Synaptic plasticity as probabilistic inference

13.12.2017 - 17:00
13.12.2017 - 19:00

Dr. Peter Latham

Gatsby Computational Neuroscience Unit, University College London

Organisms face a hard problem: based on noisy sensory input, they must set a
large number of synaptic weights. However, they do not receive enough
information in their lifetime to learn the correct, or optimal weights (i.e.,
the weights that ensure the circuit, system, and ultimately organism functions
as effectively as possible). Instead, the best they could possibly do is
compute a probability distribution over the optimal weights. Based on this
observation, we hypothesize that synapses represent probability distributions
over weights -- in contrast to the widely held belief that they represent point
estimates. From this hypothesis, we derive learning rules for both supervised
and unsupervised learning. This introduces a new feature: the more uncertain
the brain is about the optimal weight of a synapse, the more plastic it is.
Consequently, the learning rate of each synapse is adjusted on the fly. This
framework makes several testable predictions and, combined with the ansatz that
more uncertain synapses are more variable, it is consistent with current data.