DeWeerth Lab

Hybrid Neural-Microelectronic Systems

We are bringing models and experiments together, creating systems whose activity is a product of both the biological and the modeled components. In these hybrid systems, the model component allows us to manipulate the system in ways that are impossible with traditional neurophysiological techniques. The biological component, however, provides the system with a level of realism and relevance that cannot be achieved with modeling alone. For example, by replacing a single neuron in a biological neural network with a modeled neuron, we can investigate how the properties of that neuron affect the behavior of the network.

Legged Robot Locomotion

Legged robots are used as a method of studying both individual neuron models as well as possible neuron interconnectivities that can produce stable locomotion. Our model neural architectures maintain closed-loop control of the robot, while novel mechanical devices are used to provide a more accurate emulation of true muscle dynamics. The goal of this research area is to test the major theories of motor control and neural connectivity to determine their ability to produce stable locomotion in a real-world, mechanical system.

Microfabricated 3D Neural Interface System

The goal of this project is to produce a device that is cable of interfacing and sustaining a three dimensional network of neurons. We plan to accomplish this by integrating a traditional multi-electrode array (MEA) with scaffolding towers, microfluidics, and microelectronics. The scaffolds support cellular growth and offer structural stability, the microfluidic channels are used for cell maintenance and chemical stimulation, and the electrodes are for electrical stimulation and recording. In addition, integrated circuitry used for processing neuron signals will be incorporated directly onto the neural interfacing device.

Neuromorphic Motor-Pattern Generating Systems

The goals of this project are to develop neuromorphic systems that emulate the control and production of rhythmic movements and to use nonlinear dynamical analysis to gain a better understanding of the production of these movements. Ultimately, we plan to develop systems that have direct engineering application in fields including autonomous robots and neural prosthetics. By applying the fundamental properties of rhythmic neuromechanical systems and their advantages over present engineered systems, we expect to develop next-generation controllers that use biologically-inspired mechanisms to create efficient motion generation.

Steve DeWeerthFacultyDeweerth
JoAnna AndersonGraduate StudentDeweerth
Liang GuoGraduate StudentDeweerth
Gareth GuvanasenGraduate StudentDeweerth
Michelle KuykendalGraduate StudentDeweerth
Dustin LiGraduate StudentDeweerth
Sarah SteinmetzGraduate StudentDeweerth
Jason WhiteGraduate StudentDeweerth
J. Matthew KinnemoreUndergraduate StudentDeweerth
Safeer SiddickyUndergraduate StudentDeweerth
Seon YooUndergraduate StudentDeweerth