Conductance-based dendrites perform Bayes-optimal cue integration.

Jordan, Jakob; Sacramento, João Antonio; Wybo, Willem A M; Petrovici, Mihai A; Senn, Walter (2024). Conductance-based dendrites perform Bayes-optimal cue integration. PLoS computational biology, 20(6) Public Library of Science 10.1371/journal.pcbi.1012047

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Physiology

UniBE Contributor:

Jordan, Jakob Jürgen, Rodrigues Sacramento, Joao Antonio, Wybo, Willem, Petrovici, Mihai Alexandru, Senn, Walter

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1553-734X

Publisher:

Public Library of Science

Language:

English

Submitter:

Pubmed Import

Date Deposited:

17 Jun 2024 15:48

Last Modified:

20 Jun 2024 03:15

Publisher DOI:

10.1371/journal.pcbi.1012047

PubMed ID:

38865345

BORIS DOI:

10.48350/197794

URI:

https://boris.unibe.ch/id/eprint/197794

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