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Barbour B, Brunel N, Hakim V, Nadal JP (2007) What can we learn from synaptic weight distributions? Trends Neurosci 30:622-9 [PubMed]

References and models cited by this paper

References and models that cite this paper

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 Front. Neural Circuits 8:16 [Journal] [PubMed]
  • Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014) [Model]
London M, Roth A, Beeren L, Häusser M, Latham PE (2010) Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature 466:123-7 [Journal] [PubMed]
  • Perturbation sensitivity implies high noise and suggests rate coding in cortex (London et al. 2010) [Model]
O'Donnell C, Nolan MF, van Rossum MC (2011) Dendritic spine dynamics regulate the long-term stability of synaptic plasticity. J Neurosci 31:16142-56 [Journal] [PubMed]
  • CA1 pyramidal neuron dendritic spine with plasticity (O`Donnell et al. 2011) [Model]
Scheler G (2017) Logarithmic distributions prove that intrinsic learning is Hebbian. F1000Res 6:1222 [Journal] [PubMed]
  • Logarithmic distributions prove that intrinsic learning is Hebbian (Scheler 2017) [Model]
Smolen P (2015) Modeling maintenance of long-term potentiation in clustered synapses: long-term memory without bistability. Neural Plast 2015:185410 [Journal] [PubMed]
  • Modeling maintenance of Long-Term Potentiation in clustered synapses (Smolen 2015) [Model]
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