Dendritic cortical microcircuits approximate the backpropagation algorithm

Joao Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

136 Citations (Scopus)


Deep learning has seen remarkable developments over the last years, many of them inspired by neuroscience. However, the main learning mechanism behind these advances \u2013 error backpropagation \u2013 appears to be at odds with neurobiology. Here, we introduce a multilayer neuronal network model with simplified dendritic compartments in which error-driven synaptic plasticity adapts the network towards a global desired output. In contrast to previous work our model does not require separate phases and synaptic learning is driven by local dendritic prediction errors continuously in time. Such errors originate at apical dendrites and occur due to a mismatch between predictive input from lateral interneurons and activity from actual top-down feedback. Through the use of simple dendritic compartments and different cell-types our model can represent both error and normal activity within a pyramidal neuron. We demonstrate the learning capabilities of the model in regression and classification tasks, and show analytically that it approximates the error backpropagation algorithm. Moreover, our framework is consistent with recent observations of learning between brain areas and the architecture of cortical microcircuits. Overall, we introduce a novel view of learning on dendritic cortical circuits and on how the brain may solve the long-standing synaptic credit assignment problem.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems
Subtitle of host publicationNeurIPS 2018
Publication statusPublished - 2018

Publication series

ISSN (Electronic)1049-5258


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