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]

Avella Gonzalez OJ, van Aerde KI, Mansvelder HD, van Pelt J, van Ooyen A. (2014). Inter-network interactions: impact of connections between oscillatory neuronal networks on oscillation frequency and pattern. PloS one. 9 [PubMed]

• A two networks model of connectivity-dependent oscillatory activity (Avella OJ et al. 2014) [Model]

Banitt Y, Martin KA, Segev I. (2007). A biologically realistic model of contrast invariant orientation tuning by thalamocortical synaptic depression. The Journal of neuroscience : the official journal of the Society for Neuroscience. 27 [PubMed]

• Contrast invariance by LGN synaptic depression (Banitt et al. 2007) [Model]

Behabadi BF, Polsky A, Jadi M, Schiller J, Mel BW. (2012). Location-dependent excitatory synaptic interactions in pyramidal neuron dendrites. PLoS computational biology. 8 [PubMed]

• Excitatory synaptic interactions in pyramidal neuron dendrites (Behabadi et al. 2012) [Model]

Chadderdon GL et al. (2014). Motor cortex microcircuit simulation based on brain activity mapping. Neural computation. 26 [PubMed]

• Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014) [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]

Djurfeldt M. (2012). The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models. Neuroinformatics. 10 [PubMed]

• Connection-set Algebra (CSA) for the representation of connectivity in NN models (Djurfeldt 2012) [Model]

Esposito U, Giugliano M, Vasilaki E. (2014). Adaptation of short-term plasticity parameters via error-driven learning may explain the correlation between activity-dependent synaptic properties, connectivity motifs and target specificity. Frontiers in computational neuroscience. 8 [PubMed]

• Adaptation of Short-Term Plasticity parameters (Esposito et al. 2015) [Model]

Ferrario A, Merrison-Hort R, Soffe SR, Borisyuk R. (2018). Structural and functional properties of a probabilistic model of neuronal connectivity in a simple locomotor network. eLife. 7 [PubMed]

• Model of the Xenopus tadpole swimming spinal network (Roberts et al. 2014) [Model]

Hagen E et al. (2017). Focal Local Field Potential Signature of the Single-Axon Monosynaptic Thalamocortical Connection. The Journal of neuroscience : the official journal of the Society for Neuroscience. 37 [PubMed]

• LFP signature of monosynaptic thalamocortical connection (Hagen et al 2017) [Model]

Hay E, Segev I. (2015). Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cerebral cortex (New York, N.Y. : 1991). 25 [PubMed]

• Microcircuits of L5 thick tufted pyramidal cells (Hay & Segev 2015) [Model]
• Layer V pyramidal cell model with reduced morphology (Mäki-Marttunen et al 2018) [Model]

Heinzle J, Hepp K, Martin KA. (2007). A microcircuit model of the frontal eye fields. The Journal of neuroscience : the official journal of the Society for Neuroscience. 27 [PubMed]

• A microcircuit model of the frontal eye fields (Heinzle et al. 2007) [Model]

Hilgetag CC, Kaiser M. (2010). Optimal hierarchical modular topologies for producing limited sustained activation of neural networks Front. Neuroinform.. 4(8)

• Network topologies for producing limited sustained activation (Kaiser and Hilgetag 2010) [Model]

Hooks BM et al. (2011). Laminar analysis of excitatory local circuits in vibrissal motor and sensory cortical areas. PLoS biology. 9 [PubMed]

• Laminar analysis of excitatory circuits in vibrissal motor and sensory cortex (Hooks et al. 2011) [Model]

Iannella N, Tanaka S. (2006). Synaptic efficacy cluster formation across the dendrite via STDP. Neuroscience letters. 403 [PubMed]

Kerr CC et al. (2012). Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 20 [PubMed]

• Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012) [Model]

Kerr CC et al. (2013). Cortical information flow in Parkinson's disease: a composite network/field model. Frontiers in computational neuroscience. 7 [PubMed]

• Composite spiking network/neural field model of Parkinsons (Kerr et al 2013) [Model]

Lavzin M, Rapoport S, Polsky A, Garion L, Schiller J. (2012). Nonlinear dendritic processing determines angular tuning of barrel cortex neurons in vivo. Nature. 490 [PubMed]

• Nonlinear dendritic processing in barrel cortex spiny stellate neurons (Lavzin et al. 2012) [Model]

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]

Lundqvist M, Rehn M, Djurfeldt M, Lansner A. (2006). Attractor dynamics in a modular network model of neocortex. Network (Bristol, England). 17 [PubMed]

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]

Martínez-Cañada P et al. (2018). Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells. PLoS computational biology. 14 [PubMed]

• Cortical feedback alters visual response properties of dLGN relay cells (Martínez-Cañada et al 2018) [Model]

Mejias JF, Murray JD, Kennedy H, Wang XJ. (2016). Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex. Science advances. 2 [PubMed]

• Large-scale laminar model of macaque cortex (Mejias et al 2016) [Model]

Neymotin SA, Lee H, Park E, Fenton AA, Lytton WW. (2011). Emergence of physiological oscillation frequencies in a computer model of neocortex. Frontiers in computational neuroscience. 5 [PubMed]

• Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011) [Model]

Park J et al. (2019). Contribution of apical and basal dendrites to orientation encoding in mouse V1 L2/3 pyramidal neurons Nature Communications. 10

• Orientation preference in L23 V1 pyramidal neurons (Park et al 2019) [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]

Quax S, Jensen O, Tiesinga P. (2017). Top-down control of cortical gamma-band communication via pulvinar induced phase shifts in the alpha rhythm. PLoS computational biology. 13 [PubMed]

Reimann MW, Horlemann AL, Ramaswamy S, Muller EB, Markram H. (2017). Morphological Diversity Strongly Constrains Synaptic Connectivity and Plasticity. Cerebral cortex (New York, N.Y. : 1991). 27 [PubMed]

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

Rowan MS, Neymotin SA. (2013). Synaptic Scaling Balances Learning in a Spiking Model of Neocortex Adaptive and Natural Computing Algorithms. 7824

• Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013) [Model]

Rowan MS, Neymotin SA, Lytton WW. (2014). Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Frontiers in computational neuroscience. 8 [PubMed]

• Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014) [Model]

Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ. (2018). Multi-scale account of the network structure of macaque visual cortex. Brain structure & function. 223 [PubMed]

• Multi-area layer-resolved spiking network model of resting-state dynamics in macaque visual cortex [Model]

Schmidt M et al. (2018). A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLoS computational biology. 14 [PubMed]

• Multi-area layer-resolved spiking network model of resting-state dynamics in macaque visual cortex [Model]

Shai AS, Anastassiou CA, Larkum ME, Koch C. (2015). Physiology of layer 5 pyramidal neurons in mouse primary visual cortex: coincidence detection through bursting. PLoS computational biology. 11 [PubMed]

• Layer 5 Pyramidal Neuron (Shai et al., 2015) [Model]

Shimoura RO et al. (2018). [Re] The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model The ReScience Journal. 4(1)

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

Shu Y, Hasenstaub A, Duque A, Yu Y, McCormick DA. (2006). Modulation of intracortical synaptic potentials by presynaptic somatic membrane potential. Nature. 441 [PubMed]

• Intracortical synaptic potential modulation by presynaptic somatic potential (Shu et al. 2006, 2007) [Model]

Shu Y, Yu Y, Yang J, McCormick DA. (2007). Selective control of cortical axonal spikes by a slowly inactivating K+ current. Proceedings of the National Academy of Sciences of the United States of America. 104 [PubMed]

• Selective control of cortical axonal spikes by a slowly inactivating K+ current (Shu et al. 2007) [Model]

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]

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]

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]

Weiler N, Wood L, Yu J, Solla SA, Shepherd GM. (2008). Top-down laminar organization of the excitatory network in motor cortex. Nature neuroscience. 11 [PubMed]

• Laminar connectivity matrix simulation (Weiler et al 2008) [Model]