  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.

Neurons and Cognition;Machine Learning;Neural and Evolutionary Computing