Deep Learning-based Stress Determinator for Mouse Psychiatric Analysis using Hippocampus Activity

TL;DR

This paper presents a deep learning approach to decode hippocampal neuron activity for stress level determination in mice, demonstrating high accuracy and environmental stress differences.

Contribution

It introduces a novel deep learning-based method for decoding hippocampal neuron signals to assess stress levels in mice, combining neuroscience and AI techniques.

Findings

Deep learning model achieves high prediction accuracy.

Strong evidence of stress level differences across environments.

Neuroscience and AI integration enhances stress analysis.

Abstract

Decoding neurons to extract information from transmission and employ them into other use is the goal of neuroscientists' study. Due to that the field of neuroscience is utilizing the traditional methods presently, we hence combine the state-of-the-art deep learning techniques with the theory of neuron decoding to discuss its potential of accomplishment. Besides, the stress level that is related to neuron activity in hippocampus is statistically examined as well. The experiments suggest that our state-of-the-art deep learning-based stress determinator provides good performance with respect to its model prediction accuracy and additionally, there is strong evidence against equivalence of mouse stress level under diverse environments.