WG
wen-jun gao
College of Medicine, Drexel University
Philadelphia, United States
About this Research Topic
Manuscript Extension Submission Deadline 31 October 2026
Background
The past decade has delivered a step change in systems neuroscience: brain-wide recording technologies, shared large-scale datasets, and increasingly powerful computational tools have transformed how we study neural circuits, population dynamics, and behavior. Yet the field now faces a harder challenge, turning scale into understanding. This Research Topic asks: what are the conceptual and methodological approaches that made this era possible, and what does the next generation of systems neuroscience urgently need?
Rather than centering on a single disorder, brain region, model organism, or recording technique, this collection brings together contributions that cut across levels of analysis, from local circuit dynamics to brain-wide activity, from single-trial population codes to distributed computation, from high-density electrophysiology and large-scale calcium imaging to connectomics, fMRI, and closed-loop perturbation experiments. We’re particularly interested in contributions that connect these experimental advances to the analytic and theoretical toolkits that make such data interpretable: dynamical systems approaches, latent variable models, normative and Bayesian theories, representational geometry, causal inference, and, reflecting one of the most active areas in the field right now, neural foundation models and large-scale AI approaches trained on neural data.
The timing of this collection reflects a genuine inflection point. Shared datasets (IBL, Allen Brain Atlas, Human Connectome Project and its successors), open-source analysis pipelines, and a growing culture of reproducibility are making brain-wide hypotheses testable at unprecedented scale. Foundation models trained on neural recordings are beginning to generalize across individuals, brain regions, and stimulus domains, raising fundamental questions about what such generalization reveals about neural computation. This collection is designed to capture both the approaches that brought the field here and the principles that will carry it forward.
Articles in this collection emphasize (not limited to):
• Generalizable systems-level principles rather than narrow, task-specific findings
• Mechanistic depth at circuit and population levels with broad cross-species or cross-study relevance
• Integration across scales, from local microcircuits to brain-wide dynamics and behavior
• Rigorous links between data, models, and theory, including transparent assumptions and testable predictions
• Causal and computational perspectives, including perturbation-based inference and closed-loop experiments
• Critical engagement with open questions in interpretability, reproducibility, and standardization
The intersection of large-scale neural data and modern AI, including neural foundation models and self-supervised learning on neural recordings
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
- Brief Research Report
- Clinical Trial
- Data Report
- Editorial
- FAIR² Data
- General Commentary
- Hypothesis and Theory
- Methods
- Mini Review
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Keywords: Brain-wide neural dynamics, Large-scale neural recordings, Neural population coding, Data-driven modeling Circuit-to-behavior computation
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.
