Biology subjects

Chapuis, G.

Publications and source records attributed to Chapuis, G..

4 recordsLinked to original sources

bioRxiv2026

A flexible quality metric for electrophysiological recordings across brain regions and species

The increasing size of electrophysiological datasets has heightened the need for quality metrics that automatically reject neurons whose activity was recorded with low sensitivity or specificity. One key approach estimates artifactual contamination by assuming that each neuron has a refractory period (RP), a brief time interval following each action potential when further activity cannot occur. However, existing methods cannot be applied without prior knowledge of the neurons RP durations, limiting their usefulness in datasets that include neurons from brain regions or species in which RP durations have not been systematically characterized. Here, we find that neurons in some brain regions (thalamus) and species (macaque) have shorter RP durations than commonly assumed, and we introduce a new metric, the Sliding Refractory Period metric, which is robust to variation in a neurons RP duration without tuning. We validate the method using simulations, demonstrating that it improves acceptance of uncontaminated spike trains with short or long RP durations while still rejecting contaminated ones. Moreover, by incorporating Poisson statistics into the calculation, the method also improves on prior work by allowing the user to approximately control the false acceptance rate. Our new metric improves quantification of contamination in electrophysiological recordings and enables application of a single tuning-free quality metric to data recorded from diverse brain regions and species.

neuroscience↗

bioRxiv2024

Interactive data exploration websites for large-scale electrophysiology

Methodological advances in neuroscience have enabled the collection of massive datasets which demand innovative approaches for scientific communication. Existing platforms for data storage lack intuitive tools for data exploration, limiting our ability to interact effectively with these brain-wide datasets. We introduce two public websites: (Data and Atlas) developed for the International Brain Laboratory which provide access to millions of behavioral trials and hundreds of thousands of individual neurons. These interfaces allow users to discover both the raw and processed brain-wide data released by the IBL at the scale of the whole brain, individual sessions, trials, and neurons. By hosting these data interfaces as websites they are available cross-platform with no installation. By releasing each sites code as a modular open-source framework, other researchers can easily develop their own web interfaces and explore their own data. As neuroscience datasets continue to expand, customizable web interfaces offer a glimpse into a future of streamlined data exploration and act as blueprints for future tools.

neuroscience↗

bioRxiv2020

A standardized and reproducible method to measure decision-making in mice

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We designed a task for head-fixed mice that combines established assays of perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be successfully reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path towards achieving reproducibility in neuroscience through collaborative open-science approaches.

neuroscience↗

bioRxiv2019

Data architecture and visualization for a large-scale neuroscience collaboration

Effective data management is a major challenge for neuroscience labs, and even greater for collaborative projects. In the International Brain laboratory (IBL), ten experimental labs spanning 7 geographically distributed sites measure neural activity across the brains of mice making perceptual decisions. Here, we report a novel, modular architecture that allows users to contribute, access, and analyze data across this collaboration. Users contribute data using a web-based electronic lab notebook (Alyx), which automatically registers recorded data files and uploads them to a central server. Users access data with a lightweight interface, the Open Neurophysiology Environment (ONE), which searches data from all labs and loads it into MATLAB or Python. To analyze data, we have developed pipelines based on DataJoint, which automatically populate a website displaying a graphical summary of results to date. This architecture provides a new framework to contribute, access and analyze data, surmounting many challenges currently faced by neuroscientists.

neuroscience↗