ProjectsProject Details

Interpretable network analysis of the motor cortex during performance of a motor task

Project ID: 7525
Year: 2024
Student/s: Shira Lifshitz
Supervisor/s: Dr. Hadas Benisty

In this work, we aim to explore whether flavor representation builds in layers 2-3 of the motor cortex (M1) during a hand-reach task of a food pellet. Previous studies have shown that trial-by-trial outcomes are encoded by M1 neurons, with distinct populations reporting successful and failed attempts. Additionally, preliminary results suggest that flavors can be decoded from the same neuronal population. Here we analyze data collected from 2-photon calcium imaging experiments measuring the activity of cells in the motor cortex while mice perform a hand-reach tasks to retrieve different flavored pellets: grain, quinine, sucrose, and fake ones.

Our main objectives were to determine if flavor encoding emerges with exposure, how flavor presentation affects outcome representation, and the relationship between neuronal populations encoding outcome and flavor. The analysis utilizes a novel architecture for contextual feature selection called conditional Stochastic Gates (c-STG) to identify specific neurons encoding task-relevant information.

Poster for Interpretable network analysis of the motor cortex during performance of a motor task