<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Steimer</style></author><author><style face="normal" font="default" size="100%">W. Maass</style></author><author><style face="normal" font="default" size="100%">R. Douglas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Belief-propagation in networks of spiking neurons</style></title><secondary-title><style face="normal" font="default" size="100%">Neural Computation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">2502-2523</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&amp;nbsp;&lt;/p&gt;
&lt;div&gt;From a theoretical point of view, statistical inference is an attractive model   of brain operation. However, it is unclear how to implement these inferential   processes in neuronal networks. We offer a solution to this problem by   showing in detailed simulations how the Belief-Propagation algorithm on a   factor graph can be embedded in a network of spiking neurons. We use pools of   spiking neurons as the function nodes of the factor graph. Each pool gathers   'messages' in the form of population activities from its input nodes and   combines them through its network dynamics. The various output messages to be   transmitted over the edges of the graph are each computed by a group of   readout neurons that feed in their respective destination pools. We use this   approach to implement two examples of factor graphs. The first example is   drawn from coding theory. It models the transmission of signals through an   unreliable channel and demonstrates the principles and generality of our   network approach. The second, more applied example, is of a psychophysical   mechanism in which visual cues are used to resolve hypotheses about the   interpretation of an object's shape and illumination. These two examples, and   also a statistical analysis, all demonstrate good agreement between the   performance of our networks and the direct numerical evaluation of   beliefpropagation.&lt;/div&gt;
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