Clustering exploratory activity in an elevated plus-maze with neural networks

摘要

An unsupervised neural network that uses Hebbian and anti-Hebbian learning (HAHL model) was implemented to determine levels of anxiety of rats by clustering these animals based on their behavior in the elevated plus maze. The HAHL model showed capacity to generalize, being trained with only 1.6 of the total of patterns, and was able to identify fine details during the clustering, i.e. sensibility to context and scale. Analysis of the results showed that the proposed model was able to coherently cluster the animals in different exploratory activities, and consequently, in different levels of anxiety.