Augmenting learning in neuro-embodied systems through neurobiological first principles
Newcastle University（纽卡斯尔大学）；University of Tokyo（东京大学）；University of Quebec in Trois-Rivieres（魁北克大学三河分校）；IT:U Interdisciplinary Transformation University Austria（奥地利跨学科转型大学）；Okinawa Institute of Science and Technology Graduate University（冲绳科学技术大学院大学）
Alejandro Rodriguez-Garcia, Anindya Ghosh, Jie Mei, Srikanth Ramaswamy
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.
Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations
KEIM Institute, Albstadt-Sigmaringen University, Germany（KEIM研究所，阿尔布施塔特-西格马林根大学，德国）；Department of Computer Science, Chemnitz University of Technology, Germany（计算机科学系， Chemnitz技术大学，德国）
From Brain Models to Executable Digital Twins: Execution Semantics and Neuro-Neuromorphic Systems
International Laboratory on Learning Systems (ILLS)（学习与系统国际实验室）；CNRS（法国国家科学研究中心）；McGill（麦吉尔大学）；ETS（工程技术高等学院）；MILA（蒙特利尔人工智能研究院）；Université Paris-Saclay（巴黎萨克雷大学）
