Laura Grima

@lauragrima.bsky.social

Research Scientist in the Dudman lab @HHMIJanelia. PhD @Oxford, BSc @RoyalHolloway. dopamine | foraging | decision-making | hippocampus | reinforcement learning 🧠🧪👩‍🔬🐭💻 Wondering what mice are thinking about.

🚨 New Walton × Bogacz Collab Preprint! POV: dopamine refuses to have just one job.🧠 DLS dopamine carries distinct reward AND action prediction errors — parallel teaching signals that could support value learning and habit formation. Enjoyed working with Yuhao, Juan Carlos, et al. on this one!

doi.org

Looking forward to speaking at Barrels Meeting this Saturday, and a poster this Tuesday afternoon at #FENS2026 - PS02-07PM-083. Presenting mostly published work but sneaking in some new CA1 data/analyses that I’m excited about and keen to discuss…!

It’s very fun to analyze other people’s data for your favorite model and theory, but data reuse actually takes a huge amount of effort. We tested if current agentic AI can help with data reuse. Conclusion: it definitely can, but requires careful human supervision. #AIforScience #compneuro #neuroAI

Kristin Branson @kristinmbranson.bsky.social · 5mo ago

New preprint with @lingqiz.bsky.social: Neurodata Without Boredom: Benchmarking Agentic AI for Data Reuse arxiv.org/abs/2605.12808 1/10

Overview of the data conversion task. The benchmark includes eight datasets spanning a range of neural recording modalities, behavioral tasks, measurements, experiment protocols, and data formats. For each dataset, agents were also given the released paper, methods, and code, together with a structured prompt. The agent’s goal was to convert each heterogeneous source dataset into a common format suitable for a neural decoding task. 
Diagram has a column for each of 8 papers from mouse systems neuroscience. The first row shows the name of the dataset and a description of the focus of that work: 1) Allen2P: Visual change detection,2) Lee2025: Spatial coding & remapping, 3) Majnik2025: Cortical development, 4) Sosa2024: Spatial reward learning,  5) Chen2024: Memory-guided decision, 6) Hasnain2024: Context-based decision, 7) Zhang2025: Visual-motor decision, 8) Zhong2025: Visual learning. 
The next row shows the type of neural recording data: 1, 3, 4, 8: head-fixed calcium imaging, 2: miniprobe, 5-7: neuropixels or other silicon probes
The next row shows the types of behavioral and task variables. These are different for every task
The next row shows a visualization of the experiment protocol for each task. Again, these are different for every task
The next row shows that this information is all condensed into data, paper, and code. Data has the different types of information in different orders and structures. It also shows the type of files: 1: NWB files & SDK, 2,6: MATLAB files, 3, 7: Python files, 4-5: NWB files, 8: Various + API
The next part of the diagram shows the task, which is for agents to convert the data to a common data format for a downstream decoder task.

After three years of working on this review with my collaborator Emily Dennis (Janelia Farm), it is finally in press. The review provides an overview and our personal reflections on what constitutes an integrative approach to neurobiology of foraging. Enjoy www.annualreviews.org/content/jour...

Neurobiology of Foraging: An Integrative Approach

Foraging, defined as the search for food to sustain one's energetic needs, is a fundamental behavior performed by almost all animals to survive in their environment. Foraging involves a variety o...

annualreviews.org