Conductance-based dendrites perform Bayes-optimal cue integration

TL;DR

This paper proposes a Bayesian framework for conductance-based neurons, showing they can perform optimal cue integration by representing priors and likelihoods in dendritic compartments, supported by formal proofs, simulations, and testable predictions.

Contribution

It introduces a novel Bayesian model of dendritic computation, demonstrating how conductance-based neurons can naturally perform optimal information integration and learn through synaptic plasticity.

Findings

Dendrites can encode prior and likelihood information for Bayesian inference.

Somatic potentials represent the posterior distribution derived from dendritic inputs.

The model aligns with experimental data on multi-sensory integration and predicts new testable phenomena.

Abstract

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.