Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Don't Throw the Baby Iguana Out With the Bathwater
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Measuring autonomy by multivariate autoregressive modelling
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Journal of Computational Neuroscience
Journal of Computational Neuroscience
On directed information theory and Granger causality graphs
Journal of Computational Neuroscience
Journal of Computational Neuroscience
Automatic modeling of dominance effects using granger causality
HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
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We describe a theoretical network analysis that can distinguish statistically causal interactions in population neural activity leading to a specific output. We introduce the concept of a causal core to refer to the set of neuronal interactions that are causally significant for the output, as assessed by Granger causality. Because our approach requires extensive knowledge of neuronal connectivity and dynamics, an illustrative example is provided by analysis of Darwin X, a brain-based device that allows precise recording of the activity of neuronal units during behavior. In Darwin X, a simulated neuronal model of the hippocampus and surrounding cortical areas supports learning of a spatial navigation task in a real environment. Analysis of Darwin X reveals that large repertoires of neuronal interactions contain comparatively small causal cores and that these causal cores become smaller during learning, a finding that may reflect the selection of specific causal pathways from diverse neuronal repertoires.