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Related Experiment Video

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Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

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Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics.

Adam S Charles^1,2, Benjamin Falk¹, Nicholas Turner³

¹Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;

Annual Review of Neuroscience

|April 15, 2020

Summary

As experimental brain science generates more data, computational and statistical approaches must advance. Community-driven tools can democratize brain science by fostering collaboration across diverse expertise and disciplines.

Keywords:

computational infrastructure reference data statistics

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Area of Science:

Neuroscience
Computational Neuroscience
Statistical Neuroscience

Background:

Experimental brain science is producing increasingly large datasets.
Current computational and statistical methods may not fully leverage these big data.
A gap exists in translating large-scale experimental data into meaningful insights.

Purpose of the Study:

To propose a framework for advancing computational and statistical brain science.
To highlight the importance of community-driven tools in data analysis.
To foster interdisciplinary collaboration for enhanced brain data utilization.

Main Methods:

Conceptual analysis of current challenges in brain data science.
Advocating for the development and adoption of community-driven computational tools.
Promoting collaborative frameworks across different neuroscience subfields.

Main Results:

Increased data acquisition necessitates parallel advances in analytical methods.
Community-driven tools offer a scalable solution for complex data challenges.
Collaborative efforts can bridge gaps between experimental and computational neuroscience.

Conclusions:

Harnessing big data in brain science requires synergistic progress in computational and statistical approaches.
Democratizing brain science through shared, community-built tools is crucial.
This collaborative perspective can unify siloed research communities and accelerate discovery.