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 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/.
Reference: W. Maass.
On the relevance of time in neural computation and learning.
In M. Li and A. Maruoka, editors, Proc. of the 8th International
Conference on Algorithmic Learning Theory in Sendai (Japan), volume 1316 of
Lecture Notes in Computer Science, pages 364-384. Springer (Berlin),