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Reproducing and quantitatively validating a biologically-constrained point-neuron model of CA pyramidal cells

Abstract : We have attempted to reproduce a biologically-constrained point-neuron model of CA1 pyramidal cells. The original models, developed for the Brian simulator, captured the frequency-current profiles of both strongly and weakly adapting cells. As part of the present study, we reproduced the model for different simulators, namely Brian2 and NEURON. The reproductions were attempted independent of the original Brian implementation, relying solely on the published article. The different implementations were quantitatively validated, to evaluate how well they mirror the original model. Additional tests were developed and packaged into a test suite, that helped further characterize and compare various aspects of these models, beyond the scope of the original study. Overall, we were able to reproduce the core features of the model, but observed certain unaccountable discrepancies. We demonstrate an approach for undertaking these evaluations, using the SciUnit framework, that allows for such quantitative validations of scientific models, to verify their accurate replication and/or reproductions. All resources employed and developed in our study have been publicly shared via the EBRAINS Live Papers platform.
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https://hal-cnrs.archives-ouvertes.fr/hal-03846983
Contributor : Andrew Davison Connect in order to contact the contributor
Submitted on : Thursday, November 10, 2022 - 1:51:50 PM
Last modification on : Saturday, November 12, 2022 - 3:27:03 AM

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fnint-16-1041423.pdf
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Shailesh Appukuttan, Andrew Davison. Reproducing and quantitatively validating a biologically-constrained point-neuron model of CA pyramidal cells. Frontiers in Integrative Neuroscience, 2022, 16, pp.1041423. ⟨10.3389/fnint.2022.1041423⟩. ⟨hal-03846983⟩

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