# A Fast, Accurate and Cloud-based Data Processing Pipeline for High-Density, High-Site-Count Electrophysiology

> **NIH NIH R24** · VIDRIO TECHNOLOGIES, LLC · 2020 · $255,842

## Abstract

The past decade has seen major advances in the tools available to neuroscientists, making it possible to ask
increasingly specific questions regarding which neurons and circuits are correlated with, necessary for, and
sufficient for, specific behavioral or computational functions. Advancements in our understanding of neural
computation and how it leads to complex behavior critically depend on accurate measurements of coordinated
neural activities in behaving animals. In the past several years there have been major coordinated efforts to
advance neural probe technology by increasing site density, extending spatial coverage, providing high fidelity
recording, and integrating with cell-type specific stimulation tools. Extracellular recordings exhibit action
potentials (spikes) that require spike-sorting analysis to correctly detect and assign them to individual neurons.
The demand for accurate and scalable spike sorting has increased due to the wide accessibility of extracellular
recording technology and the increased requirements to record and separate activity from as many neurons as
possible. Fundamental discoveries in neuroscience such as orientation-selective cells, place cells, and grid cells
would not have been possible without reliable spike sorting of extracellular signals. These discoveries have
illuminated the cellular basis of information processing and cognitive abilities. Recently, extracellular recording
devices have also been applied to restoring motor function through prosthetics. However, as the number of
electrodes needed for these treatments increases to allow for finer motor control so does the need for automated
analysis. With more capacity for automated spike sorting the field’s progress in these domains would be
accelerated. Existing spike analysis solutions suffer from a lack of scalability and are often designed to lock a
user into a specific hardware platform. The community’s need for an integrated open-source analysis platform is
rapidly growing with the increasing capacity of extracellular electrodes and the number of new and un-validated
spike-sorting methods. JRCLUST, our free, open-source, standalone spike sorting software, offers a scalable,
automated and well-validated spike sorting workflow that can tolerate experimental recording conditions with
noise, probe drift, and motion artifacts from behaving animals. It can handle a wide range of datasets using a set
of pre-optimized parameters making it practical for wide use in the community. Also, our processing speed and
modular approach allows for rapid cycle innovation and practical pathways to interpret long recordings from
hundreds of recording sites. Thanks to its real-time performance and accurate automated analysis requiring only
a single workstation, JRCLUST has been rapidly adopted in 20+ labs worldwide since its inception less than a
year ago. Successful completion of this project will enable Vidrio to support, expand and maintain JRCLUST
thus empowering rese...

## Key facts

- **NIH application ID:** 9905557
- **Project number:** 5R24MH114811-03
- **Recipient organization:** VIDRIO TECHNOLOGIES, LLC
- **Principal Investigator:** Bruce Kimmel
- **Activity code:** R24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $255,842
- **Award type:** 5
- **Project period:** 2018-07-06 → 2021-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9905557

## Citation

> US National Institutes of Health, RePORTER application 9905557, A Fast, Accurate and Cloud-based Data Processing Pipeline for High-Density, High-Site-Count Electrophysiology (5R24MH114811-03). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9905557. Licensed CC0.

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