PT  - JOURNAL ARTICLE
AU  - Arjun Rao
AU  - Robert Legenstein
AU  - Anand Subramoney
AU  - Wolfgang Maass
TI  - A normative framework for learning top-down predictions through synaptic plasticity in apical dendrites
AID  - 10.1101/2021.03.04.433822
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.03.04.433822
4099  - http://biorxiv.org/content/early/2021/05/05/2021.03.04.433822.short
4100  - http://biorxiv.org/content/early/2021/05/05/2021.03.04.433822.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.