The Brain in a Box - An Encoding Scheme for Natural Neural Networks
Martin Pyka, Tilo Kircher, Sascha Hauke, Dominik Heider
2012
Abstract
To study the evolution of complex nervous systems through artificial development, an encoding scheme for modeling networks is needed that reflects intrinsic properties similiar to natural encodings. Like the genetic code, a description language for simulations should indirectly encode networks, be stable but adaptable through evolution and should encode functions of neural networks through architectural design as well as single neuron configurations. We propose an indirect encoding scheme based on Compositional Pattern Producing Networks (CPPNs) to fulfill these needs. The encoding scheme uses CPPNs to generate multidimensional patterns that represent the analog to protein distributions in the development of organisms. These patterns form the template for three-dimensional neural networks, in which dendrite- and axon cones are placed in space to determine the actual connections in a spiking neural network simulation.
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in Harvard Style
Pyka M., Kircher T., Hauke S. and Heider D. (2012). The Brain in a Box - An Encoding Scheme for Natural Neural Networks . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 196-201. DOI: 10.5220/0004152801960201
in Bibtex Style
@conference{ecta12,
author={Martin Pyka and Tilo Kircher and Sascha Hauke and Dominik Heider},
title={The Brain in a Box - An Encoding Scheme for Natural Neural Networks},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)},
year={2012},
pages={196-201},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004152801960201},
isbn={978-989-8565-33-4},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)
TI - The Brain in a Box - An Encoding Scheme for Natural Neural Networks
SN - 978-989-8565-33-4
AU - Pyka M.
AU - Kircher T.
AU - Hauke S.
AU - Heider D.
PY - 2012
SP - 196
EP - 201
DO - 10.5220/0004152801960201