Using Autoencoder to Perform a Population Level Survey of Synaptic Connections in the Cerebral Cortex
The brain is the most complex organ in the body, composed of billions of neurons and trillions of connections between those neurons. Those connections are known as synapses and have been for many years the subject of intense study. What is less clear, however, is how synapses are organized at a population level throughout the brain. To start to address this, we developed a method that analyzes individual synapses using spatial and intensity metrics and scaled this approach to analyze hundreds of thousands of synapses concurrently. By doing so, we found that synapses fall into previously unknown subgroups. The proposed project, which is a collaboration between 2 groups (the Au lab in Pathology and Cell Biology and Menon lab in Neurology), will be to develop the robustness of our approach by working to normalize our findings across batches and conditions.
This project is eligible for a matching fund stipend from the Data Science Institute. This is not a guarantee of payment, and the total amount is subject to available funding.
Faculty Advisor
- Professors: Edmund Au Vilas Menon
- Center/Lab: Pathology & Cell Biology
- Location: VP&S 14-401
- The Au lab studies the early events of cortical circuit assembly, with a focus on inhibitory interneurons. We hypothesize that this avenue of research will illuminate our understanding of normal brain function as well as the causes of neuropsychiatric illness.
Project Timeline
- Earliest starting date: 10/15/2022
- End date: 6/1/2023
- Number of hours per week of research expected during Fall 2022: ~10
Candidate requirements
- Skill sets: R and/or python, with experience in deep learning packages like keras or pytorch
- Student eligibility:
freshman,sophomore, junior, senior, master’s - International students on F1 or J1 visa: NOT eligible
- Academic Credit Possible: No
- Additional comments: experience with microscopy and imaging/image analysis is a plus