Overview of differences computational science
PiVR Real-Time Behavioral Mode Classification
Intro to PiVR
PiVR is a virtual reality program for small animals. It has a robust closed-loop tracking system and post-hoc data analysis tools. It uses Raspberry Pi technology combined with 3D printing, with a tracking algorithm and machine interface written in Python (Tadres and Louis, 2020). It can be used to create virtual odor realities for small animals expressing a red-light gated ion channel (Chrimson) in a genetically targeted olfactory sensory neuron. It is used by the Louis Lab for Drosophila larvae virtual odor experiments.
The one thing PiVR is lacking is the ability to identify larval behavioral modes in real time. This limitation prevents the implementation of open-loop optogenetic stimulation paradigms, in which the Drosophila larva is stimulated during a specific behavioral mode. This real-time mode classification would help advance many projects within the lab, as well as be useful to others using PiVR. The project aims to update the software to include a robust real-time mode classification algorithm. Doing this will require looking at a large pool of existing behavioral data from PiVR to determine parameters and thresholds to build the algorithm with accuracy. It will also require researching methods implemented in other tracking softwares which have real-time mode classification algorithms.
Louis Lab Coding Team
Louis Lab Coding Team
The Louis Lab coding team is a subset of members in the lab, mostly consisting of undergraduates. We are currently working on creating a streamlined post-hoc data analysis pipeline for PiVR data, to be integrated by the whole lab. We are achieving this in 3 steps: 1) a module for filtering/cleaning raw data points, 2) a module for behavioral classifications, and 3) a module for statistical analysis using the results of 1) and 2). We are on step 2, creating a module for behavioral classifications. Ideally, once the coding team has successfully created algorithms that can robustly identify behavioral modes post-hoc, these algorithms can be used as a reference for doing so in real-time.
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