A major goal in neuroscience is to determine the neural circuit dynamics and plasticity underlying behavior. In the first half of the talk, I will discuss our efforts to dissect the cellular and circuit mechanisms underlying a neural integrator circuit that accumulates and stores information in a short-term memory buffer. Using a simple computational modeling framework that enables the direct incorporation of data on cellular properties, neural recordings, perturbations of activity, and anatomical constraints, we show what features of the network connectivity can, and cannot, be directly inferred from the data. In the second half of the talk, I will discuss our work seeking to determine the sites of plasticity underlying the tuning of a simple reflexive eye movement behavior that has been at the center of a decades-old debate in the field of motor learning. Despite the seeming simplicity of this reflexive behavior, we show that inferring even the sites and signs (potentiation vs. depression) of plasticity can be highly challenging due to the presence of feedback loops in the neural circuitry and through the environment. In both halves of the talk, challenges and approaches for disambiguating different possible models underlying neural and behavioral data will be highlighted.
