Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
Article Properties
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DOI (url)
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Publication Date2015/06/01
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Indian UGC (journal)
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Refrences77
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Citations3
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Xinyu Wu ORCID (unauthenticated)
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Vishal Saxena
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Kehan Zhu
Cite
Wu, Xinyu, et al. “Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition”. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 5, no. 2, 2015, pp. 254-66, https://doi.org/10.1109/jetcas.2015.2433552.
Wu, X., Saxena, V., & Zhu, K. (2015). Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 5(2), 254-266. https://doi.org/10.1109/jetcas.2015.2433552
Wu, Xinyu, Vishal Saxena, and Kehan Zhu. “Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition”. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 5, no. 2 (2015): 254-66. https://doi.org/10.1109/jetcas.2015.2433552.
Wu X, Saxena V, Zhu K. Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 2015;5(2):254-66.
Journal Categories
Technology
Electrical engineering
Electronics
Nuclear engineering
Electric apparatus and materials
Electric circuits
Electric networks
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Izhikevich neuron circuit using stochastic logic | Electronics Letters |
| 3 | 2014 |
| Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type | 1998 | |||
| On-chip supervised learning rule for ultra high density neural crossbar using memristor for synapse and neuron | 2014 | |||
| 10.1007/978-3-540-69369-7_14 | 2008 | |||
| Plasticity in memristive devices for spiking neural networks | Frontiers in Neuroscience |
| 2015 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules | IEEE Transactions on Neural Networks and Learning Systems |
| 2024 | |
| Power and Area-Efficient XNOR-AND Hybrid Binary Neural Networks Using TFT-Type Synaptic Device | IEEE Electron Device Letters |
| 2023 | |
A system design perspective on neuromorphic computer processors | Neuromorphic Computing and Engineering |
| 6 | 2021 |
Citations Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks 3
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A system design perspective on neuromorphic computer processors and was published in 2021. The most recent citation comes from a 2024 study titled A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules. This article reached its peak citation in 2024, with 1 citations. It has been cited in 3 different journals, 33% of which are open access. Among related journals, the IEEE Electron Device Letters cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.