You are here

Event Details

MP Associates, Inc.
TUESDAY October 15, 1:30pm - 3:00pm | KC 406
Deciphering the Brain: From Mathematical Models to Computing Platforms for Cyber-Human Autonomy Symbiosis
James Weimer - Univ. of Pennsylvania
Sergio Pequito - Rensselaer Polytechnic Institute
Miroslav Pajic - Duke Univ.
Arian Ashourvan - Penns Center for Neuroengineering and Therapeutics and the Penn Epilepsy Center, University of Penns
Partha Pratim Pande - Washington State Univ.
A fundamental challenge in neuroscience is to uncover the principles governing complex interactions between the brain and its external environment. The development of functional neuroimaging techniques and tools from graph theory, network science, and computational neuroscience have markedly expanded opportunities to study the intrinsic organization of brain activity. However, many current computational models are fundamentally limited by little to no explicit assessment of the brains constant and crucial interactions with external stimuli. Moreover, the complex multiscale spatiotemporal dynamics exhibited by brain activity makes the modeling, analysis and discovery of therapeutic strategies harder highlighting new challenges for Internet-of-Medical-Things targeting the brain. To address these limitations, this special session discusses the following pioneering approaches: (1) By leveraging concepts from dynamical, control systems and fractal theories, we describe a new suite of mathematical modeling of brain activity that provides new and more efficient strategies for understanding healthy and disease regimes. We discuss the design of EEG-based non-invasive brain machine interfaces and the use of multi-dimensional fractal dynamic models for predicting the brain state for specific tasks. (2) To treat several neurological disorders (e.g., alleviating symptoms of Parkinson’s disease), we present the design of energy efficient deep brain stimulation (DBS) devices. We discuss the development of open and closed-loop DBS controllers, based on our physiological Basal Ganglia Model (BGM) and a hardware platform with a fully programable interface exposing (a) functional (continuous) electrical potentials for validation through simulation and device testing, and (b) logical signals for discrete event-based controller analysis. (3) We discuss the joint estimation of the intrinsic organization of brain activity and extrinsic stimuli. We demonstrate the utility of this scheme by accurately estimating unknown external stimuli in a synthetic example. Next, we examine brain activity at rest and task for 99 subjects from the Human Connectome Project, and find significant task-related changes in the identified system, and task-related increases in the estimated external inputs showing high similarity to known task regressors. Together, our embodied model of brain activity provides an avenue to gain deeper insight into the relationship between cortical functional dynamics and their drivers. (4) Finally, we describe a spatiotemporal fractal parallel algorithm to efficiently analyze brain activity data and discuss the design of a machine-learning-inspired wireless network-on-chip (WiNoC)-based manycore architecture for handling the compute- and communication-intensive nature of the brain-machine-body-interface application. Throughout the entire special session, we will highlight numerous open mathematical, algorithmic and hardware implementation related challenges that remain to be addressed by the IoT research community.

5D.1Towards personalized real-time closed-loop brain-machine-brain neurotechnologies
 Speaker: Sergio Pequito - Rensselaer Polytechnic Institute
 Author: Sergio Pequito - Rensselaer Polytechnic Institute
5D.2Design of Efficient Deep Brain Stimulation Devices
 Speaker: Miroslav Pajic - Duke Univ.
 Author: Miroslav Pajic - Duke Univ.
5D.3A dynamical systems framework to uncover the drivers of large-scale cortical activity
 Speaker: Arian Ashourvan - Penns Center for Neuroengineering and Therapeutics and the Penn Epilepsy Center, University of Penns
 Author: Arian Ashourvan - Penns Center for Neuroengineering and Therapeutics and the Penn Epilepsy Center, University of Penns
5D.4Network-on-Chip-enabled Manycore Architectures for Brain-Machine-Interface Applications
 Speaker: Partha Pande - Washington State Univ.
 Author: Partha Pande - Washington State Univ.