Wiki source code of Elephant Tutorials
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| 5 | = Elephant Tutorial Space = | ||
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| 7 | Video tutorials on data analysis using Elephant | ||
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| 15 | == A resource for kick-starting work with the Elephant library == | ||
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| 17 | The Python library [[Electrophysiology Analysis Toolkit (Elephant)>>https://python-elephant.org||rel="noopener noreferrer" target="_blank"]] provides tools for the analysis of neuronal activity data, such as spike trains, local field potentials and intracellular data. In addition to providing a platform for sharing analysis codes from different laboratories, Elephant provides a consistent and homogeneous framework for data analysis, built on a modular foundation. The underlying data model is the Neo library, a framework which easily captures a wide range of neuronal data types and methods, including dozens of file formats and network simulation tools. A common data description, as provided by the Neo library, is essential for developing interoperable analysis workflows. | ||
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| 19 | In this collaborative space, we provide hands on video tutorials based on Jupyter notebooks that showcase various types of data analysis, from simple to advanced. Most notebooks are based on a common dataset published at [[https:~~/~~/gin.g-node.org/INT/multielectrode_grasp>>https://gin.g-node.org/INT/multielectrode_grasp]] (for details cf. Brochier et al (2018) Scientific Data 5, 180055. [[https:~~/~~/doi.org/10.1038/sdata.2018.55>>url:https://doi.org/10.1038/sdata.2018.55]]). All video tutorials are approximately 30 minutes in length. | ||
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| 21 | == Access to the tutorials == | ||
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| 23 | To access the tutorials, check out the drive space of this Collab. Video are available for download in the (% style="color:#f39c12" %)videos(%%) section, whereas the corresponding Jupyter notebooks are available in the (% style="color:#f39c12" %)notebooks(%%) folder. Notebooks can either be run directly on the Collaboratory (currently limited to HBP affiliated members), or downloaded and run locally. For local execution, please use the provided (% style="color:#f39c12" %)requirements.txt(%%) file to generate an appropriate Python environment. | ||
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| 25 | == List of available tutorials == | ||
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| 29 | |=(% style="width: 300px;" %)Tutorial|=(% style="width: 267px;" %)Hosts and Authors|=(% style="width: 626px;" %)Content | ||
| 30 | |(% style="width:300px" %)Elephant_Tutorial_-_LFP_analysis|(% style="width:267px" %)Robin Gutzen|(% style="width:626px" %)Apply basic LFP analysis techniques, such as power spectra. | ||
| 31 | |(% style="width:300px" %)Elephant_Tutorial_-_Spike_analysis|(% style="width:267px" %)((( | ||
| 32 | Cristiano Köhler | ||
| 33 | Alexander Kleinjohann | ||
| 34 | )))|(% style="width:626px" %)Perform basic statistical analysis of spike trains from rate profiles to pair-wise correlations. | ||
| 35 | |(% style="width:300px" %)Elephant_Tutorial_-_Spatio-temporal_spike_patterns|(% style="width:267px" %)Regimatas Jurkus | ||
| 36 | Alessandra Stella|(% style="width:626px" %)Highlights two methods for detecting hidden spatio-temporal patterns in spike data. | ||
| 37 | |(% style="width:300px" %)Elephant_Tutorial_-_GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity. | ||
| 38 | |(% style="width:300px" %)Elephant_Tutorial_-_Surrogate_techniques|(% style="width:267px" %)Peter Bouss|(% style="width:626px" %)Learn how to use different surrogate methods for spike trains to assist in formulating statistical null hypotheses in the presence of non-stationarity. | ||
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| 46 | {{box title="**Contents**"}} | ||
| 47 | {{toc/}} | ||
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