  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.
