NeuroArch: Designing for the mind

Abstract This study examines the field of neural architecture, an interdisciplinary discipline that seeks to understand the structure and function of the brain for application in various areas such as artificial intelligence, neuroscience, and computing. Through a review of recent research, we present advances in biological computing, the evolution of complex brains, learning in unicellular organisms, and neuroarchitecture, as well as the development of more efficient neural network models. 1. Introduction The human brain has been an inexhaustible source of inspiration for science and technology. Neural architecture, understood as the design and modeling of artificial and biological neural networks, has enabled advancements in multiple disciplines. This paper examines recent developments in this field, including biological computing with human neurons, the evolution of complex brains in vertebrates, the explainability of decisions in artificial intelligence, learning in unicellular organisms, the optimization of neural networks in facial detection, and neuroarchitecture. 2. Materials and Methods For the development of this work, a bibliographic review of recent studies on neural architecture was conducted, covering scientific publications, specialized articles, and reports on technological advancements. Sources from various disciplines, including neuroscience, artificial intelligence, and bioengineering, were analyzed to provide a comprehensive view of the field's progress. 3. Results and Discussion 3.1 Biological Computing with Human Neurons Recently, the Australian company Cortical Labs developed CL1, a biological computer based on laboratory-grown human neurons. This system has the potential to drastically reduce energy consumption compared to conventional devices while improving decision-making efficiency. Additionally, it provides an experimental platform for understanding neural processes and developing new pharmaceuticals. 3.2 Evolution of Complex Brains in Vertebrates Recent studies have shown that mammals, birds, and reptiles have developed complex brains through independent evolutionary processes. This evolutionary convergence suggests that intelligence is not exclusive to a single lineage but can arise from diverse brain structures adapted to specific environments, with implications for biology and the modeling of artificial neural networks. 3.3 Artificial Intelligence and Decision Explainability One of the current challenges in artificial intelligence is the opacity of its models, commonly described as "black boxes." A study published in Proceedings of the National Academy of Sciences (PNAS) proposes a mathematical technique that improves the interpretability of AI systems, enabling more precise image segmentation. This advancement has applications in medicine, security, and autonomous driving. 3.4 Learning in Unicellular Organisms Research conducted at Harvard University and in Barcelona has demonstrated that the ciliate Stentor roeselii is capable of learning and developing a primitive form of memory. This finding suggests that learning mechanisms are not exclusive to organisms with brains, opening new research avenues in biology and the development of computing systems inspired by living organisms. 3.5 Neural Architecture Search for Facial Detection Neural Architecture Search (NAS) has enabled the optimization of neural network models in artificial intelligence. A notable case is FaceNAS, a system designed to improve facial detection through models adjustable to different levels of computational capacity. These advances impact security and biometric recognition. 3.6 Neuroarchitecture: Integration of Environment and the Human Brain Neuroarchitecture studies how physical environments affect brain function. Inspired by the studies of Dr. Jonas Salk and architect Louis Kahn, this field aims to design spaces that optimize creativity, concentration, and well-being. Factors such as lighting, spatial perception, and acoustics have a direct impact on people's experience and productivity. 4. Conclusion Advances in neural architecture have led to a better understanding of brain processes and their application in various technological and scientific fields. From biological computing to neural network optimization, this interdisciplinary field continues to evolve. Future research is expected to further integrate biological knowledge with the development of new technologies, fostering innovations in artificial intelligence, medicine, and architectural design. 5.

References:

Cortical Labs. (2025). The biological computer with human neurons. El País.

PNAS. (2025). Mathematical methods for AI interpretability.

Harvard University. (2025). Learning in unicellular organisms.

University of Chile. (2025). FaceNAS: optimizing neural networks for facial detection.

Salk, J., & Kahn, L. (2025). Neuroarchitecture and the relationship between the environment and the human mind. The Future of Neural Architecture: Bridging Biology and AI.