Projects2016

We are studying the basis of high level visual object recognition, namely face processing, using ultra high field functional magnetic resonance imaging (fMRI) to study face processing areas of the cerebral cortex with unprecidented resolution. The interested student will gain experience with the collection of high resolution fMRI data, learn to use of standard toolkits and custom MATLAB code to analyze imaging data, and have the opportunity to work with vision scientists and MRI physicists at the world renowned University of Minnesota Center for Magnetic Resonance Imaging.

We are able to instantly and effortlessly recognize individuals on the basis of their speech, but how this challenging task is accomplished by our brains remains a mystery. In this project, a student will assist in the acquisition and analysis of responses to monkey vocalizations while the monkey is performing a vocalization detection task. We will be using multi-cellular array recordings, and the student project will be to see whether the population responses to different vocalization in a specific auditory area are sufficiently fast and reliable to explain vocalization discrimination abilities.

Brain-computer interfaces (BCIs) aim to provide a means of communicating and interacting with one's environment in the absence of conventional motor pathways. This project involves exploration of the neurophysiology underlying SMR-based BCI use, using a form of noninvasive brain stimulation, TMS, to perturb specific neural circuits. The project will mainly involve analysis of EEG data from experiments with human subjects, and may also include helping to run experiments, and developing/validating alternate BCI task paradigms.

This project involves learning histological and immunohistochemistry techniques to section frozen brain tissue and label it for c-Fos, a proto-oncogene marker of neuronal activity in the brain. The student will use these techniques to visualize the brain regions / network that are modulated by deep brain stimulation therapy.

Computational neuron models, which predict the electrophysiological effects of electrical stimulation within the brain, have proven useful guide how to deliver deep brain stimulation therapy. However, the models make a number of assumptions with regards to axonal structure and axonal biophysics. In this project, the student will leverage microscopy techniques coupled with fast optical recording dyes to develop an approach to visualize axonal activation during extracellular stimulation, and assess how variation in stimulation parameters affects axonal activation.

This project will adapt an existing dry, long-term wearable EEG system for use under a virtual reality headset. The goal is to be able to record neural signals all day while subjects experience visual envrionments that produce neural plasticity in their visual systems. We will attempt to configure hardware and design pilot experiments to allow EEG to record neural changes over an 8-hour virtual reality session.

This project will adapt an existing dry, long-term wearable EEG system for use under a virtual reality headset. The goal is to be able to record neural signals all day while subjects experience visual envrionments that produce neural plasticity in their visual systems. We will attempt to configure hardware and design pilot experiments to allow EEG to record neural changes over an 8-hour virtual reality session.

Deep Brain Stimulation (DBS) of the Basal Ganglia (BG) is a common treatment for Parkinson's Disease (PD), and other neurological disorders. When DBS therapy is applied, different symptoms have different wash-out times, some symptoms cease immediately and others take minutes to hours to fully abate. This project will focus on incorporating synaptic plasticity into network models of DBS in the BG to try to explain this phenomenom.

Research at the nanomaterials and sustainable technology lab (NSTL) is focused on nanotechnology for sensing and energy harvesting applications. This project will focus on the fabrication of unique nanostructures such as nanowires for tactile sensors. Tactile sensors are being developed for neural interfacing applications such as restoring a sense of touch to upper limb amputees.

The Pacinian corpuscle (PC) is a mechanoreceptor that senses high-frequency vibrations in the dermis of skin. Our lab has developed a three-stage multi-physics computational model of the PC that can convert a vibratory stimulus into a neural response. Previous psychophysical experiments have shown that the perceived consonance/dissonance of vibrotactile chords is a frequency-dependent process. This project will involve performing simulations with our lab’s model using multi-frequency vibratory inputs to observe the neural responses that correlate with perceptions of consonance/dissonance.