Practically Trivial Parallel Data Processing in a Neuroscience Laboratory

Denker, Michael; Wiebelt, Bernd; Fliegner, Denny; Diesmann, Markus; Morrison, Abigail

doi:10.1007/978-1-4419-5675-0_20

Cited by 11 publications

(10 citation statements)

References 3 publications

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“…Fortunately, the progress in computer hardware and methods for trivial parallelization in high-level programming languages has now considerably expanded our capabilities compared to the time when dithering was first considered. The algorithms described below are implemented in Python (Langtangen, 2006) and executed in parallel using the techniques described in Denker et al (2010). Example code for implementing dithering in operational time is available at .…”

Section: Simulation Methodsmentioning

confidence: 99%

Surrogate spike train generation through dithering in operational time

Louis

Gerstein

Grün

et al. 2010

Front. Comput. Neurosci.

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Detecting the excess of spike synchrony and testing its significance can not be done analytically for many types of spike trains and relies on adequate surrogate methods. The main challenge for these methods is to conserve certain features of the spike trains, the two most important being the firing rate and the inter-spike interval statistics. In this study we make use of operational time to introduce generalizations to spike dithering and propose two novel surrogate methods which conserve both features with high accuracy. Compared to earlier approaches, the methods show an improved robustness in detecting excess synchrony between spike trains.

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Section: Simulation Methodsmentioning

confidence: 99%

Surrogate spike train generation through dithering in operational time

Louis

Gerstein

Grün

et al. 2010

Front. Comput. Neurosci.

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“…Thus, the evaluation of a full matrix J of 200 × 200 entries took on average less than 7 minutes. However, the fact that single entries are evaluated independently may be easily exploited by parallelizing the analysis on multi-core machines or computer clusters, where each worker process is assigned to perform the computation for a subset of the matrix entries (see [ 37 ]).…”

Section: Resultsmentioning

confidence: 99%

ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

et al. 2016

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With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity.

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“…The novelty is that we demonstrate how they may be used in combination to quickly implement sophisticated and dynamic neuroimaging workflow, with a supporting manual, lab practicals, and data and examples downloadable from NITRC. The simplicity of using a build system to track workflow has been described for spike train analysis (Denker et al, 2010) and anecdotally noted in several blog postings as a feature for reproducible science (Butler, 2012; Hyndman, 2012; Bostock, 2013; Hambley, 2013; Jones, 2013). This has driven development of several Make-like systems geared toward specific types of data analysis (e.g., Drake, Factual, 2015, Nextflow, Tommaso, 2015).…”

Section: Discussionmentioning

confidence: 99%

Using Make for Reproducible and Parallel Neuroimaging Workflow and Quality-Assurance

Askren

Day

Koh

et al. 2016

Front. Neuroinform.

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The contribution of this paper is to describe how we can program neuroimaging workflow using Make, a software development tool designed for describing how to build executables from source files. A makefile (or a file of instructions for Make) consists of a set of rules that create or update target files if they have not been modified since their dependencies were last modified. These rules are processed to create a directed acyclic dependency graph that allows multiple entry points from which to execute the workflow. We show that using Make we can achieve many of the features of more sophisticated neuroimaging pipeline systems, including reproducibility, parallelization, fault tolerance, and quality assurance reports. We suggest that Make permits a large step toward these features with only a modest increase in programming demands over shell scripts. This approach reduces the technical skill and time required to write, debug, and maintain neuroimaging workflows in a dynamic environment, where pipelines are often modified to accommodate new best practices or to study the effect of alternative preprocessing steps, and where the underlying packages change frequently. This paper has a comprehensive accompanying manual with lab practicals and examples (see Supplemental Materials) and all data, scripts, and makefiles necessary to run the practicals and examples are available in the “makepipelines” project at NITRC.

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Practically Trivial Parallel Data Processing in a Neuroscience Laboratory

Cited by 11 publications

References 3 publications

Surrogate spike train generation through dithering in operational time

Surrogate spike train generation through dithering in operational time

ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

Using Make for Reproducible and Parallel Neuroimaging Workflow and Quality-Assurance

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