In functional network models, neurons are commonly conceptualized as linearly
summing presynaptic inputs before applying a non-linear gain function to
produce output activity. In contrast, synaptic coupling between neurons in the
central nervous system is regulated by dynamic permeabilities of ion channels.
So far, the computational role of these membrane conductances remains unclear
and is often considered an artifact of the biological substrate. Here we
demonstrate that conductance-based synaptic coupling allow neurons to
represent, process and learn uncertainties. We suggest that membrane potentials
and conductances on dendritic branches code opinions with associated
reliabilities. The biophysics of the membrane combines these opinions by taking
account their reliabilities, and the soma thus acts as a decision maker. 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, and makes testable
predictions on dendritic integration and synaptic plasticity.Comment: 36 pages, 10 figures; Mihai A. Petrovici and Walter Senn share senior
authorshi