Learning Bayes-optimal dendritic opinion pooling

Pooling different opinions and weighting them according to their reliability is conducive to making good decisions. We demonstrate that single cortical neurons, through the biophysics of conductance-based coupling, perform such complex probabilistic computations via their natural dynamics. While the effective computation can be described as a feedforward process, the implementation critically relies on the bidirectional current flow along the dendritic tree. We suggest that dendritic membrane potentials and conductances encode opinions and their associated reliabilities, on which the soma acts as a decision maker. Furthermore, we derive gradient-based plasticity rules, allowing neurons to learn to represent desired target distributions and to weight afferents according to their reliability. Our theory shows how neurons perform Bayes-optimal cue integration. It also explains various experimental findings, both on the system and on the single-cell level, and makes new, testable predictions for intracortical neuron and synapse dynamics.