Computational Neuroscience: Trends in Research, 1998 by D. A. Depireux, Powen Ru, S. A. Shamma, J. Z. Simon (auth.),

By D. A. Depireux, Powen Ru, S. A. Shamma, J. Z. Simon (auth.), James M. Bower (eds.)

This quantity comprises papers provided on the 6th Annual Computational Neurosci­ ence assembly (CNS*97) held in immense Sky, Montana, July 6-10, 1997. This assortment comprises 103 of the 196 papers awarded on the assembly. popularity for assembly presentation was once in response to the peer overview of initial papers initially submitted in January of 1997. The papers during this quantity signify ultimate models of this paintings submitted in January of 1998. Taken jointly they supply a go element of computational neuroscience and symbolize good the ongoing power and development of this box. The assembly in Montana was once strange in numerous respects. First, to our wisdom it was once the 1st foreign medical assembly with beginning ceremonies on horseback. moment, after 5 days of rigorous medical dialogue and debate, assembly contributors have been capable of get to the bottom of all ultimate conflicts in barrel race competitions. differently the beauty of Montana and the massive Sky Ski inn guaranteed that the assembly won't quickly be forgotten. Scientifically, this quantity once more represents the outstanding breadth of matters that may be approached with computational instruments. This quantity and the continued CNS meet­ ings make it transparent that there's nearly no topic or quarter of modem neuroscience examine that isn't acceptable for computational studies.

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The network architecture sketched Methods The model is a neural network, and consists of 4 subparts (each a single layer) : a feature detector preprocessor, a "predisposition" network, an "imhv" network, and an output layer. The output units are taken to represent different action patterns; in particular (following Bateson and Horn 2 ), the output layer consists of an "approach" unit and a "withdraw" unit . These are the two chick behaviors which researchers use to assess a chick's degree of preference for a particular stimulus.

A. Nicoll and C. Blakemore. Patterns of local connectivity in the neocortex. Neural Computation, 5:665-680, 1993. A. L. G. Sutton III, and M. Weinrich. A competitive distribution theory of neocortical dynamics. Neural ComputatIOn, 4:287-317, 1992. IVlorasso and V. Sanguineti. How the brain can discover the existence of external egocentric space. Neurocompllting, 12:289-310, 1996. F. Frisone and P. G. Morasso. Extending the TRN model in a biologically plausible way. In ICANN97- Int. Conf. on Artificial Neural Networks, Lousanne, October 1997, In Press.

Intenoal intervals at rest were not fixed, so the subject could not anticipate the precise timings of the sounds. Filtering: I ().. 100 Hz. The rise time of the auditory evoked response is faster and the amplitude is more prominent in the task than at rest. Average evoked responses on two consecutive task days are fairly reproducible. Recordings in 6 s were nonnalized, averaged for 16 electrodes per trial then time averageS of 20 trials of spatial averages were taken. Upper curves are added standard deviations.

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