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