Pavlov's Dog Associative Learning Demonstrated on Synaptic-Like Organic Transistors

@article{Bichler2013PavlovsDA,
  title={Pavlov's Dog Associative Learning Demonstrated on Synaptic-Like Organic Transistors},
  author={O. Bichler and W. Zhao and F. Alibart and S. Pleutin and S. Lenfant and D. Vuillaume and C. Gamrat},
  journal={Neural Computation},
  year={2013},
  volume={25},
  pages={549-566}
}
In this letter, we present an original demonstration of an associative learning neural network inspired by the famous Pavlov's dogs experiment. [...] Key Method 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…Expand
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References

SHOWING 1-10 OF 42 REFERENCES
An Electronic Version of Pavlov's Dog
Neuromorphic plasticity is the basic platform for learning in biological systems and is considered as the unique concept in the brains of vertebrates, which outperform today's most powerful digitalExpand
Experimental demonstration of associative memory with memristive neural networks
TLDR
This work has demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses and opens up new possibilities in the understanding of neural processes using memory devices. Expand
An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse
TLDR
A device made of molecules and nanoparticles that exhibits the main behavior of a biological spiking synapse is demonstrated and the synaptic plasticity for real-time computing is evidenced and described by a simple model, opening the way to rate-coding utilization of the NOMFET in dynamical neuromorphic computing circuits. Expand
Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing.
TLDR
A new nanoscale electronic synapse based on technologically mature phase change materials employed in optical data storage and nonvolatile memory applications is reported, utilizing continuous resistance transitions in phase change material to mimic the analog nature of biological synapses, enabling the implementation of a synaptic learning rule. Expand
Simulation of a memristor-based spiking neural network immune to device variations
TLDR
System level simulations on a textbook case show that performance can compare with traditional supervised networks of similar complexity and show the system can retain functionality with extreme variations of various memristors' parameters, thanks to the robustness of the scheme, its unsupervised nature, and the power of homeostasis. Expand
Functional Model of a Nanoparticle Organic Memory Transistor for Use as a Spiking Synapse
Emerging synapse-like nanoscale devices such as memristive devices and synaptic transistors are of great interest to provide adaptability, high density, and robustness for the development of newExpand
Physical aspects of low power synapses based on phase change memory devices
In this work, we demonstrate how phase change memory (PCM) devices can be used to emulate biologically inspired synaptic functions in particular, potentiation and depression, important forExpand
Learning with memristive devices: How should we model their behavior?
TLDR
A new behavioral model is introduced, intended towards the nanoarchitecture community, that fits the conductance evolution of Univ. Expand
Nanoscale memristor device as synapse in neuromorphic systems.
TLDR
A nanoscale silicon-based memristor device is experimentally demonstrated and it is shown that a hybrid system composed of complementary metal-oxide semiconductor neurons and Memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Expand
Neuronal ion-channel dynamics in silicon
  • K. Hynna, K. Boahen
  • Engineering, Computer Science
  • 2006 IEEE International Symposium on Circuits and Systems
  • 2006
We present a simple silicon circuit for modelling voltage-dependent ion channels found within neural cells, capturing both the gating particle's sigmoidal activation (or inactivation) and theExpand
...
1
2
3
4
5
...