Project Description
The research project seeks to explore the complex underpinnings of social cognition in the brain, particularly the lateral entorhinal cortex (EC) role in social recognition memory. In our experiments, we collect behavioral videos of subject animals performing various social memory tasks while neural activity is simultaneously recorded from hundreds of neurons in superficial layers in the EC. With this approach, we seek to unravel how social novelty/familiarity and identity are represented in EC neural population. Given the large volume of data there is a critical need for developing analytical tools that allow for a more efficient and accurate interpretation of the data. We plan to develop and implement in our lab machine learning techniques for tracking the position and behavior of the subject mouse and for gaining insights into the social representations in the EC.
Tasks and Responsibilites
The student task and responsibilities will include: Data Collection and Management: Participate in collecting and preprocessing behavioral and neural activity data. This could involve setting up and monitoring behavioral experiments, as well as ensuring that the collected data is properly annotated, cleaned, and stored for further analysis. Algorithm Development and Implementation: Assist in the design, testing, and implementation of machine learning algorithms to track the subject mouse's behavior and analyze neural activity. This would require understanding machine learning principles and programming skills, particularly in languages commonly used in data analysis, such as Python or R. Data Analysis: Apply the developed machine learning tools to the collected data to extract meaningful insights about social representations in the EC. Research Support: Participate in literature reviews to stay updated on the latest developments in the field. Communication and Collaboration: Collaborate effectively with other team members, participate in lab meetings, and communicate findings clearly. Documentation: Maintain accurate and detailed records of work. Document the performance and accuracy of the developed algorithms, note any challenges encountered and how they were resolved, and describe the results of your analyses.
Desired Qualifications
None Listed.