Paper
Dendritic cortical microcircuits approximate the backpropagation\n algorithm
Deep learning has seen remarkable developments over the last years, many of\nthem inspired by neuroscience. However, the main learning mechanism behind\nthese advances - error backpropagation - appears to be at odds with\nneurobiology. Here, we introduce a multilayer neuronal network model with\nsimplified dendritic compartments in which error-driven synaptic plasticity\nadapts the network towards a global desired output. In contrast to previous\nwork our model does not require separate phases and synaptic learning is driven\nby local dendritic prediction errors continuously in time. Such errors\noriginate at apical dendrites and occur due to a mismatch between predictive\ninput from lateral interneurons and activity from actual top-down feedback.\nThrough the use of simple dendritic compartments and different cell-types our\nmodel can represent both error and normal activity within a pyramidal neuron.\nWe demonstrate the learning capabilities of the model in regression and\nclassification tasks, and show analytically that it approximates the error\nbackpropagation algorithm. Moreover, our framework is consistent with recent\nobservations of learning between brain areas and the architecture of cortical\nmicrocircuits. Overall, we introduce a novel view of learning on dendritic\ncortical circuits and on how the brain may solve the long-standing synaptic\ncredit assignment problem.\n
Authors: Sacramento, João · Costa, Rui Ponte · Bengio, Yoshua · Senn, Walter