CoDi: Difference between revisions
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[[File:CoDi_CA_signaling.png|100px|right|Codi signaling]] |
[[File:CoDi_CA_signaling.png|100px|right|Codi signaling]] |
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CoDi is a cellular [[Cellular automaton|automaton/automata (CA)]] model for |
'''CoDi''' is a cellular [[Cellular automaton|automaton/automata (CA)]] model for [[Spiking neural network|spiking neural networks (SNNs)]]. CoDi is an acronym for Collect and Distribute referring to the signals/spikes in a neural network. |
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[[Spiking neural network|spiking neural networks (SNNs)]]. |
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DoDi stands for Collect and Distribute referring to the signals/spikes in a neural network. |
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==General Description and Rules of the CoDi CA== |
==General Description and Rules of the CoDi CA== |
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[[File:CoDi_CA_signaling_rules.png|250px|right|Codi signaling rules]] |
[[File:CoDi_CA_signaling_rules.png|250px|right|Codi signaling rules]] |
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CoDi uses a von Neumann neighborhood, in a 3D space, thus it looks at 6 neighbors and at its own state. In a growth phase a neural network is grows in the CA-space based on an underlying chromosome. There are only 3 types of cells: neuron body, axon, dendrite and blank. The growth phase is followed by a signaling- or processing-phase.Signals are distributed from the neuron bodies vie their axons tree and collected from connection dendrites. |
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CoDi uses a von Neumann neighborhood, |
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in a 3D space, thus it looks at |
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6 neighbors and at its own state. |
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In a growth phase a neural network is grows in the CA-space based on an underlying chromosome. |
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There are only 3 types of cells: neuron body, axon, dendrite and blank. |
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The growth phase is followed by a signaling- or processing-phase. |
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Signals are distributed from the neuron bodies vie their axons tree and collected |
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from connection dendrites. |
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==History== |
==History== |
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CoDi was introduced by Gers et. al. <ref name="Gers1998">{{cite journal|last=Gers|first=Felix|coauthors=Hugo Garis, Michael Korkin|year=1998|title=CoDi-1Bit : A simplified cellular automata based neuron model|volume=1363|pages=315–333|doi=10.1007/BFb0026610}}</ref>. |
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CoDi was introduced by Gers et. al. |
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<ref>F.A. Gers, H. De Garis and M. Korkin, [http://felixgers.de/papers/codi-1bit.pdf "Codi-1bit : A simplified cellular automata based neuron model"], ''In Artificial Evolution Conference (AE), Nimes, France'', 1997.</ref>. |
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A specialized parallel machine based on FPGA Hardware to run the DoDi model on a large scale was developed by Korkin et. al. |
A specialized parallel machine based on FPGA Hardware to run the DoDi model on a large scale was developed by Korkin et. al. |
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<ref name="de Garis2001">{{cite journal|last=de Garis|first=Hugo|coauthors=Michael Korkin, Gary Fehr|year=2001|journal=Autonomous Robots|volume=10|issue=3|pages=235–249|issn=09295593|doi=10.1023/A:1011286308522}}</ref>. |
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<ref>M. Korkin, H. De Garis, F. A. Gers, and H. Hemmi, "CBM (CAM-brain machine) : A hardware tool which evolves a neural net module in a fraction of a second and runs a million neuron artificial brain in real time", In Genetic Programming Conference, Stanford, USA, 1997.</ref>. |
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The original model, where learning is based on evolutionary algorithms, has been augmented with a local learning rule via feedback from dendritic spikes by Schwarzer |
The original model, where learning is based on evolutionary algorithms, has been augmented with a local learning rule via feedback from dendritic spikes by Schwarzer |
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<ref name=" |
<ref name="SchwarzerMüller-Schloer2004">{{cite book|last1=Schwarzer|first1=Jens|last2=Müller-Schloer|first2=Christian|title=Lernverfahren für evolutionär optimierte Künstliche Neuronale Netze auf der Basis Zellulärer Automaten|url=http://books.google.com/books?id=wVuqAAAACAAJ|accessdate=7 January 2013|date=2004-08-05|publisher=Logos Verlag Berlin|isbn=9783832506285|pages=125–}}</ref>. |
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==References== |
==References== |
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Revision as of 20:07, 7 January 2013
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CoDi is a cellular automaton/automata (CA) model for spiking neural networks (SNNs). CoDi is an acronym for Collect and Distribute referring to the signals/spikes in a neural network.
General Description and Rules of the CoDi CA
CoDi uses a von Neumann neighborhood, in a 3D space, thus it looks at 6 neighbors and at its own state. In a growth phase a neural network is grows in the CA-space based on an underlying chromosome. There are only 3 types of cells: neuron body, axon, dendrite and blank. The growth phase is followed by a signaling- or processing-phase.Signals are distributed from the neuron bodies vie their axons tree and collected from connection dendrites.
History
CoDi was introduced by Gers et. al. [1]. A specialized parallel machine based on FPGA Hardware to run the DoDi model on a large scale was developed by Korkin et. al. [2]. The original model, where learning is based on evolutionary algorithms, has been augmented with a local learning rule via feedback from dendritic spikes by Schwarzer [3].
References
- ^ Gers, Felix (1998). "CoDi-1Bit : A simplified cellular automata based neuron model". 1363: 315–333. doi:10.1007/BFb0026610.
{{cite journal}}: Cite journal requires|journal=(help); Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ de Garis, Hugo (2001). Autonomous Robots. 10 (3): 235–249. doi:10.1023/A:1011286308522. ISSN 0929-5593.
{{cite journal}}: Missing or empty|title=(help); Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ Schwarzer, Jens; Müller-Schloer, Christian (2004-08-05). Lernverfahren für evolutionär optimierte Künstliche Neuronale Netze auf der Basis Zellulärer Automaten. Logos Verlag Berlin. pp. 125–. ISBN 9783832506285. Retrieved 7 January 2013.