论文标题
不同的特征值分布在复发性神经网络中编码相同的时间任务
Different eigenvalue distributions encode the same temporal tasks in recurrent neural networks
论文作者
论文摘要
不同的大脑区域，例如皮层，更具体地说，前额叶皮层，即使在早期感觉区域也会在其连接中出现很大的复发。 {已经提出了基于训练的网络的几种方法和方法来建模和描述这些区域。重要的是要了解模型背后的动态，因为它们用于构建有关大脑区域功能的不同假设并解释实验结果。这里的主要贡献是通过一组数值模拟进行的分类和解释对动力学的描述。这项研究阐明了针对相同任务获得的解决方案的多样性，并显示了线性训练的网络光谱与对应物的动力学之间的联系。研究了复发权重矩阵的特征值分布的模式，并与每个任务中的动力学正确相关。
Different brain areas, such as the cortex and, more specifically, the prefrontal cortex, show great recurrence in their connections, even in early sensory areas. {Several approaches and methods based on trained networks have been proposed to model and describe these regions. It is essential to understand the dynamics behind the models because they are used to build different hypotheses about the functioning of brain areas and to explain experimental results. The main contribution here is the description of the dynamics through the classification and interpretation carried out with a set of numerical simulations. This study sheds light on the multiplicity of solutions obtained for the same tasks and shows the link between the spectra of linearized trained networks and the dynamics of the counterparts. The patterns in the distribution of the eigenvalues of the recurrent weight matrix were studied and properly related to the dynamics in each task.
