Available: 09/01/19, Expires: 08/31/20
This project will focus on understanding the biological codes involved in transcriptional regulation as well as translational regulation. Using computational modeling, high-throughput RNA sequencing data and MS proteomic data, the goal is to unravel the biological mechanisms to control transcription and the relationships between transcript production and protein production.
Available: 09/30/19, Expires: 09/01/20
The small object next to a computer keyboard is most likely to be a computer mouse, not an elephant. In the real world, objects often co-vary with other objects and particular environments. In the project, we have two primary goals. The first is to develop an understanding for the brain’s ability to process and comprehend visual input based on surrounding context through a biological and quantified explanation. In particular, there are eight main experiments that we have tested and plan to test on both humans and machine learning algorithms with regards to the role of context in object recognition, including (a) varying amounts of context shown, (b) backward masking, (c) blurred context, (d) blurred object, (e) scrambling contextual information, (f) guessing objects from contextual information, (g) finding the effects of time on recognition, and (h) swapping context. Each of these experiments are run in-lab using eyetrackers and online through Amazon’s Mechanical Turk. Second, we further hope to utilize this understanding towards building a robust computational model, which would contribute to enabling current technology to identify visual cues more meticulously. A paper related to our project: https://arxiv.org/abs/1902.00163
NO background in computer science or neuroscience is required.