While the brain's computational abilities are in many respects unrivaled, its workings appear to be far noisier than those of almost any engineered computational system. Even in carefully controlled experiments in which the same conditions are presented over multiple trials, neural activity is strikingly variable from one trial to the next. This project aims to resolve the apparent contradiction between the brain's computational proficiency and its apparently high levels of noise. The core hypothesis is that much of the observed neural variability is driven not by noise but by internal brain modes -- that is, by coordinated patterns of activity across the brain. Thus, variability may be a signature of dynamic and uniquely biological computations rather than noisy fluctuations. If this hypothesis is correct, then observation of these global modes should explain choices that subjects make in behavioral tasks, and perturbation of the modes should alter their patterns of choices in systematic ways. To test this hypothesis, the project employs a novel joint experimental and theoretical approach to measure the variability in brain-wide neural activity across scales, to define its relationship to behavior, and to dynamically perturb these modes to impact behavioral performance both within and across individuals.
To build a transformative understanding of the link between neural and behavioral variability, the project will use multi-probe Neuropixels technology that enables simultaneous recording at submillisecond resolution from thousands of individual neurons distributed across the brain, coupled with advanced data analytic and dynamical modeling tools to extract activity modes from these data. These analyses will be performed together with behavioral assays that probe multiple aspects of behavioral performance, including engagement, perceptual sensitivity, and vigor. Statistical modeling will then be used to identify the functional role of these modes in regulating behavioral performance, and how their activity drives behavioral differences both across trials and across individuals. Beyond correlative analysis, control theory tools will design patterned optogenetic perturbations to provide direct causal tests of this novel functional role for brain-wide activity modes. If the project succeeds, the result will be a new understanding of the nature of the ongoing fluctuations in brain-wide activity patterns trial by trial and individual by individual in terms of behavior rather than noise -- a key step in deciphering the logic of distributed computation underlying perception and cognition. The project will develop and disseminate novel open data and code to impact research and training nationwide. It will also build a dynamic, team-mentored environment led by investigators from very distinct disciplines -- from neuroscience to applied mathematics to control engineering -- which will prepare both undergraduate and graduate trainees to bridge disciplines and scientific cultures.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
