CRCNS: Coordinating learning by top-down gating of plasticity in dendrites

There is a central problem in biological learning known as the “credit assignment problem”: how does information about the outcome of a decision or behavior modify the right synapses in the right neurons across multiple brain regions to improve future performance? The standard solution to this problem in artificial neural networks is to perform direct gradient descent, which minimizes error in the output of a network by precisely adjusting the strengths of every connection in proportion to that error. However, it is unlikely that the brain is able to compute the impact of each synapse on performance error and “backpropagate” fine-grained error signals across multiple layers of neuronal circuitry to every synapse. Recent work identified a new candidate biological mechanism for supervised learning in the brain. In addition to “bottom-up” connections that process sensory inputs, neurons also send “top-down” connections to the dendrites of neurons in lower layers. This feedback drives special events called “dendritic calcium spikes” that induce a potent form of synaptic plasticity and cause neurons to become selective for stimulus features in as few as a single trial, a phenomenon called “one-shot learning.” This project aims to develop new learning theory inspired by these experimental observations, and to experimentally test predictions of this theory in awake, behaving mice to better understand how top-down instructive signals in the brain coordinate learning across multiple layers of neuronal circuitry by regulating dendritic calcium spiking and associated plasticity. The team synergizes expertise in neuronal cellular and synaptic physiology, systems and computational neuroscience, and machine learning to better understand an important cognitive function - memory formation during goal-directed learning. A major objective is to develop and critically test a new theory of learning based on the regulation of dendritic calcium spikes and associated synaptic plasticity. Computational modeling will directly inform the proposed experiments, which entail imaging and manipulating neuronal population activity in vivo during spatial foraging behavior in mice. Preliminary results suggest that incorporation of these insights from biology into artificial neural networks leads to enhanced performance compared to standard techniques, highlighting the transformative potential of the proposed approach.