Speaker
Dr. Babak Mahmoudi
Date
Location
University of Houston
Abstract
Current therapeutic devices to treat neuropsychiatric disorders are often ineffective, in part
due to an incomplete understanding of the mechanisms of actions of neuromodulation
therapies. Next-generation closed-loop neuromodulation systems provide powerful tools for
elucidating the causal effects of regulating physiological biomarkers and promise to enable
precision therapies for a wide range of diseases. Real-time sensing and computing capabilities
of closed-loop neuromodulation systems allows for precise measurement of the physiological
states and generation of adaptive neuromodulatory actions. However, the complexity of
optimally controlling the neuromodulatory actuators to induce desired physiological or
behavioral states, in real-time, is a major barrier for developing more effective therapies. Our
long-term goal is to develop an end-to-end platform for designing, prototyping and
implementing intelligent Closed-Loop Neuromodulation systems that can automatically learn
the optimal mapping between neural states and neuromodulatory actions from interacting
with the neural systems.
due to an incomplete understanding of the mechanisms of actions of neuromodulation
therapies. Next-generation closed-loop neuromodulation systems provide powerful tools for
elucidating the causal effects of regulating physiological biomarkers and promise to enable
precision therapies for a wide range of diseases. Real-time sensing and computing capabilities
of closed-loop neuromodulation systems allows for precise measurement of the physiological
states and generation of adaptive neuromodulatory actions. However, the complexity of
optimally controlling the neuromodulatory actuators to induce desired physiological or
behavioral states, in real-time, is a major barrier for developing more effective therapies. Our
long-term goal is to develop an end-to-end platform for designing, prototyping and
implementing intelligent Closed-Loop Neuromodulation systems that can automatically learn
the optimal mapping between neural states and neuromodulatory actions from interacting
with the neural systems.