Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. ssMRP state detection for brain computer interfacing using hidden Markov models

    Nazarpour, K., Stastny, J. & Miall, R. C., 6 Oct 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing. Institute of Electrical and Electronics Engineers (IEEE), p. 29-32 4 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  2. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells

    Clark, S. J., Argelaguet, R., Kapourani, A., Stubbs, T. M., Lee, H. J., Alda-Catalinas, C., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J. C., Stegle, O. & Reik, W., 22 Feb 2018, In : Nature Communications. 9, 17 p., 781.

    Research output: Contribution to journalArticle

  3. riboviz: analysis and visualization of ribosome profiling datasets

    Carja, O., Xing, T., Wallace, E. W. J., Plotkin, J. B. & Shah, P., 25 Oct 2017, In : BMC Bioinformatics. 18, 461

    Research output: Contribution to journalArticle

  4. qpMerge: Merging different peptide isoforms using a motif centric strategy

    Hindle, M. M., Le Bihan, T., Krahmer, J., Martin, S. F., Noordally, Z. B., Simpson, T. I. & Millar, A. J., 5 Apr 2016, (Submitted) bioRxiv, at Cold Spring Harbor Laboratory, 9 p.

    Research output: Working paper

  5. puma: a Bioconductor package for propagating uncertainty in microarray analysis

    Pearson, R., Liu, X., Sanguinetti, G., Milo, M., Lawrence, N. & Rattray, M., 2009, In : BMC Bioinformatics. 10, 1, 10 p.

    Research output: Contribution to journalArticle

  6. ptype: probabilistic type inference

    Ceritli, T., Williams, C. K. I. & Geddes, J., 31 May 2020, In : Data Mining and Knowledge Discovery. 34, 3, p. 870-904 35 p.

    Research output: Contribution to journalArticle

  7. mRNA Cap Methyltransferase, RNMT-RAM, Promotes RNA Pol II-Dependent Transcription

    Varshney, D., Lombardi, O., Schweikert, G., Dunn, S., Suska, O. & Cowling, V. H., 2 May 2018, In : Cell Reports. 23, 5, p. 1530-1542 14 p.

    Research output: Contribution to journalArticle

  8. iBehave - applications of supervised machine learning to behaviour analysis.

    Heward, J. A., Crook, P. A., Lukins, T. C. & Armstrong, D., 2008, Proceedings of Measuring Behaviour 2008. 6th International Conference on Methods and Techniques in Behavioural Research. Spink, A., Ballintijn, M., Bogers, N., Grieco, F., Loijens, L., Noldus, L., Smit, G. & Zimmerman, P. (eds.). p. 314-315 2 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  9. fMRI correlates of state and trait effects in subjects at genetically enhanced risk of schizophrenia

    Whalley, H. C., Simonotto, E., Flett, S., Marshall, I., Ebmeier, K. P., Owens, D. G. C., Goddard, N. H., Johnstone, E. C. & Lawrie, S. M., Mar 2004, In : Brain. 127, Pt 3, p. 478-90 13 p.

    Research output: Contribution to journalArticle

  10. eCAT: Online electronic lab notebook for scientific research

    Goddard, N., Macneil, R. & Ritchie, J., 2009, In : Automated Experimentation. 1, p. 1-7 7 p., 4.

    Research output: Contribution to journalArticle

  11. Wrattler: Reproducible, live and polyglot notebooks

    Petricek, T., Geddes, J. & Sutton, C., 2018, 10th USENIX Workshop on Theory and Practice of Provenance (TaPP 2018). London, UK: Usenix, p. 1-4 4 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  12. Word Storms: Multiples of Word Clouds for Visual Comparison of Documents

    Castella, Q. & Sutton, C. A., 2014, Proceedings of the 23rd international conference on World wide web. International World Wide Web Conferences Steering Committee, p. 665-676 12 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  13. Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio

    Jastrzębski, S., Kenton, Z., Arpit, D., Ballas, N., Fischer, A., Bengio, Y. & Storkey, A., Oct 2018, Proceedings of 27th International Conference on Artificial Neural Networks. Rhodes, Greece: Springer, Cham, p. 392-402 10 p. (Lecture Notes in Computer Science; vol. 11141)(Theoretical Computer Science and General Issues; vol. 11141).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  14. Wide-band information transmission at the calyx of Held

    Hennig, M., Graham, B. P., Yang, Z., Postlethwaite, M. & Forsythe, I. D., Apr 2009, In : Neural Computation. 21, 4, p. 991-1017 27 p.

    Research output: Contribution to journalLetter

  15. Why, when, and what: Analyzing Stack Overflow questions by topic, type, and code

    Allamanis, M. & Sutton, C., 2013, Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on. Institute of Electrical and Electronics Engineers (IEEE), p. 53-56 4 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  16. When Training and Test Sets Are Different: Characterizing Learning Transfer

    Storkey, A., Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. & Lawrence, ND., Dec 2008, Dataset Shift in Machine Learning. Cambridge: Yale University Press in association with the Museum of London, p. 3-28 26 p. (Neural Information Processing Series).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  17. What, if anything, are topological maps for?

    Wilson, S. P. & Bednar, J. A., 11 Feb 2015, In : Developmental neurobiology.

    Research output: Contribution to journalArticle

  18. What can MaxEnt reveal about high-density recordings and what can high-density recordings reveal about MaxEnt?

    Panas, D., Maccione, A., Berdondini, L. & Hennig, M., 2011, In : BMC Neuroscience. 12, Supplement 1, 2 p., P146.

    Research output: Contribution to journalMeeting abstract

  19. Weak Epistasis May Drive Adaptation in Recombining Bacteria

    Arnold, B. J., Gutmann, M., Grad, Y. H., Sheppard, S. K., Corander, J., Lipsitch, M. & Hanage, W. P., 1 Mar 2018, In : Genetics. 208, 3, p. 1247-1260 39 p.

    Research output: Contribution to journalArticle

  20. Wasserstein distances for estimating parameters in stochastic reaction networks

    Öcal, K., Grima, R. & Sanguinetti, G., 15 Nov 2019, Computational Methods in Systems Biology - 17th International Conference, CMSB 2019, Proceedings. Bortolussi, L. & Sanguinetti, G. (eds.). Springer-Verlag, p. 347-351 5 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11773 LNBI).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  21. W:Ti flexible transversal electrode array for peripheral nerve stimulation: a feasibility study

    Silveira, C., Brunton, E., Escobedo-Cousin, E., Gupta, G., Whittaker, R., O'Neill, A. & Nazarpour, K., 13 Aug 2020, In : IEEE Transactions on Neural Systems and Rehabilitation Engineering. 9 p.

    Research output: Contribution to journalArticle

  22. Volume transmission as a new homeostatic mechanism

    Sweeney, Y. A., Hellgren-Kotaleski, J. & Hennig, M., 2013.

    Research output: Contribution to conferencePoster

  23. Visual Cortex as a General-Purpose Information-Processing Device

    Bednar, J. A., 2012, Computer Vision – ECCV 2012. Workshops and Demonstrations: Florence, Italy, October 7-13, 2012, Proceedings, Part I. Fusiello, A., Murino, V. & Cucchiara, R. (eds.). Springer Berlin Heidelberg, p. 480-485 6 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  24. Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection

    Kivinen, J., Williams, C. K. I. & Heess, N., 2014, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. Reykjavik, Iceland: Journal of Machine Learning Research: Workshop and Conference Proceedings, Vol. 33. p. 512-521 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  25. Visual Aftereffects, Models of

    Bednar, J., 2014, Encyclopedia of Computational Neuroscience. Jaeger, D. & Jung, R. (eds.). Springer New York, p. 1-8

    Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionary

  26. Vision2Sensor: Knowledge Transfer Across Sensing Modalities for Human Activity Recognition

    Radu, V. & Henne, M., 9 Sep 2019, In : PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 3, 3, p. 84:1-84:21 21 p., 84.

    Research output: Contribution to journalArticle

  27. Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image

    Romaszko, L., Williams, C. K. I., Moreno, P. & Kohli, P., 23 Jan 2018, ICCV 2017 Workshop on Geometry Meets Deep Learning. Institute of Electrical and Electronics Engineers (IEEE), p. 940-948 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  28. Virtual fly brain - Using OWL to support the mapping and genetic dissection of the drosophila brain

    Osumi-Sutherland, D., Costa, M., Court, R. & O'Kane, C. J., 2014, In : CEUR Workshop Proceedings. 1265, p. 85-96 12 p.

    Research output: Contribution to journalArticle

  29. Virtual Fly Brain 3D Interaction Tool

    Milyaev, N., Armstrong, D., Osumi-Sutherland, D., Ashburner, M., Baldock, R. A., Burton, N. & Husz, Z. L., Dec 2010, In : Journal of neurogenetics. 24, p. 50-50 1 p.

    Research output: Contribution to journalMeeting abstract

  30. Virtual Fly Brain

    Osumi-Sutherland, D., Milyaev, N., Reeve, S., Armstrong, J. D. & Ashburner, M., Dec 2010, In : Journal of neurogenetics. 24, p. 49-50 2 p.

    Research output: Contribution to journalMeeting abstract

  31. Very Predictive Ngrams for Space-Limited Probabilistic Models

    Cohen, P. R. & Sutton, C. A., 2003, Advances in Intelligent Data Analysis V: 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003. Proceedings. R. Berthold, M., Lenz, H-J., Bradley, E., Kruse, R. & Borgelt, C. (eds.). Springer-Verlag GmbH, p. 134-142 9 p. (Lecture Notes in Computer Science; vol. 2810).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  32. Verifying Anti-Security Policies Learnt from Android Malware Families

    Chen, W., Sutton, C., Aspinall, D., Gordon, A., Shen, Q. & Muttik, I., 21 Oct 2015, Fourth International Seminar on Program Verification, Automated Debugging and Symbolic Computation. 6 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  33. Variational inference for Markov jump processes

    Opper, M. & Sanguinetti, G., 2008, Advances in Neural Information Processing Systems 20 (NIPS 2007). Platt, J. C., Koller, D., Singer, Y. & Roweis, S. T. (eds.). p. 1105-1112 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  34. Variational Relevance Vector Machines

    Bishop, C. M. & Tipping, M. E., 1 Jan 2000, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, p. 46-53 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  35. Variational Principal Components

    Bishop, C. M., 1 Jan 1999, Proceedings Ninth International Conference on Artificial Neural Networks, ICANN'99. IEE, p. 509-514 6 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  36. Variational Noise-Contrastive Estimation

    Rhodes, B. & Gutmann, M., 25 Apr 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019). Naha, Okinawa, Japan: PMLR, Vol. 89. p. 2741-2750 14 p. (Proceedings of Machine Learning Research; vol. 89).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  37. Variational Message Passing

    Winn, J. & Bishop, C., 2005, In : Journal of Machine Learning Research. 6, p. 661-694 34 p.

    Research output: Contribution to journalArticle

  38. Variational Learning in Graphical Models and Neural Networks

    Bishop, C., 1998, ICANN 98: Proceedings of the 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998. Niklasson, L., Boden, M. & Ziemke, T. (eds.). Springer London, p. 13-22 10 p. (Perspectives in Neural Computing).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  39. Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations

    Zammit-Mangion, A., Sanguinetti, G. & Kadirkamanathan, V., 1 Jul 2012, In : IEEE Transactions on Signal Processing. 60, 7, p. 3449-3459 11 p.

    Research output: Contribution to journalArticle

  40. Variational Bayesian Model Selection for Mixture Distributions

    Corduneanu, A. & Bishop, C. M., 2001, Proceedings Eighth International Conference on Artificial Intelligence and Statistics. Morgan Kaufmann, p. 27-34 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  41. Validity conditions for moment closure approximations in stochastic chemical kinetics

    Schnoerr, D., Sanguinetti, G. & Grima, R., 1 Jan 2014, In : Journal of Chemical Physics. 141, 8, 084103.

    Research output: Contribution to journalArticle

  42. Validating a standardised test battery for synesthesia: Does the Synesthesia Battery reliably detect synesthesia?

    Carmichael, D. A., Down, M. P., Shillcock, R. C., Eagleman, D. M. & Simner, J., May 2015, In : Consciousness and Cognition. 33, p. 375-385 11 p.

    Research output: Contribution to journalArticle

  43. VIBES: A Variational Inference Engine for Bayesian Networks

    Bishop, C. M., Spiegelhalter, D. J. & Winn, J., 2002, Advances in Neural Information Processing Systems 15 (NIPS 2002). 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  44. VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

    Srivastava, A., Valkov, L., Russell, C., Gutmann, M. & Sutton, C., 9 Dec 2017, Advances in Neural Information Processing Systems 30 (NIPS 2017). Long Beach, CA, USA: Curran Associates Inc, p. 3308-3318 18 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  45. Utilising disaggregated energy data in feedback designs – the IDEAL project

    Goddard, N., Pullinger, M., Webb, L., Farrow, E., Farrow, E., Kilgour, J., Morgan, E. & Moore, J., Jul 2016, TEDDINET Energy Feedback Symposium 2016. p. 74-77 4 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  46. Using the Nyström Method to Speed Up Kernel Machines

    Williams, C. K. I. & Seeger, M., 2001, Advances in Neural Information Processing Systems 13 (NIPS 2000). Leen, T. K., Dietterich, T. G. & Tresp, V. (eds.). MIT Press, p. 682-688 7 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  47. Using affective and behavioural sensors to explore aspects of collaborative music making

    Morgan, E., Gunes, H. & Bryan-Kinns, N., Oct 2015, In : International Journal of Human-Computer Studies. 82, p. 31-47 17 p.

    Research output: Contribution to journalArticle

  48. Using a neural net to instantiate a deformable model

    Williams, C. K. I., Revow, M. & Hinton, G. E., 1995, Advances in neural information processing systems. p. 965-972 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  49. Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains

    Onken, A., Liu, J. K., Karunasekara, P. P. C. R., Delis, I., Gollisch, T. & Panzeri, S., 4 Nov 2016, In : PLoS Computational Biology. 12, 11, p. 1-46 46 p., e1005189.

    Research output: Contribution to journalArticle

  50. Using Machine Learning to Focus Iterative Optimization

    Agakov, F., Bonilla, E., Cavazos, J., Franke, B., Fursin, G., O'Boyle, M. F. P., Thomson, J., Toussaint, M. & Williams, C. K. I., 2006, Proceedings of the International Symposium on Code Generation and Optimization. Washington, DC, USA: Institute of Electrical and Electronics Engineers (IEEE), p. 295-305 11 p. (CGO '06).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  51. Using Generative Models for Handwritten Digit Recognition

    Revow, M., Williams, C. K. I. & Hinton, G. E., Jun 1996, In : IEEE Transactions on Pattern Analysis and Machine Intelligence. 18, 6, p. 592-606 15 p.

    Research output: Contribution to journalArticle

  52. Using Bayesian neural networks to classify segmented images

    Vivarelli, F. & Williams, C. K. I., 1 Jul 1997, Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440). IET, p. 268-273 6 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  53. Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data

    Huang, Y. & Sanguinetti, G., 2019, Computational Methods for Single-Cell Data Analysis. Yuan, G-C. (ed.). New York, NY: Springer New York LLC, p. 175-185 11 p. (Methods in Molecular Biology; vol. 1935).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  54. Use of Regularized Discriminant Analysis Improves Myoelectric Hand Movement Classification

    Krasoulis, A., Nazarpour, K. & Vijayakumar, S., 15 Aug 2017, 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). Institute of Electrical and Electronics Engineers (IEEE), p. 395-398 4 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  55. Unsupervised spike sorting for large scale, high density multielectrode arrays

    Hilgen, G., Sindaci, M. S., Pirmoradian, S., Muthmann, J-O., Kepiro, I., Ullo, S., Juarez Ramirez, C., Encinas, A. P., Maccione, A., Berdondini, L., Murino, V., Sona, D., Cella Zanacchi, F., Sernagor, E. & Hennig, M. H., 7 Mar 2017, In : Cell Reports. 18, 10, p. 2521-2532 12 p.

    Research output: Contribution to journalArticle

  56. Unsupervised deduplication using cross-field dependencies

    Hall, R., Sutton, C. & McCallum, A., 2008, Proceedings of the 14th ACM SIGKDD international conference on Knowledge Discovery and Data mining (KDD '08). New York, NY, USA: ACM, p. 310-317 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  57. Unsupervised Monocular Depth Estimation with Left-Right Consistency

    Godard, C., Mac Aodha, O. & Brostow, G. J., 9 Nov 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Institute of Electrical and Electronics Engineers (IEEE), p. 6602-6611 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  58. Universal fluctuations in a simple disordered system

    Nieuwenhuizen, T. M. & Van Rossum, M., 1991, In : Physics letters a. 160, 5, p. 461 - 464 4 p.

    Research output: Contribution to journalArticle

  59. Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning

    Costa, R. P., Froemke, R. C., Sjöström, P. J. & Van Rossum, M. C. W., 26 Aug 2015, In : eLIFE. 4, 24 p.

    Research output: Contribution to journalArticle

  60. Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners

    Chen, Y., Singla, A., Mac Aodha, O., Perona, P. & Yue, Y., 8 Dec 2018, Advances in Neural Information Processing Systems 31. Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N. & Garnett, R. (eds.). Neural Information Processing Systems, Vol. 31. p. 1476-1486 11 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  61. Understanding synaptic pathways - modelling Parkinson's Disease

    Heil, K. F., Sorokina, O., Hellgren-kotaleski, J. & Armstrong, JD., 18 Jun 2014.

    Research output: Contribution to conferencePoster

  62. Understanding Neural Population Coding: Information Theoretic Insights from the Auditory System

    Onken, A., Karunasekara, P. P. C. R., Kayser, C. & Panzeri, S., 19 Oct 2014, In : Advances in Neuroscience. 2014, p. 1-14 15 p., 907851.

    Research output: Contribution to journalArticle

  63. Understanding Neural Maps with Topographica

    Bednar, J. A., 2008, In : Brains, Minds & Media. 1

    Research output: Contribution to journalArticle

  64. Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations

    Georgoulas, A., Hillston, J. & Sanguinetti, G., 1 Jul 2017, In : Statistics and Computing. 27, 4, p. 991–1002 12 p.

    Research output: Contribution to journalArticle

  65. Ultrasensitive Magnetoelectric Sensing Systemfor pico-Tesla MagnetoMyoGraphy

    Zuo, S., Schmalz, J., Ozden, M-O., Ozden, M-O., Su, J., Niekiel, F., Lofink, F., Nazarpour, K. & Heidari, H., 28 May 2020, In : IEEE Transactions on Biomedical Circuits and Systems. 14 p.

    Research output: Contribution to journalArticle

  66. U-check: Model Checking and Parameter Synthesis under Uncertainty

    Bortolussi, L., Milios, D. & Sanguinetti, G., 2015, Quantitative Evaluation of Systems: 12th International Conference, QEST 2015, Madrid, Spain, September 1-3, 2015, Proceedings. Springer International Publishing, p. 89-104 16 p. (Lecture Notes in Computer Science; vol. 9259).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  67. Tuning Curve Sharpening for orientation selectivity: coding efficiency and the impact of correlations

    Series, P., Latham, P. & Pouget, A., 2004, In : Nature Neuroscience. 7, p. 1129-35

    Research output: Contribution to journalArticle

  68. Truncated covariance matrices and Toeplitz methods in Gaussian processes

    Storkey, AJ., 1999, Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470) (Volume:1 ) . EDISON: INST ELECTRICAL ENGINEERS INSPEC INC, p. 55-60 6 p. (IEE CONFERENCE PUBLICATIONS).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  69. Trends and challenges in Computational RNA biology

    Selega, A. & Sanguinetti, G., 7 Dec 2016, In : Genome Biology. 17, 253, p. 1-4 4 p.

    Research output: Contribution to journalMeeting abstract

  70. Tree-structured belief networks as models of images

    Feng, X. & Williams, C. K. I., 1 Jan 1999, Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470). IET, p. 31-36 5 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  71. Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3

    Huynh-Thu, V. A. & Sanguinetti, G., 2019, Gene Regulatory Networks: Methods and Protocols. Sanguinetti, G. & Huynh-Thu, V. A. (eds.). New York, NY: Springer New York LLC, p. 217-233 17 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  72. Transmission of population-coded information

    Renart, A. & van Rossum, M. C. W., Feb 2012, In : Neural Computation. 24, 2, p. 391-407 17 p.

    Research output: Contribution to journalArticle

  73. Transition of Escherichia coli from Aerobic to Micro-aerobic Conditions Involves Fast and Slow Reacting Regulatory Components

    Partridge, J. D., Sanguinetti, G., Dibden, D. P., Roberts, R. E., Poole, R. K. & Green, J., Apr 2007, In : Journal of Biological Chemistry. 282, 15, p. 11230-11237 8 p.

    Research output: Contribution to journalArticle

  74. Transformation Equivariant Boltzmann Machines

    Kivinen, J. J. & Williams, C. K. I., 2011, Artificial Neural Networks and Machine Learning - ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I. Honkela, T., Duch, W., Girolami, M. & Kaski, S. (eds.). Springer-Verlag GmbH, p. 1-9 9 p. (Lecture Notes in Computer Science; vol. 6791).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  75. Transcriptional Analysis of Gli3 Mutants Identifies Wnt Target Genes in the Developing Hippocampus

    Hasenpusch-Theil, K., Magnani, D., Amaniti, E-M., Han, L., Armstrong, D. & Theil, T., Dec 2012, In : Cerebral Cortex. 22, 12, p. 2878-2893 16 p.

    Research output: Contribution to journalArticle

  76. Transcription rate strongly affects splicing fidelity and cotranscriptionality in budding yeast

    Aslanzadeh, V., Huang, Y., Sanguinetti, G. & Beggs, J., 2018, In : Genome Research. 12 p.

    Research output: Contribution to journalArticle

  77. Transcription factor binding predicts histone modifications in human cell lines

    Benveniste, D., Sonntag, H-J., Sanguinetti, G. & Sproul, D., 16 Sep 2014, In : Proceedings of the National Academy of Sciences. 111, 37, p. 13367-72 6 p.

    Research output: Contribution to journalArticle

  78. Transcript Profiling and Inference of Escherichia coli K-12 ArcA Activity across the Range of Physiologically Relevant Oxygen Concentrations

    Rolfe, M. D., Ter Beek, A., Graham, A. I., Trotter, E. W., Asif, H. M. S., Sanguinetti, G., de Mattos, J. T., Poole, R. K. & Green, J., 25 Mar 2011, In : Journal of Biological Chemistry. 286, 12, p. 10147-10154 8 p.

    Research output: Contribution to journalArticle

  79. Training Deep Convolutional Neural Networks to Play Go

    Clark, C. & Storkey, A., 2015, Proceedings of the 32nd International Conference on Machine Learning (IMCL 2015). Lille, France: PMLR, Vol. 37. p. 1766-1774 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  80. TractoR: Magnetic Resonance Imaging and Tractography with R

    Clayden, J. D., Maniega, S. M., Storkey, A. J., King, M. D., Bastin, M. E. & Clark, C. A., Oct 2011, In : Journal of statistical software. 44, 8, p. 1-18 18 p.

    Research output: Contribution to journalArticle

  81. Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

    Adams, R. P., Murray, I. & MacKay, D. J. C., 2009, Proceedings of the 26th International Conference on Machine Learning. 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  82. Tract shape modelling provides evidence of topological change in corpus callosum genu during normal ageing

    Bastin, M. E., Piatkowski, J. P., Storkey, A. J., Brown, L. J., MacLullich, A. M. J. & Clayden, J. D., 15 Oct 2008, In : NeuroImage. 43, 1, p. 20-28 9 p.

    Research output: Contribution to journalArticle

  83. Tracking of Individual Mice in a Social Setting Using Video Tracking Combined with RFID tags

    Armstrong, J. D., Acevedo-Arozena, A., Bains, R. S., Cater, H., Chartsias, A., Nolan, P., Sneddon, D., Sillito, R. & Wells, S., 25 May 2016, Proceedings of Measuring Behavior 2016: 10th International Conference on Methods and Techniques in Behavioral Research. Spink, A. J., Riedel, G., Zhou, L., Teekens, L., Albatal, R. & Gurrin, C. (eds.). Dublin, Ireland, p. 413-416 4 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  84. Towards tracking homeostais on high-density multi-electrode arrays

    Panas, D., Maccione, A., Berdondini, L. & Hennig, M., Sep 2012.

    Research output: Contribution to conferencePoster

  85. Towards learning analytics adoption: A mixed methods study of data-related practices and policies in Latin American universities

    Hilliger, I., Ortiz-Rojas, M., Pesántez-Cabrera, P., Scheihing, E., Tsai, Y-S., Muñoz-Merino, P. J., Broos, T., Whitelock-Wainwright, A., Gašević, D. & Pérez-Sanagustín, M., 31 Jul 2020, In : British Journal of Educational Technology. 51, 4, p. 915–937 23 p.

    Research output: Contribution to journalArticle

  86. Towards a virtual fly brain

    Armstrong, J. D. & van Hemert, J. I., Jun 2009, In : Philosophical Transactions A: Mathematical, Physical and Engineering Sciences. 367, 1896, p. 2387-2397 11 p.

    Research output: Contribution to journalArticle

  87. Towards a quantitative model of the post-synaptic proteome

    Sorokina, O., Sorokin, A. & Armstrong, J. D., 2011, In : Molecular BioSystems. 7, 10, p. 2813-2823 11 p.

    Research output: Contribution to journalArticle

  88. Towards a Neural Statistician

    Edwards, H. & Storkey, A., 26 Apr 2017, 5th International Conference on Learning Representations (ICLR 2017). 13 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  89. Towards NeuroML: model description methods for collaborative modelling in neuroscience.

    Goddard, N., Hucka, M., Howell, F., Cornelis, H., Shankar, K. & Beeman, D., 1 Aug 2001, In : Philosophical Transactions of the Royal Society B: Biological Sciences. 356, 1412, p. 1209-1228 20 p.

    Research output: Contribution to journalArticle

  90. Towards Multimodal Deep Learning for Activity Recognition on Mobile Devices

    Radu, V., Lane, N. D., Bhattacharya, S., Mascolo, C., Marina, M. K. & Kawsar, F., 12 Sep 2016, Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. New York, NY, USA: ACM, p. 185-188 4 p. (UbiComp '16).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  91. Towards Low-Dimensionsal Proportional Myoelectric Control

    Krasoulis, A., Nazarpour, K. & Vijayakumar, S., 2015, Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. Institute of Electrical and Electronics Engineers (IEEE), p. 7155 - 7158 4 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  92. Toward data representation with spiking neurons

    Gutmann, M. & Aihara, K., 2008, In : Artificial Life and Robotics. 12, 1, p. 223-226 4 p.

    Research output: Contribution to journalArticle

  93. Toward a low-cost gait analysis system for clinical and free-living assessment

    Ladha, C., Din, S. D., Nazarpour, K., Hickey, A., Morris, R., Catt, M., Rochester, L. & Godfrey, A., 18 Oct 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers (IEEE), p. 1874-1877 4 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  94. Topographica: building and analyzing map-level simulations from Python, C/C++, MATLAB, NEST, or NEURON components

    Bednar, J. A., 2009, In : Frontiers in Neuroinformatics. 3, 8, p. 1-9 9 p., 8.

    Research output: Contribution to journalArticle

  95. Topographica: Computational Modeling of Neural Maps

    Ball, C. E. & Bednar, J. A., 2009.

    Research output: Contribution to conferencePoster

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