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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

LNetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model

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Romaro, Cecilia [1] ; Najman, Fernando Araujo [2] ; Lytton, William W. [3] ; Roque, Antonio C. [1] ; Dura-Bernal, Salvador [3, 4]
Total Authors: 5
[1] Univ Sao Paulo, Dept Phys, Sch Philosophy Sci & Letters Ribeirao Preto, BR-14049 Ribeirao Preto, SP - Brazil
[2] Univ Sao Paulo, Inst Math & Stat, BR-05508 Sao Paulo, SP - Brazil
[3] State Univ New York Downstate Hlth Sci Univ, Dept Physiol & Pharmacol, Brooklyn, NY 11203 - USA
[4] Nathan S Kline Inst Psychiat Res, New York, NY 10962 - USA
Total Affiliations: 4
Document type: Journal article
Source: NEURAL COMPUTATION; v. 33, n. 7, p. 1993-2032, JUL 2021.
Web of Science Citations: 0

The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. Here, we reimplemented the model using NetPyNE, a high-level Python interface to the NEURON simulator, and reproduced the findings of the original publication. We also implemented a method for scaling the network size that preserves first- and second-order statistics, building on existing work on network theory. Our new implementation enabled the use of more detailed neuron models with multicompartmental morphologies and multiple biophysically realistic ion channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, the relation to local field potentials, and other multiscale interactions. The scaling method we used provides flexibility to increase or decrease the network size as needed when running these CPU-intensive detailed simulations. Finally, NetPyNE facilitates modifying or extending the model using its declarative language; optimizing model parameters; running efficient, large-scale parallelized simulations; and analyzing the model through built-in methods, including local field potential calculation and information flow measures. (AU)

FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 18/20277-0 - Computational and systems neuroscience
Grantee:Antonio Carlos Roque da Silva Filho
Support Opportunities: Regular Research Grants
FAPESP's process: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat
Grantee:Oswaldo Baffa Filho
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC