1. The emergence of NeuroAI: bridging neuroscience and artificial intelligence #
Source: https://www.nature.com/articles/s41583-025-00954-x
Summary: This perspective article discusses how neuroscience has inspired AI for decades, but recent years have seen AI tools revolutionizing neuroscience research. The emerging field of NeuroAI holds potential to transform large-scale neural modeling and data-driven discovery, though it must balance computational power with interpretability and biological insight. The authors highlight recent methodological advances like SAM 2, Cellpose, RoboEM, foundation models of neural activity, neuroprosthetics for speech, learnable latent embeddings, and whole-brain Drosophila annotation as enabling this interdisciplinary approach.
Future Research Directions:
- Develop unified frameworks that integrate multimodal neural data (electrophysiology, imaging, behavior) with AI models to uncover principled representations of brain computation
- Investigate how neuroscience-inspired architectural innovations (e.g., sparse coding, predictive coding, neuromodulation mechanisms) can improve AI system robustness and efficiency
- Create closed-loop AI-neuroscience systems where AI models generate testable hypotheses about neural mechanisms that are empirically validated through targeted perturbations
2. Reinforcement learning in artificial intelligence and neurobiology #
Source: https://www.sciencedirect.com/science/article/pii/S2772528625000354
Summary: This article reviews reinforcement learning (RL) as a multidisciplinary field with roots in psychology, neuroscience, operations research, and AI. It traces the evolution of ideas about learning through rewards and punishments, sequential decision-making, and optimization, highlighting RL’s role in bridging computational theory with biological implementations of learning systems.
Future Research Directions:
- Develop biologically plausible RL models that incorporate neuromodulatory systems (dopamine, serotonin) to better explain flexible decision-making in uncertain environments
- Apply inverse reinforcement learning to infer reward functions from naturalistic behavior, enabling more accurate modeling of motivation and goal-directed behavior in healthy and clinical populations
- Investigate meta-RL approaches that capture how biological systems learn learning strategies, with applications to creating more adaptable AI agents
3. Psychiatry in the age of AI: transforming theory, practice, and medical education #
Source: https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1660448/full
Summary: This review article examines how AI is transforming psychiatric theory and clinical practice through precision diagnosis, mechanistic insight, and personalized intervention, while addressing challenges including data privacy, algorithmic bias, and inequitable access. It emphasizes the need for medical education to evolve through curricular redesign, computational competencies, integrative pedagogical models, and bioethical literacy to equip future psychiatrists to harness AI responsibly.
Future Research Directions:
- Develop federated learning frameworks that enable multi-institutional AI model training while preserving patient privacy and addressing data heterogeneity across diverse populations
- Create interpretable AI models that link molecular, cellular, and circuit-level mechanisms to clinical phenotypes, facilitating translation from basic science to psychiatric nosology
- Design and evaluate AI-augmented preventive interventions that identify individuals at risk for mental health disorders before symptom onset, leveraging digital phenotyping and ecological momentary assessment