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author = {Marc-Oliver Gewaltig and Markus Diesmann},
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title = {NEST (NEural Simulation Tool)},
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journal = {Scholarpedia},
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year = {2007},
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volume = {2},
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pages = {1430},
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number = {4}
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}
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@article{Vlachos2011,
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title={Context-dependent encoding of fear and extinction memories in a large-scale network model of the basal amygdala},
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author={Vlachos, I. and Herry, C. and L{\"u}thi, A. and Aertsen, A. and Kumar, A.},
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journal={PLoS Comput. Biol.},
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volume={7},
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pages={e1001104},
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year={2011},
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}
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@article{Herry2008,
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title={Switching on and off fear by distinct neuronal circuits},
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author={Herry, C. and Ciocchi, S. and Senn, V. and Demmou, L. and M{\"u}ller, C. and L{\"u}thi, A.},
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journal={Nature},
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volume={454},
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pages={600--606},
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year={2008},
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}
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@article{Stimberg2019,
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abstract = {Brian 2 allows scientists to simply and efficiently simulate spiking neural network models. These models can feature novel dynamical equations, their interactions with the environment, and experimental protocols. To preserve high performance when defining new models, most simulators offer two options: Low-level programming or description languages. The first option requires expertise, is prone to errors, and is problematic for reproducibility. The second option cannot describe all aspects of a computational experiment, such as the potentially complex logic of a stimulation protocol. Brian addresses these issues using runtime code generation. Scientists write code with simple and concise high-level descriptions, and Brian transforms them into efficient low-level code that can run interleaved with their code. We illustrate this with several challenging examples: A plastic model of the pyloric network, a closed-loop sensorimotor model, a programmatic exploration of a neuron model, and an auditory model with real-time input.},
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author = {Stimberg, Marcel and Brette, Romain and Goodman, Dan F.M.},
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doi = {10.7554/eLife.47314},
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file = {:C$\backslash$:/Users/Renan/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Stimberg, Brette, Goodman - 2019 - Brian 2, an intuitive and efficient neural simulator.pdf:pdf},
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issn = {2050084X},
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journal = {eLife},
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mendeley-groups = {Amygdala},
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month = {aug},
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publisher = {eLife Sciences Publications Ltd},
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title = {{Brian 2, an intuitive and efficient neural simulator}},
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volume = {8},
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year = {2019}
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}
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@article{kumar2008high,
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title={The high-conductance state of cortical networks},
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author={Kumar, A. and Schrader, S. and Aertsen, A. and Rotter, S.},
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journal={Neural Comput},
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pages={1--43},
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year={2008},
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}
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@article{sah2003amygdaloid,
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title={The amygdaloid complex: anatomy and physiology},
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author={Sah, P. and Faber, E. S. L. and Lopez de Armentia, M. and Power, J. M. J. P. R.},
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@article{woodruff2007networks,
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title={Networks of parvalbumin-positive interneurons in the basolateral amygdala},
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author={Woodruff, A. R. and Sah, P.},
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journal={J. Neurosci.},
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pages={553--563},
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year={2007},
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}
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@article{Golomb2007,
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author = {Golomb, David},
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doi = {10.4249/scholarpedia.1347},
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issn = {1941-6016},
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journal = {Scholarpedia},
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number = {1},
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pages = {1347},
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publisher = {Scholarpedia},
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title = {{Neuronal synchrony measures}},
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}
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author = {Golomb, D. and Rinzel, J.},
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doi = {10.1103/PhysRevE.48.4810},
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journal = {Phys. Rev. E},
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pages = {4810--4814},
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}
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@article{wilson1972excitatory,
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title={Excitatory and inhibitory interactions in localized populations of model neurons},
# Information about the original article that has been replicated
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replication:
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- cite: Vlachos, I., Herry, C., Lüthi, A., Aertsen, A., & Kumar, A. "Context-dependent encoding of fear and extinction memories in a large-scale network model of the basal amygdala". PLoS Comput Biol, v. 7, n. 3, p. e1001104, 2011. # Full textual citation
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- bib: Vlachos2011 # Bibtex key (if any) in your bibliography file
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- url: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1001104 # URL to the PDF, try to link to a non-paywall version
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- doi: 10.1371/journal.pcbi.1001104 # Regular digital object identifier
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# Don't forget to surround abstract with double quotes
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abstract: "The basal nucleus of the amygdala (BA) is related to the process
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of creating memories of conditioned fear and extinction that are both dependent
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on the context. Vlacho and collaborators developed two models of neural networks
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to study the effect of plasticity based on a specific phenomenological rule in BA
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excitatory neurons. When an excitatory subpopulation receives conditioned stimulus
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(CS) and contextual inputs in narrow time windows, synaptic weights are potentiated
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by this effect, increasing the subpopulation's firing rate related to the specified
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context. In this replication, we implemented the models using Python (for the mean-field
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model) and Brian 2 (for the spiking neuron model), and we were able to reproduce the
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original results qualitatively. In order to replicate the model, it was necessary to
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estimate a considerable amount of parameters and to adapt some of the protocols that
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were either ambiguous or absent in the methodological descriptions of the original work."
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# Bibliography file (yours)
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bibliography: bibliography.bib
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# Type of the article
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# Type can be:
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# * Editorial
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# * Letter
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# * Replication
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type: Replication
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# Scientific domain of the article (e.g. Computational Neuroscience)
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# (one domain only & try to be not overly specific)
- number: 9# Article number will be automatically assigned during publication
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- doi: 10.5281/zenodo.5657320 #10.5281/zenodo.5573985 # DOI from Zenodo
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- url: https://zenodo.org/record/5657320/files/article.pdf #https://zenodo.org/record/5573985/files/article.pdf # Final PDF URL (Zenodo or rescience website?)
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