CAREER: Extracting principles of neural computation from large scale neural recordings through neural network theory and high dimensional statistics

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Recent technological advances now enable recordings of thousands of neurons during complex behaviors. Such experimental capabilities could potentially reveal how the brain encodes sensations, forms memories, learns tasks, makes decisions, and generates motor actions. However, there exist major obstacles to attaining a scientific understanding of how the psychological capabilities of the mind emerge from the biological wetware of the brain. First, data analytic methods are not adequate to make sense of the massive datasets currently being gathered from the brain. Second, theoretical methods are not adequate for both optimally designing large-scale neural recordings, and bridging scales from the collective biophysics of many neurons to psychological processes underlying sensations, thoughts and actions. This project will develop novel data analytic and theoretical methods to extract a conceptual understanding of how the brain gives rise to cognition. These methods will be tested in large-scale recordings from many experimental labs studying perception, memory, learning, decision making and motor control. They will also be applied to developing better learning protocols and neural prosthetic devices.This project will pursue three overarching aims. It will build on advances in high dimensional statistics to develop a theory of when and how subsets of neurons reflect the collective dynamics of the much larger unobserved circuit in which they are embedded. This theory will provide quantitative guidance for the efficient design of future large-scale recording experiments. Second, it will build on advances in deep learning to develop algorithmic methods for extracting a conceptual understanding of how complex neural networks solve tasks. These algorithmic methods will elucidate which aspects of network connectivity and dynamics are essential to understanding how neural circuits perform their computations, thereby providing guidance for what to measure in future neuroscience experiments. Finally, it will advance theories of neural network learning to better understand how the structure of prior experience determines learned neural connectivity, and how this learning process can be optimized. These general theoretical advances will be refined and tested in specific, close experimental collaborations, involving: identifying feedback control laws in motor cortex, finding signatures of attractor dynamics in the hippocampal memory circuits, understanding the neural algorithms for perception in the retina and decision making in prefrontal cortex, and developing frameworks for understanding rapid rodent learning built upon prior experiences.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.