Document
NOT PEER REVIEWED
Technical report
Call for community review of PyNN — A simulator-independent language for building neuronal network models
Published 18 Jan 2019
(https://doi.org/10.7490/f1000research.1116399.1)
Author Affiliations
Copyright © 2019 Martone M 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 18 Jan 2019
Technical report
Call for community review of PyNN — A simulator-independent language for building neuronal network models
[version 1; not peer reviewed]Author Affiliations
Abstract
Competing Interests
No competing interests were disclosed
Keywords
PyNN, neuronal network models, computational neuroscience, modelling language, Python, standard, best practice, INCF, call for feedback, simulator independent
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I support the endorsement of PyNN from INCF standard and best practices because it is a valuable effort to create a common interface to multiple neural network simulators,plus two neuromorphic platforms.
Regarding the comments raised by the reviewers, it seems to me that the governance improvements can be implemented following the example of other related communities (e.g, the NEST Initiative), while the permanent position of some PyNN developers is a sufficient guarantee to its future support.
About the adoption, I would also include other countries such as Italy, Denmark, Switzerland, and Spain, who are using PyNN in the context of neurorobotics experiments within the HBP Neurorobotics Platform.
In my opinion, the overlap between PyNN and NeuroML is quite limited, since the two tools have different approaches and especially different applications (e.g. the interaction with neuromorphic hardware and neurorobotics).
I support the endorsement of PyNN from INCF standard and best practices because it is a valuable effort to create a... READ MORE
I support the endorsement of PyNN from INCF standard and best practices because it is a valuable effort to create a common interface to multiple neural network simulators,plus two neuromorphic platforms.
Regarding the comments raised by the reviewers, it seems to me that the governance improvements can be implemented following the example of other related communities (e.g, the NEST Initiative), while the permanent position of some PyNN developers is a sufficient guarantee to its future support.
About the adoption, I would also include other countries such as Italy, Denmark, Switzerland, and Spain, who are using PyNN in the context of neurorobotics experiments within the HBP Neurorobotics Platform.
In my opinion, the overlap between PyNN and NeuroML is quite limited, since the two tools have different approaches and especially different applications (e.g. the interaction with neuromorphic hardware and neurorobotics).
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