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

2020/06/11 by Donghan Liu, Benjamin C. M. Fung, Liu, Donghan +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · #Applications (stat.AP) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Bioinformatics #Neurons and Cognition (q-bio.NC) #Neuroscience and Neuropharmacology Research

paper · pdf · doi:10.48550/arxiv.2006.06862

openalex publication_date 2020/06/11 · openalex created_date 2020/06/19 · openalex updated_date 2026/07/28

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.

Citations

• The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat
• The Probable Error of a Mean
• Bidirectional recurrent neural networks
• An improvement of the convergence proof of the ADAM-Optimizer
• Deep Learning with Long Short-Term Memory for Time Series Prediction
• PyTorch: An Imperative Style, High-Performance Deep Learning Library

Related

• Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review
• Early Detection of Mental Stress Using Advanced Neuroimaging and Artificial Intelligence
• Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders
• Decoding the Stressed Brain with Geometric Machine Learning
• Brain2Vec: A Deep Learning Framework for EEG-Based Stress Detection Using CNN-LSTM-Attention