- Poster presentation
- Open Access
Beyond dynamical mean-field theory of neural networks
© Muratori and Cessac; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Firing Rate
- Lyapunov Exponent
- Bifurcation Diagram
- Thermodynamic Limit
- Time Dynamic
This work was supported by INRIA, ERC-NERVI number 227747, KEOPS ANR-CONICYT and European Union Project # FP7-269921 (BrainScales), Renvision grant agreement N 600847 and Mathemacs FP7-ICT_2011.9.7.
- Cessac B, Doyon B, Quoy M, Samuelides M: Mean-field equations, bifurcation map and route to chaos in discrete time neural networks. Physica D. 1994, 74: 24-44. 10.1016/0167-2789(94)90024-8.View ArticleGoogle Scholar
- Cessac B: Increase in Complexity in Random Neural Networks. J Phys I France. 1995, 5: 409-432. 10.1051/jp1:1995135.View ArticleGoogle Scholar
- Moynot O, Samuelides M: Large deviations and mean-field theory for asymmetric random recurrent neural networks. Probability Theory and Related Fields. 2002, 123: 41-75. 10.1007/s004400100182. Springer-VerlagView ArticleGoogle Scholar
- Legenstein R, Maass W: Edge of Chaos and Prediction of Computational Performance for Neural Circuit Models. Neural Networks. 2007, 20: 323-334. 10.1016/j.neunet.2007.04.017.View ArticlePubMedGoogle Scholar
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