There is a relevant transforming convergence between artificial intelligence (AI) and neuroscience, particularly in the field of behavioral neuroscience around the modeling of adaptive behaviors and the identification of behavioral phenotypes.
AI presents not only as a computational tool but also as a theoretical framework able to model neural systems, decode behavioral patterns, and predict disease trajectories with hitherto unheard-of accuracy as we try to untangle the complicated interactions between brain structure, function, and behavior.

Traditionally, behavioral neuroscience has understood the processes behind cognition, emotion, and behavior by depending on experimental methods. Still, the fast growing complexity and volume of neurobiological and behavioral data now demand more scalable, integrated approaches. Unique possibilities to process high-dimensional datasets, detect subtle or non-linear patterns, and replicate behavior across developmental, pathological, and therapeutic settings present themselves from AI and machine learning (ML) techniques.
AI is reshaping our ability to investigate the brain-behavior axis: from decoding neural signals and predicting behavioral outputs to creating synthetic data that might enhance small experimental cohorts. It offers unique capabilities to process naturalistic behavior and build predictive behavioral models across ecological and developmental contexts.
Moreover, the synergy between AI and behavioral neuroscience enables fine-grained modeling of individual variability, facilitating the identification of individual behavioral phenotypes and neurobiological correlates of psychiatric and neurodegenerative diseases.
For early identification and intervention plans in underprivileged populations, the combination of AI-based predictive modeling with longitudinal data shows especially promise.

Concurrent with this, the application of AI in neuroscience begs significant problems and questions. Problems with model interpretability, bias in training datasets, reproducibility, and ethical deployment have to be resolved such that these technologies significantly advance neuroscience without supporting current inequalities or oversimplifying complicated biological reality.
From data integration and interpretation to mechanistic modeling and translational applications, this Research Topic seeks to investigate the many functions of AI tailored to behavioral neuroscience. Emphasizing how AI may help identify the neurological substrates of behavior, increase diagnostic accuracy, and direct treatment innovation, will show the reciprocal enrichment between computational intelligence and behavioral research.

We welcome submissions examining how AI approaches, including supervised and unsupervised learning, deep learning, neural network modeling, natural language processing, and generative AI, are used to analyze brain-behavior links. We especially welcome studies grounded in behavioral outputs or behavioral constructs, and we support entries looking at ethical issues, constraints of present models, and future viewpoints for including AI into experimental and clinical behavioral neuroscience.

Articles on the following subtopics are especially welcome:

1. Computational modeling and neural decoding:
o AI-driven decoding of neural activity associated with behavior
o Modeling of decision-making, emotional regulation, or learning using deep neural networks
o Simulations of behavioral outputs from synthetic or biologically inspired AI models
2. Data-driven discovery in behavioral neuroscience:
o Machine learning for behavioral phenotyping in animal and human studies
o Predictive models for psychiatric and neurodegenerative outcomes
o Integration of multimodal data (neuroimaging, genomics, behavior) using AI frameworks
3. Personalized neuroscience and digital biomarkers:
o AI for individual behavioral trajectories’ prediction
o Digital phenotyping and real-world behavioral monitoring using wearable sensors and AI
o Identification of early biomarkers of disease progression through longitudinal modeling
4. Ethical and translational perspectives:
o Interpretability, explainability, and fairness in AI models applied to neuroscience
o Ethical implications of using AI in behavioral monitoring and psychiatric prediction
o Challenges in clinical translation and regulatory considerations
5. Innovations in experimental design and methodology:
o Use of AI to enhance experimental paradigms in animal models
o Closed-loop systems integrating AI and real-time behavioral manipulation
o Generative AI for augmenting behavioral datasets or simulating experimental conditions

This Research Topic aims to build a cross-disciplinary platform that will stimulate invention at the junction of AI and neuroscience by aggregating contributions from behavioral neuroscientists, translational researchers, and computational scientists.
Our aim is to hasten research, help the creation of more mechanistic and predictive models of behavior, and eventually guide individualized, data-driven treatments for brain-related diseases.

Are you interested in contributing a manuscript to this Research Topic?

Artificial Intelligence for Behavioral Neuroscience: Unlocking mechanisms, modeling behavior, and advancing prediction