Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations

By Kühn Simone   Beyn Wolf-Jürgen   Cruse Holk  

Biological Cybernetics, Vol. 96, Iss. 5, 2007-05 ,pp. : 455-470

Springer Publishing Company

Access to resources Recommend Favorite

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. II. Input selectivity—symmetry breaking

By Gilson Matthieu  

Biological Cybernetics, Vol. 101, Iss. 2, 2009-08 ,pp. : 103-114

Springer Publishing Company

Access to resources Recommend Favorite

Elements for a general memory structure: properties of recurrent neural networks used to form situation models

By Makarov Valeri  

Biological Cybernetics, Vol. 98, Iss. 5, 2008-05 ,pp. : 371-395

Springer Publishing Company

Access to resources Recommend Favorite

Compact internal representation of dynamic situations: neural network implementing the causality principle

By Villacorta-Atienza José   Velarde Manuel   Makarov Valeri  

Biological Cybernetics, Vol. 103, Iss. 4, 2010-10 ,pp. : 285-297

Springer Publishing Company

Access to resources Recommend Favorite

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

By Bitzer Sebastian   Kiebel Stefan  

Biological Cybernetics, Vol. 106, Iss. 4-5, 2012-07 ,pp. : 201-217

Springer Publishing Company

Access to resources Recommend Favorite