Modeling and analysis of neural activity in visual cortex through the lens of machine learning
Reza Abbasi-Asl, Professor
Neuroscience
Applications for Spring 2025 are closed for this project.
Characterizing the the neural function in the brain and its relationship with connectivity is an eminent question of visual sensory processing. With the recent increase in the amount of the data collected from brain, tools based on machine learning principles play an essential role in understanding the brain function. The data collected from brain has a wide variety of modalities such as electrophysiology, calcium imaging, electron microscopy, and gene expression. In this project, we explore the functions in the brain via two distinct datasets: The first dataset is collected in collaboration with Allen Institute. It consists of visual responses from excitatory neurons within an 800X800 um region of primary visual cortex in mouse, spanning all visual layers from pia to white matter. This includes 750 2-photon and 35 3-photon calcium imaging planes spaced by ~16 um. The second dataset is electrophysiological recordings from visual cortex in macaque. This includes data from V1, V2, V4 and IT. Our goal is to examine the single-cell and population activity in visual cortex, build predictive models of spike rates and decoding models to reconstruct the visual experience from brain activity.
Role: The successful candidates will start by reading relevant papers and learning about the dataset. Then the candidate will use our recently-developed analysis and modeling pipeline to study the dataset in more depth. This includes assessing multiple metrics, including receptive field profile, direction and orientation selectivity indices, reliability of response, signal and noise correlations, and sparseness of response. In another phase, the candidate will work on the predictive models based on convolutional neural networks to predict spike rates. Then an inverse model will be developed to reconstruct the visual experience from the neural activity. Finally, the results will be documented and presented in form of a research publication. Note that the successful candidate will be working remotely on this project.
Qualifications: Candidate should have experience programming with Python. Familiarity with computational neuroscience and convolutional neural networks is desirable but not essential.
Hours: 12 or more hours
Off-Campus Research Site: remote
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