Implantable devices provide an alternative for treating neurological disorders in patients who do not respond well to available drugs. A good example are deep brain stimulators for the treatment of Parkinson’s Disease, which have been very successful. Many neurological and psychiatric disorders are associated with cognitive deficits, which tend to be difficult to ameliorate through drug therapy. In the future, it might be possible to treat cognitive deficits with implantable devices, but this requires much more advanced technology than the currently available stimulators. This project will lay the foundation for interfacing technology with neural cognitive systems. Being able to steer or control a system of any kind requires a model or mathematical description of the system. The project will establish a framework for deriving such a model directly from neural activity. It will further develop strategies for moving the cognitive system into a desired target state through the application of electrical microstimulation, with the intent to allow for correction of disease-related maladaptive states.

This interdisciplinary project, which involves investigators from both neuroscience and engineering, has the following components: 1) System identification: Using a large number of implanted electrodes, the recorded neural activity will be used to identify a dynamical system that is able to capture (and predict) the temporal pattern of neural activity. The focus will be on piecewise linear models for approximating nonlinear dynamics. 2) State decoding: With the help of the identified model and based on the current and recent activity pattern, the current internal state of the neural system can be identified. 3) System control: In the case of the current state of the system deviating from a desired target state, the identified model in combination with control-theoretic strategies can be used to determine a stimulation pattern that needs to be applied to drive the system towards the desired state. The project will use simple cognitive tasks, like working memory and perceptual decision-making tasks, to provide a proof of principle that the state of a cognitive neural system can be adjusted in real time to improve, for example, overall performance on the task. This is an ambitious goal, which, in this form, has not been achieved before, but a successful outcome has the potential to revolutionize how mental disorders might be treated in the future. This work is supported by the Integrative Strategies for Neural and Cognitive Systems (NCS) Program.

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.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Ellen Carpenter
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University of California Davis
United States
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