Modeling Single-Neuron Dynamics and Computations: A Balance of Detail and Abstraction
By Andreas V. M. Herz, Tim Gollisch, Christian K. Machens, D. Jaeger. Science, 2006
475 citations recorded by the source corpus.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/9e4fd2799e506c855e8bfdcf85ec3abb51d43e19
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cellular and Molecular Neuroscience
- Matthew E. Larkum
- G. Major
- Maurizio Mattia
- Daniele Linaro
- Marco Storace
- Padraig Gleeson
- Jackie Schiller
- Guy O. Billings
In Cognitive Neuroscience
- Matthew E. Larkum
- G. Major
- Andrew P. Davison
- Jackie Schiller
- Maurizio Mattia
- Daniele Linaro
- Marco Storace
- Padraig Gleeson
In Molecular Biology
Citations in the corpus
- Active properties of neocortical pyramidal neuron dendrites. (2013, background): “Actually understanding any given neuron requires the right level of abstraction (Branco & Häusser 2010, Häusser & Mel 2003, Herz et al. 2006, Koch et al. 1983, Koch & Segev 2000, Larkum et al. 2001, Mel 1994, Poirazi et al. 2003, Spruston 2008).”
- NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail (2010, background): “Models of neuronal systems can vary greatly in the amount of biological detail incorporated [6].”
- Inferring Network Dynamics and Neuron Properties from Population Recordings (2011, background): “Complexity reduction in single-neuron modeling is the result of a trade-off between the tractability of the description and the capability of mimicking almost all the behaviors exhibited by isolated nervous cells (in particular the rich firing patterns; Herz et al., 2006).”