Research Projects
Neural Representation for Temporal Dynamics in Auditory Cortex during Gap Detection: A Population Analysis
September 2024 - Present
Supervised by Professor Maneesh Sahani, Gatsby Computational Neuroscience Unit and Professor Jennifer Linden, UCL Ear Institute
Statistical Analysis on Naturalistic Long-Duration Mouse Foraging Data
September 2023 - Present
Supervised by Professor Maneesh Sahani and Dr Joaquin Rapela, Gatsby Computational Neuroscience Unit
Applied advanced statistical methods to characterize mice naturalistic behavior in continual experiments lasting several days. Used linear dynamical systems to infer mice kinematics from position observations, hidden Markov models to infer discrete states from kinematics and other measurements, and linear regression to relate state occupancy to behavioral events. Found that mice kinematics were substantially more active in the first experimental day, repeatable sequences of behavioral states across sessions and mice, and that foraging events were predictable from the inferred states. Assisting with monitoring mice foraging experiments. [Code]
Short-Term Synaptic Plasticity in Cerebellar Information Processing
October 2023 - March 2024
Supervised by Professor R. Angus Silver, Silver Lab
Conducted a 7500-word literature review on the mechanism and functional roles of short-term synaptic plasticity in the cerebellar cortical circuit. [PDF]
Mouse V1 Neuron Patterns in Different Luminance Levels
May 2022 - October 2023
Supervised by Professor Kenneth Harris and Dr Maxwell Shinn, Harris/Carandini Lab
Performed dimensionality analysis on 2-photon calcium imaging neural data to explore the possible factors of mouse V1 neuron pattern distinction in different light conditions. Proposed a dynamical systems model of mice cortical neural circuit to predict high-dimensional neuronal activities during locomotion. The influence of ambient light on the cor- relation between locomotion and neural activities can be simulated by altering the dynamics of the modelled cortical neural network.