Understanding Multi-Layer Learning in a Biological Circuit

Work on learning in neural systems has focused largely on the effects of plasticity at synapses that provide direct input to the neurons being studied. Learning a model of the environment or a complex skill, however, relies on plasticity that is widely distributed and may occur at synapses far from the neurons driving decisions or actions. As is well-known from multi-layer (or 'deep') artificial networks, distributing learning over multiple layers is substantially more powerful but also more difficult to implement than learning at a single layer. The fact that computer scientists have solved such problems has revolutionized artificial intelligence and is rapidly reshaping the human world. Understanding how the brain solves such problems is, undoubtedly, one of the biggest challenges facing neuroscience today. However, progress along these lines has been slow, due in part to the high degree of complexity of learning and memory circuits in mammals, such as hippocampus and neocortex, that have been a major focus of research. This proposal applies integrated experimental and theoretical approaches to a system with unique advantages for understanding learning in multi-layer networks. The electrosensory lobe (ELL) of mormyrid fish is the site of a continual learning process that predicts and cancels self-generated sensory input in order to enhance detection of behaviorally-relevant stimuli. Building on this knowledge, we propose to develop a model of the ELL spanning from cellular biophysics to network dynamics with the goal of explaining how synaptic plasticity widely distributed across processing layers and cell types gives rise to learning. To accomplish this, we will leverage cutting-edge approaches for mapping synaptic connectivity at high-resolution and monitoring neural population activity over the entire time course of learning. The proposed research is expected to yield general insight into how sophisticated forms of learning are implemented in neural circuits.