Decoding Behavior: Molecular Insights and High-Resolution Phenotyping in Naturalistic Settings

Understanding behavior at multiple levels of resolution—from molecules to actions—is a hallmark and complex topic in neuroscience. This research topic invites contributions that bridge molecular neuroscience with next-generation behavioral phenotyping, aiming to deepen our understanding of how cellular and molecular mechanisms translate into, or contribute to, complex, naturalistic behaviors.


Currently, molecular neuroscience is yielding powerful tools to probe the mechanisms that contribute to such behavior. Contributions focusing on the role of extracellular vesicles in neural communication, the plasticity of synaptic networks, the interplay between neurons and glial cells, neuromodulation, or molecular responses to environmental stimuli are all highly relevant to this research topic. Studies that link molecular changes to specific behavioral patterns, especially those revealed by high-resolution phenotyping, are particularly encouraged.


At the same time, advances in computational tools and experimental design—especially with the emergence of machine learning—have transformed behavioral neuroscience. Markerless pose estimation technologies such as DeepLabCut and SLEAP now enable high-throughput, sub-second tracking of animal posture and movement in both enriched homecage environments and controlled artificial arenas like virtual reality settings. These technologies allow for long-term, unobtrusive monitoring of spontaneous behavior with unprecedented spatial and temporal resolution, capturing nuances far beyond what traditional behavioral scoring could reveal.

Behavioral interpretation packages, including DeepOF, SimBA, BORIS, B-SOID, and others, offer un- or semi-supervised classification of complex behavioral patterns. These tools, when integrated with pose estimation or multi-animal tracking systems (including RFID-based and marker-based solutions), are redefining how we quantify behaviors—whether social interaction, exploration, anxiety-like behavior, or decision-making—in ethologically relevant contexts.

We are particularly interested in studies that make use of novel modulations and innovative measurements in “naturalistic” settings, whether that involves group-housed animals in enriched homecages, complex spatial navigation in virtual reality systems, or stress paradigms that mimic real-world challenges.

By integrating molecular and behavioral data at multiple scales and in semi-naturalistic conditions, this research topic aims to move beyond reductionist paradigms and toward a systems-level understanding of behavioral heterogeneity.

Contributions from molecular neuroscientists, behavioral neuroscientists, systems biologists, computer vision specialists, and computational biologists are all warmly welcomed.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: behavioral phenotyping, environmental stimuli, pose estimation, computational tools, naturalistic behavior, neural mechanisms

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.