An indirect encoding scheme for artificial neural networks based on gene regulatory networks

Abstract

Event Abstract Back to Event An indirect encoding scheme for artificial neural networks based on gene regulatory networks Martin Pyka1*, Sascha Hauke2 and Dominik Heider3 1 Department of Psychiatry, University of Marburg, Germany 2 Telecooperation Group, Technische Universität Darmstadt, Germany 3 Department of Bioinformatics, University of Duisburg-Essen, Germany The architecture of natural neural networks evolved through a gradual complexification driven by evolutionary pressure and generated through a highly indirect encoding scheme. Although the development of organic structures (such as neural networks) from the genotype happens through local interactions of proteins and cells, global coordinated structures and patterns can be observed in the phenotype. A Compositional Pattern Producing Network (CPPN) is a model for building such patterns using principles of gene regulatory networks, while exploiting shortcuts in the simulated world (Stanley, 2007). The main idea behind CPPNs is that single genes are activated at a certain concentration level of proteins generated by other genes. Thus, the concentration gradient of a gene can be regarded as the activation function of another protein/gene concentration. Given that different genes have different activation functions and differing influences on other genes, pattern formation can be represented as a graph of concatenated activation functions. The output of the last function builds the pattern for a given substrate. CPPNs are a highly indirect encoding scheme. However, the phenotype is stable and robust against continues changes in the genotype, making it interesting for evolution-driven approaches. We present a model based on CPPNs, called Brain-in-a-box (BIB), that implements several mechanisms of natural developmental processes. Neurons are placed in space according to patterns generated by a BIB, potential connections between neurons are derived from axon- and dendrite cones whose properties are also controlled by the BIB. Thus, neural networks for controlling organisms can be generated and improved through evolutionary methods with indirect genotype-phenotype mechanisms, as they occur in biological systems. Several examples of this model are presented, demonstrating its functionality and how it can be extended to incorporate concepts, such as synaptic plasticity and neuromodulation. The framework and the demos are implemented in Python using the Briansimulator (Goodman and Brette, 2008). Goodman D and Brette R (2008) Brian: a simulator for spiking neural networks in Python. Front. Neuroinform. http://dx.doi.org/10.3389/neuro.11.005.2008 Stanley K (2007) Compositional pattern producing networks: A novel abstraction of development. Genetic Programming and Evolvable Machines, 8(2):131–162 Keywords: computational neuroscience, Neural Networks (Computer), Gene Regulatory Networks, Compositional Pattern Producing Network (CPPN), Brain-in-a-box (BIB) Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Pyka M, Hauke S and Heider D (2014). An indirect encoding scheme for artificial neural networks based on gene regulatory networks. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00044 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Martin Pyka, Department of Psychiatry, University of Marburg, Marburg, Germany, martin.pyka@med.uni-marburg.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Martin Pyka Sascha Hauke Dominik Heider Google Martin Pyka Sascha Hauke Dominik Heider Google Scholar Martin Pyka Sascha Hauke Dominik Heider PubMed Martin Pyka Sascha Hauke Dominik Heider Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.