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]

Gilson M, Masquelier T, Hugues E. (2011). STDP allows fast rate-modulated coding with Poisson-like spike trains. PLoS computational biology. 7 [PubMed]

• STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011) [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]

Spreizer S, Aertsen A, Kumar A. (2019). From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks. PLoS computational biology. 15 [PubMed]

• Emergence of spatiotemporal sequences in spiking neuronal networks (Spreizer et al 2019) [Model]

Tikidji-Hamburyan RA, Govindaiah G, Guido W, Colonnese MT. (2023). Synaptic and circuit mechanisms prevent detrimentally precise correlation in the developing mammalian visual system eLife. [PubMed]

• Decorrelation in the developing visual thalamus (Tikidji-Hamburyan et al, 2023) [Model]