Aristizabal F, Glavinovic MI. (2004). Simulation and parameter estimation of dynamics of synaptic depression. Biological cybernetics. 90 [PubMed]

• Vesicular pool simulations of synaptic depression (Aristizabal and Glavinovic 2004) [Model]

Barros-Zulaica N et al. (2019). Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in the Neocortex Frontiers in Synaptic Neuroscience. 11

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

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]

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]

Legenstein R, Naeger C, Maass W. (2005). What can a neuron learn with spike-timing-dependent plasticity? Neural computation. 17 [PubMed]

Legenstein R, Pecevski D, Maass W. (2008). A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. PLoS computational biology. 4 [PubMed]

• Reward modulated STDP (Legenstein et al. 2008) [Model]

Maass W, Joshi P, Sontag ED. (2007). Computational aspects of feedback in neural circuits. PLoS computational biology. 3 [PubMed]

• Computational aspects of feedback in neural circuits (Maass et al 2006) [Model]

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]

Sussillo D, Toyoizumi T, Maass W. (2007). Self-tuning of neural circuits through short-term synaptic plasticity. Journal of neurophysiology. 97 [PubMed]

Tino P, Mills AJ. (2006). Learning beyond finite memory in recurrent networks of spiking neurons. Neural computation. 18 [PubMed]