1 |
3D-printer visualization of NEURON models (McDougal and Shepherd, 2015) |

2 |
A comparative computer simulation of dendritic morphology (Donohue and Ascoli 2008) |

3 |
A CORF computational model of a simple cell that relies on LGN input (Azzopardi & Petkov 2012) |

4 |
A detailed data-driven network model of prefrontal cortex (Hass et al 2016) |

5 |
A fast model of voltage-dependent NMDA Receptors (Moradi et al. 2013) |

6 |
A finite volume method for stochastic integrate-and-fire models (Marpeau et al. 2009) |

7 |
A generic MAPK cascade model for random parameter sampling analysis (Mai and Liu 2013) |

8 |
A set of reduced models of layer 5 pyramidal neurons (Bahl et al. 2012) |

9 |
A simplified cerebellar Purkinje neuron (the PPR model) (Brown et al. 2011) |

10 |
Accelerating with FlyBrainLab discovery of the functional logic of Drosophila brain (Lazar et al 21) |

11 |
Accurate and fast simulation of channel noise in conductance-based model neurons (Linaro et al 2011) |

12 |
Activity constraints on stable neuronal or network parameters (Olypher and Calabrese 2007) |

13 |
Allosteric gating of K channels (Horrigan et al 1999) |

14 |
Analytical modelling of temperature effects on an AMPA-type synapse (Kufel & Wojcik 2018) |

15 |
Analyzing neural time series data theory and practice (Cohen 2014) |

16 |
AP shape and parameter constraints in optimization of compartment models (Weaver and Wearne 2006) |

17 |
Automated metadata suggester (McDougal et al 2018) |

18 |
Boolean network-based analysis of the apoptosis network (Mai and Liu 2009) |

19 |
Brain Dynamics Toolbox (Heitmann & Breakspear 2016, 2017, 2018) |

20 |
Brain networks simulators - a comparative study (Tikidji-Hamburyan et al 2017) |

21 |
Cell splitting in neural networks extends strong scaling (Hines et al. 2008) |

22 |
Cellular classes revealed by heartbeat-related modulation of extracellular APs (Mosher et al 2020) |

23 |
Cellular function given parametric variation in the HH model of excitability (Ori et al 2018) |

24 |
Channel density variability among CA1 neurons (Migliore et al. 2018) |

25 |
Channel parameter estimation from current clamp and neuronal properties (Toth, Crunelli 2001) |

26 |
Code to calc. spike-trig. ave (STA) conduct. from Vm (Pospischil et al. 2007, Rudolph et al. 2007) |

27 |
Collection of simulated data from a thalamocortical network model (Glabska, Chintaluri, Wojcik 2017) |

28 |
Combining modeling, deep learning for MEA neuron localization, classification (Buccino et al 2018) |

29 |
Comparison of full and reduced globus pallidus models (Hendrickson 2010) |

30 |
Composite spiking network/neural field model of Parkinsons (Kerr et al 2013) |

31 |
Connection-set Algebra (CSA) for the representation of connectivity in NN models (Djurfeldt 2012) |

32 |
Constructed Tessellated Neuronal Geometries (CTNG) (McDougal et al. 2013) |

33 |
Data-driven, HH-type model of the lateral pyloric (LP) cell in the STG (Nowotny et al. 2008) |

34 |
Detailed analysis of trajectories in the Morris water maze (Gehring et al. 2015) |

35 |
Dipole Localization Kit (Mechler & Victor, 2012) |

36 |
Discrete event simulation in the NEURON environment (Hines and Carnevale 2004) |

37 |
Distinct current modules shape cellular dynamics in model neurons (Alturki et al 2016) |

38 |
Distributed computing tool for NEURON, NEURONPM (screensaver) (Calin-Jageman and Katz 2006) |

39 |
DynaSim: a MATLAB toolbox for neural modeling and simulation (Sherfey et al 2018) |

40 |
Efficient estimation of detailed single-neuron models (Huys et al. 2006) |