Dendritic error backpropagation in deep cortical microcircuits

TL;DR

This paper proposes a biologically plausible model of cortical microcircuits that uses dendritic prediction errors for synaptic learning, approximating backpropagation and enabling attention-like functions.

Contribution

It introduces a novel multi-area neuronal network model where dendritic prediction errors drive synaptic plasticity, bridging deep learning algorithms with cortical microcircuit mechanisms.

Findings

Model approximates classical backpropagation algorithm.

Dendritic errors occur at apical dendrites integrating feedback and inhibition.

Disinhibitory mechanisms enable attention-like stimulus denoising.

Abstract

Animal behaviour depends on learning to associate sensory stimuli with the desired motor command. Understanding how the brain orchestrates the necessary synaptic modifications across different brain areas has remained a longstanding puzzle. Here, we introduce a multi-area neuronal network model in which synaptic plasticity continuously adapts the network towards a global desired output. In this model synaptic learning is driven by a local dendritic prediction error that arises from a failure to predict the top-down input given the bottom-up activities. Such errors occur at apical dendrites of pyramidal neurons where both long-range excitatory feedback and local inhibitory predictions are integrated. When local inhibition fails to match excitatory feedback an error occurs which triggers plasticity at bottom-up synapses at basal dendrites of the same pyramidal neurons. We demonstrate the learning capabilities of the model in a number of tasks and show that it approximates the classical error backpropagation algorithm. Finally, complementing this cortical circuit with a disinhibitory mechanism enables attention-like stimulus denoising and generation. Our framework makes several experimental predictions on the function of dendritic integration and cortical microcircuits, is consistent with recent observations of cross-area learning, and suggests a biological implementation of deep learning.