Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Systematic biases in early ERP and ERF components as a result of high-pass filtering

    Acunzo, D., MacKenzie, G. & van Rossum, M. C. W., 2012, In : Journal of Neuroscience Methods. 209, 1, p. 212-218 7 p.

    Research output: Contribution to journalArticle

  2. The Gaussian process density sampler

    Adams, R. P., Murray, I. & MacKay, D. J. C., 2009, Advances in Neural Information Processing Systems 21: 22nd Annual Conference on Neural Information Processing Systems 2008. Koller, D. (ed.). Curran Associates Inc, p. 9-16 8 p.

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

  3. Comparing Mean Field and Exact EM in Tree Structured Belief Networks

    Adams, N. J., Williams, C. K. I. & Storkey, A. J., 2001, In Fourth International ICSC Symposium on Soft Computing and Intelligent Systems for Industry. ICSC-NAISO Adademic. Thoemmes Press, 6 p.

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

  4. MFDTs: Mean field dynamic trees

    Adams, NJ., Storkey, AJ., Ghahramani, Z. & Williams, CKI., 2000, 15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS. Sanfeliu, A., Villanueva, JJ., Vanrell, M., Alquezar, R., Huang, T. & Serra, J. (eds.). LOS ALAMITOS: Institute of Electrical and Electronics Engineers (IEEE), p. 147-150 4 p. (INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION).

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

  5. 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

  6. 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

  7. 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

  8. Incorporating side information into probabilistic matrix factorization using Gaussian Processes

    Adams, R. P., Dahl, G. E. & Murray, I., 2010, Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010). 9 p.

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

  9. SDTs: sparse dynamic trees

    Adams, N. J. & Williams, C. K. I., 1999, Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470). IET, p. 527-532 vol.2 6 p.

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

  10. Sparse instrumental variables (SPIV) for genome-wide studies

    Agakov, F. V., McKeigue, P., Krohn, J. & Storkey, A., 1 Jan 2010, Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  11. 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

  12. 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

  13. Synaptic interactome mining reveals p140Cap as a new hub for PSD proteins involved in psychiatric and neurological disorders

    Alfieri, A., Sorokina, O., Adrait, A., Angelini, C., Russo, I., Morellato, A., Matteoli, M., Menna, E., Erba, E. B., McLean, C., Armstrong, J. D., Ala, U., Buxbaum, J. D., Brusco, A., Couté, Y., De Rubeis, S., Turco, E. & Defilippi, P., 30 Jun 2017, In : Frontiers in Molecular Neuroscience. p. 1-15 15 p., 212.

    Research output: Contribution to journalArticle

  14. 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

  15. Mining Idioms from Source Code

    Allamanis, M. & Sutton, C., 2014, Proceedings of the 22Nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. New York, NY, USA: ACM, p. 472-483 12 p.

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

  16. A Survey of Machine Learning for Big Code and Naturalness

    Allamanis, M., Barr, E. T., Devanbu, P. & Sutton, C., 31 Jul 2018, In : ACM Computing Surveys. 51, 4, 36 p., 81.

    Research output: Contribution to journalArticle

  17. Learning Natural Coding Conventions

    Allamanis, M., Barr, E. T., Bird, C. & Sutton, C., 2014, Proceedings of the 22Nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. New York, NY, USA: ACM, p. 281-293 13 p.

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

  18. 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

  19. Learning Continuous Semantic Representations of Symbolic Expressions

    Allamanis, M., Chanthirasegaran, P., Kohli, P. & Sutton, C., 11 Aug 2017, The 34th International Conference on Machine Learning (ICML 2017). Sydney, Australia: PMLR, Vol. 70. p. 80-88 9 p.

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

  20. Suggesting Accurate Method and Class Names

    Allamanis, M., Barr, E. T., Bird, C. & Sutton, C., 2015, Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. ACM, p. 38-49 12 p.

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

  21. Mining source code repositories at massive scale using language modeling

    Allamanis, M. & Sutton, C., 2013, Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on. Institute of Electrical and Electronics Engineers (IEEE), p. 207-216 10 p.

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

  22. Mining Semantic Loop Idioms

    Allamanis, M., Barr, E. T., Bird, C., Devanbu, P., Marron, M. & Sutton, C., 1 Jul 2018, In : IEEE Transactions on Software Engineering. 44, 7, 18 p.

    Research output: Contribution to journalArticle

  23. A Convolutional Attention Network for Extreme Summarization of Source Code

    Allamanis, M., Peng, H. & Sutton, C., 24 Jun 2016, Proceedings of The 33rd International Conference on Machine Learning, PMLR. New York, United States: PMLR, Vol. 48. p. 2091-2100 10 p.

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

  24. Object localisation using the Generative Template of Features

    Allan, M. & Williams, C. K. I., Jul 2009, In : Computer Vision and Image Understanding. 113, 7, p. 824-838 15 p.

    Research output: Contribution to journalArticle

  25. Harmonising chorales by probabilistic inference

    Allan, M. & Williams, C. K. I., 2005, Advances in Neural Information Processing Systems 17 (NIPS 2004). MIT Press, p. 25-32 8 p.

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

  26. Fast Learning of Sprites using Invariant

    Allan, M., Titsias, M. K. & Williams, C. K. I., 2005, Proceedings of the British Machine Vision Conference 2005. BMVA Press, 10 p.

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

  27. The Effects of Ectopic White and Transformer Expression on Drosophila Courtship Behavior

    An, X., Armstrong, J. D., Kaiser, K. & O'Dell, K. M. C., 2000, In : Journal of neurogenetics. 14, 4, p. 227-243 17 p.

    Research output: Contribution to journalArticle

  28. Reconciling models of V1 development and adult function

    Antolik, J., Law, J. S. & Bednar, J., 2009.

    Research output: Contribution to conferencePoster

  29. Modeling the development of maps of complex cells in V1

    Antolik, J. & Bednar, J., 2007.

    Research output: Contribution to conferenceAbstract

  30. 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

  31. Developing maps of complex cells in a computational model

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

    Research output: Contribution to conferencePoster

  32. Modelling surround modulation in the LGN

    Antolik, J. & Bednar, J., Nov 2007.

    Research output: Contribution to conferencePoster

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

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

    Research output: Contribution to conferencePoster

  34. 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

  35. How to train your MAML

    Antoniou, A., Edwards, H. & Storkey, A., 2019. 11 p.

    Research output: Contribution to conferencePaper

  36. Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks

    Antoniou, A., Storkey, A. & Edwards, H., 27 Sep 2018, Artificial Neural Networks and Machine Learning – ICANN 2018. Rhodes, Greece, p. 594-603 10 p. (Lecture Notes in Computer Science; vol. 11141).

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

  37. Sex, Flies and no Videotape

    Armstrong, D., Baker, D. A., Heward, J. A. & Lukins, T. C., 2005, 5th International Conference on Methods and Techniques in Behavioral Research.

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

  38. A general mathematical model of transduction events in mechano-sensory stretch receptors

    Armstrong, J. D., Jarman, A. P. & Suslak, T., 2011, In : Network: Computation in Neural Systems. 22, 1-4, p. 133-42 10 p.

    Research output: Contribution to journalArticle

  39. Gravitaxis in Drosophila melanogaster: a forward genetic screen

    Armstrong, J. D., Texada, M. J., Munjaal, R., Baker, D. A. & Beckingham, K. M., 2006, In : Genes Brain and Behavior. 5, 3, p. 222-239 18 p.

    Research output: Contribution to journalArticle

  40. Evolution of the Cognitive Proteome: From Static to Dynamic Network Models

    Armstrong, J. D. & Sorokina, O., 2012, Advances in Systems Biology. Goryanin, I. I. & Goryachev, A. B. (eds.). Springer New York, p. 119-134 16 p. (Advances in Experimental Medicine and Biology; vol. 736).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  41. 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

  42. Metamorphosis of the Mushroom Bodies; Large-Scale Rearrangements of the Neural Substrates for Associative Learning and Memory in Drosophila

    Armstrong, J. D., de Belle, J. S., Wang, Z. & Kaiser, K., 1998, In : Learning and Memory. 5, 1, p. 102-114 13 p.

    Research output: Contribution to journalArticle

  43. 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

  44. Neuroinformatics in Model Organisms

    Armstrong, D., Goddard, N. & Shepherd, D., 2003, In : Journal of neurogenetics. 17, 2-3, p. 103–116 14 p.

    Research output: Contribution to journalArticle

  45. Evolution of the synapse proteome

    Armstrong, J. D., Malik, B., Pocklington, A., Emes, R. & Grant, S., 2009, In : Journal of neurogenetics. 23, p. S34-S35 2 p.

    Research output: Contribution to journalMeeting abstract

  46. Flytracker

    Armstrong, D., Baker, D. A. & Heward, J. A., 2008, Patent No. PCT/GB06/001113

    Research output: Patent

  47. Studying neuronal function using the Drosophila genetic system

    Armstrong, D. & Goodwin, S. F., 2006, Molecular Biology of the Neuron. p. 1-14 15 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  48. Reconstructing protein complexes: From proteomics to systems biology

    Armstrong, J. D., Pocklington, A. J., Cumiskey, M. A. & Grant, S. G. N., 2006, In : Proteomics. 6, 17, p. 4724-4731 8 p.

    Research output: Contribution to journalArticle

  49. 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

  50. TFInfer: a tool for probabilistic inference of transcription factor activities

    Asif, H. M. S., Rolfe, M. D., Green, J., Lawrence, N. D., Rattray, M. & Sanguinetti, G., Oct 2010, In : Bioinformatics. 26, 20, p. 2635-2636 2 p.

    Research output: Contribution to journalArticle

  51. Large-scale learning of combinatorial transcriptional dynamics from gene expression

    Asif, H. M. S. & Sanguinetti, G., 1 May 2011, In : Bioinformatics. 27, 9, p. 1277-1283 7 p.

    Research output: Contribution to journalArticle

  52. 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

  53. Data-Intensive Research Workshop (15-19 March 2010) Report

    Atkinson, M., Roure, D. D., van Hemert, J., Jha, S., McNally, R., Mann, R., Viglas, S. & Williams, C. K. I., 1 May 2010, University of Edinburgh. 88 p.

    Research output: Book/ReportOther report

  54. Assessing mouse behaviour throughout the light/dark cycle using automated in-cage analysis tools

    Bains, R. S., Sillito, R. R., Armstrong, J. D., Cater, H. L., Banks, G. & Nolan, P. M., 26 Apr 2017, In : Journal of Neuroscience Methods. 32 p.

    Research output: Contribution to journalArticle

  55. Analysis of individual mouse activity in group housed animals of different inbred strains using a novel automated home cage analysis system.

    Bains, R. S., Cater, H. L., Sillito, R. R., Chartsias, A., Sneddon, D., Concas, D., Keskivali-Bond, P., Lukins, T. C., Wells, S., Acevedo Arozena, A., Nolan, P. M. & Armstrong, J. D., 10 Jun 2016, In : Frontiers in behavioral neuroscience. 10, 106, 22 p.

    Research output: Contribution to journalArticle

  56. Functional dissection of the neural substrates for gravitaxic maze behavior in Drosophila melanogaster

    Baker, D., Beckingham, K. M. & Armstrong, J. D., 2007, In : Journal of Comparative Neurology. 501, 5, p. 756-764 9 p.

    Research output: Contribution to journalArticle

  57. Topographica: Computational Modeling of Neural Maps

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

    Research output: Contribution to conferencePoster

  58. Motion aftereffects in a self-organizing model of the primary visual cortex

    Ball, C. & Bednar, J., 2006, p. 34.

    Research output: Contribution to conferencePoster

  59. Large Developing Receptive Fields Using a Distributed and Locally Reprogrammable Address-Event Receiver

    Bamford, S., Murray, A. & Willshaw, D. J., Feb 2010, In : IEEE Transactions on Neural Networks. 21, 2, p. 286-304 19 p.

    Research output: Contribution to journalArticle

  60. Synaptic rewiring for topographic mapping and receptive field development

    Bamford, S., Murray, A. & Willshaw, D. J., May 2010, In : Neural Networks. 23, 4, p. 517-527 11 p.

    Research output: Contribution to journalArticle

  61. Spike-timing-dependent plasticity with weight dependence evoked from physical constraints

    Bamford, S., Murray, A. F. & Willshaw, D. J., Aug 2012, In : IEEE Transactions on Biomedical Circuits and Systems. 6, 4, p. 385-98 14 p.

    Research output: Contribution to journalArticle

  62. Large Developing Axonal Arbors Using a Distributed and Locally-Reprogrammable Address-Event Receiver

    Bamford, S., Murray, A. & Willshaw, D. J., 1 Jun 2008, IEEE International Joint Conference on Neural Networks (IJCNN). p. 1464-1471 8 p.

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

  63. Synaptic Rewiring for Topographic Map Formation

    Bamford, S., Murray, A. & Willshaw, D. J., 3 Sep 2008, International Conference on Artificial Neural Networks (ICANN). p. 218-227 10 p.

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

  64. 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

  65. Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo

    Barber, D. & Williams, C. K. I., 1997, Advances in Neural Information Processing Systems 9. MIT Press, p. 340-346 7 p.

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

  66. Restrictions on modularity - Does would prime timber?

    Bard, E. G. & Shillcock, R., 1992, In : International Journal of Psychology. 27, 3-4, p. 83-83 1 p.

    Research output: Contribution to journalArticle

  67. State Based Model of Long-Term Potentiation and Synaptic Tagging and Capture

    Barrett, A. B., Billings, G., Morris, R. G. M. & van Rossum, M. C. W., Jan 2009, In : PLoS Computational Biology. 5, 1, p. 1-12 12 p., e1000259.

    Research output: Contribution to journalArticle

  68. A Biophysical model of long-term potentiation and synaptic tagging

    Barrett, A., Billings, G., Morris, R. G. M. & van Rossum, M. C. W., 2007.

    Research output: Contribution to conferencePoster

  69. Optimal Learning Rules for Discrete Synapses

    Barrett, A. B. & van Rossum, M. C. W., Nov 2008, In : PLoS Computational Biology. 4, 11, p. 1-7 7 p., e1000230.

    Research output: Contribution to journalArticle

  70. Policy learning in Continuous-Time Markov Decision Processes using Gaussian Processes

    Bartocci, E., Bortolussi, L., Brázdil, T., Milios, D. & Sanguinetti, G., 1 Nov 2017, In : Performance Evaluation. 116, p. 84-100 28 p.

    Research output: Contribution to journalArticle

  71. Policy learning for time-bounded reachability in Continuous-Time Markov Decision Processes via doubly-stochastic gradient ascent

    Bartocci, E., Bortolussi, L., Brázdil, T., Milios, D. & Sanguinetti, G., 3 Aug 2016, Quantitative Evaluation of Systems: 13th International Conference, QEST 2016, Quebec City, QC, Canada, August 23-25, 2016, Proceedings. Springer International Publishing, p. 244-259 16 p. (Lecture Notes in Computer Science (LNCS); vol. 9826).

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

  72. Studying Emergent Behaviours in Morphogenesis Using Signal Spatio-Temporal Logic

    Bartocci, E., Bortolussi, L., Milios, D., Nenzi, L. & Sanguinetti, G., 2015, Hybrid Systems Biology: Fourth International Workshop, HSB 2015, Madrid, Spain, September 4-5, 2015. Revised Selected Papers. Abate, A. & Safránek, D. (eds.). Springer International Publishing, p. 156-172 17 p. (Lecture Notes in Computer Science; vol. 9271).

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

  73. Categorial mirror symmetry for K3 surfaces

    Bartocci, C., Bruzzo, U. & Sanguinetti, G., Oct 1999, In : Communications in Mathematical Physics. 206, 2, p. 265-272 8 p.

    Research output: Contribution to journalArticle

  74. Data-driven statistical learning of temporal logic properties

    Bartocci, E., Bortolussi, L. & Sanguinetti, G., 2014, Formal Modeling and Analysis of Timed Systems: 12th International Conference, FORMATS 2014, Florence, Italy, September 8-10, 2014. Proceedings. Springer International Publishing, p. 23-37 15 p. (Lecture Notes in Computer Science; vol. 8711).

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

  75. 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

  76. Centre Report 2019: Centre for Research in Digital Education

    Bayne, S., Ewins, A., Evans, P., Gallagher, M., Ghazali-Mohammed, Z., Manches, A., Odai, L., Knox, J., Menzies, J., O'shea, C., Plowman, L., Ross, J., Sowton, C., Robertson, J., Sheail, P., Tsai, Y-S., Wang, Y., Williamson, B., Farrell, K., Lawson, T. & 1 others, Priztker, R., 1 May 2019, The Centre for Research in Digital Education. 21 p.

    Research output: Book/ReportCommissioned report

  77. 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

  78. Genetics of Graviperception in Animals

    Beckingham, K. M., Texada, M. J., Baker, D. A., Munjaal, R. & Armstrong, J. D., 2005, In : Advances in genetics. 55, p. 105-145 41 p.

    Research output: Contribution to journalArticle

  79. Building a mechanistic model of the development and function of the primary visual cortex

    Bednar, J. A., Sep 2012, In : Journal of Physiology-Paris. 106, 5-6, p. 194-211 18 p.

    Research output: Contribution to journalArticle

  80. Modeling the visual cortex using the Topographica cortical map simulator

    Bednar, J., Choe, Y., De Paula, J., Miikkulainen, R. & Provost, J., 2005.

    Research output: Contribution to conferencePoster

  81. Hebbian Learning of the Statistical and Geometrical Structure of Visual Input

    Bednar, J. A., 2014, Neuromathematics of Vision: Lecture Notes in Morphogenesis. Springer Berlin Heidelberg, p. 335-366 Chapter 8. (Neuromathematics of Vision).

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

  82. Neural Maps: Their Function and Development

    Bednar, J. & Williams, C. K. I., Nov 2016, From Neuron to Cognition via Computational > Neuroscience. Arbib, M. & Bonaiuto, J. (eds.). MIT Press, p. 409-432 34 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  83. 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

  84. The Role of Internally Generated Neural Activity in Newborn and Infant Face Preferences

    Bednar, J., 2003, The Development of Face Processing in Infancy and Early Childhood: Current Perspectives. Pascalis, O. & Slater, A. (eds.). Nova Science Publishers, p. 133-142

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

  85. Self-Organization of Spatiotemporal Receptive Fields and laterally connected direction and orientation maps

    Bednar, J. A. & Miikkulainen, R., 2003, Neurocomputing. Thoemmes Press, p. 52-54 3 p.

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

  86. Neonatal Learning of Faces: Environmental and Genetic Influences

    Bednar, J. A. & Miikkulainen, R., 2002, Proceedings of the 24th Annual Conference of the Cognitive Science Society. p. 107-112 6 p.

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

  87. Internally-generated activity, non-episodic memory, and emotional salience in sleep

    Bednar, J. A., 1 Dec 2000, In : Behavioral and Brain Sciences. 23, 6, p. 908-909

    Research output: Contribution to journalArticle

  88. ImaGen: Generic library for 0D, 1D and 2D pattern distributions

    Bednar, J. A. & Ball, C. E., 2012.

    Research output: Contribution to conferencePoster

  89. Tilt Aftereffects In A Self-Organizing Model Of The Primary Visual Cortex

    Bednar, J. A. & Miikkulainen, R., 2000, In : Neural Computation. 12, p. 1721-1740 20 p.

    Research output: Contribution to journalArticle

  90. 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

  91. The Topographica cortical map simulator

    Bednar, J. A., Choe, Y., De Paula, J., Miikkulainen, R. & Provost, J., 2005.

    Research output: Contribution to conferencePoster

  92. 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

  93. Learning multi-whisker spatial-temporal cortical receptive fields from robotic whisker input

    Bednar, J., Wilson, S. P., Mitchinson, B., Pearson, M. & Prescott, T. J., Oct 2009.

    Research output: Contribution to conferencePoster

  94. A Neural Network Model of Visual Tilt Aftereffects

    Bednar, J. A. & Miikkulainen, R., 1997, In Proceedings of the 19th Annual Conference of the Cognitive Science Society. Shafto, M. G. & Langley, P. (eds.). Erlbaum, p. 37-42 6 p.

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

  95. Orthogonal coding of tactile stimulus features in a self-organising model of barrel cortex development

    Bednar, J., Wilson, S. P., Mitchinson, B. & Prescott, T. J., 2012.

    Research output: Contribution to conferencePoster

  96. Modeling Cortical Maps with Topographica

    Bednar, J. A., Choe, Y., Paula, J. D., Miikkulainen, R., Provost, J. & Tversky, T., 2004, In : Neurocomputing. 58-60, p. 1129-1135 7 p.

    Research output: Contribution to journalArticle

  97. Understanding Neural Maps with Topographica

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

    Research output: Contribution to journalArticle

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