Eguchi A, Neymotin SA, Stringer SM. (2014). Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity Frontiers in neural circuits. 8 [PubMed]

• Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014) [Model]

Pilly PK, Grossberg S. (2013). Spiking neurons in a hierarchical self-organizing map model can learn to develop spatial and temporal properties of entorhinal grid cells and hippocampal place cells PloS one. 8 [PubMed]

• Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013) [Model]

Rabinowitch I, Segev I. (2006). The endurance and selectivity of spatial patterns of long-term potentiation/depression in dendrites under homeostatic synaptic plasticity. The Journal of neuroscience : the official journal of the Society for Neuroscience. 26 [PubMed]

• Homeostatic synaptic plasticity (Rabinowitch and Segev 2006a,b) [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]

Schulz R, Reggia JA. (2004). Temporally asymmetric learning supports sequence processing in multi-winner self-organizing maps. Neural computation. 16 [PubMed]

Skorheim S, Razak K, Bazhenov M. (2014). Network models of frequency modulated sweep detection. PloS one. 9 [PubMed]

• Network models of frequency modulated sweep detection (Skorheim et al. 2014) [Model]