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Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing

Da Costa, L; Parr, T; Sengupta, B; Friston, K; (2021) Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing. Entropy , 23 (4) , Article 454. 10.3390/e23040454. Green open access

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Abstract

Active inference is a normative framework for explaining behaviour under the free energy principle—a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy—a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error—plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference.

Type: Article
Title: Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/e23040454
Publisher version: https://doi.org/10.3390/e23040454
Language: English
Additional information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Keywords: Active inference; free energy principle; process theory; natural gradient descent; information geometry; variational Bayesian inference; Bayesian brain; self-organisation; metabolic efficiency; Fisher information length
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10126711
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