The Modern Neuroscience Lab in the Age of AI: Exploring the Future of Research at FENS Forum 2026

Artificial intelligence is rapidly transforming the way neuroscience is conducted. From literature discovery and data annotation to knowledge integration and scientific reasoning, AI-powered tools are reshaping how researchers interact with increasingly large and complex datasets. Yet as these technologies become more deeply embedded in research workflows, important questions remain: How can AI enhance scientific rigor and reproducibility? What new opportunities does it create for discovery? And how can the neuroscience community ensure that these tools are used responsibly and effectively?

To explore these questions, the International Neuroinformatics Coordinating Facility (INCF) and The Transmitter are partnering to host a scientific symposium and panel discussion at FENS Forum 2026 entitled "The Modern Neuroscience Lab in the Age of AI."

The symposium will take place on Wednesday, July 8, from 09:45–11:15 in Hall B as part of the General Neuroscience Topics program at FENS Forum 2026. The session will be chaired by Emily Singer, Chief Opinion Editor at The Transmitter, and Mathew Abrams, Director of Science and Training at the INCF Secretariat.

AI Is Changing How Neuroscience Gets Done

Modern neuroscience increasingly relies on sophisticated experimental approaches that generate vast amounts of multimodal data. While advances in data acquisition have opened new possibilities for understanding the brain, they have also created new challenges in data management, integration, analysis, and interpretation.

Large language models (LLMs) and related AI technologies are beginning to address these challenges by lowering technical barriers and creating more intuitive ways for researchers to interact with data, software, and scientific knowledge. From improving data quality and discoverability to supporting literature synthesis and knowledge generation, AI is poised to become a core component of the modern neuroscience laboratory.

This symposium brings together leading researchers developing and applying AI technologies across neuroscience to discuss both the opportunities and challenges that lie ahead.

Featured Talks

• AI for Better Neuroscience: Boosting Data Integrity, Quality, and Impact with LLMs
  Sean Hill (Senscience)
  • Sean Hill will explore how LLMs can strengthen key research workflows, including literature review, data curation, annotation, and analysis. His presentation will highlight emerging AI-powered infrastructure designed to improve reproducibility, reusability, and interoperability through automated metadata generation, community standards, and interactive data portals that make neuroscience data more accessible and impactful.
     
• Improving Large Language Models as Model Organisms of Language in the Human Brain
  Mariya Toneva (Max Planck Institute for Software Systems)

  • Can large language models serve as scientific models for understanding language processing in the human brain? Mariya Toneva will discuss recent advances in "brain-tuning" LLMs using neural recordings and examine how researchers can evaluate whether AI systems not only resemble human behavior but also share underlying computational principles. Her work offers new perspectives on the intersection of neuroscience, cognitive science, and AI.

• A Computational Knowledge Ecosystem for Neuroscience Research
  Satrajit Ghosh (TheMcGovern Institute for Brain Research at MIT)

  • Drawing on work within major U.S. BRAIN Initiative programs, Satrajit Ghosh will present efforts to create a computational knowledge ecosystem capable of integrating data across scales, species, and experimental modalities. The vision is an infrastructure that supports reusable workflows, scalable computation, and continuously improving knowledge generation to accelerate reproducible and collaborative neuroscience.

• From Bees to Brains: Building Better Benchmarks for LLMs in Biology
  Rachel Parkinson (Queen Mary University of London)

  • Rachel Parkinson will introduce MetaBeeAI, a project that leverages pollinator ecotoxicology as a testbed for evaluating AI-assisted scientific review. By comparing LLM-generated outputs against expert assessments, the project provides valuable insights into common AI failure modes while helping establish rigorous, domain-specific benchmarks that can improve AI systems for biological research.

Beyond the Technology

While AI tools are advancing rapidly, the future of neuroscience will depend on more than technological innovation alone. Researchers must also develop new skills for evaluating AI-generated outputs, understanding model limitations, and integrating these tools into robust scientific workflows.

The symposium concludes with a panel discussion bringing together speakers and attendees to examine what the next generation of neuroscience research environments might look like. Topics will include reproducibility, data stewardship, scientific reasoning, infrastructure development, and the evolving relationship between researchers and AI systems.

As neuroscience enters a new era of data-intensive discovery, conversations like these will play an essential role in shaping how the community harnesses AI to advance our understanding of the brain.

We invite all FENS Forum attendees interested in neuroinformatics, AI, data science, and research infrastructure to join us for this timely discussion on the future of neuroscience.