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  Predicting spike timing of neocortical pyramidal neurons by simple threshold models

Jolivet, R., Rauch, A., Lüscxher, H.-R., & Gerstner, W. (2006). Predicting spike timing of neocortical pyramidal neurons by simple threshold models. Journal of Computational Neuroscience, 21(1), 35-49. doi:10.1007/s10827-006-7074-5.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-836D-E Version Permalink: http://hdl.handle.net/21.11116/0000-0004-836E-D
Genre: Journal Article

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Jolivet, R, Author
Rauch, A1, 2, Author              
Lüscxher, H-R, Author
Gerstner, W, Author
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Neurons generate spikes reliably with millisecond precision if driven by a fluctuating current—is it then possible to predict the spike timing knowing the input? We determined parameters of an adapting threshold model using data recorded in vitro from 24 layer 5 pyramidal neurons from rat somatosensory cortex, stimulated intracellularly by a fluctuating current simulating synaptic bombardment in vivo. The model generates output spikes whenever the membrane voltage (a filtered version of the input current) reaches a dynamic threshold. We find that for input currents with large fluctuation amplitude, up to 75% of the spike times can be predicted with a precision of ±2 ms. Some of the intrinsic neuronal unreliability can be accounted for by a noisy threshold mechanism. Our results suggest that, under random current injection into the soma, (i) neuronal behavior in the subthreshold regime can be well approximated by a simple linear filter; and (ii) most of the nonlinearities are captured by a simple threshold process.

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 Dates: 2006-08
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1007/s10827-006-7074-5
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Title: Journal of Computational Neuroscience
Source Genre: Journal
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Publ. Info: Boston : Kluwer Academic Publishers
Pages: - Volume / Issue: 21 (1) Sequence Number: - Start / End Page: 35 - 49 Identifier: ISSN: 0929-5313
CoNE: https://pure.mpg.de/cone/journals/resource/954925568787