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The format of the weights file

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Bibliographic Details
Title: The format of the weights file
Authors: Joseph James Tharayil, Jorge Blanco Alonso, Silvia Farcito, Bryn Lloyd, Armando Romani, Elvis Boci, Antonino Cassara, Felix Schürmann, Esra Neufeld, Niels Kuster, Michael Reimann
Publication Year: 2025
Subject Terms: Biophysics, Cell Biology, Genetics, Neuroscience, Physiology, Biotechnology, Evolutionary Biology, Developmental Biology, Cancer, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, standalone python code, sonata data format, rat somatosensory cortex, new avenues open, network simulations accessible, coreneuron computation engine, computational neuroscience grows, complex tissue anatomy, permit online calculation, signal information content, e ., non, simulate eeg recordings, enable efficient calculation, div >< p, dipolar current sources
Description: As the size and complexity of network simulations accessible to computational neuroscience grows, new avenues open for research into extracellularly recorded electric signals. Biophysically detailed simulations permit the identification of the biological origins of the different components of recorded signals, the evaluation of signal sensitivity to different anatomical, physiological, and geometric factors, and selection of recording parameters to maximize the signal information content. Simultaneously, virtual extracellular signals produced by these networks may become important metrics for neuro-simulation validation. To enable efficient calculation of extracellular signals from large neural network simulations, we have developed BlueRecording , a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. In particular, we implement a general form of the reciprocity theorem, which is capable of handling non-dipolar current sources, such as may be found in long axons and recordings close to the current source, as well as complex tissue anatomy, dielectric heterogeneity, and electrode geometries. To our knowledge, this is the first application of this generalized (i.e., non-dipolar) reciprocity-based approach to simulate EEG recordings. We use these tools to calculate extracellular signals from an in silico model of the rat somatosensory cortex and hippocampus and to study signal contribution differences between regions and cell types.
Document Type: dataset
Language: unknown
Relation: https://figshare.com/articles/dataset/The_format_of_the_weights_file/29138136
DOI: 10.1371/journal.pcbi.1013023.t001
Availability: https://doi.org/10.1371/journal.pcbi.1013023.t001
https://figshare.com/articles/dataset/The_format_of_the_weights_file/29138136
Rights: CC BY 4.0
Accession Number: edsbas.DC800BC7
Database: BASE
Description
Abstract:As the size and complexity of network simulations accessible to computational neuroscience grows, new avenues open for research into extracellularly recorded electric signals. Biophysically detailed simulations permit the identification of the biological origins of the different components of recorded signals, the evaluation of signal sensitivity to different anatomical, physiological, and geometric factors, and selection of recording parameters to maximize the signal information content. Simultaneously, virtual extracellular signals produced by these networks may become important metrics for neuro-simulation validation. To enable efficient calculation of extracellular signals from large neural network simulations, we have developed BlueRecording , a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. In particular, we implement a general form of the reciprocity theorem, which is capable of handling non-dipolar current sources, such as may be found in long axons and recordings close to the current source, as well as complex tissue anatomy, dielectric heterogeneity, and electrode geometries. To our knowledge, this is the first application of this generalized (i.e., non-dipolar) reciprocity-based approach to simulate EEG recordings. We use these tools to calculate extracellular signals from an in silico model of the rat somatosensory cortex and hippocampus and to study signal contribution differences between regions and cell types.
DOI:10.1371/journal.pcbi.1013023.t001