PanNeuro: leveraging a community-based approach for big data neuroscience

BRAIN Initiative PI meeting, April, 2019

Ariel Rokem
The University of Washington eScience Institute

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Computational and circuit mechanisms underlying rapid learning

NINDS/NIH U19 funded through the BRAIN Initiative


Identify the neural mechanisms that support schema development and rapid learning in association and categorization paradigms in monkeys and humans.

Develop and validate novel techniques for large-scale single unit recordings from multiple distributed regions of the nonhuman and human primate brain, during learning, through reversible inactivation, and during sleep.

Generate and test a multi-region computational understanding of circuit mechanisms that underlie schema development and rapid learning.

Learning to learn

Harlow, 1949
Multi-channel recordings in NHP
ECoG recordings in human
Single unit recordings in human

Data types:

Behavioral results

Non-human primate multichannel recordings

Human grid and electrode recordings

Model and simulation results

Data volumes:

~5-20 TB/week at steady-state

=> ~1-2 PB to store at steady-state

~10%-20% of that needs to be routinely accessed

Challenges of data-intensive neuroscience

Computational resources

Statistical methods and algorithms

Integration of different data types

Integration of theory and experiments

Patterns of collaboration

Reproducibility and extensibility

What are the requirements?

Bring the compute to the data

Scalable computing

Provide useful tools and interfaces

Facilitate interoperability (between datasets, between software libraries)

Control access

Package into a reproducible unit

Inspiration from other fields of science

Big data in the geosciences

Big data in the geosciences

A community platform for Big Data Geoscience

Open-source scientific computing tools and large open datasets on high-performance computing platforms served through the web-browser


Cloud computing

Open source scientific computing in Python

Interactive computing with Jupyter

A selection of standardized datasets generated by the collaboration

Why use the cloud?

Data and compute are colocalized

Minimal data transfers

Access control

Consistent and centrally managed

Portable and reproducible

Scientific computing in Python

Python: an ecosystem for scientific computing

Free and open source

High-level interpreted language

Very wide adoption

Both in academic research and in industry

Interactive computing through the web browser

The Jupyter notebook

Standardized data formats

BIDS: a scientist-friendly data standard


Adam Dede (UW)

Demo video

Binder demo

Try it out yourself!

Barriers to adoption

Concerns about cost

Reluctance to share data

New skills required

The tools are rapidly evolving

Data formats and data standardization


Leveraging existing investments in Pangeo

Open source software and cyberinfrastructure

Interdisciplinary collaboration

Accessible scalable computing in the browser

The team

Beth Buffalo (UW)
Adrienne Fairhall (UW)
Xiao-jing Wang (NYU)
David Freedman (U Chicago)
Bob Knight (UC Berkeley)
Jack Lin (UC Irvine)
Jeff Ojemann (UW)
Joe Hamman (NCAR)
Ryan Abernathy (Columbia)

We are hiring!


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