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  PyRates - A Python framework for rate-based neural simulations

Gast, R., Rose, D., Möller, H. E., Weiskopf, N., & Knösche, T. R. (2019). PyRates - A Python framework for rate-based neural simulations. PLOS One, 14(12): e0225900. doi:10.1371/journal.pone.0225900.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-CCA5-C Version Permalink: http://hdl.handle.net/21.11116/0000-0005-AA23-4
Genre: Journal Article

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 Creators:
Gast, Richard1, Author              
Rose, Daniel2, Author              
Möller, Harald E.3, Author              
Weiskopf, Nikolaus2, Author              
Knösche, Thomas R.1, Author              
Affiliations:
1Methods and Development Group MEG and Cortical Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205650              
2Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              
3Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              

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 Abstract: In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical simulations. While many such tools exist for different families of neural models, there is a lack of tools allowing for both a generic model definition and efficiently parallelized simulations. In this work, we present PyRates, a Python framework that provides the means to build a large variety of rate-based neural models. PyRates provides intuitive access to and modification of all mathematical operators in a graph, thus allowing for a highly generic model definition. For computational efficiency and parallelization, the model is translated into a compute graph. Using the example of two different neural models belonging to the family of rate-based population models, we explain the mathematical formalism, software structure and user interfaces of PyRates. We show via numerical simulations that the behavior of the PyRates model implementations is consistent with the literature. Finally, we demonstrate the computational capacities and scalability of PyRates via a number of benchmark simulations of neural networks differing in size and connectivity.

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Language(s): eng - English
 Dates: 2019-04-242019-11-142019-12-16
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pone.0225900
PMC: PMC6913930
PMID: 31841550
Other: eCollection 2019
 Degree: -

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Project name : Non-Invasive In-Vivo Histology in Health and Disease Using Magnetic Resonance Imaging (MRI) / HMRI
Grant ID : 616905
Funding program : Funding Programme 7
Funding organization : European Commission (EC)
Project name : Entschlüsselung der pathophysiologischen Prozesse induziert durch eine Querschnittlähmung: Anwendung von MRT basierter in vivo und ex vivo Histologie / hMRIofSCI
Grant ID : 01EW1711A ; 01EW1711B
Funding program : -
Funding organization : German Federal Ministry of Education and Research (BMBF)
Project name : Antibodies against Nogo-A to enhance plasticity, regeneration and functional recovery after acute spinal cord injury, a multicenter European clinical proof of concept trial / NISCI
Grant ID : 681094
Funding program : Horizon 2020
Funding organization : European Commission (EC)
Project name : -
Grant ID : -
Funding program : -
Funding organization : Max Planck Society
Project name : -
Grant ID : -
Funding program : -
Funding organization : Studienstiftung des Deutschen Volkes
Project name : -
Grant ID : -
Funding program : -
Funding organization : International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity (IMPRS NeuroCom)

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Title: PLOS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 14 (12) Sequence Number: e0225900 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850