Dendritic error backpropagation in deep cortical microcircuits
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.
Date:
9 May 2018, 13:30 (Wednesday, 3rd week, Trinity 2018)
Venue:
Le Gros Clark Building, off South Parks Road OX1 3QX
Venue Details:
Lecture theatre
Speaker:
João Sacramento (University of Bern, Switzerland)
Organiser:
Dr Friedemann Zenke (University of Oxford)
Organiser contact email address:
friedemann.zenke@cncb.ox.ac.uk
Hosts:
Dr Friedemann Zenke (University of Oxford),
Dr Tim Vogels
Part of:
Oxford Neurotheory Forum
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Friedemann Zenke
