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In vitro neural networks minimise variational free energy

View ORCID ProfileTakuya Isomura, View ORCID ProfileKarl Friston
doi: https://doi.org/10.1101/323550
Takuya Isomura
1Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
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Karl Friston
2Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3BG, UK
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Abstract

In this work, we address the neuronal encoding problem from a Bayesian perspective. Specifically, we ask whether neuronal responses in an in vitro neuronal network are consistent with ideal Bayesian observer responses under the free energy principle. In brief, we stimulated an in vitro cortical cell culture with stimulus trains that had a known statistical structure. We then asked whether recorded neuronal responses were consistent with variational message passing (i.e., belief propagation) based upon free energy minimisation (i.e., evidence maximisation). Effectively, this required us to solve two problems: first, we had to formulate the Bayes-optimal encoding of the causes or sources of sensory stimulation, and then show that these idealised responses could account for observed electrophysiological responses. We describe a simulation of an optimal neural network (i.e., the ideal Bayesian neural code) and then consider the mapping from idealised in silico responses to recorded in vitro responses. Our objective was to find evidence for functional specialisation and segregation in the in vitro neural network that reproduced in silico learning via free energy minimisation. Finally, we combined the in vitro and in silico results to characterise learning in terms of trajectories in a variational information plane of accuracy and complexity.

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Posted May 16, 2018.
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In vitro neural networks minimise variational free energy
Takuya Isomura, Karl Friston
bioRxiv 323550; doi: https://doi.org/10.1101/323550
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In vitro neural networks minimise variational free energy
Takuya Isomura, Karl Friston
bioRxiv 323550; doi: https://doi.org/10.1101/323550

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