Python in neuroscience

Müller, Eilif; Bednar, James A.; Diesmann, Markus; Gewaltig, Marc‐Oliver; Hines, Michael L.; Davison, Andrew P.

doi:10.3389/fninf.2015.00011

Cited by 67 publications

(59 citation statements)

References 41 publications

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“…Neuroscience researchers doing simulation and modeling often use Python as a programming medium (Antolík and Davison, 2013; Davison et al, 2009; Hines et al, 2009; Muller et al, 2015; Van Geit et al, 2016), whereas researchers on the experimental side often use MATLAB (Baek et al, 2016; Delorme and Makeig, 2004; Englitz et al, 2013; Felice et al, 2016; Lawhern et al, 2013; Schrouff et al, 2013; Shamlo et al, 2015; Vidaurre et al, 2011); there are exceptions to both as well as hybrids. On ModelDB as of February 28, 2016, there were 247 MATLAB models and 104 Python models hosted or linked to; NEURON models numbered 523 (McDougal et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow

Stockton

Santamarı́a

2017

Neuroinform

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We developed software tools to download, extract features, and organize the Cell Types Database from the Allen Brain Institute (ABI) in order to integrate its whole cell patch clamp characterization data into the automated modeling/data analysis cycle. To expand the potential user base we employed both Python and MATLAB. The basic set of tools downloads selected raw data and extracts cell, sweep, and spike features, using ABI’s feature extraction code. To facilitate data manipulation we added a tool to build a local specialized database of raw data plus extracted features. Finally, to maximize automation, we extended our NeuroManager workflow automation suite to include these tools plus a separate investigation database. The extended suite allows the user to integrate ABI experimental and modeling data into an automated workflow deployed on heterogeneous computer infrastructures, from local servers, to high performance computing environments, to the cloud. Since our approach is focused on workflow procedures our tools can be modified to interact with the increasing number of neuroscience databases being developed to cover all scales and properties of the nervous system.

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Section: Methodsmentioning

confidence: 99%

Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow

Stockton

Santamarı́a

2017

Neuroinform

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“…Handling and cleaning these data and including baseline corrections typically requires specific statistical analyses (e.g., multi-level or mixed model; Zhang et al, 2014). Yet, both the rise of plug and play devices, which often return immediately usable data, and the growing amount of open source software packages and algorithms to process, clean, and analyze data contribute to optimizing neuroscientific dataanalysis (e.g., several packages in Python, PhysioToolkit; Goldberger et al, 2000;Massaro and Pecchia, 2019;Muller et al, 2015).…”

Section: Data-analysis and Interpretation Neuro-data Creates New Chamentioning

confidence: 99%

Neuroscience in service research: an overview and discussion of its possibilities

Verhulst¹,

Keyser²,

Gustafsson³

et al. 2019

JOSM

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2 NEUROSCIENCE IN SERVICE RESEARCH: AN OVERVIEW AND DISCUSSION OF ITS POSSIBILITIES STRUCTURED ABSTRACTPurpose: The paper discusses recent developments in neuroscientific methods and demonstrates its potential for the service field. This work is a call to action for more service researchers to adopt promising and increasingly accessible neuro-tools that allow the service field to benefit from neuroscience theories and insights.Design/methodology/approach: The paper synthesizes key literature from a variety of domains (e.g., neuroscience, consumer neuroscience, organizational neuroscience) to provide an in-depth background to start applying neuro-tools. Specifically, this paper outlines the most important neuro-tools today and discusses their theoretical and empirical value.Findings: To date, the use of neuro-tools in the service field is limited. This is surprising given the great potential they hold to advance service research. To stimulate the use of neurotools in the service area, the authors provide a roadmap to enable neuroscientific service studies and conclude with a discussion on promising areas (e.g., service experience, servicescape) ripe for neuroscientific input.Originality/value: The paper offers service researchers a starting point to understand the potential benefits of adopting the neuroscientific method and shows their complementarity with traditional service research methods like surveys, experiments, and qualitative research.In addition, this paper may also help reviewers and editors to better assess the quality of neuro-studies in service.

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“…Another goal of this work was to provide a Python code of these signal decomposition methods for 269 the community. Python is a fast-growing programming language and is currently the third most popular 270 programming language in the world [51] and with widespread applications in neuroscience [52]. The 271 distribution of these packages could further encourage the use of open source programming languages for 272 works involving these specific signal processing methods.…”

Section: Feature Selection and Classification 194mentioning

confidence: 99%

Evaluating three different adaptive decomposition methods for EEG signal seizure detection and classification

Carvalho

Moraes

Braga

et al. 2019

Preprint

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Signal processing and machine learning methods are valuable tools in epilepsy research, potentially assisting in diagnosis, seizure detection, prediction and real-time event detection during long term monitoring. Recent approaches involve the decomposition of these signals in different modes or functions in a data-dependent and adaptive way. These approaches may provide advantages over commonly used Fourier based methods due to their ability to work with nonlinear and nonstationary data. In this work, three adaptive decomposition methods (Empirical Mode Decomposition, Empirical Wavelet Transform and Variational Mode Decomposition) are evaluated for the classification of normal, ictal and inter-ictal EEG signals using a freely available database. We provide a previously unavailable common methodology for comparing the performance of these methods for EEG seizure detection, with the use of the same classifiers, parameters and spectral and time domain features. It is shown that the outcomes using the three methods are quite similar, with maximum accuracies of 97.5% for Empirical Mode Decomposition, 96.7% for Empirical Wavelet Transform and 98.2% for Variational Mode Decomposition. Features were also extracted from the original non-decomposed signals, yielding inferior, but still fairly accurate (95.3%) results. The evaluated decomposition methods are promising approaches for seizure detection, but their use should be judiciously analysed, especially in situations that require real-time processing and computational power is an issue. An additional methodological contribution of this work is the development of two python packages, already available at the PyPI repository: One for the Empirical Wavelet Transform (ewtpy) and another for Variational Mode Decomposition (vmdpy).

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Python in neuroscience

Cited by 67 publications

References 41 publications

Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow

Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow

Neuroscience in service research: an overview and discussion of its possibilities

Evaluating three different adaptive decomposition methods for EEG signal seizure detection and classification

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