<?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%">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%">Real-time Computing without stable states: A New Framework for Neural Computation Based on Perturbations</style></title><secondary-title><style face="normal" font="default" size="100%">Neural Computation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">LSM</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">11</style></number><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">2531–2560</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&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 framework for neural computation that provides an alternative to previous approaches based on attractor neural networks. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a neural circuit may serve as a universal source of information about past stimuli, from which readout neurons can extract particular aspects needed for diverse tasks in real-time. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that unlike Turing machines, does not require sequential transitions between discreteNatschlager:04 internal states. Like the Turing machine paradigm it allows for universal computational power under idealized conditions, but for real-time processing of time-varying input. The resulting new framework for neural computation has novel implications for the interpretation of neural coding, for the design of experiments and data-analysis in neurophysiology, and for neuromorphic engineering.&lt;/p&gt;</style></abstract></record></records></xml>