CAJ | 학술논문

背景:海马是一个与记忆相关的重要脑区,在联想记忆中起着关键的作用.海马CA3区是形成联想记忆最重要的脑区,它从功能上被分为自联想记忆和异联想记忆.理解记忆的形成和读取需要建立海马功能的详细模型.目的:根据海马CA3区的结构,建立一个海马CA3区功能的详细模型.方法:该模型是一个三层的类Hopfield神经网络,由280个lzhikevich人工神经元组成.以lzhikevich神经元模型做为节点,用Hebbian学习法则调节权值.存给模型的输入加入白噪声的情况下用MATLAB对其仿真.在仿真中,记忆用同步放电序列表示.结果与结论:仿真结果表明,模型的第三层具有异联想记忆功能;模型的第一层和第二层能完成自联想记忆功能.该模型很好的重现了海马CA3区三个子区域的记忆功能.但真实生物神经网络的功能极其动力学特性不可能通过建立一个简单的模型完全理解.该模型由280个神绎元组成,远远小于实际神经元的数量,这也说明所建模型的特性与CA3实际生物神经网络的特性之间还存在很大差距.
배경:해마시일개여기억상관적중요뇌구,재련상기억중기착관건적작용.해마CA3구시형성련상기억최중요적뇌구,타종공능상피분위자련상기억화이련상기억.리해기억적형성화독취수요건립해마공능적상세모형.목적:근거해마CA3구적결구,건립일개해마CA3구공능적상세모형.방법:해모형시일개삼층적류Hopfield신경망락,유280개lzhikevich인공신경원조성.이lzhikevich신경원모형주위절점,용Hebbian학습법칙조절권치.존급모형적수입가입백조성적정황하용MATLAB대기방진.재방진중,기억용동보방전서렬표시.결과여결론:방진결과표명,모형적제삼층구유이련상기억공능;모형적제일층화제이층능완성자련상기억공능.해모형흔호적중현료해마CA3구삼개자구역적기억공능.단진실생물신경망락적공능겁기동역학특성불가능통과건립일개간단적모형완전리해.해모형유280개신역원조성,원원소우실제신경원적수량,저야설명소건모형적특성여CA3실제생물신경망락적특성지간환존재흔대차거.
BACKGROUND: Hippocampus is one of an important brain areas related with memory,and plays a critical role in associative memory function.Hippocampal CA3 is one of the most important regions to form associative memory.CA3 is functionally divided into autoassociative and heteroassociative memories,and memory formation and retrieval require the development of detailed models of hippocampal function.OBJECTIVE: To establish a detailed model of hippocampal CA3 function according to CA3 structure.METHODS: The model was a three-layered Hopfield-like neural network and was constituted by 280 Izhikevich artificial neurons,and is modulated by Hebbian rules.The model was simulated using MATLAB under the condition of adding the Gaussian white noise to its input.In the simulation,memories were represented by synchronous firing sequences.RESULTS AND CONCLUSION: The simulating results show that the third layer of model had heteroassociative memory function;the first and the second layer of the model could implement autoassociative memory.The model implemented well the memory functions of three subregions of hippecampal CA3.But it is impossible to understand the functions and dynamics of a real biological neural network by constructing a simple model.The model proposed has 280 neurons,which are far less than the real number of neurons.It suggests that there is a big gap between the properties of the model and a real biological neural network of CA3.