Finding iterative roots with a spiking neural network

Authors:
Nicolangelo Iannella;Lars Kindermann
Affiliations:
Laboratory for Visual Neurocomputing, Brain Science Institute, RIKEN, Saitama, Japan;Laboratory for Mathematical Neuroscience, Brain Science Institute, RIKEN, Saitama, Japan and Department of Climate Systems and Ocean Acoustics, Alfred Wegener Institute for Polar and Marine Resear ...
Venue:
Information Processing Letters - Special issue on applications of spiking neural networks
Year:
2005

Citing 2
Cited 5

Synchronization of Elliptic Bursters

SIAM Review
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks

IEEE Transactions on Neural Networks

Computing iterative roots of polygonal functions

Journal of Computational and Applied Mathematics
Spike-timing error backpropagation in theta neuron networks

Neural Computation
2009 Special Issue: Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models

Neural Networks
Quantum-inspired feature and parameter optimisation of evolving spiking neural networks with a case study from ecological modeling

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Continuity of iteration and approximation of iterative roots

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In recent years, both multilayer perceptrons and networks of spiking neurons have been used in applications ranging from detailed models of specific cortical areas to image processing. A more challenging application is to find solutions to functional equations in order to gain insights to underlying phenomena. Finding the roots of real valued monotonically increasing function mappings is the solution to a particular class of functional equation. Furthermore, spiking neural network approaches in solving problems described by functional equations, may be an useful tool to provide important insights to how different regions of the brain may co-ordinate signaling within and between modalities, thus providing a possible basis to construct a theory of brain function. In this letter, we present for the first time a spiking neural network architecture based on integrate-and-fire units and delays, that is capable of calculating the functional or iterative root of nonlinear functions, by solving a particular class of functional equation.