Projects

This project investigates the distinct roles of key neuromodulators (dopamine, serotonin, norepinephrine, and acetylcholine) in shaping decision-making and adaptive behavior. Using the International Brain Laboratory’s standardized decision-making task, which has been validated for reproducibility across behavior and neural recordings, we systematically examine neuromodulatory activity across multiple brain regions and task events. This approach allows us to link neuromodulatory dynamics to learning, behavioral strategy shifts, and theoretical models of decision making.

To support this scientific effort, the project includes the development of integrated hardware for high-throughput fiber photometry acquisition, fully compatible with the IBL behavioral platform. In parallel, we are building a modular software toolbox to enable flexible yet standardized analysis of photometry data. These technical developments ensure reproducibility, scalability, and ease of adoption. Both the tools and the full neuromodulator dataset will be made openly available, providing a valuable resource for the broader neuroscience community and advancing open, collaborative science.

This project aims to generate a rich, large-scale dataset capturing the activity of defined neural populations during the IBL decision-making task, using a 2-photon random access mesoscope. By imaging calcium activity in excitatory neurons across the dorsal cortex of transgenic mice, we seek to characterize how population-level neural activity encodes key task variables such as stimulus, choice, and bias context. Imaging functionally connected regions simultaneously allows us to investigate interregional interactions and trial-to-trial dynamics, while repeated recordings across days will help assess the stability of these neural representations over time and their relationship to expert performance.

Complementing the IBL’s existing Neuropixels electrophysiology data, this project adds a critical new dimension by visualizing the spatial organization and long-term evolution of cortical population codes. To support this, we are developing and refining a technical pipeline for the entire calcium imaging workflow, from data curation and preprocessing to analysis and public dissemination. The mesoscope platform thus serves not only as a scientific tool but also as a testbed for evaluating and standardizing open-source methods that enable reproducible, scalable imaging research. This technical effort ensures that the dataset becomes a robust, long-term resource for understanding cortical function during decision making.

This project aims to build a brain-wide reference atlas of electrophysiological signatures by leveraging the IBL’s large-scale, standardized datasets. By characterizing spike shapes, spike trains, local field potentials, and their relationships across brain areas, we seek to map neural signals to anatomical location and genetic class. This atlas will support both offline analyses and real-time applications, such as improving probe targeting accuracy during recordings, and will serve as a foundation for adapting processing algorithms like spike sorting to region-specific signal characteristics.

To support this vision, we are developing predictive models, visualization tools, and integration with existing IBL infrastructure including the trajectory planner and potentially SpikeGLX. The atlas will be openly accessible and designed for community contributions, with plans to incorporate data from external partners like the Allen Institute. Alongside this, we are creating tools to improve histological alignment, automate region boundary detection, and refine brain parcellations by linking gene expression data to electrophysiological patterns. Together, these technical developments will enhance accuracy, reproducibility, and accessibility in large-scale electrophysiology.

This project addresses a central challenge in neuroscience: understanding how distributed brain regions integrate sensory input, prior expectations, and motor planning to drive behavior. To overcome the limitations of fragmented experimental approaches, the IBL coordinated a large-scale, standardized effort across 12 laboratories, recording from 139 mice performing a unified decision-making task. Using 699 Neuropixels probe insertions, neural activity was captured across 241 brain areas spanning the forebrain, midbrain, hindbrain, and cerebellum.

This unprecedented dataset enables a comprehensive appraisal of brain-wide activity during decision making, revealing broad and overlapping representations of stimuli, movement, and reward throughout the brain. The dataset also captures more selective encoding of prior expectations in specific regions. By making this data openly available, along with standardized preprocessing pipelines and metadata, the project provides an essential technical resource for the field, allowing researchers to explore distributed neural computations with consistency and reproducibility. This effort sets a new standard for collaborative, scalable neuroscience.

To support the International Brain Laboratory’s large-scale effort in brain-wide electrophysiology, this project documents and refines a robust, standardized spike sorting pipeline tailored to over a thousand Neuropixels recordings collected across multiple labs. As data volume and diversity grew, earlier processing approaches revealed inconsistencies in spike sorting outcomes. In response, the team developed a modular, reproducible workflow that includes quality control tools at each stage, enabling researchers to visualize, score, and troubleshoot sorting performance systematically.

This pipeline, openly available through the ibl-sorter repository, improves the reliability and comparability of spike sorting across heterogeneous datasets and labs. It represents a critical piece of technical infrastructure for brain-wide electrophysiology, ensuring that downstream analyses rest on a consistent foundation. By documenting known limitations and proposing areas for future improvement, the project not only advances IBL’s scientific goals but also contributes to the broader neuroscience community’s efforts to make high-density electrophysiology more scalable, transparent, and reproducible.

Precise alignment between electrophysiological recordings and anatomical location is essential for interpreting brain-wide neural data. To support this, the International Brain Laboratory developed a graphical user interface (GUI) that enables researchers to align Neuropixels probe trajectories with histological data in a standardized and intuitive way. This tool facilitates the mapping of recording sites onto a common coordinate framework, helping to ensure consistency across experiments and laboratories.

The GUI integrates seamlessly with IBL’s data architecture and supports both manual and semi-automated alignment workflows. It is actively used across the collaboration for verifying probe placements, assigning brain region labels to recording channels, and improving the anatomical accuracy of analyses. Comprehensive usage instructions are available on the IBL Apps Wiki, making it a widely accessible and practical resource for the neuroscience community.

To support the International Brain Laboratory’s mission to understand neural dynamics underlying decision-making, this project documents and refines a robust, standardized video analysis pipeline for tracking head-fixed mouse behavior during cognitive tasks. As neural dynamics control movements, and movements in turn produce sensory input that modulates neural dynamics, detailed behavioral monitoring is essential for interpreting brain-wide neural data. The pipeline successfully tracks pupils, paws, tongue, and nose across hundreds of heterogeneous videos from a dozen experimental labs, achieving high accuracy despite significant variance in camera placement, lighting conditions, and animal appearance. This standardized approach provides the foundation necessary for relating neural activity to behavior at scale.

The pipeline processes video data from a multi-camera setup (two side cameras and one body camera) with specialized workflows including markerless pose estimation with Lightning Pose and motion energy computation in pre-defined regions of interest. Key technical innovations include a novel pose estimation architecture and unsupervised losses, ensemble kalman smoother post-processor, comprehensive quality control metrics for both raw video and tracking outputs, and automated infrastructure for processing hundreds of datasets at scale. This technical framework, openly available through the IBL video repository, provides the neuroscience community with a scalable, reproducible solution for high-throughput behavioral analysis in large-scale studies.