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11-July-2008

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Neuroscience: Transient Dynamics For Neural Processing, Science
 









Excerpts: Neural networks are complicated dynamical entities, whose properties
are understood only in the simplest cases. When the complex biophysical
properties of neurons and their connections (synapses) are combined with
realistic connectivity rules and scales, network dynamics are usually difficult
to predict. Yet, experimental neuroscience is often based on the implicit
premise that the neural mechanisms underlying sensation, perception, and
cognition are well approximated by steady-state measurements (of neuron
activity) or by models in which the behavior of the network is simple (steady
state or periodic). Transient states--ones in which no stable equilibrium is
reached--may sometimes better describe neural network behavior. An intuition for
such properties arises from mathematical and computational modeling of some
appropriately simple experimental systems.
Source: Neuroscience: Transient Dynamics For Neural Processing[
http://www.sciencemag.org/cgi/content/full/321/5885/48 ], Misha Rabinovich,
Ramon Huerta, Gilles Laurent, DOI: DOI: 10.1126/science.1155564, Science,
08/07/04

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