Reverse-engineering in spiking neural networks parameters: exact deterministic parameters estimation

Horacio Rostro-Gonzalez 1, * Juan Carlos Vasquez 1 Bruno Cessac 1, 2 Thierry Viéville 3
* Auteur correspondant
1 NEUROMATHCOMP
CRISAM - Inria Sophia Antipolis - Méditerranée , INRIA Rocquencourt, ENS Paris - École normale supérieure - Paris, UNS - Université Nice Sophia Antipolis, CNRS - Centre National de la Recherche Scientifique : UMR8548
3 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We consider the deterministic evolution of a time-discretized network with spiking neurons, where synaptic transmission has delays, modeled as a neural network of the generalized integrate-and-fire (gIF) type. The purpose is to study a class of algorithmic methods allowing one to calculate the proper parameters to reproduce exactly a given spike train, generated by an hidden (unknown) neural network. This standard problem is known as NP-hard when delays are to be calculated. We propose here a reformulation, now expressed as a Linear-Programming (LP) problem, thus allowing us to provide an efficient resolution. This allows us to “reverse engineer” a neural network, i.e. to find out, given a set of initial conditions, which parameters (i.e., synaptic weights in this case), allow to simulate the network spike dynamics. More precisely we make explicit the fact that the reverse engineering of a spike train, is a Linear (L) problem if the membrane potentials are observed and a LP problem if only spike times are observed. Numerical robustness is discussed. We also explain how it is the use of a generalized IF neuron model instead of a leaky IF model that allows us to derive this algorithm. Furthermore, we point out how the L or LP adjustment mechanism is local to each unit and has the same structure as an “Hebbian” rule. A step further, this paradigm is easily generalizable to the design of input-output spike train transformations. This means that we have a practical method to “program” a spiking network, i.e. find a set of parameters allowing us to exactly reproduce the network output, given an input. Numerical verifications and illustrations are provided.
Type de document :
Rapport
[Research Report] RR-7199, INRIA. 2010, pp.41
Liste complète des métadonnées

Littérature citée [49 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00455415
Contributeur : Horacio Rostro-Gonzalez <>
Soumis le : mercredi 10 février 2010 - 13:38:22
Dernière modification le : jeudi 26 avril 2018 - 10:28:51
Document(s) archivé(s) le : jeudi 18 octobre 2012 - 15:00:28

Fichier

RR-7199.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00455415, version 1

Citation

Horacio Rostro-Gonzalez, Juan Carlos Vasquez, Bruno Cessac, Thierry Viéville. Reverse-engineering in spiking neural networks parameters: exact deterministic parameters estimation. [Research Report] RR-7199, INRIA. 2010, pp.41. 〈inria-00455415〉

Partager

Métriques

Consultations de la notice

614

Téléchargements de fichiers

166