I used Matlab to analyze data collected from a primate task in which the primate reaches with two arms simultaneously to different targets. This data included neural recordings of population and single neuron firing. In addition to generating basic figures, such as raster plots and tuning curves, I also quantified primate movement data and correlated them to neural activities. I also worked on collecting behavioral data for visual prosthesis in rats. I trained the rats to incorporate an implanted infrared sensor as a novel source of sensory information for behavioral tasks and recorded the underlying neural activity.
Relation to Grand Challenge:
Through this project, I learned various techniques in analyzing electrode recording of neural activities and how these data relate to movement and behavior. These quantitative skillset would allow me to tease out additional properties and functions of the brain using animal models in the future and possibly provide reliable models for certain neurological diseases.
Supervisor: Dr. Miguel Nicolelis
Start Date : 01/20/2017
End Date: 05/20/2018
NIH Functional and Applied Biomechanics Section
I performed analysis on motion capture (biomechanics), electromyography (EMG), and electroencephalography (EEG) data collected in a cohort of children with unilateral cerebral palsy and a cohort of age- and gender-matched children with typical development. The primary objective was to analyze EEG and EMG data of walking under several different conditions to identify the primary muscle synergies utilized by each individual and to determine if cortical (EEG) activity is correlated with the EMG synergies. I then compared these outcome measures across groups to identify differences in muscle and brain activity to assess the effects of CP on muscle synergy deployment during walking. The analysis was be performed in MATLAB utilizing custom scripts and the open-source EEGLAB toolbox. In addition to analyzing this previously collected data set, I also assisted in collection of motion capture, EMG, and EEG data in the Functional and Applied Biomechanics Center under a different experimental protocol. Through this internship, I gained experience and understanding of the data collection methods, as well as clinical exposure to evaluation of children with movement disorders.
Relation to Grand Challenge:
This project allowed me to learn to decipher neural activity human subjects. These skillsets will be useful for me to investigate brain function and neurological disorders in human subjects.
Supervisor: Dr. Diane Damiano, Dr. Thomas Bulea
Start Date: 06/18/2018
End Date: 08/15/2018
This project was motivated by the general interest in the higher-level cognitive processes that drive saccade target selection. More specifically, this study aims to investigate whether the human brain can flexibly generalize learned stimuli-reward associations across space. While spatial generalization is trivial in machine learning due to convolutional layers, this structure is biologically implausible. Little has been done to investigate whether the flexible learning of stimuli-reward associations is spatially invariant and what mechanisms may support it. Better understanding and characterization of these mechanisms may provide further insight into working memory deficits, which is common in various psychiatric disorders, such as schizophrenia. Psychophysics data was collected in human subjects, which were then fitted using reinforcement learning models. An artificial neural network was then used to provide a more mechanistic model to describe the observed phenomenon.
Supervisor: Dr. Marc Sommer
Start Date: 8/20/2018
End Date: 5/20/2020