Information Transmitting and Cognition with a Spiking Neural Network Model  

作　　者：Ying Du Jiaqi Liu Shihui Fu 杜莹;刘嘉琦;付士慧(School of Science,East China University of Science and Technology;School of Science,Zhengzhou University)

机构地区：[1]School of Science,East China University of Science and Technology,Shanghai 200237 [2]School of Science,Zhengzhou University,Zhengzhou 450001

出　　处：《Chinese Physics Letters》2018年第9期24-27,共4页中国物理快报（英文版）

基　　金：Supported by the National Natural Science Foundation of China under Grant Nos 11672107,11402294,11602224 and 11502062

摘　　要：Information encoding plays a crucial role in neuroscience. One of the fundamental questions in cognitive ncu- roscienee is how the brain encodes external stimuli in the sensory cortex. We use a network model based on the Hodgkin-Huxley type to study the information transmitting including its storage and recall. The model is inspired by psychological and neurobiological evidence on sequential memories. The model contains excitatory and inhibitory neurons with all-to-all connections whose architecture has two layers. A lower layer represents consecutive events during the information encoding process, and the upper layer is used to tag sequences of events represented in the lower layer. The spike-timing-dependent plasticity learning rule is used for sequential storage of excitatory connections between the modules. Computer simulations demonstrate that the synchronization status of multiple neurons is dependent on the network connectivity patterns, and also this model has good performance for different sequences of storage and recall.Information encoding plays a crucial role in neuroscience. One of the fundamental questions in cognitive ncu- roscienee is how the brain encodes external stimuli in the sensory cortex. We use a network model based on the Hodgkin-Huxley type to study the information transmitting including its storage and recall. The model is inspired by psychological and neurobiological evidence on sequential memories. The model contains excitatory and inhibitory neurons with all-to-all connections whose architecture has two layers. A lower layer represents consecutive events during the information encoding process, and the upper layer is used to tag sequences of events represented in the lower layer. The spike-timing-dependent plasticity learning rule is used for sequential storage of excitatory connections between the modules. Computer simulations demonstrate that the synchronization status of multiple neurons is dependent on the network connectivity patterns, and also this model has good performance for different sequences of storage and recall.

分 类 号：Q42[生物学—神经生物学]