Modeling the statistical structures of brain-wide activity using recurrent neural circuits
Project Description
Recent experimental advances have made it possible to record the activity of a large number of neurons (the information processing cells) simultaneously in behaving animals (e.g., https://www.youtube.com/watch?v=eKkaYDTOauQ). Such data reveal unprecedented complexity and promise unique opportunities for understanding neural circuit dynamics across multiple brain areas via data-driven modeling.
In this project, we focus on recently discovered statistical spatial-temporal
structures in brain-wide neural activity in zebrafish and the mouse cortex. Several phenomenological models have been proposed to explain the scale-invariance seen in the data’s covariance eigenvalue distribution (Morrell et al. 2020). However, a mechanistic model based on the complex network of connections between neurons and brain areas is still lacking. We aim to close this gap by combining numerical analysis of experimental data, simulations, and mathematical analysis of the resulting model.
In this project, we focus on recently discovered statistical spatial-temporal
structures in brain-wide neural activity in zebrafish and the mouse cortex. Several phenomenological models have been proposed to explain the scale-invariance seen in the data’s covariance eigenvalue distribution (Morrell et al. 2020). However, a mechanistic model based on the complex network of connections between neurons and brain areas is still lacking. We aim to close this gap by combining numerical analysis of experimental data, simulations, and mathematical analysis of the resulting model.
Supervisor
HU Yu
Quota
2
Course type
UROP1100
Applicant's Roles
(i) Apply and adapt machine learning and statistical analysis codes in the literature to experimental data.
(ii) Build and simulate the data-driven recurrent circuit models. The applicants will also interpret the results regarding biological insights and summarize the project activity in written reports and oral presentations.
Requirements are fundamental solid math skills (i.e., high scores in calculus, linear algebra), proficiency in any programming language (e.g., Python, Matlab, C++), ability to learn quickly, and strong motivation and responsibility. Basic probability or statistics are required for this project. Knowledge of neuroscience is favorable but not required.
The minimum time commitment is 5 hours per week.
(ii) Build and simulate the data-driven recurrent circuit models. The applicants will also interpret the results regarding biological insights and summarize the project activity in written reports and oral presentations.
Requirements are fundamental solid math skills (i.e., high scores in calculus, linear algebra), proficiency in any programming language (e.g., Python, Matlab, C++), ability to learn quickly, and strong motivation and responsibility. Basic probability or statistics are required for this project. Knowledge of neuroscience is favorable but not required.
The minimum time commitment is 5 hours per week.
Applicant's Learning Objectives
(i) Learn practical skills in mathematical modeling and data analysis
(ii) Understand the goals and basic models of neural circuits in computational neuroscience.
(ii) Understand the goals and basic models of neural circuits in computational neuroscience.
Complexity of the project
Challenging