Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons
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DOI: 10.1371/journal.pcbi.1002294
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References listed on IDEAS
- Attila Losonczy & Judit K. Makara & Jeffrey C. Magee, 2008. "Compartmentalized dendritic plasticity and input feature storage in neurons," Nature, Nature, vol. 452(7186), pages 436-441, March.
- Lars Buesing & Johannes Bill & Bernhard Nessler & Wolfgang Maass, 2011. "Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-22, November.
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Cited by:
- Stefan Habenschuss & Zeno Jonke & Wolfgang Maass, 2013. "Stochastic Computations in Cortical Microcircuit Models," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-28, November.
- Bernhard Nessler & Michael Pfeiffer & Lars Buesing & Wolfgang Maass, 2013. "Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity," PLOS Computational Biology, Public Library of Science, vol. 9(4), pages 1-30, April.
- Robert Legenstein & Wolfgang Maass, 2014. "Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-27, October.
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