Neurodata Without Boredom: Benchmarking Agentic AI for Data Reuse

Neuroscience data are highly fragmented across labs, formats, and experimental paradigms, and reuse often requires substantial manual effort. A persistent roadblock to data reuse and integration is the need to decipher bespoke and diverse data formatting choices. Common data formats have been proposed in response, but the field continues to struggle with a fundamental tension: formats flexible enough to accommodate diverse experiments are rarely descriptive enough to be self-explanatory, and sufficiently descriptive formats demand detailed documentation and curation effort that few labs can sustain. Agentic AI is a natural candidate to solve this problem: LLMs read code and text faster and with sustained attention to the low-level details humans tend to skim over. To measure how well agentic AI performs on this task, we selected eight recent papers studying large-scale mouse neural population recordings that shared both data and code, spanning diverse recording modalities, behavioral paradigms, and dataset formats (e.g., NWB, specialized APIs, and general-purpose Python or MATLAB files). We provided agents with the data, code, and paper, and prompted them to load, understand, and reformat the data for a common downstream task: training a decoder from neural activity to task or behavioral variables. General-purpose coding agents commonly used by scientists performed well on each sub-task, but rarely strung together a fully error-free end-to-end solution. We characterize the types of mistakes agents made and the dataset properties that elicited them, and propose data-sharing best practices for the agentic-AI era. We further find that agents-as-judges are unreliable at catching errors, especially without ground-truth references, so interactive, human-in-the-loop coding remains necessary.

Optimized Linear Measurements for Inverse Problems using Diffusion-Based Image Generation

We re-examine the problem of reconstructing a high-dimensional signal from a small set of linear measurements, in combination with image prior from a diffusion probabilistic model. Well-established methods for optimizing such measurements include principal component analysis (PCA), independent component analysis (ICA) and compressed sensing (CS), all of which rely on axis- or subspace-aligned statistical characterization. But many naturally occurring signals, including photographic images, contain richer statistical structure. To exploit such structure, we introduce a general method for obtaining an optimized set of linear measurements, assuming a Bayesian inverse solution that leverages the prior implicit in a neural network trained to perform denoising. We demonstrate that these measurements are distinct from those of PCA and CS, with significant improvements in minimizing squared reconstruction error. In addition, we show that optimizing the measurements for the SSIM perceptual loss leads to perceptually improved reconstruction. Our results highlight the importance of incorporating the specific statistical regularities of natural signals when designing effective linear measurements.