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SpikeHistogram

The Spike Histogram Module provides the ability to detect, discriminate and analyze extracellular neural spike activity online. Recorded spikes can also be reanalyzed offline with modified discrimination settings.

WindowsFerramentas de Sistema

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Sobre

SpikeHistogram is a Windows software module for detecting, discriminating, and analyzing extracellular neural spike activity. It is intended for work with recorded neural signals in situations where spike events need to be examined as they occur. The module addresses both initial online handling and later offline review of recorded spikes.

During online operation, it can detect spike activity as recordings are being made. It can discriminate among recorded spike activity, supporting separation of spikes for analysis. It also provides analysis of extracellular neural spikes within the recording workflow. Recorded spikes can be reanalyzed offline when different discrimination settings are needed.

This online and offline arrangement allows users to revisit recordings rather than relying only on the settings used during the original session. Offline reanalysis is specifically tied to modifying discrimination settings, so it can be used to examine how those settings affect the treatment of recorded spikes. The available information describes the product by these signal-processing functions and does not specify additional tools or components.

SpikeHistogram belongs in the Windows category because its stated platform is Windows. MirrorNest does not mirror the software package; it links directly to the official release. The license is unknown, so readers should check the official release source for licensing information before using or redistributing it. This listing identifies the module and its documented capabilities while directing acquisition to the official release.

Arquitetura: x86

Novidades na v2.6.3400

  • Fixed various crashes that could occur while sampling when Analyze while sampling is enabled.
  • Fixed an issue where histogram views would show incorrect analysis while sampling.

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