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Related Experiment Video

Updated: May 30, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Recurrent kernel machines: computing with infinite echo state networks.

Michiel Hermans¹, Benjamin Schrauwen

¹Department of Electronics and Information Systems, Ghent University, 9000 Ghent, Belgium. michiel.hermans@ugent.be

Neural Computation

|August 20, 2011

Summary

This summary is machine-generated.

Echo state networks (ESNs) are extended to infinite-sized recurrent neural networks, acting as recursive kernels. These kernels enable the creation of recursive support vector machines for temporal data tasks.

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Area of Science:

Computational neuroscience
Machine learning
Recurrent neural networks

Background:

Echo state networks (ESNs) are large, random recurrent neural networks with a trainable linear readout layer.
ESNs excel at universal computations on temporal data due to their inherent nonlinear spatiotemporal mappings.

Purpose of the Study:

To extend the concept of ESNs to infinite-sized recurrent neural networks.
To introduce the idea of recursive kernels derived from these infinite ESNs.
To apply these recursive kernels in the development of recursive support vector machines.

Main Methods:

Theoretical framework development for infinite-sized recurrent neural networks.
Formulation of recursive kernels based on the extended ESN concept.
Application of recursive kernels to support vector machines for temporal tasks.

Main Results:

Demonstration of the theoretical framework for infinite recurrent neural networks.
Practical examples of recursive kernels derived from the framework.
Successful application of recursive kernels to standard temporal data processing tasks.

Conclusions:

Infinite-sized recurrent neural networks can be conceptualized as recursive kernels.
Recursive kernels offer a novel approach for building advanced machine learning models like recursive support vector machines.
The proposed framework and methods are effective for temporal data analysis.