On the relevance of time in neural computation and learning

Abstract

We discuss models for computation in biological neural systems that are based on the current state of knowledge in neurophysiology. Differences and similarities to traditional neural network models are highlighted. It turns out that many important questions regarding computation and learning in biological neural systems cannot be adequately addressed in traditional neural network models. In particular the role of time is quite different in biologically more realistic models, and many fundamental questions regarding computation and learning have to be rethought for this context. Simultaneously a new generation of VLSI-chips is emerging (“pulsed VLSI”) where new ideas about computing and learning with temporal coding can be tested.Articles with details and further pointers to the literature can be found at http://www.cis.tu-graz.ac.at/igi/maass/.KeywordsFiring RateSpike TrainNeural Information Processing SystemDendritic TreeInterspike IntervalThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.