Variational learning for recurrent spiking networks

Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Volumn , Issue , 2011, Pages

  Rezende, Danilo Jimenez ^a   Wierstra, Daan ^a   Gerstner, Wulfram ^a  

^a EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

FEEDBACK CONNECTION; FEEDFORWARD CONNECTIONS; GENERATIVE MODEL; LATERAL CONNECTIONS; LEARNING MECHANISM; LEARNING RULES; RECURRENT NETWORKS; SPIKE SEQUENCES; SPIKING NETWORKS; SPIKING NEURONES;

NEURAL NETWORKS;

EID: 85162503676     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Konrad P Körding and Daniel M Wolpert. Bayesian integration in sensorimotor learning. Nature, 427(6971):244-7, January 2004.
2. P. Berkes, G. Orban, M. Lengyel, and J. Fiser. Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment. Science, 331(6013):83-87, January 2011.
3. Wei Ji Ma, Jeffrey M Beck, and Alexandre Pouget. Spiking networks for Bayesian inference and choice. Current opinion in neurobiology, 18(2):217-22, April 2008.
4. Joshua B Tenenbaum, Thomas L Griffiths, and Charles Kemp. Theory-based Bayesian models of inductive learning and reasoning. Trends in cognitive sciences, 10(7):309-18, 2006.
5. Konrad P Körding and Daniel M Wolpert. Bayesian decision theory in sensorimotor control. Trends in cognitive sciences, 10(7):319-26, July 2006.
6. D. Acuna and P. Schrater. Bayesian modeling of human sequential decision-making on the multi-armed bandit problem. In Proceedings of the 30th Annual Conference of the Cognitive Science Society. Washington, DC: Cognitive Science Society, 2008.
7. Michael D. Lee. A Hierarchical Bayesian Model of Human Decision-Making on an Optimal Stopping Problem. Cognitive Science, 30(3):1-26, May 2006.
8. G. Bi and M. Poo. Synaptic modification by correlated activity: Hebb's postulate revisited. Annual review of neuroscience, 24(1):139-166, 2001.
9. W. Gerstner andW. K. Kistler. Mathematical Formulations of Hebbian Learning. Biological Cybernetics, 87(5-6):404-415, 2002. article.
10. W. Gerstner. Spike-response model. Scholarpedia, 3(12):1343, 2008.
11. J W Pillow, J Shlens, L Paninski, A Sher, A M Litke, E J Chichilnisky, and E P Simoncelli. Spatiotemporal correlations and visual signaling in a complete neuronal population. Nature, 454(7206):995-999, Aug 2008.
12. Renaud Jolivet, Alexander Rauch, Hans R. Lüscher, and Wulfram Gerstner. Predicting spike timing of neocortical pyramidal neurons by simple threshold models. J Comput Neurosci, 21(1):35-49, August 2006.
13. J.P. Pfister, Taro Toyoizumi, D. Barber, and W. Gerstner. Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning. Neural Computation, 18(6):1318-1348, 2006.
14. S. Kullback and R. A. Leibler. On Information and Sufficiency. The Annals of Mathematical Statistics, 22(1):79-86, March 1951.
15. Taro Toyoizumi, Kamiar Rahnama Rad, and Liam Paninski. Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractoriness. Neural computation, 21(5):1203-43, May 2009.
16. Brendan J. Frey and Geoffrey E. Hinton. Variational Learning in Nonlinear Gaussian Belief Networks. Neural Computation, 11(1):193-213, January 1999.
17. Karl Friston, Jérémie Mattout, Nelson Trujillo-Barreto, John Ashburner, and Will Penny. Variational free energy and the Laplace approximation. NeuroImage, 34(1):220-34, January 2007.
18. Matthew J Beal and Zoubin Ghahramani. Variational Bayesian Learning of Directed Graphical Models with Hidden Variables. Bayesian Analysis, 1(4):793-832, 2006.
19. T.S. Jaakkola and M.I. Jordan. Bayesian parameter estimation via variational methods. Statistics and Computing, 10(1):25-37, 2000.
20. Jayant E Kulkarni and Liam Paninski. Common-input models for multiple neural spike-train data. Network (Bristol, England), 18(4):375-407, December 2007.
21. Ian H Stevenson, James M Rebesco, Nicholas G Hatsopoulos, Zach Haga, Lee E Miller, and Konrad P Körding. Bayesian inference of functional connectivity and network structure from spikes. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 17(3):203-13, June 2009.
22. C. Archambeau, Dan Cornford, Manfred Opper, and J. Shawe-Taylor. Gaussian process approximations of stochastic differential equations. In Journal of Machine Learning Research Workshop and Conference Proceedings, volume 1, pages 1-16. Citeseer, 2007.
23. Daniel H O'Connor, Gayle M Wittenberg, and Samuel S-H Wang. Graded bidirectional synaptic plasticity is composed of switch-like unitary events. Proceedings of the National Academy of Sciences of the United States of America, 102(27):9679-84, July 2005.
24. C C Petersen, R C Malenka, R a Nicoll, and J J Hopfield. All-or-none potentiation at CA3-CA1 synapses. Proceedings of the National Academy of Sciences of the United States of America, 95(8):4732-7, April 1998.
25. Rajesh P N Rao. Bayesian computation in recurrent neural circuits. Neural computation, 16(1):1-38, January 2004.
26. Bernhard Nessler, Michael Pfeiffer, andWolfgang Maass. STDP enables spiking neurons to detect hidden causes of their inputs. Advances in Neural Information Processing Systems (NIPS09), pages 1357-1365, 2009.