On the relevance of time in neural computation and learning
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.