Dynamics of Evolving Feed-Forward Neural Networks and Their Topological Invariants

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• 关键词：Graph theory ; Network invariant ; Directed clique complex ; Recurrent neural dynamics ; Synfire chain ; Synaptic plasticity
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9886
• 期：1
• 页码：99-106
• 全文大小：261 KB
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• 作者单位：Paolo Masulli (16)
  Alessandro E. P. Villa (16)
  
  16. NeuroHeuristic Research Group, University of Lausanne, Quartier Dorigny, 1015, Lausanne, Switzerland
  
• 丛书名：Artificial Neural Networks and Machine Learning – ICANN 2016
• ISBN：978-3-319-44778-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9886

文摘

The evolution of a simulated feed-forward neural network with recurrent excitatory connections and inhibitory forward connections is studied within the framework of algebraic topology. The dynamics includes pruning and strengthening of the excitatory connections. The invariants that we define are based on the connectivity structure of the underlying graph and its directed clique complex. The computation of this complex and of its Euler characteristic are related with the dynamical evolution of the network. As the network evolves dynamically, its network topology changes because of the pruning and strengthening of the onnections and algebraic topological invariants can be computed at different time steps providing a description of the process. We observe that the initial values of the topological invariant computed on the network before it evolves can predict the intensity of the activity.