Bower JM, Beeman D. (1998). The Book Of Genesis: Exploring Realistic Neural Models With The General Neural Simulation System.

Brette R, Guigon E. (2003). Reliability of spike timing is a general property of spiking model neurons. Neural computation. 15 [PubMed]

• Reliability of spike timing is a general property of spiking model neurons (Brette & Guigon 2003) [Model]

Destexhe A, Contreras D, Steriade M. (1998). Mechanisms underlying the synchronizing action of corticothalamic feedback through inhibition of thalamic relay cells. Journal of neurophysiology. 79 [PubMed]

• Pyramidal Neuron: Deep, Thalamic Relay and Reticular, Interneuron (Destexhe et al 1998, 2001) [Model]

Destexhe A, Rudolph M, Fellous JM, Sejnowski TJ. (2001). Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience. 107 [PubMed]

• Fluctuating synaptic conductances recreate in-vivo-like activity (Destexhe et al 2001) [Model]
• Thalamocortical Relay cell under current clamp in high-conductance state (Zeldenrust et al 2018) [Model]

Destexhe A, Rudolph M, Paré D. (2003). The high-conductance state of neocortical neurons in vivo. Nature reviews. Neuroscience. 4 [PubMed]

Ermentrout B. (1996). Type I membranes, phase resetting curves, and synchrony. Neural computation. 8 [PubMed]

Fourcaud-Trocmé N, Hansel D, van Vreeswijk C, Brunel N. (2003). How spike generation mechanisms determine the neuronal response to fluctuating inputs. The Journal of neuroscience : the official journal of the Society for Neuroscience. 23 [PubMed]

Goldman MS, Golowasch J, Marder E, Abbott LF. (2001). Global structure, robustness, and modulation of neuronal models. The Journal of neuroscience : the official journal of the Society for Neuroscience. 21 [PubMed]

• Global structure, robustness, and modulation of neuronal models (Goldman et al. 2001) [Model]

HODGKIN AL, HUXLEY AF. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology. 117 [PubMed]

• Squid axon (Hodgkin, Huxley 1952) (NEURON) [Model]
• Squid axon (Hodgkin, Huxley 1952) (SNNAP) [Model]
• Squid axon (Hodgkin, Huxley 1952) (LabAXON) [Model]
• Squid axon (Hodgkin, Huxley 1952) (SBML, XPP, other) [Model]
• Squid axon (Hodgkin, Huxley 1952) used in (Chen et al 2010) (R language) [Model]

Hille B. (2001). Classic mechanisms of block Ion Channels of Excitable Membranes (3rd edn).

Hines ML, Carnevale NT. (1997). The NEURON simulation environment. Neural computation. 9 [PubMed]

Izhikevich EM. (2003). Simple model of spiking neurons. IEEE transactions on neural networks. 14 [PubMed]

• Artificial neuron model (Izhikevich 2003, 2004, 2007) [Model]

Jolivet R, Lewis TJ, Gerstner W. (2004). Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy. Journal of neurophysiology. 92 [PubMed]

• Spike Response Model simulator (Jolivet et al. 2004, 2006, 2008) [Model]

Keat J, Reinagel P, Reid RC, Meister M. (2001). Predicting every spike: a model for the responses of visual neurons. Neuron. 30 [PubMed]

Kistler WM, van Hemmen JL, Gerstner W. (1997). Reduction of Hodgkin-Huxley equations to a single-variable threshold model. Neural Comput. 9

Lapicque L. (1907). Recherches quantitatives sur lexcitation electrique des nerfs traitee comme une polarisation J Physiol Pathol Gen. 9

Latham PE, Richmond BJ, Nelson PG, Nirenberg S. (2000). Intrinsic dynamics in neuronal networks. I. Theory. Journal of neurophysiology. 83 [PubMed]

Mainen ZF, Sejnowski TJ. (1995). Reliability of spike timing in neocortical neurons. Science (New York, N.Y.). 268 [PubMed]

McCormick DA, Wang Z, Huguenard J. (1993). Neurotransmitter control of neocortical neuronal activity and excitability. Cerebral cortex (New York, N.Y. : 1991). 3 [PubMed]

Paninski L, Pillow JW, Simoncelli EP. (2004). Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. Neural computation. 16 [PubMed]

Paré D, Shink E, Gaudreau H, Destexhe A, Lang EJ. (1998). Impact of spontaneous synaptic activity on the resting properties of cat neocortical pyramidal neurons In vivo. Journal of neurophysiology. 79 [PubMed]

Prinz AA, Billimoria CP, Marder E. (2003). Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. Journal of neurophysiology. 90 [PubMed]

Richardson MJ, Brunel N, Hakim V. (2003). From subthreshold to firing-rate resonance. Journal of neurophysiology. 89 [PubMed]

Roth A, Häusser M. (2001). Compartmental models of rat cerebellar Purkinje cells based on simultaneous somatic and dendritic patch-clamp recordings. The Journal of physiology. 535 [PubMed]

Segev I, Burke RE, Fleshman JW. (1989). Compartmental models of complex neurons Methods In Neuronal Modelling: From Synapses To Networks.

Sharp AA, O'Neil MB, Abbott LF, Marder E. (1993). The dynamic clamp: artificial conductances in biological neurons. Trends in neurosciences. 16 [PubMed]

Stein RB. (1967). Some models of neuronal variability. Biophysical journal. 7 [PubMed]

Steriade M. (2004). Neocortical cell classes are flexible entities. Nature reviews. Neuroscience. 5 [PubMed]

Tuckwell HC. (1988). Introduction To Theoretical Neurobiology: Vol 1, Linear Cable Theory And Dendritic Structure. 1

Ai H, Kai K, Kumaraswamy A, Ikeno H, Wachtler T. (2017). Interneurons in the Honeybee Primary Auditory Center Responding to Waggle Dance-Like Vibration Pulses. The Journal of neuroscience : the official journal of the Society for Neuroscience. 37 [PubMed]

• Vibration-sensitive Honeybee interneurons (Ai et al 2017) [Model]

Arsiero M, Lüscher HR, Lundstrom BN, Giugliano M. (2007). The impact of input fluctuations on the frequency-current relationships of layer 5 pyramidal neurons in the rat medial prefrontal cortex. The Journal of neuroscience : the official journal of the Society for Neuroscience. 27 [PubMed]

• Input Fluctuations effects on f-I curves (Arsiero et al. 2007) [Model]

Aubie B, Becker S, Faure PA. (2009). Computational models of millisecond level duration tuning in neural circuits. The Journal of neuroscience : the official journal of the Society for Neuroscience. 29 [PubMed]

• Duration-tuned neurons from the inferior colliculus of the big brown bat (Aubie et al. 2009) [Model]

Badel L et al. (2008). Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. Journal of neurophysiology. 99 [PubMed]

Bernard C, Jirsa VK, Woodman MM, Melozzi F. (2017). The Virtual Mouse Brain: A Computational Neuroinformatics Platform to Study Whole Mouse Brain Dynamics eNeuro.

• The Virtual Mouse Brain (TVMB) (Melozzi et al. 2017) [Model]

Brette R. (2013). Sharpness of spike initiation in neurons explained by compartmentalization. PLoS computational biology. 9 [PubMed]

• Sharpness of spike initiation in neurons explained by compartmentalization (Brette 2013) [Model]

Brette R et al. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. Journal of computational neuroscience. 23 [PubMed]

• Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007) [Model]
• Thalamocortical Relay cell under current clamp in high-conductance state (Zeldenrust et al 2018) [Model]

Buchin A, Rieubland S, Häusser M, Gutkin BS, Roth A. (2016). Inverse Stochastic Resonance in Cerebellar Purkinje Cells. PLoS computational biology. 12 [PubMed]

• Inverse stochastic resonance of cerebellar Purkinje cell (Buchin et al. 2016) [Model]

Cakan C, Obermayer K. (2020). PLoS computational biology. 16 [PubMed]

• Mean-field models of neural populations under electrical stimulation (Cakan & Obermayer 2020) [Model]

Carnevale NT, Morse TM. (1996). Research reports that have used NEURON Web published citations at the NEURON website.

Chavlis S, Petrantonakis PC, Poirazi P. (2017). Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity. Hippocampus. 27 [PubMed]

• Dentate Gyrus model including Granule cells with dendritic compartments (Chavlis et al 2017) [Model]

Clopath C, Büsing L, Vasilaki E, Gerstner W. (2010). Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nature neuroscience. 13 [PubMed]

• Voltage-based STDP synapse (Clopath et al. 2010) [Model]

Clopath C, Ziegler L, Vasilaki E, Büsing L, Gerstner W. (2008). Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression. PLoS computational biology. 4 [PubMed]

• Tag Trigger Consolidation (Clopath and Ziegler et al. 2008) [Model]

Cohen MX. (2014). Fluctuations in oscillation frequency control spike timing and coordinate neural networks. The Journal of neuroscience : the official journal of the Society for Neuroscience. 34 [PubMed]

• Input strength and time-varying oscillation peak frequency (Cohen MX 2014) [Model]

Costa RP, Froemke RC, Sjöström PJ, van Rossum MC. (2015). Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning. eLife. 4 [PubMed]

• Memory savings through unified pre- and postsynaptic STDP (Costa et al 2015) [Model]

Danielson NB et al. (2017). In Vivo Imaging of Dentate Gyrus Mossy Cells in Behaving Mice. Neuron. 93 [PubMed]

• In vivo imaging of dentate gyrus mossy cells in behaving mice (Danielson et al 2017) [Model]

Destexhe A. (2009). Self-sustained asynchronous irregular states and Up-Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons. Journal of computational neuroscience. 27 [PubMed]

• Asynchronous irregular and up/down states in excitatory and inhibitory NNs (Destexhe 2009) [Model]

Diwakar S, Parasuram H, Nair B, Medini C, Nair M. (2017). Computational Neuroscience of Timing, Plasticity and Function in Cerebellum Microcircuits (Chapter 12) Computational Neurology and Psychiatry, Springer Series in Bio-/Neuroinformatics.

• Modeling single neuron LFPs and extracellular potentials with LFPsim (Parasuram et al. 2016) [Model]

Dragly SA et al. (2017). Neuronify: An Educational Simulator for Neural Circuits. eNeuro. 4 [PubMed]

• Neuronify: An Educational Simulator for Neural Circuits (Dragly et al 2017) [Model]

Ermentrout GB, Terman DH. (2010). Mathematical Foundations of Neuroscience Interdisciplinary Applied Mathematics. 35

• Mathematical Foundations of Neuroscience (Ermentrout and Terman 2010) [Model]

Fardet T, Levina A. (2020). Simple Models Including Energy and Spike Constraints Reproduce Complex Activity Patterns and Metabolic Disruptions PLoS computational biology. 16 [PubMed]

• eLIF and mAdExp: energy-based integrate-and-fire neurons (Fardet and Levina 2020) [Model]

Ferguson KA, Njap F, Nicola W, Skinner FK, Campbell SA. (2015). Examining the limits of cellular adaptation bursting mechanisms in biologically-based excitatory networks of the hippocampus. Journal of computational neuroscience. 39 [PubMed]

• Mean-field systems and small scale neural networks (Ferguson et al. 2015) [Model]
• CA1 pyramidal neuron network model (Ferguson et al 2015) [Model]

Fiebig F, Lansner A. (2017). A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation. The Journal of neuroscience : the official journal of the Society for Neuroscience. 37 [PubMed]

Geminiani A et al. (2018). Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness. Frontiers in neuroinformatics. 12 [PubMed]

• Complex dynamics: reproducing Golgi cell electroresponsiveness (Geminiani et al 2018, 2019ab) [Model]

Goodman DF, Brette R. (2010). Spike-timing-based computation in sound localization. PLoS computational biology. 6 [PubMed]

• Spike-Timing-Based Computation in Sound Localization (Goodman and Brette 2010) [Model]

Górski T et al. (2018). Dendritic sodium spikes endow neurons with inverse firing rate response to correlated synaptic activity. Journal of computational neuroscience. 45 [PubMed]

• Response to correlated synaptic input for HH/IF point neuron vs with dendrite (Górski et al 2018) [Model]

Hass J, Hertäg L, Durstewitz D. (2016). A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity. PLoS computational biology. 12 [PubMed]

• A detailed data-driven network model of prefrontal cortex (Hass et al 2016) [Model]

Huang C, Zeldenrust F, Celikel T. (2022). Cortical Representation of Touch in Silico Neuroinformatics. 20 [PubMed]

• Realistic barrel cortical column - Matlab (Huang et al., 2022) [Model]
• Realistic barrel cortical column - NetPyNE (Huang et al., 2022) [Model]

Jolivet R, Gerstner W. (2004). Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival. Journal of physiology, Paris. 98 [PubMed]

Jolivet R et al. (2008). A benchmark test for a quantitative assessment of simple neuron models. Journal of neuroscience methods. 169 [PubMed]

• Spike Response Model simulator (Jolivet et al. 2004, 2006, 2008) [Model]

Jolivet R, Rauch A, Lüscher HR, Gerstner W. (2006). Predicting spike timing of neocortical pyramidal neurons by simple threshold models. Journal of computational neuroscience. 21 [PubMed]

• Spike Response Model simulator (Jolivet et al. 2004, 2006, 2008) [Model]

Jones A, Singh N, Huyck CR, Gandhi V. (2016). Neuron-Based Control Mechanisms for a Robotic Arm and Hand ICMRRS 2017: International Conference on Medical Robotics and Robotics for Surgery, Paris, France. 3(2)

• Neuron-based control mechanisms for a robotic arm and hand (Singh et al 2017) [Model]

Khalil R, Moftah MZ, Moustafa AA. (2017). The effects of dynamical synapses on firing rate activity: a spiking neural network model. The European journal of neuroscience. 46 [PubMed]

Kobayashi R, Tsubo Y, Shinomoto S. (2009). Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Frontiers in computational neuroscience. 3 [PubMed]

• Multi-timescale adaptive threshold model (Kobayashi et al 2009) (NEURON) [Model]
• Multi-timescale adaptive threshold model (Kobayashi et al 2009) [Model]

Komarov M et al. (2018). New class of reduced computationally efficient neuronal models for large-scale simulations of brain dynamics. Journal of computational neuroscience. 44 [PubMed]

Köndgen H et al. (2008). The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro. Cerebral cortex (New York, N.Y. : 1991). 18 [PubMed]

• Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008) [Model]

Linaro D, Ocker GK, Doiron B, Giugliano M. (2019). Correlation Transfer by Layer 5 Cortical Neurons Under Recreated Synaptic Inputs In Vitro. The Journal of neuroscience : the official journal of the Society for Neuroscience. 39 [PubMed]

• L5 cortical neurons with recreated synaptic inputs in vitro correlation transfer (Linaro et al 2019) [Model]

Lonardoni D et al. (2017). Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks. PLoS computational biology. 13 [PubMed]

Maes A, Barahona M, Clopath C. (2020). Learning spatiotemporal signals using a recurrent spiking network that discretizes time. PLoS computational biology. 16 [PubMed]

• Learning spatiotemporal sequences using recurrent spiking NN that discretizes time (Maes et al 2020) [Model]

Malerba P, Krishnan GP, Fellous JM, Bazhenov M. (2016). Hippocampal CA1 Ripples as Inhibitory Transients. PLoS computational biology. 12 [PubMed]

• CA1 pyramidal cells, basket cells, ripples (Malerba et al 2016) [Model]

Marasco A et al. (2023). An Adaptive Generalized Leaky Integrate-and-Fire Model for Hippocampal CA1 Pyramidal Neurons and Interneurons. Bulletin of mathematical biology. 85 [PubMed]

• Adaptive Generalized Leaky Integrate-and-Fire Model (AGLIF) (Marasco et al., 2023) [Model]

Mensi S et al. (2012). Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. Journal of neurophysiology. 107 [PubMed]

• Extraction and classification of three cortical neuron types (Mensi et al. 2012) [Model]

Morén J, Shibata T, Doya K. (2013). The mechanism of saccade motor pattern generation investigated by a large-scale spiking neuron model of the superior colliculus. PloS one. 8 [PubMed]

• A spatial model of the intermediate superior colliculus (Moren et. al. 2013) [Model]

Muller E, Buesing L, Schemmel J, Meier K. (2007). Spike-frequency adapting neural ensembles: beyond mean adaptation and renewal theories. Neural computation. 19 [PubMed]

Nicola W, Campbell SA. (2013). Bifurcations of large networks of two-dimensional integrate and fire neurons. Journal of computational neuroscience. 35 [PubMed]

• Mean Field Equations for Two-Dimensional Integrate and Fire Models (Nicola and Campbell, 2013) [Model]

Pena RFO, Zaks MA, Roque AC. (2018). Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise : Spontaneous activity in networks with synaptic noise. Journal of computational neuroscience. 45 [PubMed]

• Dynamics in random NNs with multiple neuron subtypes (Pena et al 2018, Tomov et al 2014, 2016) [Model]

Platkiewicz J, Brette R. (2010). A threshold equation for action potential initiation. PLoS computational biology. 6 [PubMed]

• A threshold equation for action potential initiation (Platkiewicz & Brette 2010) [Model]

Platkiewicz J, Brette R. (2011). Impact of fast sodium channel inactivation on spike threshold dynamics and synaptic integration. PLoS computational biology. 7 [PubMed]

• Impact of fast Na channel inact. on AP threshold & synaptic integration (Platkiewicz & Brette 2011) [Model]

Pospischil M et al. (2008). Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons. Biological cybernetics. 99 [PubMed]

• Hodgkin-Huxley models of different classes of cortical neurons (Pospischil et al. 2008) [Model]

Richert M, Nageswaran JM, Dutt N, Krichmar JL. (2011). An efficient simulation environment for modeling large-scale cortical processing. Frontiers in neuroinformatics. 5 [PubMed]

• Efficient simulation environment for modeling large-scale cortical processing (Richert et al. 2011) [Model]

Stewart RD, Bair W. (2009). Spiking neural network simulation: numerical integration with the Parker-Sochacki method. Journal of computational neuroscience. 27 [PubMed]

• Numerical Integration of Izhikevich and HH model neurons (Stewart and Bair 2009) [Model]

Susi G et al. (2021). FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency Scientific reports. 11 [PubMed]

• FNS spiking neural simulator; LIFL neuron model, event-driven simulation (Susi et al 2021) [Model]

Susin E, Destexhe A. (2021). Integration, coincidence detection and resonance in networks of spiking neurons expressing gamma oscillations and asynchronous states PLoS computational biology. 17 [PubMed]

• PING, ING and CHING network models for Gamma oscillations in cortex (Susin and Destexhe 2021) [Model]

Tchumatchenko T, Clopath C. (2014). Oscillations emerging from noise-driven steady state in networks with electrical synapses and subthreshold resonance. Nature communications. 5 [PubMed]

• Oscillations emerging from noise-driven NNs (Tchumatchenko & Clopath 2014) [Model]

Tikidji-Hamburyan RA, El-Ghazawi TA, Narayana V, Bozkus Z. (2017). Software for Brain Network Simulations: A Comparative Study Front. Neuroinform..

• Brain networks simulators - a comparative study (Tikidji-Hamburyan et al 2017) [Model]

Tomm C, Avermann M, Petersen C, Gerstner W, Vogels TP. (2014). Connection-type-specific biases make uniform random network models consistent with cortical recordings. Journal of neurophysiology. 112 [PubMed]

Tomov P, Pena RF, Roque AC, Zaks MA. (2016). Mechanisms of Self-Sustained Oscillatory States in Hierarchical Modular Networks with Mixtures of Electrophysiological Cell Types. Frontiers in computational neuroscience. 10 [PubMed]

• Dynamics in random NNs with multiple neuron subtypes (Pena et al 2018, Tomov et al 2014, 2016) [Model]

Tomsett RJ et al. (2015). Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue. Brain structure & function. 220 [PubMed]

• Large-scale model of neocortical slice in vitro exhibiting persistent gamma (Tomsett et al. 2014) [Model]

Tonnelier A, Belmabrouk H, Martinez D. (2007). Event-driven simulations of nonlinear integrate-and-fire neurons. Neural computation. 19 [PubMed]

Touboul J, Brette R. (2008). Dynamics and bifurcations of the adaptive exponential integrate-and-fire model. Biological cybernetics. 99 [PubMed]

• Brette-Gerstner model (Touboul and Brette 2008) [Model]

Van Pottelbergh T, Drion G, Sepulchre R. (2018). Robust Modulation of Integrate-and-Fire Models. Neural computation. 30 [PubMed]

• Robust modulation of integrate-and-fire models (Van Pottelbergh et al 2018) [Model]

Vasilaki E, Giugliano M. (2014). Emergence of connectivity motifs in networks of model neurons with short- and long-term plastic synapses. PloS one. 9 [PubMed]

• Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014) [Model]

Venkadesh S et al. (2018). Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types. Frontiers in neuroinformatics. 12 [PubMed]

• Evolving simple models of diverse dynamics in hippocampal neuron types (Venkadesh et al 2018) [Model]

Vich C, Berg RW, Guillamon A, Ditlevsen S. (2017). Estimation of Synaptic Conductances in Presence of Nonlinear Effects Caused by Subthreshold Ionic Currents. Frontiers in computational neuroscience. 11 [PubMed]

• QIF method to estimate synaptic conductances (Vich et al 2017) [Model]

Xu X, Cang J, Riecke H. (2020). Development and Binocular Matching of Orientation Selectivity in Visual Cortex: A Computational Model. Journal of neurophysiology. 123 [PubMed]

• Development and Binocular Matching of Orientation Selectivity in Visual Cortex (Xu et al 2020) [Model]

Zerlaut Y, Chemla S, Chavane F, Destexhe A. (2018). Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons. Journal of computational neuroscience. 44 [PubMed]

• Mesoscopic dynamics from AdEx recurrent networks (Zerlaut et al JCNS 2018) [Model]
• Mesoscopic dynamics from AdEx recurrent networks (Zerlaut et al JCNS 2018) (PyNN) [Model]