The goal of this project is to develop a machine learning based modeling framework to discover new mechanisms of brain function, with a focus on how the brain supports decision-making and movement. Understanding the brain's inner workings, including how circuits of neurons compute and give rise to these behaviors, is critical for better diagnosing and treating cognitive and motor disorders. A challenge to studying brain function is its complexity: billions of neurons across multiple brain areas interact in coordinated ways. This project gains new insight into brain function by modeling these coordinated and multi-area computations with deep neural networks. These networks, unlike the brain, are fully observed: all artificial neurons, their activity, and their connections are known. As such, neural networks that are trained to compute like the brain can be analyzed to discover new mechanistic insights for brain function. This project will also use these insights to develop higher performance brain-computer interfaces that help the paralyzed by decoding thoughts into actions.
This project will use recurrent neural networks as in silico models of brain areas. New neural network architectures will be trained to do the same tasks and behaviors that animals perform in experimental labs. Critically, these neural networks will be trained to harness information from basic neuroscience including anatomy and neuron recordings, so that its artificial neurons resemble real neurons. After training, neural networks will be analyzed to propose new computational mechanisms for how populations of neurons compute to produce behaviors. New hypotheses of brain function from these networks will be tested in collaboration with experimental labs. These insights and models will be incorporated into new algorithms for brain-computer interfaces that aim to better decode one's intentions from his or her neural activity. The outcomes of this project will also be translated into educational and outreach materials to reach a broad audience, contributing to the training of a next generation of computational neuroscientists and neural engineers.
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.
