A normative framework for learning top-down predictions through synaptic plasticity in apical dendrites

A. Rao, R. Legenstein, A. Subramoney, and W. Maass

Abstract:

Predictive coding has been identified as a key aspect of computation and learning in cortical microcircuits. But we do not know how synaptic plasticity processes install and maintain predictive coding capabilites in these neural circuits. Predictions are inherently uncertain, and learning rules that aim at discriminating linearly separable classes of inputs - such as the perceptron learning rule - do not perform well if the goal is learning to predict. We show that experimental data on synaptic plasticity in apical dendrites of pyramidal cells support another learning rule that is suitable for learning to predict. More precisely, it enables a spike-based approximation to logistic regression, a well-known gold standard for probabilistic prediction. We also show that data-based interactions between apical dendrites support learning of predictions for more complex probability distributions than those that can be handled by single dendrites. The resulting learning theory for top-down inputs to pyramidal cells provides a normative framework for evaluating experimental data, and suggests further experiments for tracking the emergence of predictive coding through synaptic plasticity in apical dendrites.



Reference: A. Rao, R. Legenstein, A. Subramoney, and W. Maass. A normative framework for learning top-down predictions through synaptic plasticity in apical dendrites. submitted for publication, 2021.