NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

Significance In deep learning, the standard approach to accommodate changing task demands is to train new output layers on top of a common trunk network, and, if needed, to relearn synapses throughout the whole network. However, the brain appears to take a radically different strategy, as neurons in all processing layers are modulated by contextual information. We show that context-dependent dendritic afferents can powerfully modulate the neuronal output and that this modulation dynamically reshapes network function to solve new tasks, without adapting any feedforward synapses. We furthermore show that these dendritic modulations could underlie self-supervised learning of deep networks, without relying on the backpropagation of errors across the layers of the network.