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Revealing Neural Circuit Topography in Multi-Color
Published on: November 14, 2011
Computing Topological Invariants of Deep Neural Networks
Xiujun Zhang1, Nazeran Idrees2, Salma Kanwal3
1School of Computer Science, Chengdu University, Chengdu, China.
Abstract:
A deep neural network has multiple layers to learn more complex patterns and is built to simulate the activity of the human brain. Currently, it provides the best solutions to many problems in image recognition, speech recognition, and natural language processing. The present study deals with the topological properties of deep neural networks. The topological index is a numeric quantity associated to the connectivity of the network and is correlated to the efficiency and accuracy of the output of the network. Different degree-related topological indices such as Zagreb index, Randic index, atom-bond connectivity index, geometric-arithmetic index, forgotten index, multiple Zagreb indices, and hyper-Zagreb index of deep neural network with a finite number of hidden layers are computed in this study.
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