ArticleFrontiers in neuroinformatics2014
QSpike tools: a generic framework for parallel batch preprocessing of extracellular neuronal signals recorded by substrate microelectrode arrays.
Article in Frontiers in neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Dual developmental effects of ARX poly-alanine mutations on human cortical excitatory and inhibitory neurons.Cell reports · 2026Article
- SARS-CoV-2-induced damage to rat cortical neuronal networks ex vivo is mediated by the pro-inflammatory activation of the cGAS-STING pathway.Journal of neurovirology · 2025Article
- An Overview of In Vitro Biological Neural Networks for Robot Intelligence.Cyborg and bionic systems (Washington, D.C.) · 2023Review
- MEA-ToolBox: an Open Source Toolbox for Standardized Analysis of Multi-Electrode Array Data.Neuroinformatics · 2022Article
- The dual action of glioma-derived exosomes on neuronal activity: synchronization and disruption of synchrony.Cell death & disease · 2022Article
- Multidimensional Functional Profiling of Human NeuropathogenicInternational journal of molecular sciences · 2022Article
- Deep Learning in Mining Biological Data.Cognitive computation · 2021Review
- Foetal neural progenitors contribute to postnatal circuits formation ex vivo: an electrophysiological investigation.Molecular brain · 2020Article
- MEAnalyzer - a Spike Train Analysis Tool for Multi Electrode Arrays.Neuroinformatics · 2020Article
- Low-Impedance 3D PEDOT:PSS Ultramicroelectrodes.Frontiers in neuroscience · 2020Article
- Article
- SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings.Neuroinformatics · 2018Article
- Brief wide-field photostimuli evoke and modulate oscillatory reverberating activity in cortical networks.Scientific reports · 2016Article
- A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis.Computational intelligence and neuroscience · 2016Article
- Trends and Challenges in Neuroengineering: Toward "Intelligent" Neuroprostheses through Brain-"Brain Inspired Systems" Communication.Frontiers in neuroscience · 2016Article
- Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges.Frontiers in neuroscience · 2016Review
- ToolConnect: A Functional Connectivity Toolbox for In vitro Networks.Frontiers in neuroinformatics · 2016Article
- Modelling and Analysis of Electrical Potentials Recorded in Microelectrode Arrays (MEAs).Neuroinformatics · 2015Article
- α-Synuclein strains cause distinct synucleinopathies after local and systemic administration.Nature · 2015Article
- Going beyond the current neuroinformatics infrastructure.Frontiers in neuroinformatics · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Micro-Electrode Arrays (MEAs) have emerged as a mature technique to investigate brain (dys)functions in vivo and in in vitro animal models. Often referred to as "smart" Petri dishes, MEAs have demonstrated a great potential particularly for medium-throughput studies in vitro, both in academic and pharmaceutical industrial contexts. Enabling rapid comparison of ionic/pharmacological/genetic manipulations with control conditions, MEAs are employed to screen compounds by monitoring non-invasively the spontaneous and evoked neuronal electrical activity in longitudinal studies, with relatively inexpensive equipment. However, in order to acquire sufficient statistical significance, recordings last up to tens of minutes and generate large amount of raw data (e.g., 60 channels/MEA, 16 bits A/D conversion, 20 kHz sampling rate: approximately 8 GB/MEA,h uncompressed). Thus, when the experimental conditions to be tested are numerous, the availability of fast, standardized, and automated signal preprocessing becomes pivotal for any subsequent analysis and data archiving. To this aim, we developed an in-house cloud-computing system, named QSpike Tools, where CPU-intensive operations, required for preprocessing of each recorded channel (e.g., filtering, multi-unit activity detection, spike-sorting, etc.), are decomposed and batch-queued to a multi-core architecture or to a computers cluster. With the commercial availability of new and inexpensive high-density MEAs, we believe that disseminating QSpike Tools might facilitate its wide adoption and customization, and inspire the creation of community-supported cloud-computing facilities for MEAs users.
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