June 2024#

One of our main goals in the team is to provide fully integrated, automated analysis tools for systems neuroscience data. As a researcher, this would mean providing only a set of configurations before running the analysis for an entire dataset, with results and quality metrics output for checking. This will avoid researchers needing to write custom preprocessing and analysis scripts that often duplicate existing efforts, and allows researchers to work together on community tools.

This is a long-term goal, and we are working across multiple themes towards achieving this. We are ensuring data is organised in a standard way, a critical step towards automation. Currently, we are focused on building pipelines for behaviour, electrophysiology and anatomy that can ingest data from various acquisition systems into a common interface. These are critical steps towards the longer-term goal of multimodal integration. The detailed roadmap is split into these themes below.

Data management#

Current status

We have developed NeuroBlueprint, a simple, standard data folder specification for (systems) neuroscience and datashuttle, to automate the creation, validation and transfer of NeuroBlueprint projects. For more details on these specific projects, see the NeuroBlueprint and datashuttle roadmaps.

Q4 2024

Standardised metadata support

Q2 2025

Support for customising data structure (e.g. specific mandatory elements or new file types)

Q4 2025

Export of NeuroBlueprint projects to Neurodata Without Borders format

Behavioural analysis#

Current status

We are building movement a Python toolbox for analysis of animal tracking data. For more details on this specific project, see the movement roadmap.

Q3 2025

  • Support all common post pose-estimation analysis tasks

  • Support for single experiments and complex multi-animal, multi-session datasets (by reading and writing to NeuroBlueprint format)

No current ETA

  • A common interface to prevalent pose estimation tools, and other machine-learning based object detection methods

  • A common interface to methods for behavioural segmentation and classification

Extracellular electrophysiology analysis#

Current status

We are currently building spikewrap (built on top of SpikeInterface) for simple, routine analysis of extracellular electrophysiology analysis. For more details on this specific project, see the spikewrap roadmap.

Q3 2024

Contribute to ongoing SpikeInterface efforts to:

Q4 2024

Q2 2025

  • Easy to use tool for all common extracellular electrophysiology analysis steps

  • Support for single experiments and complex multi-animal, multi-session datasets (by reading and writing to NeuroBlueprint format)

Computational anatomy#

Current status

Our computational anatomy work is based around the BrainGlobe Initiative. BrainGlobe is centered on the BrainGlobe Atlas API which provides a common interface to multiple anatomical atlases. Currently, there are tools for:


BrainGlobe is a large project with many short- and long-term aims. Our two main goals are to:

  • Allow for all neuroscience histology analysis and visualisation tasks to be carried out easily in a single environment. This includes 2D & 3D data in all common animal models.

  • Create an end-to-end pipeline to analyse mouse brains imaged with a mesoSPIM.

For full details, please see the BrainGlobe roadmap.

Multimodal integration#

Current status

Currently, our data analysis tools are restricted to a single modality. It is important that multimodal data can be combined at the analysis stage e.g. to correlate neural activity and behaviour. There are some excellent existing packages for neurophysiological data analysis (e.g. pynapple) and so we plan to leverage these.

Q3 2024

Q2 2025

  • Export of spike times from spikewrap in Neurodata Without Borders format, so it can be loaded into pynapple

  • Integration of spikewrap and brainglobe-segmentation to use the anatomical location of probe recordings as part of the analysis


Current status

Over the past 18 months, we have delivered a series of computational skills courses at the Sainsbury Wellcome Centre (details can be found on the Software Skills website).

Q4 2025

Over the next 18 months we will develop our existing courses into a coherent set of materials that can be used to deliver an introductory one-week course on the computational skills needed in systems neuroscience, including:

  • Basic linux command line usage

  • Version control

  • Python programming

  • Software development best practices

  • Good data management

  • High performance computing

  • Video behavioural analysis

  • Multiphoton image analysis

  • Extracellular electrophysiology analysis

  • Histology analysis