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Modularization and integration of the International Brain Laboratory spike-sorting pipeline into SpikeInterface

将国际脑实验室尖峰分选流程模块化并集成到 SpikeInterface 中

基本信息

批准号：
10609320
负责人：
ANNE KATHRYN CHURCHLAND
金额：
$ 21.3万
依托单位：
COLUMBIA UNIV NEW YORK MORNINGSIDE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-15 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10609320
关键词：
Behavior Brain Code Collaborations Communities Computer software Data Data Analyses Data Files Data Science Data Science Core Data Set Decision Making Development Devices Disease Documentation Ecosystem Electrophysiology (science)Ensure Environment Evaluation Goals Impairment Individual Industry Standard Infrastructure Ingestion Inherited International Intuition Laboratories Methods Modeling Neurons Neurosciences Output Resources Running Schizophrenia Signal Transduction Software Engineering Software Framework Sorting - Cell Movement Speed Standardization Structure Testing Work Writing addiction analysis pipeline autism spectrum disorder base cohesion computational pipelines data acquisition data analysis pipeline density expectation extracellular improved improved outcome integrated circuit interest mouse model multi-electrode arrays neural circuit neuromechanism neurophysiology novel open source outreach parallel computer parent grant parent project performance tests relating to nervous system repository scale up software infrastructure tool

项目摘要

PROJECT SUMMARY The parent grant titled “State-dependent Decision-making in Brainwide Neural Circuits” has the goal of understanding how internal states influence decisions and identifying the underlying neural mechanisms. The project builds upon the International Brain Laboratory (IBL) effort, which brings together a large consortium of laboratories to standardize and parallelize neural data acquisition and analysis. The parent project includes an extensive data acquisition work package leveraging Neuropixels 2.0 probes, which are state-of-the-art high- density neural devices that can record from up to 384 channels simultaneously. The large amount of raw electrophysiology data (~100 TB) requires the development of cutting-edge analysis tools for neural data science. Specifically, “spike sorting” is the crucial processing step that enables the extraction of single-neuron activity from the recorded signals; the sorted single-neuron signals are then used for downstream analysis and modeling. In this direction, the Data Science Core of the parent project has been developing computational pipelines and novel processing steps to automate and improve the outcome of spike sorting for the IBL data. However, these novel tools are tightly bound to the IBL analysis pipeline and would benefit from a generalization and standardization effort to be more useful to the general neurophysiology community. The main goal of this supplement project is therefore to integrate such state-of-the-art tools into the well- established SpikeInterface software framework1 in order to maximize outreach and accessibility to the broad electrophysiology community. In doing so, we aim to modularize the developed pipeline into discrete, decoupled, and interchangeable processing sorting components and to improve their software implementation in terms of speed, efficiency, and scalability, as well as documentation and testing. Finally, we will build a software infrastructure to enable users to construct and run full spike sorting pipelines into containerized and cloud-ready solutions.

项目总结