Mean-field dynamics of sequence processing neural networks with finite connectivity
Theumann, W. K.
arXiv, 0303425 / Harvested from arXiv
Text on arXiv
A recent dynamic mean-field theory for sequence processing in fully connected neural networks of Hopfield-type (During, Coolen and Sherrington, 1998) is extended and analized here for a symmetrically diluted network with finite connectivity near saturation. Equations for the dynamics and the stationary states are obtained for the macroscopic observables and the precise equivalence is established with the single-pattern retrieval problem in a layered feed-forward network with finite connectivity.
Publié le : 2003-03-20
Classification:  Condensed Matter - Disordered Systems and Neural Networks,  Condensed Matter - Statistical Mechanics,  Mathematical Physics,  Quantitative Biology - Neurons and Cognition 
@article{0303425,
     author = {Theumann, W. K.},
     title = {Mean-field dynamics of sequence processing neural networks with finite
  connectivity},
     journal = {arXiv},
     volume = {2003},
     number = {0},
     year = {2003},
     language = {en},
     url = {http://dml.mathdoc.fr/item/0303425}
}

Theumann, W. K. Mean-field dynamics of sequence processing neural networks with finite
  connectivity. arXiv, Tome 2003 (2003) no. 0, . http://gdmltest.u-ga.fr/item/0303425/