On the relevance of time in neural computation and learning

W. Maass

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 somewhat related new generation of VLSI-chips is emerging ("pulsed VLSI") where new ideas about computing and learning with temporal coding can be tested in an engineering context. Articles with details to models and results that are sketched in this article can be found at http://www.tu-graz.ac.at/igi/maass/. We refer to Maass and Bishop (Eds., Pulsed Neural Network, MIT Press, Cambridge, MA, 1999) for a collection of survey articles that contain further details and references. © 2001 Elsevier Science B.V. All rights reserved. Key Words: Neural computation; Temporal coding; Spiking neurons; Computational complexity



Reference: W. Maass. On the relevance of time in neural computation and learning. Theoretical Computer Science, 261:157-178, 2001.