Conductance-based dendrites perform Bayes-optimal cue integration.

Autor: Jordan J; Department of Physiology, University of Bern, Bern, Switzerland.; Electrical Engineering, Yale University, New Haven, Connecticut, United States of America., Sacramento J; Department of Physiology, University of Bern, Bern, Switzerland.; Institute of Neuroinformatics, UZH / ETH Zurich, Zurich, Switzerland., Wybo WAM; Department of Physiology, University of Bern, Bern, Switzerland.; Institute of Neuroscience and Medicine, Forschungszentrum Jülich, Jülich, Germany., Petrovici MA; Department of Physiology, University of Bern, Bern, Switzerland., Senn W; Department of Physiology, University of Bern, Bern, Switzerland.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2024 Jun 12; Vol. 20 (6), pp. e1012047. Date of Electronic Publication: 2024 Jun 12 (Print Publication: 2024).
DOI: 10.1371/journal.pcbi.1012047
Abstrakt: A fundamental function of cortical circuits is the integration of information from different sources to form a reliable basis for behavior. While animals behave as if they optimally integrate information according to Bayesian probability theory, the implementation of the required computations in the biological substrate remains unclear. We propose a novel, Bayesian view on the dynamics of conductance-based neurons and synapses which suggests that they are naturally equipped to optimally perform information integration. In our approach apical dendrites represent prior expectations over somatic potentials, while basal dendrites represent likelihoods of somatic potentials. These are parametrized by local quantities, the effective reversal potentials and membrane conductances. We formally demonstrate that under these assumptions the somatic compartment naturally computes the corresponding posterior. We derive a gradient-based plasticity rule, allowing neurons to learn desired target distributions and weight synaptic inputs by their relative reliabilities. Our theory explains various experimental findings on the system and single-cell level related to multi-sensory integration, which we illustrate with simulations. Furthermore, we make experimentally testable predictions on Bayesian dendritic integration and synaptic plasticity.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Jordan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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