<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">W. Maass</style></author><author><style face="normal" font="default" size="100%">Natschläger, T.</style></author><author><style face="normal" font="default" size="100%">Markram, H.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Feng, J.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Computational models for generic cortical microcircuits</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Neuroscience: A Comprehensive Approach</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">CRC-Press</style></publisher><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
&lt;div&gt;The human nervous system processes a continuous stream of multi-modal input   from a rapidly changing environment. A key challenge for neural modeling is   to explain how the neural microcircuits (columns, minicolumns, etc.) in the   cerebral cortex whose anatomical and physiological structure is quite similar   in many brain areas and species achieve this enormous computational task. We   propose a computational model that could explain the potentially universal   computational capabilities and 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. This new   approach towards understanding neural computation on the micro-level also   suggests new ways of modeling cognitive processing in larger neural systems.   In particular it questions traditional ways of thinking about neural coding.&lt;/div&gt;
&lt;/p&gt;</style></abstract></record></records></xml>
