Ariav G, Polsky A, Schiller J. (2003). Submillisecond precision of the input-output transformation function mediated by fast sodium dendritic spikes in basal dendrites of CA1 pyramidal neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience. 23 [PubMed]

Arkhipov A et al. (2018). Visual physiology of the layer 4 cortical circuit in silico. PLoS computational biology. 14 [PubMed]

• Visual physiology of the layer 4 cortical circuit in silico (Arkhipov et al 2018) [Model]

Azouz R, Gray CM. (2008). Stimulus-selective spiking is driven by the relative timing of synchronous excitation and disinhibition in cat striate neurons in vivo. The European journal of neuroscience. 28 [PubMed]

Banerjee A. (2006). On the sensitive dependence on initial conditions of the dynamics of networks of spiking neurons. Journal of computational neuroscience. 20 [PubMed]

Barak O, Tsodyks M. (2007). Persistent activity in neural networks with dynamic synapses. PLoS computational biology. 3 [PubMed]

Bertschinger N, Natschläger T. (2004). Real-time computation at the edge of chaos in recurrent neural networks. Neural computation. 16 [PubMed]

Brunel N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of computational neuroscience. 8 [PubMed]

• Sparsely connected networks of spiking neurons (Brunel 2000) [Model]

Brunel N, Hakim V. (1999). Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural computation. 11 [PubMed]

• Fast global oscillations in networks of I&F neurons with low firing rates (Brunel and Hakim 1999) [Model]

Brunel N, Wang XJ. (2001). Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. Journal of computational neuroscience. 11 [PubMed]

Bugmann G, Christodoulou C, Clarkson T. (). A Spiking Neuron Model: Applications and Learning. Neural Networks. 15

Diesmann M, Gewaltig MO, Aertsen A. (1999). Stable propagation of synchronous spiking in cortical neural networks. Nature. 402 [PubMed]

• Stable propagation of synchronous spiking in cortical neural networks (Diesmann et al 1999) [Model]

Durstewitz D, Gabriel T. (2007). Dynamical basis of irregular spiking in NMDA-driven prefrontal cortex neurons. Cerebral cortex (New York, N.Y. : 1991). 17 [PubMed]

• Irregular spiking in NMDA-driven prefrontal cortex neurons (Durstewitz and Gabriel 2006) [Model]

Feng J, Brown D, Feerick S. (1999). Variability of firing of Hodgkin-Huxley and FitzHugh-Nagumo neurons with stochastic synaptic input. Physical Review Letters. 82

Golomb D et al. (2017). Mechanisms underlying a thalamocortical transformation during active tactile sensation PLoS Comput Biol. 13(6)

• L4 cortical barrel NN model receiving thalamic input during whisking or touch (Gutnisky et al. 2017) [Model]

Haeusler S, Maass W. (2007). A statistical analysis of information-processing properties of lamina-specific cortical microcircuit models. Cerebral cortex (New York, N.Y. : 1991). 17 [PubMed]

• Information-processing in lamina-specific cortical microcircuits (Haeusler and Maass 2006) [Model]

Hansel D, Mato G. (2003). Asynchronous states and the emergence of synchrony in large networks of interacting excitatory and inhibitory neurons. Neural computation. 15 [PubMed]

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]

Häusser M, Clark BA. (1997). Tonic synaptic inhibition modulates neuronal output pattern and spatiotemporal synaptic integration. Neuron. 19 [PubMed]

Ibañez S, Sengupta N, Luebke JI, Wimmer K, Weaver CM. (2024). Myelin dystrophy impairs signal transmission and working memory in a multiscale model of the aging prefrontal cortex. eLife. 12 [PubMed]

• Myelin dystrophy impairs signal transmission and working memory in a multiscale model of the aging prefrontal cortex (Ibañez, Sengupta et al., 2024) [Model]

Izhikevich EM. (2006). Polychronization: computation with spikes. Neural computation. 18 [PubMed]

• Polychronization: Computation With Spikes (Izhikevich 2005) [Model]

Jackson BS. (2004). Including long-range dependence in integrate-and-fire models of the high interspike-interval variability of cortical neurons. Neural computation. 16 [PubMed]

Kanamaru T, Sekine M. (2005). Synchronized firings in the networks of class 1 excitable neurons with excitatory and inhibitory connections and their dependences on the forms of interactions. Neural computation. 17 [PubMed]

Keane A, Henderson JA, Gong P. (2018). Dynamical patterns underlying response properties of cortical circuits. Journal of the Royal Society, Interface. 15 [PubMed]

• Dynamical patterns underlying response properties of cortical circuits (Keane et al 2018) [Model]

Kotaleski JH et al. (2011). Striatal fast-spiking interneurons: from firing patterns to postsynaptic impact Front. Syst. Neurosci.. 5:57

• Firing patterns in stuttering fast-spiking interneurons (Klaus et al. 2011) [Model]

Latham PE, Nirenberg S. (2004). Computing and stability in cortical networks. Neural computation. 16 [PubMed]

Lerchner A et al. (2006). Response variability in balanced cortical networks. Neural computation. 18 [PubMed]

Lim S, Goldman MS. (2013). Balanced cortical microcircuitry for maintaining information in working memory. Nature neuroscience. 16 [PubMed]

• Model of working memory based on negative derivative feedback (Lim and Goldman, 2013) [Model]

Lim S, Goldman MS. (2014). Balanced cortical microcircuitry for spatial working memory based on corrective feedback control. The Journal of neuroscience : the official journal of the Society for Neuroscience. 34 [PubMed]

Litvak V, Sompolinsky H, Segev I, Abeles M. (2003). On the transmission of rate code in long feedforward networks with excitatory-inhibitory balance. The Journal of neuroscience : the official journal of the Society for Neuroscience. 23 [PubMed]

Liu YH, Wang XJ. (2001). Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of computational neuroscience. 10 [PubMed]

London M, Roth A, Beeren L, Häusser M, Latham PE. (2010). Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature. 466 [PubMed]

• Perturbation sensitivity implies high noise and suggests rate coding in cortex (London et al. 2010) [Model]

Markram H et al. (2015). Reconstruction and Simulation of Neocortical Microcircuitry. Cell. 163 [PubMed]

• The neocortical microcircuit collaboration portal (Markram et al. 2015) [Model]

Marre O, Yger P, Davison AP, Frégnac Y. (2009). Reliable recall of spontaneous activity patterns in cortical networks. The Journal of neuroscience : the official journal of the Society for Neuroscience. 29 [PubMed]

Masuda N, Aihara K. (2003). Duality of rate coding and temporal coding in multilayered feedforward networks. Neural computation. 15 [PubMed]

Meffin H, Burkitt AN, Grayden DB. (2004). An analytical model for the "large, fluctuating synaptic conductance state" typical of neocortical neurons in vivo. Journal of computational neuroscience. 16 [PubMed]

Miller P, Wang XJ. (2006). Stability of discrete memory states to stochastic fluctuations in neuronal systems. Chaos (Woodbury, N.Y.). 16 [PubMed]

Miyawaki Y, Okada M. (2004). A network model of perceptual suppression induced by transcranial magnetic stimulation. Neural computation. 16 [PubMed]

Molter C, Salihoglu U, Bersini H. (2007). The road to chaos by time-asymmetric Hebbian learning in recurrent neural networks. Neural computation. 19 [PubMed]

Morrison A, Aertsen A, Diesmann M. (2007). Spike-timing-dependent plasticity in balanced random networks. Neural computation. 19 [PubMed]

Muscinelli SP, Gerstner W, Schwalger T. (2019). How single neuron properties shape chaotic dynamics and signal transmission in random neural networks. PLoS computational biology. 15 [PubMed]

• Single neuron properties shape chaos and signal transmission in random NNs (Muscinelli et al 2019) [Model]

Mäki-Marttunen T, Aćimović J, Ruohonen K, Linne ML. (2013). Structure-dynamics relationships in bursting neuronal networks revealed using a prediction framework. PloS one. 8 [PubMed]

• Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013) [Model]

Naundorf B, Geisel T, Wolf F. (2005). Action potential onset dynamics and the response speed of neuronal populations. Journal of computational neuroscience. 18 [PubMed]

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]

Potjans TC, Diesmann M. (2014). The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cerebral cortex (New York, N.Y. : 1991). 24 [PubMed]

• A full-scale cortical microcircuit spiking network model (Shimoura et al 2018) [Model]

Renart A, Moreno-Bote R, Wang XJ, Parga N. (2007). Mean-driven and fluctuation-driven persistent activity in recurrent networks. Neural computation. 19 [PubMed]

Romani S, Amit DJ, Mongillo G. (2006). Mean-field analysis of selective persistent activity in presence of short-term synaptic depression. Journal of computational neuroscience. 20 [PubMed]

Rudolph M, Destexhe A. (2003). Tuning neocortical pyramidal neurons between integrators and coincidence detectors. Journal of computational neuroscience. 14 [PubMed]

Sadeh S, Clopath C. (2020). Theory of neuronal perturbome in cortical networks Proceedings of the National Academy of Sciences of the United States of America. 117 [PubMed]

• A computational model of single-neuron perturbations (Sadeh and Clopath 2020) [Model]

Sadeh S, Clopath C, Rotter S. (2015). Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity. PLoS computational biology. 11 [PubMed]

• Functional balanced networks with synaptic plasticity (Sadeh et al, 2015) [Model]

Sadeh S, Rotter S. (2015). Orientation selectivity in inhibition-dominated networks of spiking neurons: effect of single neuron properties and network dynamics. PLoS computational biology. 11 [PubMed]

• Orientation selectivity in inhibition-dominated recurrent networks (Sadeh and Rotter, 2015) [Model]

Salinas E, Sejnowski TJ. (2001). Correlated neuronal activity and the flow of neural information. Nature reviews. Neuroscience. 2 [PubMed]

Senn W, Fusi S. (2005). Learning only when necessary: better memories of correlated patterns in networks with bounded synapses. Neural computation. 17 [PubMed]

Shelley M, McLaughlin D, Shapley R, Wielaard J. (2002). States of high conductance in a large-scale model of the visual cortex. Journal of computational neuroscience. 13 [PubMed]

Shushruth S et al. (2012). Strong recurrent networks compute the orientation tuning of surround modulation in the primate primary visual cortex. The Journal of neuroscience : the official journal of the Society for Neuroscience. 32 [PubMed]

• Surround Suppression in V1 via Withdraw of Balanced Local Excitation in V1 (Shushruth 2012) [Model]

Soltani A, Wang XJ. (2006). A biophysically based neural model of matching law behavior: melioration by stochastic synapses. The Journal of neuroscience : the official journal of the Society for Neuroscience. 26 [PubMed]

Soula H, Beslon G, Mazet O. (2005). Spontaneous Dynamics of Asymmetric Random Recurrent Spiking Neural Networks Neural Comput. 18

Soula H, Chow CC. (2007). Stochastic dynamics of a finite-size spiking neural network. Neural computation. 19 [PubMed]

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]

Sussillo D, Abbott LF. (2009). Generating coherent patterns of activity from chaotic neural networks. Neuron. 63 [PubMed]

• Generating coherent patterns of activity from chaotic neural networks (Sussillo and Abbott 2009) [Model]

Tan AY, Andoni S, Priebe NJ. (2013). A spontaneous state of weakly correlated synaptic excitation and inhibition in visual cortex. Neuroscience. 247 [PubMed]

• Spontaneous weakly correlated excitation and inhibition (Tan et al. 2013) [Model]

Thomas EA, Bornstein JC. (2003). Inhibitory cotransmission or after-hyperpolarizing potentials can regulate firing in recurrent networks with excitatory metabotropic transmission. Neuroscience. 120 [PubMed]

Tikidji-Hamburyan RA, Leonik CA, Canavier CC. (2019). Phase response theory explains cluster formation in sparsely but strongly connected inhibitory neural networks and effects of jitter due to sparse connectivity. Journal of neurophysiology. 121 [PubMed]

• Phase response theory in sparsely + strongly connected inhibitory NNs (Tikidji-Hamburyan et al 2019) [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]

Tomov P, Pena RF, Zaks MA, Roque AC. (2014). Sustained oscillations, irregular firing, and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types. Frontiers in computational neuroscience. 8 [PubMed]

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

Ullah G, Cressman JR, Barreto E, Schiff SJ. (2009). The influence of sodium and potassium dynamics on excitability, seizures, and the stability of persistent states. II. Network and glial dynamics. Journal of computational neuroscience. 26 [PubMed]

• Network model with dynamic ion concentrations (Ullah et al. 2009) [Model]

Vogels TP, Abbott LF. (2005). Signal propagation and logic gating in networks of integrate-and-fire neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience. 25 [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]

Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W. (2011). Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks. Science (New York, N.Y.). 334 [PubMed]

• Inhibitory plasticity balances excitation and inhibition (Vogels et al. 2011) [Model]

Wimmer K et al. (2015). Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT. Nature communications. 6 [PubMed]

• Hierarchical network model of perceptual decision making (Wimmer et al 2015) [Model]

Wu S, Amari S. (2005). Computing with continuous attractors: stability and online aspects. Neural computation. 17 [PubMed]

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]