• DOI:10.1016/J.CRMA.2014.08.017
Corpus ID: 119149927

Asymptotic description of stochastic neural networks. II - Characterization of the limit law

@article{Faugeras2014AsymptoticDO,
  title={Asymptotic description of stochastic neural networks. II - Characterization of the limit law},
  author={O. Faugeras and J. MacLaurin},
  journal={arXiv: Probability},
  year={2014}
}

• O. Faugeras, J. MacLaurin
• Published 2014
• Mathematics
• arXiv: Probability

We continue the development, started in of the asymptotic description of certain stochastic neural networks. We use the Large Deviation Principle (LDP) and the good rate function H announced there to prove that H has a unique minimum mu_e, a stationary measure on the set of trajectories. We characterize this measure by its two marginals, at time 0, and from time 1 to T. The second marginal is a stationary Gaussian measure. With an eye on applications, we show that its mean and covariance… Expand

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed