Electrodiffusion models of synaptic potentials in dendritic spines.
- Published in:
- Journal of Computational Neuroscience, 2019, v. 47, n. 1, p. 77, doi. 10.1007/s10827-019-00725-5
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- Publication type:
- Article
Works matching IS 09295313 AND DT 2019 AND VI 47 AND IP 1
Results: 6
1
2
Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study.
- Published in:
- Journal of Computational Neuroscience, 2019, v. 47, n. 1, p. 31, doi. 10.1007/s10827-019-00721-9
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- Publication type:
- Article
3
Modeling grid fields instead of modeling grid cells: An effective model at the macroscopic level and its relationship with the underlying microscopic neural system.
- Published in:
- Journal of Computational Neuroscience, 2019, v. 47, n. 1, p. 43, doi. 10.1007/s10827-019-00722-8
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- Publication type:
- Article
4
Biophysically interpretable inference of single neuron dynamics.
- Published in:
- Journal of Computational Neuroscience, 2019, v. 47, n. 1, p. 61, doi. 10.1007/s10827-019-00723-7
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- Publication type:
- Article
5
Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries.
- Published in:
- Journal of Computational Neuroscience, 2019, v. 47, n. 1, p. 1, doi. 10.1007/s10827-019-00714-8
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- Publication type:
- Article
6
How to correctly quantify neuronal phase-response curves from noisy recordings.
- Published in:
- Journal of Computational Neuroscience, 2019, v. 47, n. 1, p. 17, doi. 10.1007/s10827-019-00719-3
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- Publication type:
- Article