High-resolution multiple-unit EEG in cat auditory cortex reveals large spatio-temporal stochastic interactions

Wennekers, Thomas; Ay, Nihat; Andras, Peter

Abstract

It has been argued that information processing in the cortex is optimised with regard to certain information theoretic principles. We have, for instance, recently shown that spike-timing dependent plasticity can improve an information-theoretic measure called spatio-temporal stochastic interaction which captures how strongly a set of neurons cooperates in space and time. Systems with high stochastic interaction reveal Poisson spike trains but nonetheless occupy only a strongly reduced area in their global phase space, they reveal repetiting but complex global activation patterns, and they can be interpreted as computational systems operating on selected sets of collective patterns or “global states” in a rule-like manner. In the present work we investigate stochastic interaction in high-resolution EEG-data from cat auditory cortex. Using Kohonen maps to reduce the high-dimensional dynamics of the system, we are able to detect repetiting system states and estimate the stochastic interaction in the data, which turns out to be fairly high. This suggests an organised cooperation in the underlying neural networks which cause the data and may reflect generic intrinsic computational capabilities of the cortex.

Citation

Wennekers, T., Ay, N., & Andras, P. (2007). High-resolution multiple-unit EEG in cat auditory cortex reveals large spatio-temporal stochastic interactions. BioSystems, 89(1-3), 190-197. https://doi.org/10.1016/j.biosystems.2006.04.017

• Journal Article Type: Article
• Acceptance Date: Apr 28, 2006
• Online Publication Date: Nov 15, 2006
• Publication Date: 2007-06
• Deposit Date: Nov 2, 2021
• Print ISSN: 0303-2647
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 89
• Issue: 1-3
• Pages: 190-197
• DOI: https://doi.org/10.1016/j.biosystems.2006.04.017
• Keywords: Stochastic interaction, Mutual information, Pattern language, Neural computation, Complexity, Auditory cortex
• Public URL: http://researchrepository.napier.ac.uk/Output/2808864