The Advances in Neuromorphic Computing and Brain-Inspired Systems (ANCBIS).
Neuromorphic computing, inspired by the structure and functions of the human brain, is transforming the development of energy-efficient, adaptive, and highly parallel processing systems. This field seeks to bridge the gap between traditional computing architectures and biological neural networks by...
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| Format: | Article |
| Language: | English |
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Institute of Technology and Education Galileo da Amazônia
2025-01-01
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| Series: | ITEGAM-JETIA |
| Online Access: | http://itegam-jetia.org/journal/index.php/jetia/article/view/1425 |
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| author | DanielRaj K Ponseka G Bharath Sanjai Lordwin D J3 |
| author_facet | DanielRaj K Ponseka G Bharath Sanjai Lordwin D J3 |
| author_sort | DanielRaj K |
| collection | DOAJ |
| description |
Neuromorphic computing, inspired by the structure and functions of the human brain, is transforming the development of energy-efficient, adaptive, and highly parallel processing systems. This field seeks to bridge the gap between traditional computing architectures and biological neural networks by replicating brain-like functionalities. This paper examines recent advancements in neuromorphic computing, with an emphasis on innovative hardware and algorithms that boost computational power while reducing energy consumption. Key technologies such as memristive devices, spiking neural networks, and brain-inspired learning algorithms show promise in applications like pattern recognition, sensory processing, and autonomous decision-making. This study also addresses challenges related to scalability, robustness, and integration with existing systems, emphasizing the importance of cross-disciplinary collaboration to overcome these limitations. By exploring applications in robotics, medical diagnostics, and environmental monitoring, this research highlights how brain-inspired systems could drive the next generation of artificial intelligence and sustainable computing, meeting the growing need for energy-efficient, intelligent systems.
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| format | Article |
| id | doaj-art-7588f365a69a4a0698fed003798269b3 |
| institution | Kabale University |
| issn | 2447-0228 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Institute of Technology and Education Galileo da Amazônia |
| record_format | Article |
| series | ITEGAM-JETIA |
| spelling | doaj-art-7588f365a69a4a0698fed003798269b32025-02-06T23:51:49ZengInstitute of Technology and Education Galileo da AmazôniaITEGAM-JETIA2447-02282025-01-01115110.5935/jetia.v11i51.1425The Advances in Neuromorphic Computing and Brain-Inspired Systems (ANCBIS).DanielRaj K0Ponseka G1Bharath Sanjai Lordwin D J32TechTrainer, Department of Computer Science and Engineering, Dr. G U POPE College of Engineering, Sawyerpuram, India.Assistant Professor, Department of Computer Science and Engineering, Dr. G U POPE College of Engineering, Sawyerpuram, India.Department of Computer Science and Engineering, Dr. G U POPE College of Engineering, Sawyerpuram, India. Neuromorphic computing, inspired by the structure and functions of the human brain, is transforming the development of energy-efficient, adaptive, and highly parallel processing systems. This field seeks to bridge the gap between traditional computing architectures and biological neural networks by replicating brain-like functionalities. This paper examines recent advancements in neuromorphic computing, with an emphasis on innovative hardware and algorithms that boost computational power while reducing energy consumption. Key technologies such as memristive devices, spiking neural networks, and brain-inspired learning algorithms show promise in applications like pattern recognition, sensory processing, and autonomous decision-making. This study also addresses challenges related to scalability, robustness, and integration with existing systems, emphasizing the importance of cross-disciplinary collaboration to overcome these limitations. By exploring applications in robotics, medical diagnostics, and environmental monitoring, this research highlights how brain-inspired systems could drive the next generation of artificial intelligence and sustainable computing, meeting the growing need for energy-efficient, intelligent systems. http://itegam-jetia.org/journal/index.php/jetia/article/view/1425 |
| spellingShingle | DanielRaj K Ponseka G Bharath Sanjai Lordwin D J3 The Advances in Neuromorphic Computing and Brain-Inspired Systems (ANCBIS). ITEGAM-JETIA |
| title | The Advances in Neuromorphic Computing and Brain-Inspired Systems (ANCBIS). |
| title_full | The Advances in Neuromorphic Computing and Brain-Inspired Systems (ANCBIS). |
| title_fullStr | The Advances in Neuromorphic Computing and Brain-Inspired Systems (ANCBIS). |
| title_full_unstemmed | The Advances in Neuromorphic Computing and Brain-Inspired Systems (ANCBIS). |
| title_short | The Advances in Neuromorphic Computing and Brain-Inspired Systems (ANCBIS). |
| title_sort | advances in neuromorphic computing and brain inspired systems ancbis |
| url | http://itegam-jetia.org/journal/index.php/jetia/article/view/1425 |
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