Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. 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

  2. Sparse, redundancy-reduced visual coding through self-organized lateral connections

    Miikkulainen, R., Bednar, J. A. & Choe, Y., Oct 2004.

    Research output: Contribution to conferenceAbstractpeer-review

  3. Spatial Quantification of Cytosolic Ca2+ Accumulation in Nonexcitable Cells: An Analytical Study

    López-Caamal, F., Oyarzún, D. A., Middleton, R. H. & García, M. R., 1 May 2014, In: IEEE/ACM Transactions on Computational Biology and Bioinformatics. 11, 3, p. 592-603 12 p.

    Research output: Contribution to journalArticlepeer-review

  4. Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

    Gutmann, M. U., Laparra, V., Hyvärinen, A. & Malo, J., 12 Feb 2014, In: PLoS ONE. 9, 2, 21 p., e86481.

    Research output: Contribution to journalArticlepeer-review

  5. Spatio-Temporal Inertial Measurements Feature Extraction Improves Hand Movement Pattern Recognition without Electromyography

    Khushaba, R. N., Krasoulis, A., Al-Jumaily, A. & Nazarpour, K., 29 Oct 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers (IEEE), p. 2108-2111 4 p.

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

  6. Speeding up the brain: when spatial facilitation translates into latency shortening

    Paradis, A-L., Morel, S., Seriès, P. & Lorenceau, J., 2012, In: Frontiers in Human Neuroscience. 6, p. 330

    Research output: Contribution to journalArticlepeer-review

  7. Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays

    Muthmann, J-O., Amin, H., Sernagor, E., Maccione, A., Panas, D., Berdondini, L., Bhalla, U. S. & Hennig, M., 18 Dec 2015, In: Frontiers in Neuroinformatics. 9, 21 p., 28.

    Research output: Contribution to journalArticlepeer-review

  8. Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

    Tully, P. J., Lindén, H., Hennig, M. H. & Lansner, A., 23 May 2016, In: PLoS Computational Biology. 12, 5, 35 p., e1004954.

    Research output: Contribution to journalArticlepeer-review

  9. 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 journalArticlepeer-review

  10. Spike-timing-dependent plasticity: common themes and divergent vistas

    Kepecs, A., van Rossum, M. C. W., Song, S. & Tegner, J., 2002, In: Biological Cybernetics. 87, p. 446-458 13 p.

    Research output: Contribution to journalArticlepeer-review