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Causal networks in simulated neural systems
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
History
Publication status
- Published
Journal
Cognitive NeurodynamicsISSN
1871-4080Publisher
SpringerExternal DOI
Issue
1Volume
2Page range
49-64Pages
16.0Department affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes