Aksoy T, Shouval HZ. (2022). Active intrinsic conductances in recurrent networks allow for long-lasting transients and sustained activity with realistic firing rates as well as robust plasticity Journal of computational neuroscience. 50 [PubMed]

• Active intrinsic conductances in networks, transients, activity, plasticity (Akosy and Shouval 2021) [Model]

Cone I, Shouval HZ. (2021). Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network. eLife. 10 [PubMed]

• Sequence learning via biophysically realistic learning rules (Cone and Shouval 2021) [Model]

Triesch J, Weber C. (2006). A possible representation of reward in the learning of saccades Proc. of the Sixth International Workshop on Epigenetic Robots.

• Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7) [Model]

Wilmes KA, Clopath C. (2019). Inhibitory microcircuits for top-down plasticity of sensory representations. Nature communications. 10 [PubMed]

• Inhibitory microcircuits for top-down plasticity of sensory representations (Wilmes & Clopath 2019) [Model]