As the field of neuroscience continues to produce large and increasingly complex datasets of brain structure, physiology and function, the demand for accurate and targeted behavioral paradigms to model neurological dysfunctions continues to grow. We face a significant interpretive challenge when relying on generalized animal behavioral tasks and metrics, often with human-imposed constraints, to lend meaning to functional neural datasets. A lack of novel, updated and innovative behavioral paradigms remains an experimental and informative bottleneck in disease-specific fields, particularly for advancing foundational neural circuit discoveries to translational applications.
This collection presents a diverse set of naturalistic behavioral paradigms and analysis methodologies tailored to neural circuit studies. Methods in this collection detail examples of how to appropriately conceive of, design, validate, modify, measure and interpret behavioral outputs from animal models of neurological dysfunctions and disease. These behavioral methodologies may be paired with neural circuit datasets obtained using a variety of techniques, including in vivo optical imaging, optogenetics, electrophysiology and physical or pharmacological perturbations.
These behavioral paradigms and analysis methodologies are sensitive to the natural environments, cognitive capabilities and behavioral tendencies of the animal model, and are careful to avoid or minimize human bias in task design and data interpretation to better inform the neural circuit data gathered in parallel.