Causal networks in simulated neural systems |
| |
Authors: | Anil K Seth |
| |
Institution: | (1) Department of Informatics, University of Sussex, Brighton, BN1 9QJ, UK |
| |
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.
|
| |
Keywords: | Granger causality Causal core Consciousness Neurorobotics Hippocampus Mesoscale |
本文献已被 SpringerLink 等数据库收录! |
|