A citation-based method for searching scientific literature

Shaul Druckmann, Yoav Banitt, Albert Gidon, Felix Schürmann, Henry Markram, Idan Segev. Front Neurosci 2007
Times Cited: 144







List of co-cited articles
968 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties.
Etay Hay, Sean Hill, Felix Schürmann, Henry Markram, Idan Segev. PLoS Comput Biol 2011
124
28



The NEURON simulation environment.
M L Hines, N T Carnevale. Neural Comput 1997
23

Reconstruction and Simulation of Neocortical Microcircuitry.
Henry Markram, Eilif Muller, Srikanth Ramaswamy, Michael W Reimann, Marwan Abdellah, Carlos Aguado Sanchez, Anastasia Ailamaki, Lidia Alonso-Nanclares, Nicolas Antille, Selim Arsever,[...]. Cell 2015
473
23

Complex parameter landscape for a complex neuron model.
Pablo Achard, Erik De Schutter. PLoS Comput Biol 2006
132
21

Automated neuron model optimization techniques: a review.
W Van Geit, E De Schutter, P Achard. Biol Cybern 2008
59
35


Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data.
Shaul Druckmann, Thomas K Berger, Sean Hill, Felix Schürmann, Henry Markram, Idan Segev. Biol Cybern 2008
32
53

Efficient estimation of detailed single-neuron models.
Quentin J M Huys, Misha B Ahrens, Liam Paninski. J Neurophysiol 2006
73
21

Effective stimuli for constructing reliable neuron models.
Shaul Druckmann, Thomas K Berger, Felix Schürmann, Sean Hill, Henry Markram, Idan Segev. PLoS Comput Biol 2011
25
64

NEURON and Python.
Michael L Hines, Andrew P Davison, Eilif Muller. Front Neuroinform 2009
169
15

Modeling single-neuron dynamics and computations: a balance of detail and abstraction.
Andreas V M Herz, Tim Gollisch, Christian K Machens, Dieter Jaeger. Science 2006
171
14

Global structure, robustness, and modulation of neuronal models.
M S Goldman, J Golowasch, E Marder, L F Abbott. J Neurosci 2001
212
13

BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience.
Werner Van Geit, Michael Gevaert, Giuseppe Chindemi, Christian Rössert, Jean-Denis Courcol, Eilif B Muller, Felix Schürmann, Idan Segev, Henry Markram. Front Neuroinform 2016
39
33


Failure of averaging in the construction of a conductance-based neuron model.
Jorge Golowasch, Mark S Goldman, L F Abbott, Eve Marder. J Neurophysiol 2002
162
12

Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data.
Armin Bahl, Martin B Stemmler, Andreas V M Herz, Arnd Roth. J Neurosci Methods 2012
40
30


Variability, compensation and homeostasis in neuron and network function.
Eve Marder, Jean-Marc Goaillard. Nat Rev Neurosci 2006
621
11


Preserving axosomatic spiking features despite diverse dendritic morphology.
Etay Hay, Felix Schürmann, Henry Markram, Idan Segev. J Neurophysiol 2013
31
35



A synaptic organizing principle for cortical neuronal groups.
Rodrigo Perin, Thomas K Berger, Henry Markram. Proc Natl Acad Sci U S A 2011
333
10

Similar network activity from disparate circuit parameters.
Astrid A Prinz, Dirk Bucher, Eve Marder. Nat Neurosci 2004
490
10

Smoothing of, and parameter estimation from, noisy biophysical recordings.
Quentin J M Huys, Liam Paninski. PLoS Comput Biol 2009
41
21

Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: a new unifying principle.
Matthew E Larkum, Thomas Nevian, Maya Sandler, Alon Polsky, Jackie Schiller. Science 2009
357
9


NEURON: a tool for neuroscientists.
M L Hines, N T Carnevale. Neuroscientist 2001
330
9

Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons.
Martin Pospischil, Maria Toledo-Rodriguez, Cyril Monier, Zuzanna Piwkowska, Thierry Bal, Yves Frégnac, Henry Markram, Alain Destexhe. Biol Cybern 2008
109
8

Neuroscience. How good are neuron models?
Wulfram Gerstner, Richard Naud. Science 2009
103
8

The blue brain project.
Henry Markram. Nat Rev Neurosci 2006
380
8



Interneurons of the neocortical inhibitory system.
Henry Markram, Maria Toledo-Rodriguez, Yun Wang, Anirudh Gupta, Gilad Silberberg, Caizhi Wu. Nat Rev Neurosci 2004
8


The quantitative single-neuron modeling competition.
Renaud Jolivet, Felix Schürmann, Thomas K Berger, Richard Naud, Wulfram Gerstner, Arnd Roth. Biol Cybern 2008
61
13

The use of automated parameter searches to improve ion channel kinetics for neural modeling.
Eric B Hendrickson, Jeremy R Edgerton, Dieter Jaeger. J Comput Neurosci 2011
15
53


LTP regulates burst initiation and frequency at mossy fiber-granule cell synapses of rat cerebellum: experimental observations and theoretical predictions.
Thierry Nieus, Elisabetta Sola, Jonathan Mapelli, Elena Saftenku, Paola Rossi, Egidio D'Angelo. J Neurophysiol 2006
84
9

Large-scale model of mammalian thalamocortical systems.
Eugene M Izhikevich, Gerald M Edelman. Proc Natl Acad Sci U S A 2008
390
8

Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models.
Christian Pozzorini, Skander Mensi, Olivier Hagens, Richard Naud, Christof Koch, Wulfram Gerstner. PLoS Comput Biol 2015
32
25


Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models.
Werner Van Geit, Pablo Achard, Erik De Schutter. Front Neuroinform 2007
22
31

Neuronal firing sensitivity to morphologic and active membrane parameters.
Christina M Weaver, Susan L Wearne. PLoS Comput Biol 2008
53
13

Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces.
Laurent Badel, Sandrine Lefort, Romain Brette, Carl C H Petersen, Wulfram Gerstner, Magnus J E Richardson. J Neurophysiol 2008
115
7

Spatio-temporal correlations and visual signalling in a complete neuronal population.
Jonathan W Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M Litke, E J Chichilnisky, Eero P Simoncelli. Nature 2008
592
7




Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.