Recent experimental advances have made it possible to record 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 circuits dynamics across multiple brain areas via data-driven modeling. In this project, we will work with the whole-brain activity datasets of larval zebrafish, where essentially ALL the neurons in the brain are being recorded thanks to the transparency and small size of the animal. We will apply and adapt latest machine learning approaches to extract key biological information such as functional cell types and connectivity between neurons. Based on these results, we will build multi-scale models of neural circuits that link dynamics from single neurons to brain areas and to the behaviors of the animal.
The applicants will (i) apply and adapt machine learning and statistical analysis codes to analyze the data, and (ii) build and simulate mathematical models for the dynamics of neural circuits. The applicants will also interpret the results in terms of biological insights and summarize the project activity in written reports and oral presentations.
Requirements are solid skills of basic math (i.e. calculus, linear algebra, and basic probability), proficiency in some programming language, ability to learn quickly, and strong motivation and responsibility. Knowledge of neuroscience is favorable but not required.
The minimum time commitment is 8 hours per week.
(i) Learn practical skills of modeling and analyzing complex data.
(ii) Understand the goals and basic models of neural circuits in computational neuroscience.