Model Concept: Methods

Research on numerical, mathematical, or computational neuroscience algorithms.

  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. CA1 pyramidal populations after high frequency head impacts (Chapman, et al., 2023)
  22. Cell splitting in neural networks extends strong scaling (Hines et al. 2008)
  23. Cellular classes revealed by heartbeat-related modulation of extracellular APs (Mosher et al 2020)
  24. Cellular function given parametric variation in the HH model of excitability (Ori et al 2018)
  25. Channel density variability among CA1 neurons (Migliore et al. 2018)
  26. Channel parameter estimation from current clamp and neuronal properties (Toth, Crunelli 2001)
  27. Code to calc. spike-trig. ave (STA) conduct. from Vm (Pospischil et al. 2007, Rudolph et al. 2007)
  28. Collection of simulated data from a thalamocortical network model (Glabska, Chintaluri, Wojcik 2017)
  29. Combining modeling, deep learning for MEA neuron localization, classification (Buccino et al 2018)
  30. Comparison of full and reduced globus pallidus models (Hendrickson 2010)
  31. Composite spiking network/neural field model of Parkinsons (Kerr et al 2013)
  32. Connection-set Algebra (CSA) for the representation of connectivity in NN models (Djurfeldt 2012)
  33. Constructed Tessellated Neuronal Geometries (CTNG) (McDougal et al. 2013)
  34. Data-driven, HH-type model of the lateral pyloric (LP) cell in the STG (Nowotny et al. 2008)
  35. Detailed analysis of trajectories in the Morris water maze (Gehring et al. 2015)
  36. Dipole Localization Kit (Mechler & Victor, 2012)
  37. Discrete event simulation in the NEURON environment (Hines and Carnevale 2004)
  38. Distinct current modules shape cellular dynamics in model neurons (Alturki et al 2016)
  39. Distributed computing tool for NEURON, NEURONPM (screensaver) (Calin-Jageman and Katz 2006)
  40. DynaSim: a MATLAB toolbox for neural modeling and simulation (Sherfey et al 2018)
  41. Efficient estimation of detailed single-neuron models (Huys et al. 2006)
  42. Efficient simulation environment for modeling large-scale cortical processing (Richert et al. 2011)
  43. Efficient simulation of 3D reaction-diffusion in models of neurons (McDougal et al, 2022)
  44. Electrodiffusive astrocytic and extracellular ion concentration dynamics model (Halnes et al. 2013)
  45. Evaluation of stochastic diff. eq. approximation of ion channel gating models (Bruce 2009)
  46. Extracellular fields for a three-dimensional network of cells using NEURON (Appukuttan et al 2017)
  47. Fast population coding (Huys et al. 2007)
  48. Fully Implicit Parallel Simulation of Single Neurons (Hines et al. 2008)
  49. Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks (Magalhaes et al 2019)
  50. Gap junction subtypes (Appukuttan et al 2016)
  51. GC model (Beining et al 2017)
  52. Generating neuron geometries for detailed 3D simulations using AnaMorph (Morschel et al 2017)
  53. Generic Bi-directional Real-time Neural Interface (Zrenner et al. 2010)
  54. GLMCC validation neural network model (Kobayashi et al. 2019)
  55. Globus pallidus multi-compartmental model neuron with realistic morphology (Gunay et al. 2008)
  56. Graph-theoretical Derivation of Brain Structural Connectivity (Giacopelli et al 2020)
  57. High-Res. Recordings Using a Real-Time Computational Model of the Electrode (Brette et al. 2008)
  58. Hippocampal CA1 NN with spontaneous theta, gamma: full scale & network clamp (Bezaire et al 2016)
  59. Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010)
  60. Impedance spectrum in cortical tissue: implications for LFP signal propagation (Miceli et al. 2017)
  61. Implementation issues in approximate methods for stochastic Hodgkin-Huxley models (Bruce 2007)
  62. Increased computational accuracy in multi-compartmental cable models (Lindsay et al. 2005)
  63. Inferring connection proximity in electrically coupled networks (Cali et al. 2007)
  64. Ion channel modeling with whole cell and a genetic algorithm (Gurkiewicz and Korngreen 2007)
  65. Kernel method to calculate LFPs from networks of point neurons (Telenczuk et al 2020)
  66. KInNeSS : a modular framework for computational neuroscience (Versace et al. 2008)
  67. Large scale neocortical model for PGENESIS (Crone et al 2019)
  68. Local variable time step method (Lytton, Hines 2005)
  69. Mapping function onto neuronal morphology (Stiefel and Sejnowski 2007)
  70. Markov Chain-based Stochastic Shielding Hodgkin Huxley Model (Schmandt, Galan 2012)
  71. MATLAB for brain and cognitive scientists (Cohen 2017)
  72. Mature and young adult-born dentate granule cell models (T2N interface) (Beining et al. 2017)
  73. Mean Field Equations for Two-Dimensional Integrate and Fire Models (Nicola and Campbell, 2013)
  74. Mean field model for Hodgkin Huxley networks of neurons (Carlu et al 2020)
  75. Mean-Field models of conductance-based NNs of spiking neurons with adaptation (di Volo et al 2019)
  76. Measuring neuronal identification quality in ensemble recordings (isoitools) (Neymotin et al. 2011)
  77. Method for counting motor units in mice (Major et al 2007)
  78. Method for deriving general HH neuron model`s spiking input-output relation (Soudry & Meir 2014)
  79. Method of probabilistic principle surfaces (PPS) (Chang and Ghosh 2001)
  80. Model predictive control model for an isometric motor task (Ueyama 2017)
  81. Modeling single neuron LFPs and extracellular potentials with LFPsim (Parasuram et al. 2016)
  82. Modelling large scale electrodiffusion near morphologically detailed neurons (Solbra et al 2018)
  83. ModelView: online structural analysis of computational models (McDougal et al. 2015)
  84. ModFossa: a library for modeling ion channels using Python (Ferneyhough et al 2016)
  85. Moose/PyMOOSE: interoperable scripting in Python for MOOSE (Ray and Bhalla 2008)
  86. Motion Clouds: Synthesis of random textures for motion perception (Leon et al. 2012)
  87. Motoneuron simulations for counting motor units (Major and Jones 2005)
  88. NETMORPH: creates NNs with realistic neuron morphologies (Koene et al. 2009, van Ooyen et al. 2014)
  89. Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)
  90. Neural Interactome: interactive simulation of a neuronal system (Kim et al 2019)
  91. Neural mass model based on single cell dynamics to model pathophysiology (Zandt et al 2014)
  92. Neural Query System NQS Data-Mining From Within the NEURON Simulator (Lytton 2006)
  93. NEUROFIT: fitting HH models to voltage clamp data (Willms 2002)
  94. NeuroManager: a workflow analysis based simulation management engine (Stockton & Santamaria 2015)
  95. NeuroMatic: software for acquisition, analysis and simulation of e-phys data (Rothman & Silver 2018)
  96. NEURON + Python (Hines et al. 2009)
  97. NEURON interfaces to MySQL and the SPUD feature extraction algorithm (Neymotin et al. 2008)
  98. Neuron-based control mechanisms for a robotic arm and hand (Singh et al 2017)
  99. Neuronvisio: a gui with 3D capabilities for NEURON (Mattioni et al. 2012)
  100. Norns - Neural Network Studio (Visser & Van Gils 2014)
  101. Numerical Integration of Izhikevich and HH model neurons (Stewart and Bair 2009)
  102. On stochastic diff. eq. models for ion channel noise in Hodgkin-Huxley neurons (Goldwyn et al. 2010)
  103. Oversampling method to extract excitatory and inhibitory conductances (Bedard et al. 2012)
  104. Parallel network simulations with NEURON (Migliore et al 2006)
  105. Parallel STEPS: Large scale stochastic spatial reaction-diffusion simulat. (Chen & De Schutter 2017)
  106. Parallelizing large networks in NEURON (Lytton et al. 2016)
  107. Parameter optimization using CMA-ES (Jedrzejewski-Szmek et al 2018)
  108. Phase-locking analysis with transcranial magneto-acoustical stimulation (Yuan et al 2017)
  109. PLS-framework (Tikidji-Hamburyan and Colonnese 2021)
  110. Properties of aconitine-induced block of KDR current in NG108-15 neurons (Lin et al. 2008)
  111. PyRhO: A multiscale optogenetics simulation platform (Evans et al 2016)
  112. Python demo of the VmT method to extract conductances from single Vm traces (Pospischil et al. 2009)
  113. Quantitative assessment of computational models for retinotopic map formation (Hjorth et al. 2015)
  114. Recording from rod bipolar axon terminals in situ (Oltedal et al 2007)
  115. Reduction of nonlinear ODE systems possessing multiple scales (Clewley et al. 2005)
  116. Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008)
  117. Reverse-time correlation analysis for idealized orientation tuning dynamics (Kovacic et al. 2008)
  118. ROOTS: An Algorithm to Generate Biologically Realistic Cortical Axons (Bingham et al 2020)
  119. Simulating ion channel noise in an auditory brainstem neuron model (Schmerl & McDonnell 2013)
  120. Single neuron properties shape chaos and signal transmission in random NNs (Muscinelli et al 2019)
  121. Sloppy morphological tuning in identified neurons of the crustacean STG (Otopalik et al 2017)
  122. Smoothing of, and parameter estimation from, noisy biophysical recordings (Huys & Paninski 2009)
  123. Software (called Optimizer) for fitting neuronal models (Friedrich et al. 2014)
  124. Spatial gridding and temporal accuracy in NEURON (Hines and Carnevale 2001)
  125. Spectral method and high-order finite differences for nonlinear cable (Omurtag and Lytton 2010)
  126. Spike exchange methods for a Blue Gene/P supercomputer (Hines et al., 2011)
  127. Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013)
  128. Synchronization in a realistic model of CA1 pyramidal neurons (Fiasconaro and Migliore 2024)
  129. The cannula artifact (Chandler & Hodgkin 1965)
  130. The microcircuits of striatum in silico (Hjorth et al 2020)
  131. The neuro-electronic junction (planar and engulfed electrodes) (Massobrio et al 2018)
  132. Theta phase precession in a model CA3 place cell (Baker and Olds 2007)
  133. Translating network models to parallel hardware in NEURON (Hines and Carnevale 2008)
  134. Tutorial: Using NEURON for Neuromechanical Simulations (Fietkiewicz et al., 2023)
  135. Vectorized algorithms for spiking neural network simulation (Brette and Goodman 2011)
  136. Voltage and light-sensitive Channelrhodopsin-2 model (ChR2) (Williams et al. 2013)
  137. Voltage imaging calibration in tuft dendrites of mitral cells (Djurisic et al 2004)
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