Computational Learning in Behavioral Neuropharmacology

Abstract

Behavioral neuropharmacology is an area of neuroscience, which is responsible for the study of behavioral modifications through the administration of substances, treatments or experimental manipulations. Particularly to determine the effect on behavior, this area uses classic statistical techniques for comparing measures of central tendency; the analysis of variables is mostly carried out in a univariate manner, where the interpretation of the results obtained is often limited. There are other areas that also provide tools for data analysis, such as computational learning, through prediction models we can determine the characteristic behavioral patterns of each treatment administered. In the present study, computational learning data analysis techniques were used, specifically, supervised machine learning applied to a behavioral neuropharmacology experiment, where 3 doses of allopregnanolone (0.5, 1, and 2 mg) were evaluated in maze tests. Raised arms and motor activity test. We identified with classical statistical methods that the 2 mg dose of allopregananolone has an anxiolytic-type effect, similar to that exerted by the reference drug diazepam. Additionally, with computational learning methods, we can identify the characteristic patterns of each treatment based on the combination of the variables of both behavioral tests, likewise, we demonstrate with mathematical support the most important variables for the identification of anxioselective effects. In conclusion, computational learning methods promote enrichment in the results of neuropharmacology reflected in the characteristic patterns that are modified by the administration of different drugs, and provide foundations to support the importance of the most relevant variables of behavioral tests.