A dendritic nexus in the circuits that coordinate learning

Abstract When we learn a complex behavior the nervous system must continuously drive new actions, compare predictions for the actions against outcomes, and strengthen or weaken the connections between neurons (synapses) in order to improve future actions. However, within the multilayer brain networks that control behavior, the behavioral impact of modifying a synapse depends upon many downstream connections. Thus, learning requires the brain solve a ‘credit assignment’ problem: information about which synaptic modifications should be made is distributed across the network, yet must somehow be leveraged by local processes to guide change at individual synapses. A major gap in our ability to relate behavioral events to synaptic change is the current lack of knowledge of these local processes that guide synaptic changes at individual neurons. Recent theories of learning suggest that spikes generated in the apical dendrites of cortical neurons may play a key role in solving this credit assignment problem. The experiments in this proposal will test the hypothesis that the apical dendrites of neurons in the pre-motor cortex integrate multiple learning-instructive feedback sources, and – under appropriate conditions – generate dendritic spikes that rapidly reconfigure the connectivity and function of neurons. In these experiments we will use advanced optical techniques to monitor and manipulate activity in the dendrites of a subset of neurons in the frontal cortex that have a well-delineated role in action planning. A key prediction of our hypothesis is that the activity of the apical dendrites reflects local credit-related calculations and that this activity is distinct from the activity transmitted to other neurons by action potential generation near the cell body. We will test this using longitudinal two-photon calcium imaging of cortical neurons during learning to determine how the behavioral selectivity of dendrites and cell bodies change with changing behavior. In order to identify the contribution of dendritic spikes to learning, we will also use optogenetics to selectively suppress activity in the apical dendrites during learning. Computational models also predict that dendritic spikes are generated by a mismatch between outcome information arriving from long-range feedback projections and local inhibition that predicts this feedback. To test this, we will combine synaptic glutamate imaging and optogenetics to map the selectivity and anatomical identity of feedback projections to the apical dendrites, and calcium imaging to determine the selectivity of local inhibitory neurons that target the apical dendrites. Together, these studies will provide critical new insights into the circuit mechanisms governing cortical plasticity and credit assignment. In doing so, they will provide a key framework for connecting complex learning with modifications at the individual synapse level, and will build bridges between machine learning algorithms and models of biological neural networks.