Low-Cost Approaches in Neuroscience to Teach Machine Learning Using a Cockroach Model
In an effort to increase access to neuroscience education in underserved communities, we created an educational program that utilizes a simple task to measure place preference of the cockroach ( Gromphadorhina portentosa ) and the open-source free software, SLEAP Estimates Animal Poses (SLEAP) to quantify behavior. Cockroaches ( n = 18) were trained to explore a linear track for 2 min while exposed to either air, vapor, or vapor with nicotine from a port on one side of the linear track over 14 d. The time the animal took to reach the port was measured, along with distance traveled, time spent in each zone, and velocity. As characterizing behavior is challenging and inaccessible for nonexperts new to behavioral research, we created an educational program using the machine learning algorithm, SLEAP, and cloud-based (i.e., Google Colab) low-cost platforms for data analysis. We found that SLEAP was within a 0.5% margin of error when compared with manually scoring the data. Cockroaches wer
