文献类型：期刊文献

中文题名：Information Transmitting and Cognition with a Spiking Neural Network Model

英文题名：Information Transmitting and Cognition with a Spiking Neural Network Model

作者：Du, Ying[1];Liu, Jiaqi[1];Fue, Shihui[2]

机构：[1]East China Univ Sci & Technol, Sch Sci, Shanghai 200237, Peoples R China;[2]Zhengzhou Univ, Sch Sci, Zhengzhou 450001, Henan, Peoples R China

年份：2018

卷号：35

期号：9

中文期刊名：Chinese Physics Letters

外文期刊名：CHINESE PHYSICS LETTERS

收录：CSTPCD;;EI(收录号：20220711653098);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000448134000006)】；CSCD:【CSCD2017_2018】；

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

语种：英文

外文关键词：Encoding (symbols) - Neural networks - Signal encoding

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 neuroscience 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.