Quantifying exploratory behavior in the human behavioral pattern monitor using automated video tracking

Exploration is an essential adaptive function across many species. Distinct exploratory behavior patterns have been observed across several neuropsychiatric conditions, such as schizophrenia and bipolar disorder, leading to the refinement of animal models of such neuropsychiatric diseases. The development of methodology to properly quantify exploratory behavior is critical for studying neuropsychiatric conditions. The human behavioral pattern monitor is a cross-species translatable open field test designed to measure exploratory behavior in clinical populations. Here we introduce the first example of how advances in machine learning techniques, can be used to bolster the collection of exploratory behavior in a laboratory setting.