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Vertechi P, Brendel W, Machens CK (2014) Unsupervised learning of an efficient short-term memory network Advances in Neural Information Processing Systems 27:1-9

   Unsupervised learning of an efficient short-term memory network (Vertechi, Brendel & Machens 2014)

References and models cited by this paper

References and models that cite this paper

Barrett DG, Deneve S, Machens CK (2013) Firing rate predictions in optimal balanced networks Advances in Neural Information Processing Systems 26:1538-1546

Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129-59 [PubMed]

Boerlin M, Machens CK, Denève S (2013) Predictive coding of dynamical variables in balanced spiking networks. PLoS Comput Biol 9:e1003258 [Journal] [PubMed]

Bourdoukan R, Et_al (2012) Learning optimal spike-based representations Advances in Neural Information Processing Systems

Fusi S, Drew PJ, Abbott LF (2005) Cascade models of synaptically stored memories. Neuron 45:599-611 [Journal] [PubMed]

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735-80 [PubMed]

Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A 79:2554-8 [PubMed]

Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci U S A 81:3088-92 [PubMed]

Hu T, Genkin A, Chklovskii DB (2012) A network of spiking neurons for computing sparse representations in an energy-efficient way. Neural Comput 24:2852-72 [Journal] [PubMed]

Jaeger H (2001) The echo state approach to analyzing and training recurrent neural networks GMD Report 148

Lazar A, Pipa G, Triesch J (2009) SORN: a self-organizing recurrent neural network. Front Comput Neurosci 3:23 [Journal] [PubMed]

Lukosevicius M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training Computer Science Review 3.3 3.3:127-149

Maass W, Natschläger T, Markram H (2002) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14:2531-60 [Journal] [PubMed]

Machens CK, Romo R, Brody CD (2005) Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science 307:1121-4 [Journal] [PubMed]

   Neural model of two-interval discrimination (Machens et al 2005) [Model]

Major G, Tank D (2004) Persistent neural activity: prevalence and mechanisms. Curr Opin Neurobiol 14:675-84 [Journal] [PubMed]

Olshausen BA, Field DJ (1997) Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision Res 37:3311-25 [PubMed]

Rao RP, Ballard DH (1999) Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat Neurosci 2:79-87 [Journal] [PubMed]

Renart A, Brunel N, Wang X (2004) Mean-Field Theory of Irregularly Spiking Neuronal Populations and Working Memory in Recurrent Cortical Networks Computational Neuroscience: A Comprehensive Approach

Rozell CJ, Johnson DH, Baraniuk RG, Olshausen BA (2008) Sparse coding via thresholding and local competition in neural circuits. Neural Comput 20:2526-63 [Journal] [PubMed]

Simoncelli EP, Olshausen BA (2001) Natural image statistics and neural representation. Annu Rev Neurosci 24:1193-216 [Journal] [PubMed]

Wang XJ (2002) Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36:955-68 [PubMed]

Werbos PJ (1990) Backpropagation through time: what it does and how to do it. Proc IEEE 78:1550-1560

Zylberberg J, Murphy JT, DeWeese MR (2011) A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields. PLoS Comput Biol 7:e1002250 [Journal] [PubMed]

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