervous sytem dynamics or anatomy, whereas other models are
simplifying ones and can demonstrate how the nervous system could
be governed by specific principles. A conceptual neural model can be
at different levels: it can model the internal behavior of neurons, the
interactions of neurons in neural networks, or even the passing of
information from one neural assemblage to another. Conceptual
models are important for understanding complex systems; however,
when simplifying assumptions are made in a conceptual model, they
allow one to focus at the desired level of complexity so that key