Towards neuromorphic machine intelligence : spike -based representation, learning, and applications.
Towards Neuromorphic Machine Intelligence explores the field of spiking neural networks (SNNs), the third generation of artificial neural networks that aim to mimic the dynamics of biological neurons more accurately. The book covers fundamental theories, specialized neuron models, and learning algor...
保存先:
| 第一著者: | |
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| フォーマット: | 電子媒体 eBook |
| 言語: | 英語 |
| 出版事項: |
[S.l.] :
Academic Press,
2024.
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| 主題: | |
| オンライン･アクセス: | ScienceDirect |
| タグ: |
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| 要約: | Towards Neuromorphic Machine Intelligence explores the field of spiking neural networks (SNNs), the third generation of artificial neural networks that aim to mimic the dynamics of biological neurons more accurately. The book covers fundamental theories, specialized neuron models, and learning algorithms for both shallow and deep networks, with a focus on applications such as energy-efficient image classification and fault diagnosis. The authors provide an extensive understanding of SNNs for researchers and practitioners in artificial intelligence, particularly those interested in neuromorphic computing, offering insights into novel algorithms and their practical implementations. |
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| 物理的記述: | 1 online resource |
| ISBN: | 9780443328213 0443328218 |