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The length of attractors in asymmetric random neural networks with deterministic dynamics

K Nutzel

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The author has developed a method to detect attractors of any length in large neural networks with up to 1024 neurons within a reasonable period of CPU-time. In networks with symmetric couplings only stable states and, in the case of parallel dynamics, cycles of length 2 exist. The presented simulations suggest that, in sufficiently large systems, this holds also for couplings up to a distinct value of asymmetry. Beyond this value extremely long cycles are detected and the average cycle length depends exponentially on system size.


PACS

07.05.Mh Neural networks, fuzzy logic, artificial intelligence

MSC

62M45 Neural nets and related approaches

Subjects

Instrumentation and measurement

Dates

Issue 3 (7 February 1991)



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