Quantitative descriptions of animal behavior are essential to understand the underlying neural substrates. Fear conditioning in rodents is a widely used assay that allows neuroscientists to probe the neural mechanisms of memory. To date, quantification of freezing behavior, a proxy for fear memory strength, is usually performed by hand or with expensive and inflexible commercial software. To overcome these barriers, we developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a MATLAB-based application containing six independent modules. The Experiment Module runs fear conditioning experiments using an Arduino-based design that interfaces with commercial hardware. The Analysis Module classifies freezing and analyzes spatiotemporal behavioral statistics in user-defined ways. The remaining modules can develop custom classifiers. Of note, the Inter-Rater Module establishes reliable ground-truth human labels, making it broadly useful for scientists developing classifiers with any application. BehaviorDEPOT provides a simple, flexible, automated pipeline to move from pose tracking to reliably quantifying task-relevant behaviors.
Neural mechanisms of memory: the case of imprinting
