| _version_ | 1866901597862232064 |
|---|---|
| author | Subhasis Kundu |
| author_facet | Subhasis Kundu |
| contents | <p>This study introduces an innovative Unrestricted Neural Morphing Framework, inspired by biological systems, designed to develop adaptive artificial intelligence models capable of real-time restructuring. This framework employs deep learning techniques to enable neural networks to dynamically modify their architecture based on sensory input, thereby enhancing learning and performance [1] [2]. The fundamental principles of the framework are delineated, emphasizing its ability to add or remove neurons, adjust synaptic connections, and reorganize network layers in response to environmental stimuli. Experimental results demonstrate significant improvements in task performance and adaptability across various domains, including computer vision, natural language processing, and robotics. The proposed framework facilitates the development of more flexible and efficient AI systems that can continuously evolve and optimize their structure to meet changing requirements. The findings suggest that this approach has the potential to transform the field of artificial intelligence by bridging the gap between static neural architectures and the dynamic, adaptive nature of biological neural systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15086778 |
| institution | Zenodo |
| language | |
| publishDate | 2023 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Unrestricted Neural Morphing Framework: Biologically Inspired Deep Networks for Dynamic Restructuring Subhasis Kundu <p>This study introduces an innovative Unrestricted Neural Morphing Framework, inspired by biological systems, designed to develop adaptive artificial intelligence models capable of real-time restructuring. This framework employs deep learning techniques to enable neural networks to dynamically modify their architecture based on sensory input, thereby enhancing learning and performance [1] [2]. The fundamental principles of the framework are delineated, emphasizing its ability to add or remove neurons, adjust synaptic connections, and reorganize network layers in response to environmental stimuli. Experimental results demonstrate significant improvements in task performance and adaptability across various domains, including computer vision, natural language processing, and robotics. The proposed framework facilitates the development of more flexible and efficient AI systems that can continuously evolve and optimize their structure to meet changing requirements. The findings suggest that this approach has the potential to transform the field of artificial intelligence by bridging the gap between static neural architectures and the dynamic, adaptive nature of biological neural systems.</p> |
| title | Unrestricted Neural Morphing Framework: Biologically Inspired Deep Networks for Dynamic Restructuring |
| url | https://doi.org/10.5281/zenodo.15086778 |