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Continuous attractors of a class of neural networks with a large number of neurons

机译:具有大量神经元的一类神经网络的连续吸引子

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A class of simplified background neural networks model with a large number of neurons is proposed. Continuous attractors of the simplified model are studied in this paper. It contains: (1) When the background inputs are set to zero and the excitatory connections are in Gaussian shape, continuous attractors of the new network are obtained under some condition. (2) When the background inputs are nonzero and the excitatory connections are still in Gaussian shape, continuous attractors are achieved under some appropriately selected condition. (3) Discussions and examples are used to illustrate the theories developed.
机译:提出了一类具有大量神经元的简化背景神经网络模型。本文研究了简化模型的连续吸引子。它包含:(1)当背景输入设置为零且励磁连接为高斯形状时,在一定条件下可获得新网络的连续吸引子。 (2)当背景输入为非零且励磁连接仍为高斯形状时,在适当选择的条件下可获得连续的吸引子。 (3)讨论和实例用来说明所发展的理论。

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