The NEURON Book: Index

By Nicholas T. Carnevale, Michael L. Hines. 2006

919 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d445318c9cb2d660bb7093e2755e8944aba03fd6

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. Idan Segev
2. Felix Schürmann
3. Andrew P. Davison
4. Alberto Mazzoni
5. Eilif B. Muller
6. Gaute T. Einevoll
7. Henry Markram
8. Hermann Cuntz

In Molecular Biology

1. Andrew P. Davison
2. Eilif B. Muller
3. Henry Markram
4. Idan Segev
5. Stefano Panzeri
6. Michael L. Hines

Citations in the corpus

• NEURON and Python (2008, positive): “To show how a model neuron is implemented using Python, we repeat the example described in Chapter 6 of the NEURON Book (Carnevale and Hines, 2006), but using Python rather than Hoc.”
• Can Simple Rules Control Development of a Pioneer Vertebrate Neuronal Network Generating Behavior? (2014, methodology): “To construct model dINs with appropriate physiological firing properties, we used NEURON (controlled by a Python script; Carnevale and Hines, 2006) to build networks with 30 multicompartment dINs electrically coupled by gap junctions on their axons.”
• Simulation of networks of spiking neurons: A review of tools and strategies (2006, neutral): “Users can extend NEURON by writing new function and biophysical mechanism specifications in the NMODL language, which is then compiled and dynamically linked (Hines and Carnevale 1997) (chapter 9 in Carnevale and Hines 2006).”
• Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models (2015, methodology): “The resulting morphologies were then exported to NEURON [74,75] using the TREES toolbox functions.”
• Enabling New Flexibility in the SUNDIALS Suite of Nonlinear and Differential/Algebraic Equation Solvers (2020, background): “Over the last two decades the SUNDIALS codes have been used in a number of applications on small to large-scale computing systems [22, 28, 29, 33–35, 51, 56, 73, 76] as well as in other mathematical libraries and frameworks [3–5, 54, 61, 67, 75, 76], garnering more than 90,000 downloads in 2020.”
• A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data (2007, methodology): “Simulations were performed using NEURON 5.9 (http://www.neuron. yale.edu; Carnevale and Hines, 2005 ).”