Deep Learning-based Stress Determinator for Mouse Psychiatric Analysis using Hippocampus Activity
This work addresses stress analysis in mice for neuroscience research, but it appears incremental as it combines existing deep learning methods with neuron decoding theory without clear novel breakthroughs.
The paper tackled the problem of decoding neuron activity in the hippocampus to determine stress levels in mice, using deep learning techniques, and reported good performance in model prediction accuracy with strong evidence of stress level differences across environments.
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.