2002/04/02 by Wolfgang Kinzel, Kinzel, Wolfgang
Computer Science · Physics and Astronomy · #Algorithm #Artificial intelligence #Artificial neural network #Competition (biology) #Computability, Logic, AI Algorithms #Computer science #Computer security #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Key (lock) #Neural Networks and Applications #Phenomenon #Physics #Section (typography) #Series (stratigraphy) #State (computer science) #Theoretical computer science #cond-mat.dis-nn
paper · pdf · doi:10.48550/arxiv.cond-mat/0204054
Contribution to Networks, ed. by H.G. Schuster and S. Bornholdt, to be published by Wiley VCH
arxiv created 2002/04/02 · openalex publication_date 2002/04/02 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this contribution we give an overview over recent work on the theory of interacting neural networks. The model is defined in Section 2. The typical teacher/student scenario is considered in Section 3. A static teacher network is presenting training examples for an adaptive student network. In the case of multilayer networks, the student shows a transition from a symmetric state to specialisation. Neural networks can also generate a time series. Training on time series and predicting it are studied in Section 4. When a network is trained on its own output, it is interacting with itself. Such a scenario has implications on the theory of prediction algorithms, as discussed in Section 5. When a system of networks is trained on its minority decisions, it may be considered as a model for competition in closed markets, see Section 6. In Section 7 we consider two mutually interacting networks. A novel phenomenon is observed: synchronisation by mutual learning. In Section 8 it is shown, how this phenomenon can be applied to cryptography: Generation of a secret key over a public channel.