论文标题
使用海马活动的基于深度学习的压力决定者进行小鼠精神病分析
Deep Learning-based Stress Determinator for Mouse Psychiatric Analysis using Hippocampus Activity
论文作者
论文摘要
神经科学家研究的目的是解码神经元以从传播中提取信息并将其雇用到其他用途中。因此，神经科学领域目前正在利用传统方法，因此我们将最先进的深度学习技术与神经元解码理论结合在一起，以讨论其成就的潜力。此外，还统计检查了与海马中神经元活性有关的应力水平。实验表明，我们最新的基于深度学习的压力决定者就其模型预测准确性提供了良好的性能，此外，还有有力的证据反对在不同环境下等效小鼠应力水平的证据。
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.
