ENTRY #: 1
TITLE: Transient Dynamics for Neural Processing
Journal: Science 4 July 2008: Vol. 321. no. 5885, pp. 48 - 50
DOI: 10.1126/science.1155564
ABSTRACT: 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.
Ayush's Comments: An interesting article arguing for thinking outside the attractor states for particular systems!
Comments (1)
Ayush Gupta said
at 4:39 pm on Jul 19, 2008
If you have comments on the paper you are adding to the list, add them into the edit itself (see the red text in Entry #1, for example). Adding a comment in the comment box would add it to the whole page: as this one!
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