Neural coding and dynamics for short-term memory

The brain processes sensory stimuli and uses this information in conjunction with goals and memories to generate actions. All these steps involve the representation of information and brain states in changing patterns of neural activity, or “neural codes”. We seek to understand which codes the brain uses to represent information; to characterize the advantages of those codes with respect to efficiency, ease of readout, and so on, compared to other potential coding strategies; to learn about the circuit architecture between neurons that allows the brain to possess such codes; and finally, to understand how the brain uses these codes to compute and generate actions well-suited for survival in the world. Our group develops numerical and analytical tools to help “crack the neural code” and model how it might be substantiated in real neural circuits. The project proposed here involves topics as abstract as the mathematical analysis of neural codes, to data-driven projects involving the analysis of neural data which seek to test models of how neural circuits are work.  On the abstract end, we are focused on questions of coding efficiency, such as how the maximum amount of information that can be stored over time in various neural codes depends on inherent variability in neural activity and the size of the circuit. On the data-driven end, we plan to collaborate with laboratories studying a set of brain areas involved in spatial learning and memory in rodents, the hippocampus and the entorhinal cortex. Our work on both coding and circuit mechanisms for this project will be focused on “grid cells”, which represent animal location in space in an unusual way, and are hypothesized to be involved in navigational computations that allow mammals to estimate where they are even as they move about. We will make realistic models of the neural circuits that give rise to grid cells. We will model how grid cells interact with the hippocampus to build maps of the world and enable navigation within it. Because the challenges of spatial inference are common to rodents and humans, and because the neural representations are similar, we expect our insights to shed light on the representations and mechanisms that underlie spatial memory and navigation in humans.