Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Deep learning-based artificial vision for grasp classification in myoelectric hands

    Ghazaei, G., Alameer, A., Degenaar, P., Morgan, G. & Nazarpour, K., 3 May 2017, In : Journal of Neural Engineering. 14, 3, 18 p., 036025 .

    Research output: Contribution to journalArticle

  2. Deep multimodal autoencoders for identifying latent representations of spike counts and local field potentials

    Onken, A., Yague, J. & Sakata, S., 27 Sep 2018.

    Research output: Contribution to conferencePoster

  3. Dendritic Spine Dynamics Regulate the Long-Term Stability of Synaptic Plasticity

    O'Donnell, C., Nolan, M. & Van Rossum, M., 9 Nov 2011, In : Journal of Neuroscience. 31, 45, p. 16142-16156 15 p.

    Research output: Contribution to journalArticle

  4. Dendritic spines as devices for synaptic metaplasticity

    O'Donnell, C., Nolan, M. & van Rossum, M. C. W., 2009.

    Research output: Contribution to conferencePoster

  5. Dendritic spines can stabilize synaptic weights

    O'Donnell, C., Nolan, M. & van Rossum, M. C. W., 2010.

    Research output: Contribution to conferencePoster

  6. Density Deconvolution with Normalizing Flows

    Dockhorn, T., Ritchie, J. A., Yu, Y. & Murray, I., 29 Jun 2020, (Accepted/In press) Second workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (ICML 2020). 8 p.

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

  7. Density of states of disordered systems

    van Rossum, M., Nieuwenhuizen, T. M., Hofstetter, E. & Schreiber, M., 1 May 1994, In : Physical Review B: Condensed Matter and Materials Physics. 49, p. 13377-13382 6 p.

    Research output: Contribution to journalArticle

  8. Derivative processes for modelling metabolic fluxes

    Žurauskiene, J., Kirk, P., Thorne, T., Pinney, J. & Stumpf, M., 1 Jul 2014, In : Bioinformatics. 30, 13, p. 1892-1898 7 p.

    Research output: Contribution to journalArticle

  9. Design constraints in an operon circuit for engineered control of metabolic networks

    Oyarzún, D. A. & Stan, G., 1 Dec 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC). p. 3608-3613 6 p.

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

  10. Design of a bistable switch to control cellular uptake

    Oyarzun, D. & Madalena, C., 6 Dec 2015, In : Journal of the Royal Society Interface. 12, 113, 15 p., 20150618.

    Research output: Contribution to journalArticle

  11. Design of the TRONCO BioConductor Package for TRanslational ONCOlogy

    Antoniotti, M., Caravagna, G., De Sano, L., Graudenzi, A., Mauri, G., Mishra, B. & Ramazzotti, D., 21 Oct 2016, In : The R Journal. 8, 2, p. 39-59 21 p.

    Research output: Contribution to journalArticle

  12. Design tradeoffs in a synthetic gene control circuit for metabolic networks

    Oyarzún, D. A. & Stan, G., 1 Jun 2012, 2012 American Control Conference (ACC). p. 2743-2748 6 p.

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

  13. Detecting Artifactual Events in Vital Signs Monitoring Data

    Lal, P., Williams, C. K. I., Georgatzis, K., Hawthorne, C., McMonagle, P., Piper, I. & Shaw, M., Oct 2016, Machine Learning for Healthcare Technologies. Clifton, D. A. (ed.). IET, p. 7-32 26 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  14. Detecting and Quantifying Topographic Order in the Brain

    Yarrow, S., Razak, K., Seitz, A. R. & Series, P., 2013.

    Research output: Contribution to conferencePoster

  15. Detecting and Quantifying Topography in Neural Maps

    Yarrow, S., Seitz, A. R., Seriès, P. & Razak, K., 5 Feb 2014, In : PLoS Neglected Tropical Diseases. 9, 2, 14 p., e87178.

    Research output: Contribution to journalArticle

  16. Detecting and reconstructing vascular trees in retinal images

    Jasiobedzki, P., Williams, C. K. I. & Lu, F., 1994, Proc. SPIE 2167, Medical Imaging 1994: Image Processing. p. 815-825 11 p.

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

  17. Detecting repeated cancer evolution from multi-region tumor sequencing data

    Caravagna, G., Giarratano, Y., Ramazzotti, D., Tomlinson, I., Graham, T. A., Sanguinetti, G. & Sottoriva, A., 31 Aug 2018, In : Nature Methods. 15, 9, p. 707-714 8 p.

    Research output: Contribution to journalArticle

  18. Detection of Substructure in Receptive Fields of Retinal Ganglion Cells

    Onken, A., Liu, J. K., Delis, I., Panzeri, S. & Gollisch, T., 5 Sep 2014. 1 p.

    Research output: Contribution to conferenceAbstract

  19. Determining heat use in residential buildings using high resolution gas and domestic hot water monitoring

    Buswell, R. A., Marini, D., Webb, L. & Thomson, M., 2013, Proceedings of the 13th Conference of International Building Performance Simulation Association. Chambéry, France: International Building Performance Simulation Association, p. 2413-2420 8 p.

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

  20. Determining synaptic input properties from intra-cellular recordings in vivo

    Pelko, M., Puggioni, P., Van Rossum, M. & Bouscein, C., 2012.

    Research output: Contribution to conferencePoster

  21. Developing Complex Systems Using Evolved Pattern Generators

    Valsalam, V. K., Bednar, J. A. & Miikkulainen, R., 2007, In : IEEE Transactions on Evolutionary Computation. p. 181-198 18 p.

    Research output: Contribution to journalArticle

  22. Developing maps of complex cells in a computational model

    Antolik, J. & Bednar, J., 13 Jul 2008.

    Research output: Contribution to conferencePoster

  23. Developing maps of complex cells in a computational model of V1

    Antolik, J. & Bednar, J., 19 Nov 2008.

    Research output: Contribution to conferencePoster

  24. Developing the Technology for a Shared Demand Responsive Transport System at the University of Malta

    Attard, M., Muscat, A. & Camilleri, M., 8 Jan 2018.

    Research output: Contribution to conferencePaper

  25. Development of maps of simple and complex cells in the primary visual cortex

    Antolik, J. & Bednar, J. A., 2011, In : Frontiers in Computational Neuroscience. 5, 17

    Research output: Contribution to journalArticle

  26. Development of the ASHRAE Global Thermal Comfort Database II

    Földváry, V., Cheung, T., Zhang, H., de Dear, R., Parkinson, T., Arens, E., Chun, C., Schiavon, S., Luo, M., Brager, G., Li, P., Kaam, S., Adebamowo, M. A., Andamon, M. M., Babich, F., Bouden, C., Bukovianska, H., Candido, C., Cao, B., Carlucci, S. & 44 others, Cheong, D. K. W., Choi, J-H., Cook, M., Cropper, P., Deuble, M., Heidari, S., Indraganti, M., Jin, Q., Kim, H., Kim, J., Konis, K., Singh, M. K., Kwok, A., Lamberts, R., Loveday, D., Langevin, J., Manu, S., Moosmann, C., Nicol, F., Ooka, R., Oseland, N. A., Pagliano, L., Petráš, D., Rawal, R., Romero, R., Sekhar, C., Schweiker, M., Tartarini, F., Tanabe, S., Tham, K. W., Teli, D., Toftum, J., Toledo, L., Tsuzuki, K., De Vecchi, R., Wagner, A., Wang, Z., Wallbaum, H., Webb, L., Yang, L., Zhu, Y., Zhai, Y., Zhang, Y. & Zhou, X., Sep 2018, In : Building and Environment. 142, p. 502-512 11 p.

    Research output: Contribution to journalArticle

  27. Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies

    Belgrave, D. C. M., Granell, R., Simpson, A., Guiver, J., Bishop, C., Buchan, I. E., Henderson, J. & Custovic, A., 1 Oct 2014, In : PLoS ONE. 11, 10, 14 p., e1001748.

    Research output: Contribution to journalArticle

  28. Developmental organization of central neurons in the adult Drosophila ventral nervous system

    Shepherd, D., Sahota, V., Court, R., Williams, D. W. & Truman, J. W., 15 Oct 2019, In : Journal of Comparative Neurology. 527, 15, p. 2573-2598 72 p.

    Research output: Contribution to journalArticle

  29. Developments of the generative topographic mapping

    Bishop, C. M., Svensén, M. & Williams, C. K. I., Nov 1998, In : Neurocomputing. 21, 1–3, p. 203-224 22 p.

    Research output: Contribution to journalArticle

  30. Deviations from the Gaussian distribution of mesoscopic conductance fluctuations

    van Rossum, M., Lerner, I. V., Altshuler, B. L. & Nieuwenhuizen, T. M., 1 Feb 1997, In : Physical Review B. 55, p. 4710-4716 7 p.

    Research output: Contribution to journalArticle

  31. Device modeling of MgO-barrier tunneling magnetoresistors for hybrid spintronic-CMOS

    Zuo, S., Nazarpour, K. & Heidari, H., 1 Nov 2018, In : IEEE Electron Device Letters. 39, 11, p. 1784 - 1787 4 p.

    Research output: Contribution to journalArticle

  32. Dictionary of Computer Vision and Image Processing, 2nd Edition

    Fisher, B., Breckon, T. P., Dawson-Howe, K., Fitzgibbon, A., Roberston, C., Trucco, E. & Williams, C. K. I., Dec 2013, 2 ed. Wiley. 384 p.

    Research output: Book/ReportBook

  33. Dietary Salt Levels Affect Salt Preference and Learning in Larval Drosophila

    Russell, C., Wessnitzer, J., Young, J. M., Armstrong, J. D. & Webb, B., 1 Jun 2011, In : PLoS ONE. 6, 6, 8 p., e20100.

    Research output: Contribution to journalArticle

  34. Differences of split and non-split architectures emerged from modelling Chinese character pronunciation

    Hsiao, J. H. & Shillcock, R., 2005, Proceedings of the Twenty Seventh Annual Conference of the Cognitive Science Society. p. 989-994 6 p.

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

  35. Different binocular foveal strategies in reading: younger and older readers

    Paul, S. A., Obregón, M. & Shillcock, R., 1 Aug 2013.

    Research output: Contribution to conferencePoster

  36. Differential vergence movements in reading Chinese and English: Greater fixation-initial binocular disparity is advantageous in reading the denser orthography

    Hsiao, Y., Shillcock, R., Obregón, M., Kreiner, H., Roberts, M. A. J. & McDonald, S., 11 Jul 2017, In : Quarterly Journal of Experimental Psychology. p. 1-33 33 p.

    Research output: Contribution to journalArticle

  37. Digging Into Self-Supervised Monocular Depth Estimation

    Godard, C., Mac Aodha, O., Firman, M. & Brostow, G. J., 27 Feb 2020, 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Institute of Electrical and Electronics Engineers (IEEE), p. 3827-3837 11 p.

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

  38. Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation

    Liu, S., Quinn, J. A., Gutmann, M. U., Suzuki, T. & Sugiyama, M., 1 Mar 2014, In : Neural Computation. 26, 6, p. 1169-1197 29 p.

    Research output: Contribution to journalArticle

  39. Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation

    Liu, S., Quinn, J. A., Gutmann, M. U. & Sugiyama, M., Sep 2013, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II. Springer Berlin Heidelberg, p. 596-611 16 p. (Lecture Notes in Computer Science; vol. 8189).

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

  40. Directional-unit boltzmann machines

    Zemel, R. S., Williams, C. K. I. & Mozer, M. C., 1993, Advances in Neural Information Processing Systems 5. Morgan Kaufmann Publishers Inc., p. 172-179 8 p.

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

  41. Disciplinary Differences in Blended Learning Design: A Network Analytic Study

    Whitelock-Wainwright, A., Tsai, Y-S., Lyons, K., Khalif, S., Bryant, M., Ryan, K. & Gasevic, D., 23 Mar 2020, LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. Association for Computing Machinery (ACM), p. 579-588 10 p.

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

  42. Discovering hidden features with Gaussian processes regression

    Vivarelli, F. & Williams, C. K. I., 1999, Advances in Neural Information Processing Systems 11 (NIPS 1998). MIT Press, p. 613-619 7 p.

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

  43. Discriminative Mixtures of Sparse Latent Fields for Risk Management

    Agakov, F. V., Orchard, P. & Storkey, A. J., 2012, Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12). Lawrence, N. D. & Girolami, M. A. (eds.). Vol. 22. p. 10-18 9 p.

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

  44. Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring

    Georgatzis, K. & Williams, C. K. I., 2015, 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015). 10 p. 77

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

  45. Disruption of early events in thalamocortical tract formation in mice lacking the transcription factors Pax6 or Foxg1

    Pratt, T., Quinn, J. C., Simpson, T. I., West, J. D., Mason, J. O. & Price, D. J., 2002, In : Journal of Neuroscience. 22, 19, p. 8523-31 9 p.

    Research output: Contribution to journalArticle

  46. Dissecting Magnetar Variability with Bayesian Hierarchical Models

    Huppenkothen, D., Brewer, B. J., Hogg, D. W., Murray, I., Frean, M., Elenbaas, C., Watts, A. L., Levin, Y., Van Der Horst, A. J. & Kouveliotou, C., 1 Sep 2015, In : Astrophysical Journal. 810, 1, 21 p.

    Research output: Contribution to journalArticle

  47. Dissecting the Shared and Context-Dependent Pathways Mediated by the p140Cap Adaptor Protein in Cancer and in Neurons

    Chapelle, J., Sorokina, O., McLean, C., Salemme, V., Alfieri, A., Angelini, C., Morellato, A., Adrait, A., Menna, E., Matteoli, M., Couté, Y., Ala, U., Turco, E., Defilippi, P. & Armstrong, J. D., 15 Oct 2019, In : Frontiers in Cell and Developmental Biology. 7, 19 p., 222.

    Research output: Contribution to journalArticle

  48. Dissociation between sustained single-neuron spiking β-rhythmicity and transient β-LFP oscillations in primate motor cortex

    Rule, M. E., Vargas-Irwin, C. E., Donoghue, J. P. & Truccolo, W., 18 Jan 2017, In : Journal of Neurophysiology. p. 1-52 52 p.

    Research output: Contribution to journalArticle

  49. Distilling Intractable Generative Models

    Papamakarios, G. & Murray, I., 2 Aug 2015, (Accepted/In press) Probabilistic Integration Workshop at the Neural Information Processing Systems Conference, 2015. 5 p.

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

  50. Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex

    Khan, A. G., Poort, J., Chadwick, A., Blot, A., Sahani, M., Mrsic-Flogel, T. D. & Hofer, S. B., 1 Jun 2018, In : Nature Neuroscience. 21, 6, p. 851-859 9 p.

    Research output: Contribution to journalArticle

  51. Distributed inference and query processing for RFID tracking and monitoring

    Cao, Z., Sutton, C., Diao, Y. & Shenoy, P., Feb 2011, In : Proceedings of the VLDB Endowment (PVLDB). 4, 5, p. 326-337 12 p.

    Research output: Contribution to journalArticle

  52. Diverse Ensembles Improve Calibration

    Cooper Stickland, A. & Murray, I., 1 Jul 2020, (Accepted/In press) ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning. 6 p.

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

  53. Does Morphology Influence Temporal Plasticity?

    van Ooyen, A. & Sterratt, D., 2002, Artificial Neural Networks — ICANN 2002: International Conference Madrid, Spain, August 28–30, 2002 ProceedingsInternational Conference Madrid, Spain, August 28–30, 2002 Proceedings. Dorronsoro, J. (ed.). Springer Berlin Heidelberg, p. 186-191 6 p. (Lecture Notes in Computer Science; vol. 2415).

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

  54. Double objective optimal multivariable ripple-free deadbeat control

    Salgado, M. E. & Oyarzún, D. A., 2007, In : International journal of control. 80, 5, p. 763-773 11 p.

    Research output: Contribution to journalArticle

  55. Drosophila circadian rhythms in semi-natural environments; the summer afternoon component is not an artifact and requires TrpA1 channels

    Green, E. W., O'Callaghan, E. K., Hansen, C. N., Bastianello, S., Bhutani, S., Vanin, S., Armstrong, J. D., Costa, R. & Kyriacou, C. P., 2015, In : Proceedings of the National Academy of Sciences (PNAS). 112, 28, p. 8702-8707 6 p.

    Research output: Contribution to journalArticle

  56. Drosophila melanogaster: The model organism of choice for the complex biology of multicellular organisms

    Beckingham, K. M., Armstrong, D., Texada, M. J., Munjaal, R. & Baker, D. A., 2005, In : Gravitational and Space Biology. 18, 2

    Research output: Contribution to journalArticle

  57. Dying mRNA Tells a Story of Its Life

    Wallace, EW. J. & Drummond, D. A., 4 Jun 2015, In : Cell. 161, 6, p. 1246 - 1248 3 p.

    Research output: Contribution to journalArticle

  58. Dynamic Conditional Random Fields for Jointly Labeling Multiple Sequences

    McCallum, A., Rohanimanesh, K. & Sutton, C., 2003, NIPS Workshop on Syntax, Semantics, and Statistics. 8 p.

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

  59. Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data

    Sutton, C., Rohanimanesh, K. & McCallum, A., 2004, Proceedings of the Twenty-first International Conference on Machine Learning. New York, NY, USA: ACM, 8 p.

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

  60. Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data

    Sutton, C., McCallum, A. & Rohanimanesh, K., 1 May 2007, In : Journal of Machine Learning Research. 8, p. 693-723 31 p.

    Research output: Contribution to journalArticle

  61. Dynamic Evaluation of Neural Sequence Models

    Krause, B., Kahembwe, E., Murray, I. & Renals, S., 15 Jul 2018, Proceedings of the 35th International Conference on Machine Learning. Dy, J. & Krause, A. (eds.). Stockholmsmässan, Stockholm Sweden: PMLR, p. 2766-2775 10 p. (Proceedings of Machine Learning Research; vol. 80).

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

  62. Dynamic Evaluation of Transformer Language Models

    Krause, B., Mbabazi, E., Murray, I. & Renals, S., 17 Apr 2019, 6 p.

    Research output: Working paper

  63. Dynamic Positional Trees for Structural Image Analysis

    Storkey, A. & Williams, C. K. I., 2001, In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics. p. 298-304 7 p.

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

  64. Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules

    J. Storkey, A., 16 Jan 2000, UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence. Morgan Kaufmann, p. 566-573 8 p.

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

  65. Dynamic Trees: Learning to Model Outdoor Scenes

    Adams, N. J. & Williams, C. K. I., 2002, Computer Vision — ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28–31, 2002 Proceedings, Part IV. Springer Berlin Heidelberg, p. 82-96 15 p. (Lecture Notes in Computer Science; vol. 2353).

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

  66. Dynamic competition between contour integration and contour segmentation probed with moving stimuli

    Lorenceau, J., Giersch, A. & Seriés, P., 1 Jan 2005, In : Vision Research. 45, 1, p. 103-116

    Research output: Contribution to journalArticle

  67. Dynamic metabolic control: towards precision engineering of metabolism

    Liu, D., Mannan, A. A., Han, Y., Oyarzun, D. & Zhang, F., 1 Jul 2018, In : Journal of Industrial Microbiology & Biotechnology. 45, 7, p. 535-543 9 p.

    Research output: Contribution to journalArticle

  68. Dynamic optimization of metabolic networks coupled with gene expression

    Waldherr, S., Oyarzún, D. A. & Bockmayr, A., 21 Jan 2015, In : Journal of Theoretical Biology. 365, p. 469-485 17 p.

    Research output: Contribution to journalArticle

  69. Dynamic structure super-resolution

    Storkey, A. J., 2002, Advances in Neural Information Processing Systems 15 (NIPS 2002). MIT Press, p. 1295-1302 8 p.

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

  70. Dynamic trees for image modelling

    Adams, N. J. & Williams, C. K. I., Sep 2003, In : Image and vision computing. 21, 10, p. 865-877 13 p.

    Research output: Contribution to journalArticle

  71. Dynamical Constraints on Using Precise Spike Timing to Compute in Recurrent Cortical Networks

    Banerjee, A., Seriés, P. & Pouget, A., 1 Apr 2008, In : Neural Computation. 20, 4, p. 974-993

    Research output: Contribution to journalArticle

  72. Dynamical Inference from a Kinematic Snapshot: The Force Law in the Solar System

    Bovy, J., Murray, I. & Hogg, D. W., Mar 2010, In : Astrophysical Journal. 711, 2, p. 1157-1167 11 p.

    Research output: Contribution to journalArticle

  73. Dynamics and Robustness of Familiarity Memory

    Cortes, J. M., Greve, A., Barrett, A. B. & van Rossum, M. C. W., 20 Oct 2009, In : Neural Computation. 22, 2, p. 448-466 19 p.

    Research output: Contribution to journalArticle

  74. Dynamics of Elongation Factor 2 Kinase Regulation in Cortical Neurons in Response to Synaptic Activity

    Kenney, J. W., Sorokina, O., Genheden, M., Sorokin, A., Armstrong, J. D. & Proud, C. G., 18 Feb 2015, In : Journal of Neuroscience. 35, 7, p. 3034-3047 14 p.

    Research output: Contribution to journalArticle

  75. Dynamics of a starvation-to-surfeit shift: a transcriptomic and modelling analysis of the bacterial response to zinc reveals transient behaviour of the Fur and SoxS regulators

    Graham, A. I., Sanguinetti, G., Bramall, N., McLeod, C. W. & Poole, R. K., 2012, In : Microbiology. 158, 1, p. 284-292 9 p.

    Research output: Contribution to journalArticle

  76. Dynamics of complex feedback architectures in metabolic pathways

    Chaves, M. & Oyarzun, D., 1 Jan 2019, In : Automatica. 99, p. 323-332 10 p.

    Research output: Contribution to journalArticle

  77. ELFI: Engine for Likelihood-Free Inference

    Lintusaari, J., Vuollekoski, H., Kangasrääsiö, A., Skyten, K., Järvenpää, M., Marttinen, P., Gutmann, M., Vehtari, A., Corander, J. & Kaski, S., 20 Jun 2018, (Accepted/In press) In : Journal of Machine Learning Research. 8 p.

    Research output: Contribution to journalArticle

  78. EM optimization of latent-variable density models

    Bishop, C. M., Svensén, M. & Williams, C. KI., 1996, Advances in Neural Information Processing Systems 8 (NIPS 1995). MIT Press, p. 465-471 7 p.

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

  79. EMG prediction from motor cortical recordings via a nonnegative point-process filter

    Nazarpour, K., Ethier, C., Paninski, L., Rebesco, J. M., Miall, R. C. & Miller, L. E., 1 Jul 2012, In : IEEE Transactions on Biomedical Engineering. 59, 7, p. 1829 - 1838

    Research output: Contribution to journalArticle

  80. EMG-Based Hand Gesture Classification with Long Short-Term Memory Deep Recurrent Neural Networks

    Jabbari, M., Khushaba, R. N. & Nazarpour, K., 27 Aug 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). Institute of Electrical and Electronics Engineers (IEEE), p. 3302-3305 4 p.

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

  81. EXOTica: An Extensible Optimization Toolset for Prototyping and Benchmarking Motion Planning and Control

    Ivan, V., Yang, Y., Merkt, W., Camilleri, M. P. & Vijayakumar, S., 2019, Robot Operating System (ROS): The Complete Reference (Volume 3). Koubaa, A. (ed.). Cham: Springer International Publishing, p. 211-240 30 p. (Studies in Computational Intelligence ; vol. 778).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  82. Early-Stage Waves in the Retinal Network Emerge Close to a Critical State Transition between Local and Global Functional Connectivity

    Hennig, M. H., Adams, C., Willshaw, D. & Sernagor, E., Jan 2009, In : The Journal of Neuroscience. 29, 4, p. 1077-1086 10 p.

    Research output: Contribution to journalArticle

  83. Edge co-occurrences are sufficient to categorize natural versus animal images

    Perrinet, L. U. & Bednar, J. A., 22 Aug 2014, In : Journal of Vision. 14, 10, p. 1310-1310

    Research output: Contribution to journalArticle

  84. Editorial: Special Issue on Probabilistic Models for Image Understanding

    Triggs, B. & Williams, C. K. I., 10 Jun 2010, In : International Journal of Computer Vision. 88, 2, p. 145-146 2 p.

    Research output: Contribution to journalEditorial

  85. Effect of Parkinson’s disease and two therapeutic interventions on muscle activity during walking: a systematic review

    Islam, A., Alcock, L., Nazarpour, K., Rochester, L. & Pantall, A., 9 Sep 2020, In : npj Parkinson's Disease. 6, 16 p., 22.

    Research output: Contribution to journalArticle

  86. Effect of User Practice on Prosthetic Finger Control With an Intuitive Myoelectric Decoder

    Krasoulis, A., Vijayakumar, S. & Nazarpour, K., 10 Sep 2019, In : Frontiers in Neuroscience. 13, 16 p., 891.

    Research output: Contribution to journalArticle

  87. Effect of downstream feedback on the achievable performance of feedback control loops for serial processes

    Oyarzun, D. & Silva, E., 2007, 2007 European Control Conference, ECC 2007. p. 1727-1733 7 p.

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

  88. Effects of Noise on the Spike Timing Precision of Retinal Ganglion Cells

    van Rossum, M. C. W., O'Brien, B. J. & Smith, R. G., 2003, In : Journal of Neurophysiology. 89, 5, p. 2406-2419 14 p.

    Research output: Contribution to journalArticle

  89. Effects of ambient luminance on retinal information coding

    Alizadeh, A., Onken, A., Mutter, M., Münch, T. & Panzeri, S., 14 Sep 2017. 2 p.

    Research output: Contribution to conferenceAbstract

  90. Effects of fixational eye movements on retinal ganglion cell responses: a modelling study

    Hennig, M. H. & Wörgötter, F., 2007, In : Frontiers in Computational Neuroscience. 1, 2, p. 69-84

    Research output: Contribution to journalArticle

  91. Efficient Bayesian Experimental Design for Implicit Models

    Kleinegesse, S. & 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. 476-485 10 p. (Proceedings of Machine Learning Research; vol. 89).

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

  92. Efficient acquisition rules for model-based approximate Bayesian computation

    Järvenpää, M., Gutmann, M., Pleska, A., Vehtari, A. & Marttinen, P., 18 Sep 2018, In : Bayesian analysis. 28 p.

    Research output: Contribution to journalArticle

  93. Efficient low-order approximation of first-passage time distributions

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    Research output: Contribution to journalArticle

  94. Efficient stochastic simulation of systems with multiple time scales via statistical abstraction

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

  95. Elemental and non-elemental olfactory learning in Drosophila

    Young, J., Wessnitzer, J., Armstrong, D. & Webb, B., 2011, In : Neurobiology of Learning and Memory. 96, 2, p. 339-352 14 p.

    Research output: Contribution to journalArticle

  96. Elliptical slice sampling

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

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