Overview

SpikeInterface is a powerful Python-based extracellular data processing framework supporting a broad range of features and functions. For those well versed in Python programming and needing full control over the extracellular data processing process, working directly with SpikeInterface is the way to go.

Spikely, on the other hand, is for users who want to take advantage of a subset SpikeInterface’s processing power without having to program in Python. Instead, Spikely provides a GUI on top of SpikeInterface optimized for a specific use case: pipelining extracelluar data from a source to a sink while enabling one or more data transformations along the way.

In addition to being familiar with SpikeInterface, taking full advantage of spikely requires an understanding of a few key concepts specific to it:

• Element - An element in Spikely corresponds to capabilites exposed by the data processing nodes in SpikeInterface. Specifically, spikely elements consist of:

  • Extractors - Extractors read raw extracelluar data from files, and make those data available to downstream elements in the pipeline. Extractor names correspond to the raw extracellular data format they support. Spikely requires one, and only one, Extractor per pipeline.
  • Pre-Processors - Pre-Processors transform data sourced into the pipeline by the Extractor before it is sent to the Sorter. Pre-processors are optional. Spikely supports multiple Pre-Preprocessors per pipeline betwween the Extractor and the Sorter.
  • Sorters - Spike sorting is a big part of SpikeInterface, and spikely’s Sorters correspond closely to spike sorters in SpikeInterface. Spikely requires the presence of one, and only one, Sorter in the pipeline. Sorters write their results out to a file (unless specified not to) allowing a Sorter to act as a terminating sink in a spikely pipeline.
  • Curators - Curators, also known as post-processors, automatically curate sorted data produced by the Sorter and output them downstream to either another Curator or to a pipeline terminating Exporter. Curators are optional. Spikely supports multiple Curators per pipeline.
  • Exporters - Exporters act as data sinks, and transform sorted datasets to a formatted output file. Exporters are optional, and spikely only supports a single Exporter per pipeline.

• Parameter - Most elements have one or more parameters associated with them that can be edited by the user in spikely to customize the behavior of that element during the execution of a pipeline. Parameters are element specific, and some familiarity with the proxied node in SpikeInterface is required to correctly configure an element.

• Pipeline - The user organizes elements in spikely in a series where extracelluar data “flows” from the first element in the Pipeline to the last when the pipeline is run. Pipelines, and their associated parameterized elements, can be saved for future use therby enabling greater efficiency and repeatability.