Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings.
- Published in:
- Journal of Computational Neuroscience, 2016, v. 41, n. 3, p. 269, doi. 10.1007/s10827-016-0617-5
- By:
- Publication type:
- Article
Works matching IS 09295313 AND DT 2016 AND VI 41 AND IP 3
Results: 7
1
2
A negative group delay model for feedback-delayed manual tracking performance.
- Published in:
- Journal of Computational Neuroscience, 2016, v. 41, n. 3, p. 295, doi. 10.1007/s10827-016-0618-4
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- Publication type:
- Article
3
Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks.
- Published in:
- Journal of Computational Neuroscience, 2016, v. 41, n. 3, p. 305, doi. 10.1007/s10827-016-0619-3
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- Publication type:
- Article
4
A hidden Markov model for decoding and the analysis of replay in spike trains.
- Published in:
- Journal of Computational Neuroscience, 2016, v. 41, n. 3, p. 339, doi. 10.1007/s10827-016-0621-9
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- Publication type:
- Article
5
Linking dynamics of the inhibitory network to the input structure.
- Published in:
- Journal of Computational Neuroscience, 2016, v. 41, n. 3, p. 367, doi. 10.1007/s10827-016-0622-8
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- Publication type:
- Article
6
Multiple timescale mixed bursting dynamics in a respiratory neuron model.
- Published in:
- Journal of Computational Neuroscience, 2016, v. 41, n. 3, p. 245, doi. 10.1007/s10827-016-0616-6
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- Publication type:
- Article
7
Modelling zinc changes at the hippocampal mossy fiber synaptic cleft.
- Published in:
- Journal of Computational Neuroscience, 2016, v. 41, n. 3, p. 323, doi. 10.1007/s10827-016-0620-x
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- Publication type:
- Article