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 researchers to elucidate how functionally defined subpopulations of neurons mediate specific information-processing functions at key moments during behavior, in healthy animals and in animal models of neurological diseases.

Public Health Relevance

By enabling researchers to measure activity in neural tissue, multi-electrode array based electrophysiology has become an indispensable tool for neurobiology, used by hundreds of research laboratories to study normal brain function and brain disorders. Our spike sorting software, JRCLUST, has become widely adopted by researchers wanting to quickly and accurately analyze ever larger datasets arising from high definition multi-electrode arrays with 100s or even 1,000 recording sites. Here we propose to build on and enhance JRCLUST such that researchers can: 1) more rapidly process and evaluate data from multi-electrode array experiments, 2) perform real-time analyses, and 3) more effectively analyze and share their data on a cloud based platform.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Resource-Related Research Projects (R24)
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Special Emphasis Panel (ZMH1)
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Zhan, Ming
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Vidrio Technologies, LLC
United States
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