Complex Systems Dynamics & Control Group

    University of Idaho

  • A Unified Dynamical Modeling and Control Framework for Rewiring Brain Circuits

    It is well-established in both neurobiology and neuroscience that the basis for the operation of the brain is neither entirely chemical nor exclusively electrical but rather involves an intriguing combination of both. To understand the interplay between chemical and electrical signals, we are developing a generalized dynamical modeling and control framework that integrates the chemical level descriptions (such as neurotransmitter, receptors, signal transduction of second messengers) of brain dynamics with the electrical level descriptions (such as neuronal spiking activity, Local-field Potentials, EEG). Using this framework, we are interested in investigating the extent to which neuroplasticity could be harnessed to rewire pathological brain circuits and thus, dynamical and functional recovery to healthy brain circuits.

    • Complex Interplay between Stress Hormones and Neuromodulators on Learning and Memory

      Stress-mediated psychiatric disorders, such as post-traumatic stress disorder and major depressive disorders, adversely affect the quality of life of more than 322 million human beings globally. Stress induces hormonal changes in our brain, which play a significant role in learning and memory impairments. By developing novel systems-theoretic approaches, my research group is investigating how stress-induced hormones and brain chemical modulators act in concert to impact hippocampal learning and memory.

    • Balance of Brain Chemicals in Regulating Basal Ganglia Circuit Dynamics

      Brain modulators such as dopamine, acetylcholine, and adenosine act in concert to control the dynamics of Basal Ganglia circuit relevant to various motor tasks. By developing detailed biochemical models of direct and indirect pathways of medium spiny neurons, my research group is investigating how dopamine, acetylcholine, and adenosine work together to maintain the balance between these two pathways, which otherwise could lead to various motor disorders such as Parkinsons' disease.

  • Data-Driven Predictive Dynamical Modeling and Control of Complex Systems

    Availability of abundant amount of data and advances in machine learning have recently revolutionized the field of predictive data-driven dynamical modeling of complex systems using neural networks and deep learning approaches. In this direction, my research group is investigating novel deep learning based neural network architectures and algorithms to make multi-step time-series predictions of complex dynamical systems.

  • Noninvasive Brain-Machine Interfaces

    Brain-machine interfaces (BMIs) are artificial systems that read and decode task-relevant brain signals allowing severely paralyzed patients and amputees to convert their thoughts into actions by translating the decoded information to control external devices such as robotic arms. This project focuses on developing novel brain decoding approaches for EEG-based brain-machine interfaces. We are particularly interested in applying deep learning approaches to develop robust task-relevant decoding algorithms.