Unified, Scalable, and Reproducible Neurostatistical Software

New York University, New York NY

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Abstract

Project Summary Many advances in modern neuroscience rely on electrophysiological recordings of large neural populations (e.g. many hundreds of cells) or high-resolution measurements of animal behavior (e.g. from video). These datasets have unlocked a wide range of genuinely transformational scientific opportunities, as they enable us to draw reliable statistical inferences about individual animal subjects at precisely encapsulated moments in time. However, these statistical models are complex and non-trivial to implement in computer software. Over the past decade, an initially nascent sub-field of neural data science and statistics grew precipitously, producing a broad array of modeling approaches and a voluminous, fractured landscape of “one-off” software packages that support a single statistical modeling approach. This exploration of diverse statistical methodologies has been, and will continue to be, a crucial component to advancing the field. Nevertheless, a concerted effort to consolidate existing models into a unified and reliable software package is long overdue. Moreover, this effort must address the exponentially growing scale of neural and behavioral data, as well as the escalating intricacy of modeling workflows. To address these needs, this application will develop novel software implementations of a curated set of time-tested statistical models in neuroscience. To accommodate the exponentially growing data sizes, this software will be built on top of recently innovated infrastructure for large-scale machine learning, including flexible procedures for specifying GPU-accelerated computations and cloud computing frameworks to sweep across model parameters in parallel across many machines. Finally, we will develop procedures for neuroscience labs to share reproducible analysis workflows alongside raw datasets formatted by BRAIN Initiative standards, including a novel framework for building URL-shareable, interactive data visualizations that operate within any web browser. Altogether, these new software tools will accelerate neuroscience discoveries by enabling laboratories to rapidly iterate on in-house analyses and share them in a manner that is transparent and reproducible.