@Article{	  rostro-gonzalez-cessac-etal:12,
author = {Horacio Rostro-Gonzalez and Bruno Cessac and Thierry Viéville},
title = {Parameters estimation in spiking neural networks: a reverse-engineering approach},
journal = {J. Neural. Eng.},
year = {2012},
volume = {9},
number = {026024},
url = {http://iopscience.iop.org/1741-2552/9/2/026024/},
keywords = {Spike trains, neural networks},
topic = {Modeling of spiking neurons},
owner = {bcessac},
group = {Neuromathcomp},
annote = {This paper presents a reverse engineering approach for parameter estimation in spiking neural
networks (SNNs). 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 type. Our approach aims at by-passing the fact that the parameter
estimation in SNN results in a non-deterministic polynomial-time hard problem when delays are to
be considered. Here, this assumption has been reformulated as a linear programming (LP) problem
in order to perform the solution in a polynomial time. Besides, the LP problem formulation makes
the fact that the reverse engineering of a neural network can be performed from the observation
of the spike times explicit. Furthermore, we point out how the LP adjustment mechanism is local
to each neuron and has the same structure as a 'Hebbian' rule. Finally, we present a
generalization of this approach to the design of input�??output (I/O) transformations as 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.},
x-editorial-board = {yes},
x-international-audience = {yes},
doi = {10.1088/1741-2560/9/2/026024},
x-pays = {MX}


26 August 2016