<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">W. Maass</style></author><author><style face="normal" font="default" size="100%">T. Natschlaeger</style></author><author><style face="normal" font="default" size="100%">Markram, H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A model for real-time computation in generic neural microcircuits</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS 2002: Advances in Neural Information Processing Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2003</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">213–220</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&amp;nbsp;&lt;/p&gt;
&lt;div&gt;A key challenge for neural modeling is to explain how a continuous stream of   multi-modal input from a rapidly changing environment can be processed by   stereotypical recurrent circuits of integrate-and-fire neurons in real-time.   We propose a new computational model that does not require a task-dependent   construction of neural circuits. Instead it is based on principles of high   dimensional dynamical systems in combination with statistical learning   theory, and can be implemented on generic evolved or found recurrent   circuitry.&lt;/div&gt;
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