Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons

By M. Pospischil, Maria Toledo-Rodriguez, Cyril Monier, Z. Piwkowska, T. Bal, Yves Frégnac, Henry Markram, Alain Destexhe. Biological cybernetics, 2008

333 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/356a59100c06df4c6f1b9ee9e57fd0475d19dbda

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Iain Murray
2. George Papamakarios
3. Jakob H. Macke
4. Giacomo Bassetto
5. Jan-Matthis Lueckmann
6. Gilles Louppe
7. Conor Durkan
8. Joeri Hermans

In Computer Networks and Communications

1. Iain Murray
2. Jakob H. Macke

In Statistical and Nonlinear Physics

1. Jakob H. Macke
2. George Papamakarios
3. Marcel Nonnenmacher
4. Iain Murray
5. David C. Sterratt
6. Giacomo Bassetto
7. Jan-Matthis Lueckmann
8. Gilles Louppe

Citations in the corpus

• Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows (2018, methodology): “We use the published equations [62] for α m ( V ), β m ( V ), α h ( V ), β h ( V ) and α n ( V ), which contain a parameter V T , and the published equations for p ∞ ( V ) and τ p ( V ), the latter of which contains a parameter τ max .”
• Flexible statistical inference for mechanistic models of neural dynamics (2017, methodology): “We applied SNPE to a Hodgkin-Huxley model with channel kinetics as in [45] and inferred the posterior over 12 biophysical parameters, given 20 voltage features of the simulated data.”