对不平衡兴奋/抑制具有鲁棒性的海马放电神经网络的模式分离
Pattern Separation in a Spiking Neural Network of Hippocampus Robust to Imbalanced Excitation/Inhibition
摘要：
齿状回的有效模式分离在海马的信息存储中起着重要作用。目前的知识的结构和功能的海马，内嗅皮层和齿状回，在图案分离被纳入我们的模型。一个三层前馈尖峰神经网络的灵感来自啮齿动物海马，配备了简化的突触和分子机制。本研究的目的是使一个尖峰神经网络能够在由不同水平的刺激或网络损伤引起的兴奋/抑制比不平衡的情况下进行模式分离。本工作提出了一个新的理论，在齿状回神经元的突触和连接的损害，导致不平衡的兴奋-抑制活动的神经元的鲁棒性的细胞机制。这种尖峰神经网络使用简化的分子和细胞假设机制，并证明了在不同水平的刺激下信息的有效存储，并且可以在认知机器人中实现。
英文摘要：
Efficient pattern separation in dentate gyrus plays an important role in storing information in the hippocampus. Current knowledge of the structure and function of the hippocampus, entorhinal cortex and dentate gyrus, in pattern separation are incorporated in our model. A three-layer feedforward spiking neural network inspired by the rodent hippocampus an equipped with simplified synaptic and molecular mechanisms is developed. The aim of the study is to make an spiking neural network capable of pattern separation in imbalanced excitation/inhibition ratios caused by different levels of stimulations or network damage. This work present a novel theory on the cellular mechanisms of robustness to damges to synapses and connectivity of neurons in dentate gyrus that results in imbalanced excitation-inhibition activity of neurons. This spiking neural network uses simplified molecular and cellular hypothetical mechanisms and demonstrates efficient storing of information in different levels of stimulation and can be implemented in cognitive robotics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
