Spiking neurons and the induction of finite state machines
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. (C) 2002 Elsevier Science B.V. All rights reserved.
spiking neurons finite state machines computational neuroscience dynamic synapses grammatical inference