Neuromorphic Systems to Reverse Engineer Reflex Function
Thu, Feb 09, 2017 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Professor Francisco Valero-Cuevas, USC
Talk Title: Neuromorphic Systems to Reverse Engineer Reflex Function
Abstract: The objective of this work is to build a neuromorphic robotic system that can interact with the physical world by implementing neuromechanical principles. It is a faithful implementation of the spinal circuitry responsible for the afferentation of muscles and is capable of producing both normal and pathological functions.
We used state-of-the-art models of muscle spindle mechanoreceptors with fusimotor drive, monosynaptic circuitry of the stretch reflex, and alpha motoneuron recruitment and rate coding. This multi-scale, hybrid system driven by populations of 1024 spiking neurons, emulated the physiological characteristics of the afferented mammalian muscles. We implemented these models on field-programmable gate arrays (FPGAs) which are capable of running these complex computations in real-time. The FPGAs control the forces of two muscles acting on a joint via long tendons. We performed ramp-and-hold perturbations and systematically explored a range of muscle spindle gains (fusimotor drive) to characterize the stretch reflex response in different phases of the perturbation. Finally, we explored the fidelity of four models for isometric muscle force production by testing their responses to rate-coding using spike trains and produced force ramps.
This autonomous integrated system was self-stable and the closed-loop behavior of populations of muscle spindles, alpha and gamma motoneurones, and muscle fibers emulated muscle tone and function. Sweeping the range of muscle spindle gains provided us with a subset of values that produced tenable physiological and pathological responses. Moreover, isometric force generation revealed that the dynamic response in the tendons is very sensitive to tendon elasticity, especially at high firing rates.
This hybrid, neuromorphic, neuromechanical system is a precursor to neuromorphic robotic systems. It provides a platform to study healthy function and the potential spinal and/or supraspinal sources of pathologic behavior.
Biography: I attended Swarthmore College from 1984-88 where I obtained a BS degree in Engineering. After spending a year in the Indian subcontinent as a Thomas J Watson Fellow, I joined Queen's University in Ontario and worked with Dr. Carolyn Small. The research for my Masters Degree in Mechanical Engineering at Queen's focused on developing non-invasive methods to estimate the kinematic integrity of the wrist joint.
In 1991, I joined the doctoral program in the Design Division of the Mechanical Engineering Department at Stanford University. I worked with Dr. Felix Zajac developing a realistic biomechanical model of the human digits. This research, done at the Rehabilitation R & D Center in Palo Alto, focused on predicting optimal coordination patterns of finger musculature during static force production.
After completing my doctoral degree in 1997, I joined the core faculty of the Biomechanical Engineering Division at Stanford University as a Research Associate and Lecturer. In 1999, I joined the faculty of the Sibley School of Mechanical and Aerospace Engineering at Cornell University as Assistant Professor, and was tenured in 2005. In 2007, I joined the faculty at the Department of Biomedical Engineering, and the Division of Biokinesiology & Physical Therapy at the University of Southern California as Associate Professor; where I was promoted to Full Professor in 2011. In 2013 I was elected Senior Member of the IEEE, and in 2014 to the College of Fellows of the American Institute for Medical and Biological Engineers.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher