Real-time mapping and adaptive testing for neural population hypotheses

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ABSTRACT Recent advances in neural recording technologies have made it possible to study increasingly large and di- verse subsets of neurons, producing a growing interest in the collective computational properties of neural pop- ulations. Ideally, causally testing these population hypotheses requires timing and selecting experimental ma- nipulations based on the current state of neural dynamics, but technical limitations have rendered this difficult in practice. However, recent work on real-time preprocessing and modeling of neural data has demonstrated that up-to-the minute estimates of neural population dynamics are indeed possible, opening the door to adap-tive experiments in which the design of the task changes based on incoming data. The goal of this proposal is to disseminate these advances to the widest possible audience of systems neuroscientists by: 1) Designing and validating new methods for mapping neural states and behavior online. 2) Developing algorithms for optimally timing and selecting experimental manipulations based on these instantaneous neural and behavioral states. 3) Making improv, our platform for adaptive experiments, easier to install, use, and configure for diverse model organisms and hardware setups. By allowing researchers to test ideas online, such tools will facilitate rapid “drill-down” from the whole brain to the local circuit levels, maximizing statistical efficiency in limited experi- mental time and providing stronger causal inferences for neural population hypotheses, with broad implications for systems neuroscience. 1