Robotics & machine learning for understanding neural networks

Biological neural networks are immensely complex systems underlying all aspects of cognition and behavior. Despite significant advances in neuroscience, a comprehensive understanding of their intricate computational principles remains a grand challenge. Concurrently, artificial neural networks (ANNs) and deep learning have revolutionized artificial intelligence, demonstrating impressive capabilities often inspired by biological architectures. However, translating insights from ANNs back to fundamental biological mechanisms is often non-trivial. Robotics offers a unique avenue to bridge this gap, providing embodied platforms to test computational models of neural function in dynamic, interactive environments. This Research Topic seeks to leverage the analytical power of machine learning and the experimental validation capabilities of robotics to gain deeper insights into the working principles of biological neural networks.

The primary goal of this Research Topic is to address the formidable challenge of elucidating the functional principles and emergent properties of biological neural networks. While ANNs can achieve impressive feats, their 'black box' nature often hinders direct interpretation for biological insights. We aim to overcome this by fostering interdisciplinary research that harnesses machine learning for sophisticated analysis, modeling, and interpretation of neural data, in conjunction with robotic platforms for embodying and validating these models. Robotics provides a crucial link, enabling the testing of neural hypotheses in dynamic, interactive contexts, generating rich sensory-motor data that closer mimics biological input. By integrating these fields, we seek to uncover the computational strategies employed by biological brains for perception, action, learning, and adaptation, ultimately fostering a deeper, mechanistic understanding of neural function through embodied cognition and active inference.

This Research Topic welcomes articles that explore the synergistic application of robotics and machine learning to advance our understanding of biological neural networks. We particularly encourage submissions that present novel computational models, experimental paradigms, or analytical techniques leveraging embodied agents.

Specific themes include, but are not limited to:

- Embodied neural models and their validation on robotic platforms.
- Machine learning techniques for interpreting neural activity in closed-loop robotic systems.
- Bio-inspired robotic control architectures derived from neural principles.
- Robotics as a testbed for theories of brain function, such as predictive coding or active inference.
- Computational approaches to understanding neural circuits for learning, navigation, and decision-making in embodied systems.
- The use of robots to generate and explore sensory-motor data for neural network analysis.
- Developing and testing novel learning rules and architectures inspired by biological brains.