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Distinguishing causal interactions in neural populations

journal contribution
posted on 2023-06-07, 14:17 authored by Anil SethAnil Seth, Gerald M. Edelman
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.


Publication status

  • Published


Neural Computation




MIT Press





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Department affiliated with

  • Informatics Publications


Originality: Introduces a novel theoretical framework and a practical method for analyzing causal interactions in neural populations. This work has the potential to substantially alter prevailing views of neural population dynamics in neuroscience. Rigour: Applies a combination of time series analysis (drawn from econometric theory) and graph theory to characterize causal pathways through neural populations and investigate how they are modulated by plasticity. Significance: As well as introducing a new view of neural population dynamics, this work provides novel insights into the poorly understood relationship between synaptic plasticity and behavioral learning.

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