Document
NOT PEER REVIEWED
Policy document
SBP Review Neo
Published 15 Jun 2022
(https://doi.org/10.7490/f1000research.1119000.1)
Author Affiliations
Copyright © 2022 Davison A et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Published 15 Jun 2022
Policy document
SBP Review Neo
[version 1; not peer reviewed]Author Affiliations
Abstract
Competing Interests
No competing interests were disclosed
Keywords
standard, community review, electrophysiology, optophysiology
All commenters must hold a formal affiliation as per our Policies. The information that you give us will be displayed next to your comment.
User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions. Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available.
My organization has been able to use the current Neo library to very good effect. We have used it to integrate a variety of difference laboratory standards for human single neuron recording in a set of common analyses. This has greatly improved the ability to accurately analyze this type of recording.
While I think the current specification of the model can be made more precise and improved, it has already facilitated a lot of good extensions, such as EPhyViewer.
My organization has been able to use the current Neo library to very good effect. We have used it to integrate a variety of... READ MORE
My organization has been able to use the current Neo library to very good effect. We have used it to integrate a variety of difference laboratory standards for human single neuron recording in a set of common analyses. This has greatly improved the ability to accurately analyze this type of recording.
While I think the current specification of the model can be made more precise and improved, it has already facilitated a lot of good extensions, such as EPhyViewer. READ LESS
My experience contributing to NEO has been great. The core maintainers and other community members are very active and encouraging, all while ensuring the code base maintains a high standard.
Some of my clients are now using NEO instead of their own tools they developed for their proprietary format because NEO is intuitive, fast, and well-maintained.
My experience contributing... READ MORE
My experience contributing to NEO has been great. The core maintainers and other community members are very active and encouraging, all while ensuring the code base maintains a high standard.
Some of my clients are now using NEO instead of their own tools they developed for their proprietary format because NEO is intuitive, fast, and well-maintained. READ LESS
We are developing NeuroConv, a tools for automated conversions from many popular proprietary formats to NWB. Early in developing NeuroConv, we discovered the diversity of electrophysiology formats in the community and realized that building tools to read each of these format from scratch would have been an enormous task. Luckily for us, NEO already provided a common interface to the vast majority of these formats. Using NEO has provided a foundation that has been essential to the success of our mission. We count ourselves lucky to have started this project after NEO has reached a level of stability and maturity, with support for metadata standardization and efficient reading of large data files. The NEO team has also been very receptive to our requests for updates to include new data formats and to update existing readers as the existing formats are updated.
In addition to the python-neo library, we have also greatly benefited from the ephy-testing-data repo on GIN, which provides example files from these formats, enabling us to build a testing suite on real data. Here as well, we have started to collaborate with the NEO team to add new data files, creating a central repo of testing data that can be used to test many different libraries.
In my opinion, NEO is a shining example of the type of longitudinally supported and generally useful software that is particularly hard to develop and maintain in academia, and I strongly support the induction of this software as an INCF SBP.
We are developing NeuroConv, a tools for automated conversions from many popular proprietary... READ MORE
We are developing NeuroConv, a tools for automated conversions from many popular proprietary formats to NWB. Early in developing NeuroConv, we discovered the diversity of electrophysiology formats in the community and realized that building tools to read each of these format from scratch would have been an enormous task. Luckily for us, NEO already provided a common interface to the vast majority of these formats. Using NEO has provided a foundation that has been essential to the success of our mission. We count ourselves lucky to have started this project after NEO has reached a level of stability and maturity, with support for metadata standardization and efficient reading of large data files. The NEO team has also been very receptive to our requests for updates to include new data formats and to update existing readers as the existing formats are updated.
In addition to the python-neo library, we have also greatly benefited from the ephy-testing-data repo on GIN, which provides example files from these formats, enabling us to build a testing suite on real data. Here as well, we have started to collaborate with the NEO team to add new data files, creating a central repo of testing data that can be used to test many different libraries.
In my opinion, NEO is a shining example of the type of longitudinally supported and generally useful software that is particularly hard to develop and maintain in academia, and I strongly support the induction of this software as an INCF SBP. READ LESS
I would like to endorse for the widespread use of the neo object model. I use it on a regular basis for many years now, mainly for simulations by way of the pyNN language description. This allowed me to integrate seamlessly the same model on different architectures (see https://github.com/SpikeAI/2020-12_brainhack_Project7 ) and also to envision multi-architecture models that would be compatible with neuromorphic chips or event-based cameras.
This model is now being used in recent project with electrophysiological data that was saved using this object model. This allows to use similar tools as those developed in the modelling community and a more robust development of analysis at the crossroads between theoretical and experimental neuroscience.
LP
I would like to endorse for the widespread use of the neo object model. I use it on a regular basis for many years now, mainly for simulations by way of the pyNN language... READ MORE
I would like to endorse for the widespread use of the neo object model. I use it on a regular basis for many years now, mainly for simulations by way of the pyNN language description. This allowed me to integrate seamlessly the same model on different architectures (see https://github.com/SpikeAI/2020-12_brainhack_Project7 ) and also to envision multi-architecture models that would be compatible with neuromorphic chips or event-based cameras.
This model is now being used in recent project with electrophysiological data that was saved using this object model. This allows to use similar tools as those developed in the modelling community and a more robust development of analysis at the crossroads between theoretical and experimental neuroscience.
LP
Use of this website is subject to the F1000 Research Limited (F1000) General Terms and Conditions.
Submission of user comments to this website is subject to additional Terms and Conditions. By clicking "I accept the User Comment Terms and Conditions" before you submit your first comment, you agree to be bound by these conditions every time you submit a comment.
Terms relating to user comments