arXiv:2407.04525v5 Announce Type: replace-cross 
Abstract: Recent progress in artificial intelligence (AI) has been driven by insights from physics and neuroscience, particularly through the development of artificial neural networks (ANNs) capable of complex cognitive tasks such as vision and language processing. Despite these advances, they struggle with continual learning, adaptable knowledge transfer, robustness, and resource efficiency -- capabilities that biological systems handle seamlessly. Specifically, neuromorphic systems and artificial neural networks often overlook two key biophysical properties of neural circuits: neuronal diversity and cell-specific neuromodulation. These mechanisms, essential for regulating dynamic learning across brain scales, allow neuromodulators to introduce degeneracy in biological neural networks, ensuring stability and adaptability under changing conditions. In this article, we summarize recent bioinspired models, learning rules, and architectures, and propose a framework for augmenting ANNs, which has the potential to bridge the gap between neuroscience and AI through neurobiological first principles. Our proposed dual-framework approach leverages spiking neural networks to emulate diverse spiking behaviors and dendritic compartmental dynamics, thereby simulating the morphological and functional diversity of neuronal computations. Finally, we outline how integrating these biophysical principles into task-driven spiking neural networks and neuromorphic systems provides scalable solutions for continual learning, adaptability, robustness, and resource-efficiency. Additionally, this approach will not only provide insights into how emergent behaviors arise in neural networks but also catalyze the development of more efficient, reliable, and intelligent neuromorphic systems and robotic agents.

تُحدث التقدمات الأخيرة في الذكاء الاصطناعي، وخاصة من خلال الشبكات العصبية الاصطناعية، تحولًا في فهمنا للمهام المعرفية مثل الرؤية ومعالجة اللغة. هذا التقدم مهم لأنه يستند إلى رؤى من الفيزياء وعلم الأعصاب، مما يبرز إمكانية تطور الذكاء الاصطناعي. ومع ذلك، لا تزال هناك تحديات في مجالات مثل التعلم المستمر والقدرة على التكيف، والتي تديرها الأنظمة البيولوجية بسهولة. يمكن أن يؤدي معالجة هذه القضايا إلى أنظمة ذكاء اصطناعي أكثر قوة، مما يجعل هذا البحث حاسمًا لمستقبل التكنولوجيا.

Los recientes avances en inteligencia artificial, especialmente a través de redes neuronales artificiales, están transformando nuestra comprensión de tareas cognitivas como la visión y el procesamiento del lenguaje. Este progreso es significativo porque se basa en conocimientos de la física y la neurociencia, destacando el potencial de evolución de la IA. Sin embargo, persisten desafíos en áreas como el aprendizaje continuo y la adaptabilidad, que los sistemas biológicos manejan sin esfuerzo. Abordar estos problemas podría llevar a sistemas de IA aún más robustos, lo que hace que esta investigación sea crucial para el futuro de la tecnología.

Les récentes avancées en intelligence artificielle, notamment grâce aux réseaux de neurones artificiels, redéfinissent notre compréhension des tâches cognitives telles que la vision et le traitement du langage. Ce progrès est important car il s'inspire des connaissances en physique et en neurosciences, soulignant le potentiel d'évolution de l'IA. Cependant, des défis subsistent dans des domaines comme l'apprentissage continu et l'adaptabilité, que les systèmes biologiques gèrent sans effort. S'attaquer à ces problèmes pourrait conduire à des systèmes d'IA encore plus robustes, rendant cette recherche cruciale pour l'avenir de la technologie.

Recent advancements in artificial intelligence, particularly through artificial neural networks, are reshaping our understanding of cognitive tasks like vision and language processing. This progress is significant because it draws from insights in physics and neuroscience, highlighting the potential for AI to evolve. However, challenges remain in areas like continual learning and adaptability, which biological systems manage effortlessly. Addressing these issues could lead to even more robust AI systems, making this research crucial for the future of technology.

Augmenting learning in neuro-embodied systems through neurobiological first principles

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Augmenting learning in neuro-embodied systems through neurobiological first principles

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