Computational neuroethology is an emerging field that combines neuroscience, ethology, and computational modeling to understand how the brain generates natural behavior in real-world environments. Unlike traditional neuroscience approaches that often rely on simplified, lab-based tasks, neuroethology emphasizes studying animals in ecologically relevant contexts. By simulating natural behaviors—such as foraging, navigation, communication, and social interaction—researchers can uncover how neural circuits process sensory information, guide motor actions, and adapt through learning. Computational models play a key role in linking neural dynamics to behavior, enabling the exploration of brain-body-environment interactions across species. Advances in neural recording, machine learning, and behavioral tracking now allow for more accurate and scalable simulations of naturalistic behavior. This Research Topic, Computational Neuroethology: Simulating Natural Behaviors, aims to highlight interdisciplinary work that leverages computational tools to bridge the gap between neural function and behavior in complex, dynamic settings.

A key challenge in neuroscience is understanding how neural circuits generate complex, natural behaviors in real-world environments. Traditional lab-based tasks often oversimplify behavior, limiting insight into how the brain adapts to dynamic and ecologically relevant situations. Computational neuroethology aims to bridge this gap by modeling natural behaviors and simulating brain-body-environment interactions. However, the complexity of real-world behavior makes this a difficult task, requiring the integration of neural data, detailed behavioral tracking, and realistic simulations.

Recent advances in machine learning, neural recording, and motion tracking have made it increasingly feasible to model natural behaviors with high fidelity. This Research Topic invites contributions that use computational tools to simulate and analyze naturalistic behaviors, model sensorimotor integration, or explore decision-making in dynamic settings. The goal is to advance our understanding of how neural systems support adaptive behavior in the real world.

This Research Topic invites original research, reviews, and methods papers focused on computational models of natural behavior. We welcome studies that simulate brain-body-environment interactions, model behaviors like navigation, foraging, or social interaction, and explore sensorimotor integration and decision-making in dynamic settings. Contributions using neural networks, agent-based models, or data-driven approaches to link neural activity with behavior are encouraged. Interdisciplinary work combining neuroscience, ethology, robotics, or AI is also welcome. The goal is to advance our understanding of how neural systems generate adaptive behavior in real-world contexts.

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Computational neuroethology: simulating natural behaviors