The proposed approach to creating AGI using brain-like neural networks combines principles that teach systems to effectively generalize, remember, plan, and reason across a variety of tasks, drawing on ideas from neuromorphic architectures, dynamic hierarchical information processing, and hybrid neural-symbolic methods. Brain-like architectures dynamically process event-driven information over time, provide online learning and low power consumption, and implement research projects on hardware-based implementation of online plasticity. Within the framework of brain-like neural networks, this involves hybrid approaches to world reconstruction and action planning. The goal is to enable the network to reason, manipulate symbols, and use rules, similar to natural intelligence. This is achieved using differentiable logic, modal induction, neural memory processors, neural program interpreters, and neural-modular networks for task composition. This improves long-term memory, planning, and reasoning accuracy with a limited training set. It also enables skill transfer from one task to another, simulating alternative actions without multiple interactions with the real world. Brain-like neural networks have a modular, scalable architecture with routing and an ensemble of specialized modules. Neural modules for vision, planning, dialogue, and motor control can be dynamically assembled without complete retraining. Memory, planning, and perception are separated into separate modules with mechanisms for collaboration and joint goal learning. AGI based on brain-like neural networks enables knowledge transfer across domains. Neuromorphic modules motivate themselves to explore and investigate the environment, which supports long-term adaptability, similar to biological mechanisms. This is essential for AGI to function in the real world.
| Published in | Software Engineering (Volume 11, Issue 2) |
| DOI | 10.11648/j.se.20251102.12 |
| Page(s) | 40-48 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
AGI, Brain-like Neural Network, Neuromorphic Modules, Information Structure
| [1] | H. Furuta, K.-H. Lee, O. Nachum, Y. Matsuo, A. Faust, S. S. Gu, and I. Gur, Multimodal web navigation with instruction-finetuned foundation models, in The Twelfth International Conference on Learning Representations, 2024. |
| [2] | Evgeny Bryndin. Creation of Multi-purpose Intelligent Multimodal Self-Organizing Safe Robotic Ensembles Agents with AGI and cognitive control. COJ Robotics & Artificial Intelligence (COJRA). Vol.3, Issue 5. 2024. pp.1-10. |
| [3] | Evgeny Bryndin. Formation of Motivated Adaptive Erudite AI Twin with Reflexive Multimodal Ontology by Ensembles of Intelligent Agents. Research on Intelligent Manufacturing and Assembly. V.4, Is.2. 2025. (272-282) |
| [4] | Z. Zhao, F. Zhao, Y. Zhao, Y. Zeng, and Y. Sun. A brain-inspired theory of mind spiking neural network improves multi-agent cooperation and competition, Patterns, vol. 4, 2023. |
| [5] | K. Christakopoulou, S. Mourad, and M. Matarić, Agents thinking fast and slow: A talker-reasoner architecture, 2024. [Online]. Available: |
| [6] | Gur, H. Furuta, A. V. Huang, M. Safdari, Y. Matsuo, D. Eck, and A. Faust, “A real-world webagent with planning, long context understanding, and program synthesis,” in The Twelfth International Conference on Learning Representations, 2024. |
| [7] | Bo Yu, Jiangning Wei, Minzhen Hu, Zejie Han, Tianjian Zou, Ye He, Jun Liu. Brain-inspired AI Agent: The Way Towards AGI. 2024. |
| [8] | Y. Zeng, D. Zhao, F. Zhao, G. Shen, Y. Dong, E. Lu, Q. Zhang, Y. Sun, Q. Liang, Y. Zhao, Z. Zhao, H. Fang, Y. Wang, Y. Li, X. Liu, C. Du, Q. Kong, Z. Ruan, and W. Bi, “Braincog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired ai and brain simulation,” Patterns, vol. 4, no. 8, p. 100789, 2023. |
| [9] | Chaudhary K. The GAN book: Train stable generative adversarial networks using TensorFlow2, Keras and Python. – Kindle Edition, 2024. |
| [10] | Zhuravlev D. V., Smolin V. S. From the miracle of neural networks to ideas for AGI // Designing the Future. Problems of Digital Reality: Proceedings of the 7th International Conference. 2024. Moscow: IPM - M. V. Keldysh, 2024. pp. 96-124. |
| [11] | Robert Johansson. Machine Psychology: integrating operant conditioning with the non-axiomatic reasoning system for advancing artificial general intelligence research. Computational Intelligence in Robotics. Volume 11 - 2024 | |
| [12] | Evgeny Bryndin. Technological Stages of Neural Network AI Generation of System Program Code Based on Modular Neuro Integration. American Journal of Embedded Systems and Applications 2025. In press. |
| [13] |
Neural Networks: AGI's Impact on Advanced Pattern Recognition. FasterCapital. 2025. URL:
https://fastercapital.com/content/Neural-Networks--AGI-s-Impact-on-Advanced-Pattern-Recognition.html |
| [14] | Evgeny Bryndin. Network Training by Generative AI Assistant of Personal Adaptive Ethical Semantic and Active Ontology. International Journal of Intelligent Information Systems Volume.14, Is.2. 2025. pp. 20-25. |
| [15] | Evgeny Bryndin. Theoretical Foundations of Neural Network Integration of System Software Modules. Software Engineering. 2025. In press. |
| [16] | Amri A. OpenAI GPT for python developers/ The art and science of building AI-powered apps with GPT-4, Whisper, Weaviate, and beyond. – Kindle Edition, 2024. |
| [17] | Evgeny Bryndin. Neural Network Axiomatic Solver Coaching AGI Method for Solving Scientific and Practical Problems. American Journal of Mathematical and Computer Modelling. 2025. In press. |
| [18] | L. Zhao, L. Zhang, Z. Wu, Y. Chen, H. Dai, X. Yu, Z. Liu, T. Zhang, X. Hu, X. Jiang, X. Li, D. Zhu, D. Shen, and T. Liu, When brain-inspired ai meets agi, Meta-Radiology, vol. 1, no. 1, p. 100005, 2023. |
| [19] | Evgeny Bryndin. Theoretical Foundations of Neural Network Integration of System Software Modules. Software Engineering. Volume 11, Number 4, 2025. |
| [20] | Evgeny Bryndin. Technological Stages of Neural Network AI Generation of System Program Code Based on Modular Neuro Integration. American Journal of Embedded Systems and Applications 2025. In press. |
APA Style
Bryndin, E. (2025). Implementation of AGI on Brain-Like Neuro-Network Structure. Software Engineering, 11(2), 40-48. https://doi.org/10.11648/j.se.20251102.12
ACS Style
Bryndin, E. Implementation of AGI on Brain-Like Neuro-Network Structure. Softw. Eng. 2025, 11(2), 40-48. doi: 10.11648/j.se.20251102.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.se.20251102.12,
author = {Evgeny Bryndin},
title = {Implementation of AGI on Brain-Like Neuro-Network Structure
},
journal = {Software Engineering},
volume = {11},
number = {2},
pages = {40-48},
doi = {10.11648/j.se.20251102.12},
url = {https://doi.org/10.11648/j.se.20251102.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20251102.12},
abstract = {The proposed approach to creating AGI using brain-like neural networks combines principles that teach systems to effectively generalize, remember, plan, and reason across a variety of tasks, drawing on ideas from neuromorphic architectures, dynamic hierarchical information processing, and hybrid neural-symbolic methods. Brain-like architectures dynamically process event-driven information over time, provide online learning and low power consumption, and implement research projects on hardware-based implementation of online plasticity. Within the framework of brain-like neural networks, this involves hybrid approaches to world reconstruction and action planning. The goal is to enable the network to reason, manipulate symbols, and use rules, similar to natural intelligence. This is achieved using differentiable logic, modal induction, neural memory processors, neural program interpreters, and neural-modular networks for task composition. This improves long-term memory, planning, and reasoning accuracy with a limited training set. It also enables skill transfer from one task to another, simulating alternative actions without multiple interactions with the real world. Brain-like neural networks have a modular, scalable architecture with routing and an ensemble of specialized modules. Neural modules for vision, planning, dialogue, and motor control can be dynamically assembled without complete retraining. Memory, planning, and perception are separated into separate modules with mechanisms for collaboration and joint goal learning. AGI based on brain-like neural networks enables knowledge transfer across domains. Neuromorphic modules motivate themselves to explore and investigate the environment, which supports long-term adaptability, similar to biological mechanisms. This is essential for AGI to function in the real world.
},
year = {2025}
}
TY - JOUR T1 - Implementation of AGI on Brain-Like Neuro-Network Structure AU - Evgeny Bryndin Y1 - 2025/11/12 PY - 2025 N1 - https://doi.org/10.11648/j.se.20251102.12 DO - 10.11648/j.se.20251102.12 T2 - Software Engineering JF - Software Engineering JO - Software Engineering SP - 40 EP - 48 PB - Science Publishing Group SN - 2376-8037 UR - https://doi.org/10.11648/j.se.20251102.12 AB - The proposed approach to creating AGI using brain-like neural networks combines principles that teach systems to effectively generalize, remember, plan, and reason across a variety of tasks, drawing on ideas from neuromorphic architectures, dynamic hierarchical information processing, and hybrid neural-symbolic methods. Brain-like architectures dynamically process event-driven information over time, provide online learning and low power consumption, and implement research projects on hardware-based implementation of online plasticity. Within the framework of brain-like neural networks, this involves hybrid approaches to world reconstruction and action planning. The goal is to enable the network to reason, manipulate symbols, and use rules, similar to natural intelligence. This is achieved using differentiable logic, modal induction, neural memory processors, neural program interpreters, and neural-modular networks for task composition. This improves long-term memory, planning, and reasoning accuracy with a limited training set. It also enables skill transfer from one task to another, simulating alternative actions without multiple interactions with the real world. Brain-like neural networks have a modular, scalable architecture with routing and an ensemble of specialized modules. Neural modules for vision, planning, dialogue, and motor control can be dynamically assembled without complete retraining. Memory, planning, and perception are separated into separate modules with mechanisms for collaboration and joint goal learning. AGI based on brain-like neural networks enables knowledge transfer across domains. Neuromorphic modules motivate themselves to explore and investigate the environment, which supports long-term adaptability, similar to biological mechanisms. This is essential for AGI to function in the real world. VL - 11 IS - 2 ER -
Scientific Department, Research Center Nature Informatic, Novosibirsk, Russia
Information