The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A Leaky‐Integrate‐and‐Fire Neuron Analog Realized with a Mott Insulator
Open accessAbstract
During the last half century, the tremendous development of computers based on von Neumann architecture has led to the revolution of the information technology. However, von Neumann computers are outperformed by the mammal brain in numerous data‐processing applications such as pattern recognition and data mining. Neuromorphic engineering aims to mimic brain‐like behavior through the implementation of artificial neural networks based on the combination of a large number of artificial neurons massively interconnected by an even larger number of artificial synapses. In order to effectively implement artificial neural networks directly in hardware, it is mandatory to develop artificial neurons and synapses. A promising advance has been made in recent years with the introduction of the components called memristors that might implement synaptic functions. In contrast, the advances in artificial neurons have consisted in the implementation of silicon‐based circuits. However, so far, a single‐component artificial neuron that will bring an improvement comparable to what memristors have brought to synapses is still missing. Here, a simple two‐terminal device is introduced, which can implement the basic functions leaky integrate and fire of spiking neurons. Remarkably, it has been found that it is realized by the behavior of strongly correlated narrow‐gap Mott insulators subject to electric pulsing.
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Nanoscale Memristor Device as Synapse in Neuromorphic Systems
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A scalable neuristor built with Mott memristors
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On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex
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Cited by
In-memory computing with resistive switching devices
Ielmini, D., & Wong, H.-S. P. (2018) · Nature Electronics · doi:10.1038/s41928-018-0092-2 · Cited by 2,298
“Similar concepts of integrating neurons were shown by adopting threshold switching in a Mott insulator 64 or volatile RRAM with unstable Ag filaments 65 .”
MethodMemristive crossbar arrays for brain-inspired computing
Xia, Q., & Yang, J. J. (2019) · Nature Materials · doi:10.1038/s41563-019-0291-x · Cited by 1,800
Fully memristive neural networks for pattern classification with unsupervised learning
Wang, Z., Joshi, S., Savel’ev, S., Song, W., Midya, R., Li, Y., Rao, M., Yan, P., Asapu, S., Zhuo, Y., et al. (2018) · Nature Electronics · doi:10.1038/s41928-018-0023-2 · Cited by 1,106
A comprehensive review on emerging artificial neuromorphic devices
Zhu, J., Zhang, T., Yang, Y., & Huang, R. (2020) · Applied Physics Reviews · doi:10.1063/1.5118217 · Cited by 758
An Artificial Sensory Neuron with Tactile Perceptual Learning
Wan, C., Chen, G., Fu, Y., Wang, M., Matsuhisa, N., Pan, S., Pan, L., Yang, H., Wan, Q., Zhu, L., et al. (2018) · Advanced Materials · doi:10.1002/adma.201801291 · Cited by 418
Metadata
- Authors
- Stoliar, P., Tranchant, J., Corraze, B., Janod, E., Besland, M., Tesler, F., Rozenberg, M., & Cario, L.
- Venue
- Advanced Functional Materials
- Publisher
- Wiley
- Published
- 2017-01-30
- Volume
- 27
- Issue
- 11
- ISSN
- 1616-301X (ISSN-L)
- 1616-3028
- OpenAlex
- W2582645878
- Cited by
- 288
- References
- 29
- FWCI
- 10.74
- Type
- journal article
- Language
- en
- Open access
- bronze
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