Pavlov's dog associative learning demonstrated ...
Document type :
Compte-rendu et recension critique d'ouvrage
DOI :
Title :
Pavlov's dog associative learning demonstrated on synaptic-like organic transistors
Author(s) :
Bichler, O. [Auteur]
Département d'Architectures, Conception et Logiciels Embarqués-LIST [DACLE-LIST]
Zhao, W. [Auteur]
Département d'Architectures, Conception et Logiciels Embarqués-LIST [DACLE-LIST]
Alibart, Fabien [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Pleutin, S. [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Lenfant, Stephane [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Vuillaume, D. [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Gamrat, C. [Auteur]
Département d'Architectures, Conception et Logiciels Embarqués-LIST [DACLE-LIST]
Département d'Architectures, Conception et Logiciels Embarqués-LIST [DACLE-LIST]
Zhao, W. [Auteur]
Département d'Architectures, Conception et Logiciels Embarqués-LIST [DACLE-LIST]
Alibart, Fabien [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Pleutin, S. [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Lenfant, Stephane [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Vuillaume, D. [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Gamrat, C. [Auteur]
Département d'Architectures, Conception et Logiciels Embarqués-LIST [DACLE-LIST]
Journal title :
Neural computation
Pages :
549-566
Publisher :
Massachusetts Institute of Technology Press (MIT Press)
Publication date :
2013
ISSN :
0899-7667
HAL domain(s) :
Informatique [cs]
English abstract : [en]
In this paper we present an original demonstration of an associative learning neural network inspired by the famous Pavlov’s dogs experiment. A single nanoparticle organic memory field effect transistor (NOMFET) is used ...
Show more >In this paper we present an original demonstration of an associative learning neural network inspired by the famous Pavlov’s dogs experiment. A single nanoparticle organic memory field effect transistor (NOMFET) is used to implement each synapse. We show how the physical properties of this dynamic memristive device can be used to perform low-power write operations for the learning and implement short-term association using temporal coding and spike-timing-dependent plasticity–based learning. An electronic circuit was built to validate the proposed learning scheme with packaged devices, with good reproducibility despite the complex synaptic-like dynamic of the NOMFET in pulse regime.Show less >
Show more >In this paper we present an original demonstration of an associative learning neural network inspired by the famous Pavlov’s dogs experiment. A single nanoparticle organic memory field effect transistor (NOMFET) is used to implement each synapse. We show how the physical properties of this dynamic memristive device can be used to perform low-power write operations for the learning and implement short-term association using temporal coding and spike-timing-dependent plasticity–based learning. An electronic circuit was built to validate the proposed learning scheme with packaged devices, with good reproducibility despite the complex synaptic-like dynamic of the NOMFET in pulse regime.Show less >
Language :
Anglais
Popular science :
Non
European Project :
Source :
Files
- https://hal.archives-ouvertes.fr/hal-00795977/document
- Open access
- Access the document
- http://arxiv.org/pdf/1302.3261
- Open access
- Access the document
- https://hal.archives-ouvertes.fr/hal-00795977/document
- Open access
- Access the document
- https://hal.archives-ouvertes.fr/hal-00795977/document
- Open access
- Access the document
- document
- Open access
- Access the document
- bich1.pdf
- Open access
- Access the document
- 1302.3261
- Open access
- Access the document