Cross-Species Transfer Learning in Behavioral Neuroscience

From Mice to Rats, Zebrafish, and Beyond

In the rapidly advancing field of behavioral neuroscience, the ability to train machine learning models on one species and adapt them to another represents a powerful opportunity to streamline experimental workflows, reduce labeling effort, and enhance cross-species comparisons. This strategy—known as cross-species transfer learning—has gained traction as a way to extend the utility of pose estimation, behavior classification, and neural decoding models originally trained on mice to other laboratory organisms such as rats, zebrafish, and even non-rodent species.

This article explores the conceptual underpinnings and practical methodologies behind transfer learning in ethological research. It details how to manage differences in morphology, camera geometries, and behavioral repertoires using techniques like domain adaptation, fine-tuning and benchmarking. We also examine how modular platforms from Conduct Science support scalable cross-species behavioral research.

Why Transfer Learning Matters

Training deep learning models from scratch for every species, setup, or behavior type is prohibitively time- and labor-intensive. Transfer learning mitigates this cost by leveraging knowledge from source datasets (typically well-annotated mouse datasets) to accelerate model performance on target species. The scientific advantages include

• Reduced annotation load for less-studied species.
  
• Faster model convergence on new datasets.
  
• Improved cross-species generalization, enabling comparative ethology.
  
• Cost-effective model deployment across modular arenas and multi-species labs.
  

This is especially relevant for multi-species platforms—such as those offered by Conduct Science—where researchers may transition between rodent models and aquatic organisms like zebrafish for developmental or pharmacological studies.

Key Challenges in Cross-Species Transfer

Cross-species transfer learning offers significant promise, but it also presents complex technical and biological challenges. Transferring models trained on one species (such as mice) to others (like rats or zebrafish) requires overcoming differences not only in anatomy and movement but also in experimental setups and behavioral semantics. These differences can severely impair model generalization if not carefully addressed. Below, we explore the core obstacles that researchers must navigate to achieve effective transfer learning in behavioral neuroscience.

1. Morphological and Kinematic Divergence

One of the most fundamental challenges in cross-species transfer is the variation in body plans and movement dynamics between species.

• Rodents vs. Zebrafish: Mice and rats share a general quadrupedal structure but differ in proportions—rats have longer limbs and slower gait cycles. Zebrafish, by contrast, possess a fusiform aquatic body with tail-driven propulsion, lacking limbs altogether.
• Joint and Limb Structure: Rodents exhibit discrete jointed limbs with digit articulation, while zebrafish require modeling of continuous body curvature for accurate pose estimation.
• Behavioral Output Differences: Behaviors like “grooming” or “freezing” in mice have no direct analog in zebrafish, making one-to-one behavior label transfer ineffective.

These differences necessitate adaptable models that can learn species-invariant movement features while still respecting species-specific nuances.

2. Camera Geometry and Environmental Discrepancies

Transfer learning also suffers from inconsistencies in camera placement, lens type, resolution, and arena configuration.

• Perspective Changes: Top-down cameras used for rodents differ greatly from side-view setups often required for aquatic species. These views change the way keypoints appear and move across frames.
• Field of View and Scale: A model trained on a 50×50 cm rodent arena may not generalize to a shallow zebrafish tank or a multi-tiered home cage system.
• Lighting and Backgrounds: Lighting angle, contrast, and reflectivity affect visual features critical to deep learning models. Water reflections and distortions add further complexity for aquatic species.
• Occlusion Patterns: In group settings, different species exhibit distinct occlusion risks—rodents huddle and stack, while zebrafish school and overlap laterally.
  

These variations demand robust preprocessing and architecture design that can disentangle geometry-dependent features from behaviorally relevant ones.

3. Label Semantics and Behavior Interpretation

Another underappreciated challenge is the semantic mismatch in behavioral annotations across species.

• Species-Specific Behaviors: Actions like tail rattling (mice), upright boxing (rats), or erratic darting (zebrafish) may not translate meaningfully across species.
• Label Granularity: What qualifies as a single behavior in one species may require multiple sub-labels in another. For instance, zebrafish tail flicks vary in frequency and amplitude but are rarely segmented by behavior.
• Observer Bias: Manual labels are influenced by species familiarity, leading to inconsistent labeling across species or labs.

Such inconsistencies impair supervised behavior classifiers and call for unsupervised motif discovery, allowing behaviors to be defined from the data itself rather than imposed a priori.

4. Model Overfitting to Source Species

Deep learning models are highly expressive but also prone to overfitting—especially when trained on richly annotated but homogenous datasets.

• Pose estimation networks may learn to expect mouse-specific proportions or background textures, causing poor generalization when applied to larger-bodied rats or aquatic settings.
• Behavior classification models may key in on species-specific idiosyncrasies, such as grooming posture angles or step cadence, limiting their ability to recognize functionally similar behaviors in other species.

Without intentional regularization and domain adaptation strategies, these models can carry forward narrow biases that reduce their cross-species applicability.

5. Lack of Standardized Benchmarks and Datasets

Finally, there is a significant lack of standardized cross-species benchmarks in behavioral neuroscience.

• Public datasets tend to focus on mice or flies, with limited availability of multi-species annotated video corpora.
• Few platforms provide side-by-side performance metrics for pose or behavior models across rodents and aquatic species.
• Differences in recording hardware, arena size, and annotation protocols make cross-lab comparison difficult.
  

This absence of standardized evaluation pipelines complicates benchmarking and hinders the development of generalizable, species-agnostic behavioral models.

Addressing These Challenges

Conduct Science provides researchers with experimental tools that mitigate many of these challenges:

• Modular arenas with configurable size, lighting, and transparency to harmonize conditions across species.
• Camera rigs are adaptable to both top-down and side-view setups.
• Multi-species compatible tracking environments, including rodent open fields and aquatic tanks designed for seamless pose capture.
• Standardization in setup improves the quality of data transferability and enhances the effectiveness of fine-tuned or domain-adapted models.

To fully realize the promise of cross-species transfer learning, researchers must combine thoughtful experiment design with rigorous algorithm development—ensuring that the complexity of biological diversity is met with equally adaptable computational tools.

Strategies for Effective Transfer Learning

1. Domain Adaptation

Domain adaptation aims to align the feature spaces of source and target domains—e.g., mouse and rat video frames—without requiring full retraining.

• Feature extractor freezing: Retain the core of a pre-trained CNN (convolutional neural network) trained on mice, while adapting the final layers for the target species.
  
• Adversarial learning: Introduce a domain discriminator to reduce species-specific biases in the latent space.
  
• Style transfer: Normalize color, lighting, and background between source and target videos to minimize input differences.
  

This approach is particularly useful when using shared behavioral analysis platforms across species, such as those described in Conduct Science’s arena design solutions, which standardize lighting and camera angles for cross-lab consistency.

2. Fine-Tuning on a Subset of Target Data

Instead of building a new model, fine-tuning involves updating a pre-trained model on a small labeled subset of the new species data.

• Use transfer learning pipelines such as those found in DeepLabCut or SLEAP.
  
• Annotate only 100–200 frames of the target species.
  
• Fine-tune for a few epochs while freezing lower layers to retain general motion features.
  

Fine-tuning provides a balance between performance and efficiency, making it ideal for labs working across multiple species with limited annotation capacity.

3. Benchmarking and Evaluation

To assess how well a transferred model performs:

• Compute PCK (Percentage of Correct Keypoints) for pose estimation tasks.
  
• Analyze confusion matrices and F1 scores for behavior classification.
  
• Evaluate robustness to occlusion and identity switches, especially in multi-animal settings.
  

Benchmarking across species also enables comparative studies—e.g., whether the same motif classifier detects “chase” in mice and rats with equivalent accuracy—thereby strengthening the ethological relevance of your research.

Cross-Species Insights: From Rodents to Zebrafish

The expansion of behavioral neuroscience into multi-species domains has elevated zebrafish from a developmental biology model to a core organism in translational neuroscience. Their genetic tractability, high-throughput potential, and transparent embryonic development make them ideal for drug discovery and neurobehavioral screening. However, adapting machine learning tools trained on terrestrial rodents to aquatic species like zebrafish presents unique conceptual and technical hurdles—and fascinating opportunities.

By applying cross-species transfer learning, researchers can extend models developed for rodents to zebrafish, generating shared representations of behavior across distinct locomotor systems and body morphologies. This adaptation supports the construction of generalizable behavioral pipelines, enabling comparative studies that bridge evolutionary distance and diversify experimental insights.

Anatomical and Locomotor Divergence

Zebrafish locomotion is driven by undulatory tail movements and coordinated fin adjustments, producing fluid-based propulsion rather than limb-based walking or running. This key distinction necessitates tailored adaptations in pose estimation and behavior modeling:

• Skeletal simplification: Unlike rodents, zebrafish lack discrete limb joints for tracking; instead, pose estimation must rely on midline curvature, tail amplitude, and fin angles.
• Continuous motion dynamics: Rodent behavior often contains distinct postural transitions (e.g., rearing, grooming), while zebrafish motion is more rhythmic and oscillatory, requiring temporal models that capture wave propagation and frequency modulation.
• Body size and speed: Zebrafish exhibit rapid, high-frequency tail flicks and darting behaviors, demanding higher frame rates and denoising techniques to prevent loss of signal.

Despite these differences, certain behavioral archetypes are evolutionarily conserved, such as avoidance responses, exploration, group cohesion, and stimulus-driven approach or escape. These afford a foundation for cross-species behavior mapping using adapted models.

Pose Estimation Adaptation for Aquatic Environments

To transfer rodent-trained pose estimation models to zebrafish, several key modifications are required:

1. Keypoint Redefinition
   • Rodent models track limb joints, snout, ears, and tail base. For zebrafish, models must shift to spine segmentation—tracking the midline along the anterior-posterior axis.
   • Custom keypoints may include the swim bladder, tail tip, jaw, and pectoral fins, depending on behavioral focus.
     
2. Spline or Skeleton-Free Models
   • Instead of jointed skeletons, spline-based or centerline methods (e.g., using heatmap maxima) better capture zebrafish body curvature.
     
3. Waterborne Video Challenges
   • Refraction, reflection, and lighting inconsistency in aquatic tanks complicate video preprocessing.
   • Conduct Science addresses these issues with infrared-compatible tanks, non-reflective enclosures, and calibrated camera geometries designed for aquatic tracking.
     
4. Behavior-Specific Augmentation
   • Pose data can be augmented with tail beat frequency, swim trajectory curvature, and velocity vectors, enhancing behavior classification.

Transferring Behavioral Models: From Chase to Dart

While rodents express behaviors like chasing, rearing, or huddling, zebrafish display species-specific analogs:

• Shoaling and avoidance may parallel rodent social spacing and approach-avoidance dynamics.
• Rapid darting can serve as a proxy for flight or startle responses.
• Thigmotaxis—edge-preference behavior common in zebrafish—resembles rodent anxiety patterns in open field exploration.

By training classifiers on functional, rather than morphological, behavior categories (e.g., “approach,” “withdrawal,” “freezing”), researchers can create cross-species behavioral taxonomies that facilitate comparative neuroscience.

Use Cases and Experimental Design

Zebrafish offer powerful experimental advantages for studies where large-scale screening is essential. Cross-species transfer learning enhances the utility of these models in:

• Drug screening pipelines: Leveraging pretrained rodent classifiers to quickly evaluate compounds’ effects on exploratory behavior or social preference in zebrafish.
• Neurodevelopmental models: Applying shared behavior representations to study autism, epilepsy, and hyperactivity across species.
• Environmental toxicology: Mapping locomotor and social disruption under chemical exposure using consistent motif definitions.

Platforms from Conduct Science support these workflows with customized aquatic arenas, top-view tracking systems, and compatibility with high-throughput video analysis tools—ideal for labs working across both terrestrial and aquatic domains.

Toward Shared Behavioral Ontologies

The ultimate goal of transferring models from rodents to zebrafish is to establish shared behavioral ontologies—data-driven frameworks that define behaviors not by species-specific labels, but by underlying kinematic and social patterns.

For example:

• A tail flick in zebrafish and a head twitch in mice might both represent brief arousal responses.
• An investigative swim toward a conspecific in zebrafish may align functionally with flank sniffing in rats.
  

With continued refinement, such mappings could enable

• Evolutionary comparisons of behavior across vertebrates.
• Meta-analyses integrating data from multiple models of a single disorder.
• Machine-agnostic models that recognize the function of behavior regardless of the species or experimental setup.

Conduct Science Tools for Multi-Species Behavioral Research

As the scope of behavioral neuroscience expands to include multiple species, environments, and behavioral paradigms, the need for modular, adaptable, and scalable research infrastructure becomes paramount. Conduct Science has responded to this demand by offering a comprehensive suite of tools designed to facilitate multi-species behavioral analysis, from rodents to zebrafish and beyond. These tools are specifically engineered to support advanced tracking, pose estimation, and experimental reproducibility across a variety of research contexts.

1. Modular Arenas for Rodents and Aquatic Species

Conduct Science provides customizable arena systems tailored for different species and behavioral assays.

• Open Field Arenas: Designed with removable walls, adjustable floor dimensions, and non-reflective surfaces, these are ideal for tracking locomotion, anxiety-related behavior (e.g., thigmotaxis), and social interaction in rodents. Arena configurations can be easily modified to fit mice or rats without sacrificing data fidelity.
• Home Cage Systems: For long-term monitoring of group-housed animals, home cage setups support naturalistic, spontaneous behavior tracking over circadian cycles. These systems are compatible with camera arrays and electrophysiological recording, enabling integration of behavior and neural data.
• Aquatic Tracking Tanks: For zebrafish and other aquatic organisms, Conduct Science offers clear, infrared-compatible tanks with flat bases to eliminate refraction artifacts. These setups are optimized for top-down and side-view video tracking, and they support behavioral paradigms such as shoaling, avoidance, and novel tank exploration.

Each arena can be easily incorporated into pose estimation workflows using DeepLabCut, SLEAP, or custom neural network pipelines—making them ideal for researchers employing transfer learning or working with multi-species datasets.

2. Flexible Multi-Camera Tracking Systems

Cross-species behavioral studies often require multi-angle and high-resolution video capture, particularly when comparing species with different locomotor dynamics or when monitoring social interactions in three dimensions.

Conduct Science supports this need with:

• Synchronized camera arrays for simultaneous top, side, and oblique views.
• High frame rate options are essential for capturing fast movements in zebrafish or rearing/postural shifts in rodents.
• Infrared lighting systems for non-intrusive tracking during dark-phase activity or light-sensitive assays.

These configurations are essential for reducing occlusion, improving pose accuracy, and enabling 3D reconstruction of behavior—particularly useful when comparing rodents’ limb-driven locomotion to zebrafish’s wave-like swimming patterns.

3. Standardized Lighting and Environmental Control

Lighting conditions and visual contrast dramatically impact the performance of pose estimation models and transfer learning accuracy. Conduct Science ensures consistency and reproducibility through:

• Controlled ambient light and integrated IR backlighting.
• Optional UV-safe lighting for species with heightened photoreactivity.
• Non-reflective arena materials that minimize glare and background noise in aquatic environments.

These features reduce visual variability, which is critical when applying models across different labs, species, and behavioral tasks.

4. Compatibility with AI-Based Behavioral Pipelines

Whether researchers are performing pose estimation, motif discovery, or cross-species behavior classification, Conduct Science tools are designed to integrate with AI-based platforms such as

• DeepLabCut—for supervised keypoint tracking.
• SLEAP—for multi-animal, multi-species pose estimation.
• MoSeq, B-SOiD, or custom unsupervised clustering algorithms—for extracting behavior motifs and action transitions.

With standardized spatial dimensions, calibrated video outputs, and stable environmental variables, Conduct Science systems simplify domain adaptation and fine-tuning, both essential for effective transfer learning.

5. Customizability and Cross-Lab Replicability

A major strength of Conduct Science platforms is their custom-built flexibility. Researchers can tailor arena dimensions, wall configurations, and sensor integration (e.g., motion detectors, RFID tracking, or stimulus delivery systems) according to their unique experimental goals—whether working with social rodents or free-swimming fish.

Moreover, the modular design ensures that experiments conducted across different institutions can be standardized and replicated, a key factor in comparative and collaborative neuroscience.

6. Comprehensive Support for Behavioral Neuroscience

Conduct Science’s offerings are not limited to arenas and cameras—they extend to a full experimental pipeline:

• Surgical and tethering equipment for neural recording and stimulation.
• Software integration for real-time behavior-triggered feedback.
• Data visualization tools for plotting trajectories, heatmaps, and motif sequences.

This holistic design ensures that researchers can seamlessly integrate behavior, neural data, and computational analysis within a single experimental ecosystem.

Summary

Conduct Science enables multi-species behavioral research by providing:

• Adaptable arenas for mice, rats, and zebrafish.
• Multi-camera systems with IR and high-speed capabilities.
• Environmental control and reproducible lighting.
• Seamless integration with pose estimation and behavior analysis software.
• Modular, replicable setups for labs engaged in cross-species neuroscience.

These tools lay the groundwork for the next generation of behavioral research—one that is flexible, scalable, and scientifically rigorous. Whether developing cross-species models of social behavior, transferring pose estimators from rodents to fish, or benchmarking the effects of pharmacological treatments across vertebrates, Conduct Science equips researchers with the infrastructure to make high-impact discoveries across species boundaries.

Explore modular setups at ConductScience.com and detailed walkthroughs on the Conduct Science YouTube Channel, where video-based behavior tracking is applied to diverse experimental models.

Future Directions: Toward Universal Behavioral Models

As behavioral neuroscience continues to embrace high-throughput technologies, unsupervised learning, and cross-species frameworks, the field is approaching a pivotal transformation: the development of universal behavioral models. These models aim to generalize across species, contexts, and environments—recognizing not just species-specific actions but core movement patterns and functional behaviors that transcend anatomical and ecological differences. Achieving this vision requires a convergence of computational innovation, standardized experimental platforms, and cross-disciplinary collaboration.

The Rationale for Universal Models

Current behavior classification pipelines are often trained for one species, in one experimental setup, using one behavioral taxonomy. As a result, models trained on laboratory mice may fail when applied to rats, zebrafish, or other species, even if the core behavior—such as exploration, threat response, or social approach—is functionally similar.

A universal behavioral model would enable

• Cross-species generalization, where conserved behavioral motifs are identified regardless of morphology.
• Scalable annotation, minimizing the need for species-specific training data.
• Comparative ethology, linking behavioral evolution across taxa using shared computational language.
• Integrated neuroscience, where findings in one species can inform and validate those in another.

This would transform how researchers build models of brain function, especially in areas such as emotion, social behavior, motivation, and decision-making.

Foundations for Generalizable Behavior Modeling

To move toward universality, several foundational principles must guide model design:

1. Function Over Form
   Models should focus on the purpose and structure of behavior rather than its surface expression. For example, both a zebrafish’s dart and a mouse’s startle response may reflect a common behavioral state: escape or arousal.
2. Latent Dynamics
   Using dimensionality reduction and time-series modeling, researchers can identify latent behavioral spaces—continuous, shared axes along which actions evolve. These can provide an abstract, species-agnostic framework for describing movement.
3. Unsupervised Discovery
   Unsupervised methods allow behavior to emerge from the data itself, free from human-imposed labels. This not only avoids semantic mismatches between species but also captures novel or transitional behaviors overlooked by manual scoring.
4. Contextual Embedding
   Incorporating environmental context (e.g., arena structure, conspecific proximity, light conditions) enables models to understand how behavior is modulated, making predictions more robust and transferable.
5. Hierarchical Representation
   Behavior is structured hierarchically—micro-movements form motifs, motifs form sequences, and sequences define behavioral states. Universal models should capture this multi-scale organization, which is conserved across many species.
   

Technical Approaches

To support these goals, universal behavioral modeling will leverage:

• Graph neural networks (GNNs) for encoding body pose as relational data, allowing flexibility across different body plans.
• Temporal convolutional networks (TCNs) and transformers for modeling the sequential and time-dependent nature of behavior.
• Domain adaptation and meta-learning to transfer knowledge between species and experimental setups.
• Cross-species benchmarking datasets, where diverse animals are recorded in comparable environments using standardized hardware.

Conduct Science’s modular arenas and multi-species tracking systems are well-suited to generate this kind of structured, comparable data—supporting efforts to train and validate generalizable models.

Scientific and Translational Impact

Universal behavioral models would unlock a host of new capabilities in neuroscience and beyond:

• Cross-laboratory reproducibility: By standardizing behavioral representation, findings from different labs and species could be directly compared, increasing transparency and scientific rigor.
• Translational psychiatry: Models of depression, anxiety, or social dysfunction could be validated in mice, rats, zebrafish, and eventually humans, with behavior defined in a shared computational language.
• Automated phenotyping: Genetic and pharmacological screens could rapidly identify behaviorally relevant changes across species using common metrics.
  
• Robotic modeling: Insights from universal behavioral motifs could inform bio-inspired control systems and synthetic agent behavior.

From Vision to Reality

The path to universal behavioral models will require collaborative infrastructure:

• Open, annotated datasets across species and labs.
• Standardized arenas, like those provided by Conduct Science, ensure comparable data collection conditions.
• Community-developed taxonomies and validation protocols, bridging neuroscience, ethology, machine learning, and cognitive science.

As more labs adopt multi-species experimental designs and align around shared computational tools, the foundation for universal behavior modeling will solidify—offering a future in which behavioral insights are not bound by species but unified by function and structure.

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Berman, G. J. (2018). Measuring behavior across scales. BMC Biology, 16(1), 23. https://doi.org/10.1186/s12915-018-0494-7

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Conduct Science. (n.d.). Behavioral Neuroscience Tools & Modular Arena Systems. Retrieved from https://conductscience.com/behavior/behavior-analysis/Conduct Science YouTube Channel. (n.d.). Equipment Walkthroughs and Experimental Setups. Retrieved from https://www.youtube.com/@conductscience