Causal networks in simulated neural systems

Seth, Anil (2008) Causal networks in simulated neural systems. Cognitive Neurodynamics, 2 (1). pp. 49-64. ISSN 1871-4080

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Abstract

Neurons engage in causal interactions with one another and with the surrounding body and environment. Neural systems can therefore be analyzed in terms of causal networks, without assumptions about information processing, neural coding, and the like. Here, we review a series of studies analyzing causal networks in simulated neural systems using a combination of Granger causality analysis and graph theory. Analysis of a simple target-fixation model shows that causal networks provide intuitive representations of neural dynamics during behavior which can be validated by lesion experiments. Extension of the approach to a neurorobotic model of the hippocampus and surrounding areas identifies shifting causal pathways during learning of a spatial navigation task. Analysis of causal interactions at the population level in the model shows that behavioral learning is accompanied by selection of specific causal pathways¿¿causal cores¿¿from among large and variable repertoires of neuronal interactions. Finally, we argue that a causal network perspective may be useful for characterizing the complex neural dynamics underlying consciousness

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: Anil Seth
Date Deposited: 06 Feb 2012 18:30
Last Modified: 31 May 2012 10:06
URI: http://sro.sussex.ac.uk/id/eprint/16781
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