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Research interests |
- Bayesian Inference
- Information Theory
- Computational Neuroscience
- Neural Networks
- Formal Concept Analysis
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Concept lattice of my work |
| Legend: the graph below shows a concept lattice of my work. Each node represents a formal concept, the concept whose extent and intent are displayed on this page is highlighted in green. The graph is interactive, you can jump to a concept by clicking on it. The top concept links to my homepage. Think of the intent as the set of tags shared by the set of papers or presentations which form the extent of a concept (below the graph). Concepts are partially ordered: concept A > concept B if extent of A is a superset of B's extent (or conversely, if the intent of A is a subset of B's intent). An arrow from A to B means that A > B. I used reduced labelling, i.e. a tag is only shown in the largest concept which contains it. If you'd like to know more about Formal Concept Analysis, here is a good place to start! |
Intent: ∅ |
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Extent: |
 P. Földiák and D. Endres, Sparse Coding, Scholarpedia, 3(1):2984, 2008. |
D. Endres, M. Oram, J. Schindelin and P. Földiák, Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms,
pp. 393-400, Advances in Neural Information Processing Systems 20, MIT Press, Cambridge, MA, 2008 |
D. Endres and P. Földiák, Exact Bayesian Bin Classification: a fast alternative to Bayesian Classification and its application to neural response analysis,
Journal of Computational Neuroscience, 24(1): 24-35, 2008. DOI: 10.1007/s10827-007-0039-5. The original publication is available at www.springerlink.com |
D. Endres, Bayesian and Information-theoretic Tools for Neuroscience,
PhD Thesis, University of St. Andrews, UK, 2006 |
D. Endres and P. Földiák, Bayesian bin distribution inference and mutual information,
IEEE Transactions on Information Theory, vol. 51, no. 11, pp. 3766-3779, November 2005 |
D. Endres and J.Schindelin, A new metric for probability distributions,
IEEE Transactions on Information Theory, vol. 49, pp. 1858-60, July 2003 |
D. Endres and P. Földiák, Quadratic programming for learning sparse codes,
In Proceedings of the Ninth International Conference on Artificial Neural Networks (ICANN99),
IEE Conference Publication No. 470.
London: Institution of Electrical Engineers, vol. 2, pp. 593-596, 1999. |
D. Endres and P. Riegler, Adaptive Systems on different time scales,
J. Phys. A: Math. Gen. 32, 8655-9663(1999) |
D. Endres, Adiabatic decoupling of the learning dynamics of neural networks
(in German), Diploma thesis, Julius-Maximilians Universität, Würzburg, Germany, 1998 |
| D. Endres and P. Földiák, Interpreting the Neural Code with Formal Concept Analysis, Joint Face Lab Meeting 2008, St. Andrews, UK. |
D. Endres, P. Földiák and U. Priss, An Application of Formal Concept Analysis to Neural Decoding,
to be presented at Concept Lattices and their applications (CLA) 2008, Olomouc, Czech Republic. |
D. Endres, J. Schindelin, P. Földiák and M. Oram, Examining the joint neural code of latency and firing rate by Bayesian binning,
Neural Coding 2007, Montevideo, Uruguay. Abstract. |
D. Endres and M. Oram, Examining the joint neural code of latency and firing rate by Bayesian binning,
Spatiotemporal patterns and synfire chains workshop 2008, Newcastle, UK. |
| D. Endres, U. Priss, J. Schindelin and P. Földiák, Sparse Formal Concept Analysis (FCA), KPP 2007, Darmstadt, Germany. Abstract. |
D. Endres, Estimating Mutual Information by Bayesian Binning,
CNS 2006 - Workshop - Methods of Information Theory in Computational Neuroscience, Edinburgh, UK. Read this paper for details. |
D. Endres, M. Oram, J. Schindelin and P. Földiák, Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms,
NIPS 2007, Whistler, B.C., Canada. Listen to our bragging while you look at the spotlight. Read this paper for details. |
M.W. Oram, D. Xiao and D. Endres, Stimulus induced decorrelation of neuronal activity in the visual system, ECVP 2007, Arezzo, Italy,
PERCEPTION 36: 221-221 Suppl. S 2007 |
D. Endres and P. Földiák, Rapid presentation is efficient for testing visual neurons (area STSa): information rate peaks in the interval [9,24] stimuli/s,
CNS 2007, Toronto, Ontario, Canada. Abstract |
D. Endres and P. Földiák, Exact Bayesian Bin Classification and Its Application to Neural Response Analysis,
COSYNE 2007, Salt Lake City, UT, USA. Read this paper for details. |
| binsdfc v0.1, a command-line application that implements the PSTH/SDF estimation methods described here. Comes with Bayesian latency detection built-in! |
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