Near-maximum entropy models for binary neural representations of natural images

Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

Volumn , Issue , 2008, Pages

  Bethge, Matthias ^a   Berens, Philipp ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORKS;

DIMENSIONAL MEASUREMENTS; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL; HIGHER-DIMENSIONAL; LOW DIMENSIONAL; MAXIMUM ENTROPY MODELS; MAXIMUM-ENTROPY; NATURAL IMAGES; NEURAL REPRESENTATIONS; PAIRWISE CORRELATION;

CLUSTERING ALGORITHMS;

EID: 85162002902     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. D.H. Ackley, G.E. Hinton, and T.J. Sejnowski. A learning algorithm for boltzmann machines. Cognitive Science, 9:147-169, 1985.
2. H.B. Barlow. Sensory mechanisms, the reduction of redundancy, and intelligence. In The Mechanisation of Thought Processes, pages 535-539, London: Her Majesty's Stationery Office, 1959.
3. M. Bethge. Factorial coding of natural images: How effective are linear model in removing higher-order dependencies? J. Opt. Soc. Am. A, 23(6):1253-1268, June 2006.
4. D.R. Cox and N. Wermuth. On some models for multivariate binary variables parallel in complexity with the multivariate gaussian distribution. Biometrika, 89:462-469, 2002. (Pubitemid 41311995)
5. L.J. Emrich and M.R. Piedmonte. A method for generating high-dimensional multivariate binary variates. The American Statistician, 45(4):302-304, 1991.
6. M. Huber. A bounding chain for swendsen-wang. Random Structures & Algorithms, 22:53-59, 2002.
7. E.T. Jaynes. Where do we stand on maximum entropy inference. In R.D. Levine and M. Tribus, editors, The Maximum Entropy Formalism. MIT Press, Cambridge, MA, 1978.
8. J. Linn. Divergence measures based on the shannon entropy. IEEE Trans Inf Theory, 37:145-151, 1991.
9. D. J. C. MacKay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2003.
10. Sheila H Nirenberg and Jonathan D Victor. Analyzing the activity of large populations of neurons: how tractable is the problem? Current Opinion in Neurobiology, 17:397-400, August 2007. (Pubitemid 47531946)
11. Karl Pearson. On a new method of determining correlation between a measured character a, and a character b, of which only the percentage of cases wherein b exceeds (or falls short of) a given intensity is recorded for each grade of a. Biometrika, 7:96-105, 1909.
12. Elad Schneidman, Michael J Berry, Ronen Segev, and William Bialek. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440(7087):1007-1012, Apr 2006.
13. J Shlens, JD Field, JL Gauthier, MI Grivich, D Petrusca, A Sher, AM Litke, and EJ Chichilnisky. The structure of multi-neuron firing patterns in primate retina. J Neurosci, 26(32):8254-8266, Aug 2006. (Pubitemid 44309839)
14. G. Tkacik, E. Schneidman, M.J. Berry, andW. Bialek. Ising models for networks of real neurons. arXiv:qbio. NC/0611072, 1:1-4, 2006.