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 were found to have an increased aversive response to vapor alone compared with those that only received air. Using SLEAP, we demonstrate that the x-y coordinate data can be further classified into behavior using dimensionality-reducing clustering methods. This suggests that the linear track can be used to examine nicotine preference for the cockroach, and SLEAP can provide a fast, efficient way to analyze animal behavior. Moreover, this educational program is available for free for students to learn a complex machine learning algorithm without expensive hardware to study animal behavior.