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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.04264 |
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| _version_ | 1866913565117513728 |
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| author | Silvestri, Matteo |
| author_facet | Silvestri, Matteo |
| contents | This article delves into the Hopfield neural network model, drawing inspiration from biological neural systems. The exploration begins with an overview of the model's foundations, incorporating insights from mechanical statistics to deepen our understanding. Focusing on audio retrieval, the study demonstrates the Hopfield model's associative memory capabilities. Through practical implementation, the network is trained to retrieve different patterns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04264 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analysis of Hopfield Model as Associative Memory Silvestri, Matteo Disordered Systems and Neural Networks Artificial Intelligence Information Retrieval This article delves into the Hopfield neural network model, drawing inspiration from biological neural systems. The exploration begins with an overview of the model's foundations, incorporating insights from mechanical statistics to deepen our understanding. Focusing on audio retrieval, the study demonstrates the Hopfield model's associative memory capabilities. Through practical implementation, the network is trained to retrieve different patterns. |
| title | Analysis of Hopfield Model as Associative Memory |
| topic | Disordered Systems and Neural Networks Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2402.04264 |