Dendritic error backpropagation in deep cortical microcircuits
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.
About the speaker: Joao is a postdoc in computational neuroscience at the Department of Physiology, University of Bern with Walter Senn. He studied computer science at the Technical University of Lisbon, where he served as a teaching assistant for several years. Joao earned a PhD for his thesis on associative memory with Andreas Wichert. During his studies, Joao visited the University of Edinburgh School of Informatics where he collaborated with Mark van Rossum on energy-efficient synaptic plasticity. Joao's current research is on models of learning in cortical circuits, in particular on how prediction errors could arise and propagate across brain areas to instruct plasticity.