Data Augmentation for Automated Adaptive Rodent Training

TL;DR

This paper introduces a data-driven approach utilizing data augmentation and a novel similarity metric to enhance automated training protocols for lab rodents, aiming to reduce labor and improve training efficiency.

Contribution

It presents a new data augmentation method and a similarity metric for modeling rodent behavior, advancing automated and adaptive training systems.

Findings

Artificial rodent models successfully mimic real behavior.

The similarity metric effectively measures behavioral resemblance.

Automated training protocols show potential for efficiency improvements.

Abstract

Fully optimized automation of behavioral training protocols for lab animals like rodents has long been a coveted goal for researchers. It is an otherwise labor-intensive and time-consuming process that demands close interaction between the animal and the researcher. In this work, we used a data-driven approach to optimize the way rodents are trained in labs. In pursuit of our goal, we looked at data augmentation, a technique that scales well in data-poor environments. Using data augmentation, we built several artificial rodent models, which in turn would be used to build an efficient and automatic trainer. Then we developed a novel similarity metric based on the action probability distribution to measure the behavioral resemblance of our models to that of real rodents.