Current Computational Neuroscience Fellows
Junzi Dong, Biomedical Engineering
Mentors: Kamal Sen & Steve Colburn
Project Title: Developing a Computational Model of Sound Source Segregation to Aid Listening in Multi-Source Environments
Junzi Dong is working in Dr. Kamal Sen’s natural sounds and neural coding laboratory and in Dr. Steve Colburn’s binaural hearing laboratory. She is using modeling methods to understand the functional role of cortical auditory neurons in spatial hearing. Her work attempts to solve a piece of the cocktail party problem—our little-understood ability to selectively listen to a single sound source in noisy environments. The work currently involves building a computational network model of connections between auditory pathways based on neuro-physiological data recorded from zebra finch song birds. In the future, Junzi plans to apply the model to improve the function of hearing assistive devices in noisy environments by conducting psychoacoustic experiments in humans. The current work also opens up future avenues for better understanding of neural processes involved in selective listening.
Nicholas James, Graduate Program for Neuroscience, Computational Neuroscience specialization
Mentors: Nancy Kopell & Xue Han
Project Title: Cholinergic Enhancement of Rhythms in the Auditory Cortical Circuit
Nicholas James is a student in the Graduate Program for Neuroscience. Working with Dr. Nancy Kopell and Dr. Xue Han, Nick’s project is aimed at investigating the role of chlolinergic modulation of auditory attention and learning. He plans to use optogenetic stimulation in conjunction with in vivo recording in mouse auditory cortex to inform computational models of rhythmic network interaction. By doing so, he hopes to better characterize cortical plasticity and learning.
Naghmeh Mostofi, Brain, Behavior & Cognition – Psychology Department
Mentors: Michele Rucci; Eric Schwartz
Project Title: Computational Functions of Microsaccades
Naghmeh Mostofi is currently working in Dr. Michele Rucci’s Active Perception Laboratory, in the Department of Psychology. Her research focuses on understanding the visual functions of microsaccades, the small and fast relocation of gaze that humans and other species perform while attempting to maintain visual fixation, to visual perception. In this project she will record the eye movements of human observers in experiments of visual detection and discrimination by a Dual Purkinje Image eye tracker. She will also develop computational models of neurons in the retina, the lateral geniculate nucleus, and the primary visual cortex to simulate ongoing neuronal activity. This approach will bridge computational and perceptual levels to study perceptual responses and changes in the patterns of neural activity relative to the occurrence of microsaccades.
Kunjan Rana, Biomedical Engineering
Mentors: Eric Kolaczyk & Lucia Vaina
Project Title: Time- and frequency-defined dynamic cortical networks underlying hand movement and its recovery in stroke patients
Kunjan Rana is a PhD student working in Dr. Lucia Vaina’s Brain and Vision Research Laboratory, in the Department of Biomedical Engineering. His research focuses on modeling hand movements and characterizing motor networks underlying fine and coarse hand and finger movements. Utilizing high frequency sampling of magnetoencephalography (MEG), a technique that measures magnetic dipoles originating from pyramidal neurons in the cortex, he will uncover the dynamic causal functional networks in the time and frequency domain that characterize these underlying behaviors. In addition, he is designing and implementing statistical tools for comparing the dynamic networks in MEG.
Lena Sherakov, Graduate Program for Neuroscience, Computational Neuroscience specialization
Mentors: Dan Bullock & Max Versace
Project Title: A Computational Framework for Temporally Structured Events
To analyze continuous sensory input in real-time, one needs to go beyond the standard supervised and unsupervised machine learning classifiers, which only categorizes events as static slices through time. The primary aim of this project is to integrate biologically-inspired models of sequence processing and timing with real-world applications to create a computational framework for temporally structured events. A secondary aim is to develop this model on a software platform that allows one to build large-scale integrative models of brain systems exploiting high-performance computing resources.
Jason Sherfey, Graduate Program for Neuroscience, Computational Neuroscience specialization
Mentors: Nancy Kopell & Bernat Kocsis
Project Title: The Transmission of Beta Frequency Rhythms Through Neuronal Networks in Health and Disease
Jason Sherfey graduated from Vanderbilt University with a B.E. in Biomedical Engineering. He is a member of the computational neuroscience specialization at GPN and has extensive experience in neuroimaging, signal processing, and biomedical instrumentation from Vanderbilt, UCSD, and the pharmaceutical industry. He has broad interests in neuroscience, mostly involving the dynamics of neural networks. His is currently combining computational and experimental techniques to study the biological mechanisms that underlie gamma-frequency oscillations in prefrontal cortical networks and their pathological alteration in schizophrenia. His long term goal is to contribute to the understanding and treatment of pathological dynamics in neurological and mental disorders.
Dante Smith, Graduate Program for Neuroscience, Computational Neuroscience specialization
Mentors: Frank Guenther & Cara Stepp
Project Title: Vibrotactile Stimulation for Brain-Computer-Interfaces
Dante Smith is a PhD student in the Neural Prosthesis Lab and the Sensorimotor Rehabilitation Engineering Lab. These labs investigate brain-computer interfaces and other technologies for communication by individuals with locked-in syndrome. He plans to use vibrotactile stimulation on the fingers to create steady-state somatosensory evoked potentials in the somatosensory cortex of the brain. By doing so, he hopes to create a method of controlling a brain-computer interface that does not require any motor control by the user.
Austin Soplata, Graduate Program for Neuroscience, Computational Neuroscience specialization
Mentors: Nancy Kopell & Emery Brown
Project Title: The role of aging in dynamics of loss of consciousness and anesthesia
I will be doing my thesis work with Drs. Nancy Kopell of BU and Emery Brown of MIT and Harvard Medical School. The work relates to the neurophysiology of anesthesia and will be part of a Program Project, for which a proposal was submitted in May. This Program Project concerns the differences between younger and aging populations in the use of anesthesia. There will be a component on young populations, one on aging populations, and at least one on an animal model: rats; for the human subjects, the data will come from EEG and will include field potentials. There will also be work using structural MRI and DTI, helping to localize the effects. The fourth component, to which I will contribute especially, is a modeling component, which will help tie together the results from the previous components. For each of these, three anesthetics will be considered: propofol, dexmedetomidine, and ketamine, which are known to produce different effects. To the best of our knowledge, this is the first and only research group looking at mechanistic underpinnings of anesthesia, and the current work is focused on the completely virgin territory of anesthesia and aging. There is preliminary data showing that elderly subjects have very different and sometimes toxic responses, for reasons that are so far unknown.
Spencer Torene, Graduate Program for Neuroscience, Computational Neuroscience specialization
Mentors: Frank Guenther & Jason Ritt
Project Title: Laminar Activity and Rhythms Detected Through ECoG
Spencer Torene is a PhD student in the Graduate Program for Neuroscience, working in Dr. Frank Guenther’s neural prosthesis lab and in Dr. Jason Ritt’s sensory neuroscience and neurotechnology lab. He is using electrocorticography (ECoG) and local field potential data from somatosensory and motor cortex in mice to analyze sensorimotor rhythms that can be used as a model to improve brain-computer interface algorithms using ECoG in human patients.