This project aims to develop new mathematical and computational tools for understanding the basic information-processing strategies of neurons and neural populations in the brain. Recent technological advances have enabled large-scale recordings of neural activity from intact neural circuits, but there is a severe shortage of theoretical methods for revealing what this activity means--that is, what information it carries, and how it gives rise to behavior. The research described in this proposal will address these questions using novel statistical techniques for studying the neural code in single neurons and neural populations, using both extracellularly and intracellularly recorded neural data.
There are at least two important statistical aspects to the proposed research: first, new methods for reliably estimating the neurobiological variables of interest (e.g., spikes, membrane currents, synaptic conductances, etc.) from noisy experimental recordings; and second, powerful, flexible, model-based methods for understanding the complex, high-dimensional, and time-dependent relationship between sensory stimuli, behavioral responses, and neural activity. The three specific aims of the proposal focus on: (1) the encoding and decoding of decisions from multi-neuron spike trains in parietal cortex; (2) intracellular signals in visual cortex, at the level of membrane potential and synaptic currents, and their relationship to the information conveyed in spike trains; and (3) advanced methods for adaptive, "closed loop" neurophysiology experiments, leading to more informative experimental designs and more interpretable neural datasets. All three aims will involve intensive collaborations with experimental groups and will tightly integrate theory and experiment.
The proposed research will reveal new features of visual and cognitive representations in cortex, and will unlock the neural code at multiple levels of biophysical detail in sensory, motor and cognitive systems. More broadly, the research will shed new light on information flow in groups of neurons, with implications for both the treatment of brain disorders and the design of new technology.
