Cyclic Neural Network
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910318928592896 |
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| author | Yang, Liangwei Zhang, Hengrui Song, Zihe Zhang, Jiawei Zhang, Weizhi Ma, Jing Yu, Philip S. |
| author_facet | Yang, Liangwei Zhang, Hengrui Song, Zihe Zhang, Jiawei Zhang, Weizhi Ma, Jing Yu, Philip S. |
| contents | This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Graph (DAG) property. Drawing inspiration from biological intelligence (BI), where neurons form a complex, graph-structured network, we introduce the groundbreaking Cyclic Neural Networks (Cyclic NNs). It emulates the flexible and dynamic graph nature of biological neural systems, allowing neuron connections in any graph-like structure, including cycles. This offers greater adaptability compared to the DAG structure of current ANNs. We further develop the Graph Over Multi-layer Perceptron, which is the first detailed model based on this new design paradigm. Experimental validation of the Cyclic NN's advantages on widely tested datasets in most generalized cases, demonstrating its superiority over current BP training methods through the use of a forward-forward (FF) training algorithm. This research illustrates a totally new ANN design paradigm, which is a significant departure from current ANN designs, potentially leading to more biologically plausible AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03332 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cyclic Neural Network Yang, Liangwei Zhang, Hengrui Song, Zihe Zhang, Jiawei Zhang, Weizhi Ma, Jing Yu, Philip S. Neural and Evolutionary Computing Machine Learning This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Graph (DAG) property. Drawing inspiration from biological intelligence (BI), where neurons form a complex, graph-structured network, we introduce the groundbreaking Cyclic Neural Networks (Cyclic NNs). It emulates the flexible and dynamic graph nature of biological neural systems, allowing neuron connections in any graph-like structure, including cycles. This offers greater adaptability compared to the DAG structure of current ANNs. We further develop the Graph Over Multi-layer Perceptron, which is the first detailed model based on this new design paradigm. Experimental validation of the Cyclic NN's advantages on widely tested datasets in most generalized cases, demonstrating its superiority over current BP training methods through the use of a forward-forward (FF) training algorithm. This research illustrates a totally new ANN design paradigm, which is a significant departure from current ANN designs, potentially leading to more biologically plausible AI systems. |
| title | Cyclic Neural Network |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2402.03332 |