免疫模型的动态神经表征及其在数据分类中的应用

Shahidul Pramanik, Robert Kozma, Dipankar Dasgupta
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引用次数: 9

摘要

生发中心(germinal center, GC)是一种功能模块,位于动物体内淋巴网络的重要位置,在免疫反应中发挥重要作用。它的形成和功能可以用神经网络技术从计算的角度来解释和分析。本文的目的是根据神经网络的结构和动态对GC组织进行建模。三个Hopfield网络的级联以及Hebbian学习原理用于数据分类问题,其中连接矩阵决定了局部和全局反馈以及网络中从一种状态到另一种状态的传播。
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Dynamical neuro-representation of an immune model and its application for data classification
The germinal center (GC) is a functional module positioned in strategic locations of the lymphatic network in the animal body, which is known to play an important role in the immune response. Its formation and function can be explained and analyzed from a computational point of view using neural network technology. The objective of the paper is to model GC organization in terms of NN architecture and dynamics. A cascade of three Hopfield networks along with the Hebbian learning principle is used in a data classification problem where the connection matrices determine the local and global feedback as well as the propagation from one state to another in the network.
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