The GAIN Model: A Nature-Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908394134175744 |
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| author | Hooper, Gage K. R. |
| author_facet | Hooper, Gage K. R. |
| contents | While many neural networks focus on layers to process information, the GAIN model uses a grid-based structure to improve biological plausibility and the dynamics of the model. The grid structure helps neurons to interact with their closest neighbors and improve their connections with one another, which is seen in biological neurons. While also being implemented with the Izhikevich model this approach allows for a computationally efficient and biologically accurate simulation that can aid in the development of neural networks, large scale simulations, and the development in the neuroscience field. This adaptation of the Izhikevich model can improve the dynamics and accuracy of the model, allowing for its uses to be specialized but efficient. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04247 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The GAIN Model: A Nature-Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model Hooper, Gage K. R. Neurons and Cognition Artificial Intelligence Neural and Evolutionary Computing 92B20, 37N25, 68T05 I.2.6; I.5.1; I.6.3 While many neural networks focus on layers to process information, the GAIN model uses a grid-based structure to improve biological plausibility and the dynamics of the model. The grid structure helps neurons to interact with their closest neighbors and improve their connections with one another, which is seen in biological neurons. While also being implemented with the Izhikevich model this approach allows for a computationally efficient and biologically accurate simulation that can aid in the development of neural networks, large scale simulations, and the development in the neuroscience field. This adaptation of the Izhikevich model can improve the dynamics and accuracy of the model, allowing for its uses to be specialized but efficient. |
| title | The GAIN Model: A Nature-Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model |
| topic | Neurons and Cognition Artificial Intelligence Neural and Evolutionary Computing 92B20, 37N25, 68T05 I.2.6; I.5.1; I.6.3 |
| url | https://arxiv.org/abs/2506.04247 |