NSM

NSM Neural signal manager: a collection of classical and innovative tools for multi-channel spike train analysis. Recent developments in the neuroengineering field and the widespread use of the micro electrode arrays (MEAs) for electrophysiological investigations made available new approaches for studying the dynamics of dissociated neuronal networks as well as acute/organotypic slices maintained ex vivo. Importantly, the extraction of relevant parameters from these neural populations is likely to involve long-term measurements, lasting from a few hours to entire days. The processing of huge amounts of electrophysiological data, in terms of computational time and automation of the procedures, is actually one of the major bottlenecks for both in vivo and in vitro recordings. We present a collection of algorithms implemented within a new software package, named the Neural Signal Manager (NSM), aimed at analyzing a huge quantity of data recorded by means of MEAs in a fast and efficient way. The NSM offers different approaches for both spike and burst analysis, and integrates state-of-the-art statistical algorithms, such as the inter-spike interval histogram or the post stimulus time histogram, with some recent ones, such as the burst detection and its related statistics. In order to show the potentialities of the software, the application of the developed algorithms to a set of spontaneous activity recordings from dissociated cultures at different ages is presented in the Results section.

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References in zbMATH (referenced in 3 articles , 1 standard article )

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  1. Novellino, Antonio; Zaldívar, José-Manuel: Recurrence quantification analysis of spontaneous electrophysiological activity during development: characterization of in vitro neuronal networks cultured on multi electrode array chips (2010) ioport
  2. Novellino, Antonio; Chiappalone, Michela; Maccione, Alessandro; Martinoia, Sergio: Neural signal manager: a collection of classical and innovative tools for multi-channel spike train analysis (2009)
  3. Warwick, Kevin: Cultured neural networks (2009)