Oja E. (1982). A simplified neuron model as a principal component analyzer. Journal of mathematical biology. 15 [PubMed]

See more from authors: Oja E

References and models cited by this paper
References and models that cite this paper

Abbott LF, Nelson SB. (2000). Synaptic plasticity: taming the beast. Nature neuroscience. 3 Suppl [PubMed]

Barak O, Tsodyks M. (2006). Recognition by variance: learning rules for spatiotemporal patterns. Neural computation. 18 [PubMed]

Carnevale NT, Tsai KY, Brown TH. (1994). Hebbian learning is jointly controlled by electrotonic and input structure Network. 5

Clopath C, Büsing L, Vasilaki E, Gerstner W. (2010). Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nature neuroscience. 13 [PubMed]

Falconbridge MS, Stamps RL, Badcock DR. (2005). A Simple Hebbian/Anti-Hebbian Network Learns the Sparse, Independent Components of Natural Images Neural Comput. 18

Fiori S. (2005). Nonlinear complex-valued extensions of Hebbian learning: an essay. Neural computation. 17 [PubMed]

Frank MJ. (2006). Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making. Neural networks : the official journal of the International Neural Network Society. 19 [PubMed]

Gabbiani F, Cox SJ. (2010). Mathematics for Neuroscientists.

Gerstner W, Kistler WM. (2002). Mathematical formulations of Hebbian learning. Biological cybernetics. 87 [PubMed]

King PD, Zylberberg J, DeWeese MR. (2013). Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1. The Journal of neuroscience : the official journal of the Society for Neuroscience. 33 [PubMed]

O'Reilly RC, Frank MJ. (2006). Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural computation. 18 [PubMed]

O`Reilly RC, Frank MJ. (2005). Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia Neural Comput. 18

Porr B, Wörgötter F. (2006). Strongly improved stability and faster convergence of temporal sequence learning by using input correlations only. Neural computation. 18 [PubMed]

Porr B, Wörgötter F. (2007). Learning with "relevance": using a third factor to stabilize Hebbian learning. Neural computation. 19 [PubMed]

Sadeh S, Clopath C, Rotter S. (2015). Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity. PLoS computational biology. 11 [PubMed]

Saudargiene A, Porr B, Wörgötter F. (2004). How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural computation. 16 [PubMed]

Soman K, Chakravarthy S, Yartsev MM. (2018). A hierarchical anti-Hebbian network model for the formation of spatial cells in three-dimensional space. Nature communications. 9 [PubMed]

Toyoizumi T, Kaneko M, Stryker MP, Miller KD. (2014). Modeling the dynamic interaction of Hebbian and homeostatic plasticity. Neuron. 84 [PubMed]

Toyoizumi T, Pfister JP, Aihara K, Gerstner W. (2007). Optimality model of unsupervised spike-timing-dependent plasticity: synaptic memory and weight distribution. Neural computation. 19 [PubMed]

This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.