publication . Preprint . Research . 2013

Correlation structure of stochastic neural networks with generic connectivity matrices

Fasoli, D.; Olivier Faugeras;
Open Access English
  • Published: 10 Jul 2013
Abstract
Using a perturbative expansion for weak synaptic weights and weak sources of randomness, we calculate the correlation structure of neural networks with generic connectivity matrices. In detail, the perturbative parameters are the mean and the standard deviation of the synaptic weights, together with the standard deviations of the background noise of the membrane potentials and of their initial conditions. We also show how to determine the correlation structure of the system when the synaptic connections have a random topology. This analysis is performed on rate neurons described by Wilson and Cowan equations, since this allows us to find analytic results. Moreov...
Subjects
arXiv: Quantitative Biology::Neurons and Cognition
free text keywords: Quantitative Biology - Neurons and Cognition, Mathematics - Dynamical Systems
Funded by
EC| FACETS-ITN
Project
FACETS-ITN
Fast Analog Computing with Emergent Transient States - Initial Training Network (FACETS-ITN)
  • Funder: European Commission (EC)
  • Project Code: 237955
  • Funding stream: FP7 | SP3 | PEOPLE
,
EC| NERVI
Project
NERVI
From single neurons to visual perception
  • Funder: European Commission (EC)
  • Project Code: 227747
  • Funding stream: FP7 | SP2 | ERC
,
EC| BRAINSCALES
Project
BRAINSCALES
Brain-inspired multiscale computation in neuromorphic hybrid systems
  • Funder: European Commission (EC)
  • Project Code: 269921
  • Funding stream: FP7 | SP1 | ICT
Communities
FET FP7FET Proactive: FET proactive 8: Brain Inspired ICT
FET FP7FET Proactive: Brain-inspired multiscale computation in neuromorphic hybrid systems

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publication . Preprint . Research . 2013

Correlation structure of stochastic neural networks with generic connectivity matrices

Fasoli, D.; Olivier Faugeras;