Self-supervised learning of probabilistic prediction through synaptic plasticity in apical dendrites: A normative model

AbstractSensory information is processed by the brain not in a simple feedforward fashion. Rather, bottom-up inputs are combined in pyramidal cells of sensory cortices with top-down information from higher brain areas that arrives through synapses in apical dendrites. The exact functional role of these top-down inputs has remained unknown. A promising abstract model posits that they provide probabilistic priors for bottom-up sensory inputs. We show that this hypothesis is consistent with a large number of experimental about synaptic plasticity in apical dendrites, in particular with the prominent role of NMDA-spikes. We identify conditions under which this synaptic plasticity could approximate the gold standard for self-supervised learning of probabilistic priors: logistic regression. Furthermore, this perspective suggests an additional functional role for the complex structure of the dendritic arborization plays: It enables the neuron to learn substantially more complex landscapes of probabilistic priors.