Akin M, Onderdonk A, Guo Y. (2017). Effects of local network topology on the functional reconstruction of spiking neural network models. Applied network science. 2 [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]

Egger R et al. (2020). Cortical Output Is Gated by Horizontally Projecting Neurons in the Deep Layers Neuron. 105

• Sensory-evoked responses of L5 pyramidal tract neurons (Egger et al 2020) [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]

Esposito U, Giugliano M, van Rossum M, Vasilaki E. (2014). Measuring symmetry, asymmetry and randomness in neural network connectivity. PloS one. 9 [PubMed]

• Statistics of symmetry measure for networks of neurons (Esposito et al. 2014) [Model]

Gal E et al. (2017). Rich cell-type-specific network topology in neocortical microcircuitry. Nature neuroscience. 20 [PubMed]

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

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]

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]

Litwin-Kumar A, Doiron B. (2012). Slow dynamics and high variability in balanced cortical networks with clustered connections. Nature neuroscience. 15 [PubMed]

• Excitatory and inhibitory population activity (Bittner et al 2017) (Litwin-Kumar & Doiron 2017) [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]

Mäki-Marttunen T et al. (2018). A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells. Journal of neuroscience methods. 293 [PubMed]

• Layer V pyramidal cell model with reduced morphology (Mäki-Marttunen et al 2018) [Model]

Ramaswamy S et al. (2015). The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex. Frontiers in neural circuits. 9 [PubMed]

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

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]

Reimann MW, King JG, Muller EB, Ramaswamy S, Markram H. (2015). An algorithm to predict the connectome of neural microcircuits. Frontiers in computational neuroscience. 9 [PubMed]

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

Reimann MW et al. (2017). Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function. Frontiers in computational neuroscience. 11 [PubMed]

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

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]

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]

van Ooyen A et al. (2014). Independently outgrowing neurons and geometry-based synapse formation produce networks with realistic synaptic connectivity. PloS one. 9 [PubMed]

• NETMORPH: creates NNs with realistic neuron morphologies (Koene et al. 2009, van Ooyen et al. 2014) [Model]