2006/07/31 by Kazushi Mimura
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Artificial neural network #Binary number #Computer science #Consistency (knowledge bases) #Dynamics (music) #Fractal and DNA sequence analysis #Hopfield network #Mathematics #Neural Networks and Applications #Physics #Statistical Mechanics and Entropy #Statistical physics #Term (time) #cond-mat.dis-nn #cond-mat.stat-mech
paper · pdf · doi:10.1143/jpsj.78.033001
published as J. Phys. Soc. Jpn., 78, 3, 033001 (2009) · 4 pages, 2 figures
arxiv created 2009/01/16 · openalex publication_date 2009/02/25 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We have applied the generating functional analysis (GFA) to the continuous Hopfield model. We have also confirmed that the GFA predictions in some typical cases exhibit good consistency with computer simulation results. When a retarded self-interaction term is omitted, the GFA result becomes identical to that obtained using the statistical neurodynamics as well as the case of the sequential binary Hopfield model.