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NeuroCOLT |
Neural Networks and Computational Learning Theory |
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NeuroCOLT Technical Report NC-TR-01-117
ABSTRACT We discuss
in this short survey article some current mathematical models from
neurophysiology for the computational units of biological neural systems:
neurons and synapses. These models are contrasted with the computational
units of common artificial neural network models, which reflect the
state of knowledge in neurophysiology 50 years ago. We discuss the
problem of carrying out computations in circuits consisting of biologically
realistic computational units, focusing on the biologically particularly
relevant case of computations on time series. Finite state machines
are frequently used in computer science as models for computations
on time series. One may argue that these models provide a reasonable
common conceptual basis for analyzing computations in computers and
biological neural systems, although the emphasis in biological neural
systems is shifted more towards asynchronous computation on analog
time series. In the second half of this article some new computer
experiments and theoretical results are discussed, which address the
question whether a biological neural system can in principle learn
to behave like a given simple finite state machine.
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