The effect of the dimensionality of interconnections on the storage capacity of a threshold controlled neural network

A. Hartstein
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Abstract

The author investigates the effect of the dimensionality of the interconnections in a Hopfield-type network on the storage capacity of the network. The analysis is performed for 1D, 2D, 3D and 4D interconnection geometries. The capacity was found to be independent of the dimensionality of the interconnections and to depend only on the total number of interconnections available in a given network. In addition, no evidence of any instabilities was observed, in contrast to physical systems of reduced dimensionality.<>
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互连维数对阈值控制神经网络存储容量的影响
作者研究了hopfield型网络中互连的维数对网络存储容量的影响。对1D、2D、3D和4D互连几何形状进行了分析。研究发现,该容量与互连的维度无关,仅取决于给定网络中可用互连的总数。此外,与降维的物理系统相比,没有观察到任何不稳定的证据。
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Control of a robotic manipulating arm by a neural network simulation of the human cerebral and cerebellar cortical processes Neural network training using homotopy continuation methods A learning scheme of neural networks which improves accuracy and speed of convergence using redundant and diversified network structures The abilities of neural networks to abstract and to use abstractions Backpropagation based on the logarithmic error function and elimination of local minima
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