Publications

This page lists publications by Computational and Theoretical Neuroscience Laboratory members since June 2007.

Scholarly Books

  1. M. D. McDonnell, N. G. Stocks, C. E. M. Pearce and D. Abbott. Stochastic Resonance: From Suprathreshold Stochastic Resonance to Stochastic Signal Quantisation. Cambridge University Press, 2008 (ISBN: 9780521882620).

Editorial Work

  1. M. D. McDonnell, J. H. Goldwyn, and B. Lindner. Neuronal stochastic variability: Influences on spiking dynamics and network activity.Frontiers in Computational Neuroscience, 10: Article Number 38, 2016.
  2. M. D. McDonnell, K. Boahen, A. Ijspeert, and T. J. Sejnowski. Engineering intelligent electronic systems based on computational neuroscience. Proceedings of the IEEE, 102:646-651, 2014.

    Journal Papers since June 2007

    1. L. J. Gunn, F. Chapeau-Blondeau, M. D. McDonnell, B. R. Davis, A. Allison, and D. Abbott. Too good to be true: when overwhelming evidence fails to convince. Proceedings of the Royal Society A 472: Article no. 20150748, 2016.
    2. L. Xu, F. Duan, D. Abbott, and M. D. McDonnell. Optimal weighted suprathreshold stochastic resonance with multigroup saturating sensors. Physica A: Statistical Mechanics and its Applications, 457:348-355, 2016.
    3. M. D. Tissera and M. D. McDonnell. Deep extreme learning machines: Supervised autoencoding architecture for classification. Neurocomputing, 174:42-49, 2016.
    4. P. E. Greenwood, M. D. McDonnell and L. M. Ward. Dynamics of gamma bursts in local field potentials. Neural Computation, 27:74-103, 2015.
    5. M. D. McDonnell, M. D. Tissera, T. Vladusich, A. van Schaik and J. Tapson. Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the ‘extreme learning machine’ algorithm. PLOS One, 10: Article Number e0134254, 2015.
    6. B. Zhou and M. D. McDonnell. Optimizing threshold levels for information transmission in binary threshold networks: independent multiplicative noise on each threshold. Physica A, 419:659-667, 2015.
    7. M. D. McDonnell, N. Iannella, M.-S. To, H. C. Tuckwell, J. Jost, B. S. Gutkin and L. M. Ward. A review of methods for identifying stochastic resonance in simulations of single neuron models. Network: Computation in Neural Systems, 26:35-71, 2015.
    8. L. Xu, T. Vladusich, F. Duan, L. J. Gunn, D. Abbott and M. D. McDonnell. Decoding suprathreshold stochastic resonance with optimal weights. Physics Letters A, 379: 2277-2283, 2015.
    9. T. Vladusich and M. D. McDonnell. A unified account of perceptual layering and surface appearance in terms of gamut relativity. PLOS One, 9: Article Number e113159, 2014.
    10. M. D. McDonnell and X. Gao. M-ary suprathreshold stochastic resonance: Generalization and scaling beyond binary threshold nonlinearities. EPL (Europhysics Letters), 108: Article Number 60003, 2014.
    11. B. Moezzi, N. Iannella and M. D. McDonnell. Modelling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics. Frontiers in Computational Neuroscience, 8: Article Number 163, 2014.
    12. M. D. McDonnell, O. N. Yaveroglu, B. A. Schmerl, N. Iannella and L. M. Ward. Motif-role-fingerprints: the building-blocks of motifs, clustering-coefficients and transitivities in directed networks. PLOS One, 9: Article Number e114503, 2014.
    13. T. Vladusich. Brightness scaling according to gamut relativity. Color Research and Application, 39, 463-465, 2014.
    14. X. Gao, D. B. Grayden and M. D. McDonnell. Stochastic information transfer from cochlear implant electrodes to auditory nerve fibers. Physical Review E, 90: Article Number 022722, 2014.
    15. M. D. McDonnell and L. M. Ward. Small modifications to network topology can induce stochastic bistable spiking dynamics in a balanced cortical model. PLOS One, 9: Article number e88254, 2014.
    16. N. Iannella, T. Launey, D. Abbott and S. Tanaka. A nonlinear cable framework for bidirectional synaptic plasticityPLOS One 9: Article Number e102601, 2014.  
    17. T. Vladusich. A unified account of gloss and lightness perception in terms of gamut relativity. Journal of the Optical Society of America A, 30:1568-1579, 2013.
    18. B. A. Schmerl and M. D. McDonnell. Channel-noise-induced stochastic facilitation in an auditory brainstem neuron model. Physical Review E, 88: Article Number 052722, 2013. Download a preprint from arxiv.
    19. M. D. McDonnell, F. Li, P.-O. Amblard and A. J. Grant. Optimal sensor selection for noisy binary detection in stochastic pooling networks. Physical Review E, 88: Article Number 022118, 2013.
    20. M. D. McDonnell, A. Mohan and C. Stricker. Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of short-term synaptic depression and facilitation. Frontiers in Computational Neuroscience, 7:Article Number 58, 2013.
    21. T. Vladusich. A re-interpretation of transparency perception in terms of gamut relativity. Journal of the Optical Society of America A, 30, 418-426, 2013.
    22. T. Vladusich. Gamut relativity: A new computational approach to brightness and lightness perception. Journal of Vision, 13(1):14, 1-21, 2013.
    23. A. Mohan, M. D. McDonnell and C. Stricker. Interaction of short-term depression and firing dynamics in shaping single neuron encoding. Frontiers in Computational Neuroscience, 7:Article Number 41, 2013.
    24. L. Kostal, P Lansky  and M. D. McDonnell. Metabolic cost of neuronal information in an empirical stimulus-response model. Biological Cybernetics, 107:355-365, 2013.
    25. B. J. Prettejohn, M. J. Berryman and M. D. McDonnell. A model of the affects of authority on consensus formation in adaptive networks: impact on network topology and robustness. Physica A 392:857–868, 2013.
    26. M. D. McDonnell, A. Mohan, C. Stricker and L. M. Ward. Input-rate modulation of gamma oscillations is sensitive to network topology, delays and short-term plasticity. Brain Research 1434:162-177, 2012.
    27. M. D. McDonnell and L. M. Ward. The benefits of noise in neural systems: bridging theory and experiment. Nature Reviews Neuroscience 12:415-426, 2011.
    28. M. D. McDonnell, S. Ikeda and J. H. Manton. An introductory review of information theory in the context of computational neuroscience. Biological Cybernetics 105:55-70, 2011.
    29. B. J. Prettejohn, M. J. Berryman and M. D. McDonnell. Methods for generating complex networks with selected structural properties for simulations: A review and tutorial for neuroscientists. Frontiers in Computational Neuroscience 5:11 (1-18), 2011.
    30. M. D. McDonnell, A. J. Grant, I. Land, B. N. Vellambi, D. Abbott and K. Lever Gain from the two-envelope problem via information asymmetry: On the suboptimality of randomized switching. Proceedings of the Royal Society A 467:2825-2851 2011.
    31. M. D. McDonnell, A. N. Burkitt, D. B. Grayden, H. Meffin and A. J. Grant A channel model for inferring the optimal number of electrodes for future cochlear implants. IEEE Transactions on Information Theory, 56:928-940 2010.
    32. M. D. McDonnell, N. G. Stocks and P. O. Amblard Communication of uncoded sensor measurements through nanoscale binary-node stochastic pooling networks. Nano Communication Networks. 1:209-223, 2010.
    33. M. D. McDonnell, P. O. Amblard and N. G. Stocks Bio-inspired communication: performance limits for information transmission and compression in stochastic pooling networks with binary quantizing nodes. Journal of Computational and Theoretical Nanoscience. Nanoscience 7:876-883, 2010.
    34. M. D. McDonnell and D. Abbott. What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology. PLoS Computational Biology, 5:e1000348 2009.
    35. M. D. McDonnell and N. G. Stocks. Supratheshold Stochastic Resonance. Scholarpedia, 4:6508 2009.
    36. A. P. Nikitin, N. G. Stocks, R. P. Morse and M. D. McDonnell. Neural population coding is optimized by discrete tuning curves. Physical Review Letters, 103:138101 2009.
    37. M. D. McDonnell and D. Abbott. Randomized switching in the two-envelope problem. Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences, 465:3309-3322 2009.
    38. M. D. McDonnell and A. P. Flitney. Signal acquisition via polarization modulation in single photon sources. Physical Review E (Rapid Publication), 80: 060102(R) 2009.
    39. M. D. McDonnell. Information capacity of stochastic pooling networks is achieved by discrete inputs. Physical Review E, 79:041107 2009.
    40. M. D. McDonnell, P. O. Amblard and N. G. Stocks. Stochastic pooling networks. Journal of Statistical Mechanics: Theory and Experiment, Article no. P01012:1-18, 2009.
    41. N. G. Stocks, A. P. Nikitin, M. D. McDonnell and R. P. Morse. The role of stochasticity in an information-optimal neural population code. Journal of Physics: Conference Series, (Invited paper), 197:012015, 2009.
    42. M. D. McDonnell and N. G. Stocks. Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations. Physical Review Letters, 101:058103, 2008.
    43. M. D. McDonnell, N. G. Stocks, and D. Abbott. Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance. Physical Review E, 75:061105, 2007.

    Expository Articles

    1. M. D. McDonnell. Is electrical noise useful?. Proceedings of the IEEE, 99:242-246, 2011 [Invited "Perspectives" article].
    2. M. D. McDonnell and R. P. Morse. Noise may be music to bionic ears. Australasian Science, 29:27-30, 2008.
    3. M. D. McDonnell. A biologically inspired approach to signal compression. SPIE Newsroom (online), DOI: 10.1117/2.1200702.0644, 2007.

    Book Chapters

    1. M. D. McDonnell. Applying stochastic signal quantization theory to the robust digitization of noisy analog signals. In V. In, P. Longhini, and A. Palacios (eds.) Applications of Nonlinear Dynamics: Model and Design of Complex Systems, Springer, pp. 249-262, 2009 (ISBN: 978-3540856313).

    Refereed Full-Length Conference Papers

    1. M. D. McDonnell and T. Vladusich. Enhanced image classification with a fast-learning shallow convolutional neural network. In Proc. International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, July 12-17, 2015.
    2. X. Gao, D. B. Grayden and M. D. McDonnell. Modeling electrode place discrimination in cochlear implants: Analysis of the influence of electrode array insertion depth. Proc 7th International IEEE EMBS Neural Engineering Conference (NER’15), Montpellier, France, April 22-24, 2015.
    3. X. Gao, D. B. Grayden and M. D. McDonnell. Inferring the dynamic range of electrode current by using an information theoretic model of cochlear implant stimulation. In Proc. IEEE Information Theory Workshop (ITW),
      Hobart, Tasmania, Australia, 2-5 November 2014, pp 347-351, 2014, Invited Paper.
    4. X. Gao, D. B. Grayden and M. D. McDonnell. Using convex optimisation to compute channel capacity in a channel model of cochlear implant stimulation. In Proc. IEEE International Symposium on Information Theory (ISIT). Honolulu, July, 2014, pp 2919-2923.
    5. D. E. Padilla and M. D. McDonnell. A neurobiologically plausible vector symbolic architecture. In Proc. Eighth IEEE International Conference on Semantic Computing, June 16-18 2014, pp 242-245.
    6. M. D. Tissera and M. D. McDonnell. Enabling ‘question answering’ in the MBAT vector symbolic architecture by exploiting orthogonal random matrices. In Proc. Eighth IEEE International Conference on Semantic Computing, June 16-18 2014, pp 171-174.
    7. S. Wang, W. Guo and M. D. McDonnell. Distance distributions for real cellular networks. In Proc. IEEE International Conference on Computer Communications (INFOCOM) Workshops, Toronto, Canada, 27 April-2 May, 2014, pp 181–182.
    8. S. Wang, W. Guo and M. D. McDonnell. Transmit pulse shaping for molecular communications. In Proc. IEEE International Conference on Computer Communications (INFOCOM) Workshops, Toronto, Canada, 27 April-2 May, 2014, pp 209–210.
    9. S. Wang, W. Guo, S. Qiu and M. D. McDonnell. Performance of macro-scale molecular communications with sensor cleanse time. In Proc. IEEE ICT-2014: 21st International Conference on Telecommunications, Lisbon, Portugal, May 5-7, 2014, pp 363–368.
    10. S. Wang, W. Guo and M. D. McDonnell. Downlink interference estimation without feedback for heterogeneous network interference avoidance. In Proc. IEEE ICT-2014: 21st International Conference on Telecommunications, Lisbon, Portugal, May 5-7, 2014, pp 82–87.
    11. M. D. McDonnell. Distributed bandpass filtering and signal demodulation in cortical network models. (Invited paper) In V. In, A. Palacios, and P. Longhini, editors, Proc. International Conference on Theory and Applications in Nonlinear Dynamics (ICAND 2012), Seattle, USA, 26 - 30 August 2012, Springer series on Understanding Complex Systems, pp. 155-166, 2014.
    12. D. E. Padilla, R. Brinkworth and M. D. McDonnell. Performance of a Hierarchical Temporal Memory Network in Noisy Sequence Learning. In Proc. IEEE CyberneticsCom 2013, Yogyakarta, Indonesia, 3-5 December 2013.
    13. M. D. McDonnell, and L. M. Ward. Identifying Positive Roles for Endogenous Stochastic Noise During Computation in Neural Systems (Invited paper). In Proc. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan, 3 - 7 July 2013, pp 5232-5235.
    14. X. Gao, D. G. Grayden and M. D. McDonnell. Information Theoretic Optimization of Cochlear Implant Electrode Usage Probabilities. In Proc. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan, 3 - 7 July 2013, pp 5974-5977.
    15. A. S. Moroz, M. D. McDonnell, A. N. Burkitt, D. B. Grayden and H. Meffin.
      Information theoretic inference of the optimal number of electrodes for future cochlear implants using a spiral cochlea model. In Proc. 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, California USA, 28 August - 1 September, 2012, pp. 2965-2968.
    16. B. J. Prettejohn and M. D. McDonnell. Effect of network topology in opinion formation models. In C. Guttman, F. Dignum, and M. Georgeff, editors, Proc. Collaborative Agents – REsearch and development (CARE 2009/2010), LNAI 6066, Springer-Verlag, pages 114-124, 2011.
    17. F. Li, M. D. McDonnell, P.-O. Amblard and A. J. Grant. Sensor selection for distributed detection via multiaccess channels. In Proc. Australian Communications Theory Workshop, Sydney, Feb 4-6, pages 77-82, 2009.
    18. M. D. McDonnell and N. G. Stocks. Optimal sigmoidal tuning curves for intensity encoding sensory neurons with quasi-Poisson variability. BMC Neuroscience, 9(Suppl 1):P117, 2008.
    19. M. D. McDonnell, P.-O. Amblard, and N. G. Stocks. Stochastic pooling networks: A biologically-inspired model for robust signal detection and compression. In D. Kearney, V. Nguyen, G. Gioiosa, and T. Hendtlass, editors, Proc IEEE Third International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2008), pages 75-81, 2008.
    20. M. D. McDonnell. Reliable communication and sensing via parallel redundancy in noisy digital receivers. In Proc. Australian Communications Theory Workshop (IEEE), Christchurch, New Zealand, Jan 30-Feb 1, pages 23-28, 2008 [Won best Paper prize].
    21. P. O. Amblard, S. Zozor, N. G. Stocks, and M. D. McDonnell. Pooling networks for a discrimination task: noise-enhanced detection. In Sergey M. Bezrukov, editor, Proc. SPIE Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems, volume 6602, Article No. 66020S, 2007 (Invited Paper).
    22. M. D. McDonnell. Signal estimation via averaging of coarsely quantised signals. In Proc. IEEE Information Decision and Control, Adelaide, 12-14 February, pages 100-105, 2007.
    23. M. D. McDonnell. Signal compression in biological sensory systems: Information theoretic performance limits. In D. V. Nicolau, D. Abbott, K. Kalantar-Zadeh, T. Di Matteo, and S. M. Bezrukov, editors, Proc. SPIE BioMEMS and Nanotechnology III, volume 6799, Article No. 679913, 2007.
    24. M. D. McDonnell, N. G. Stocks and D. Abbott. Optimal coding of a random stimulus by a population of parallel neuron models. In Sergey M. Bezrukov, editor, Proc. SPIE Noise and Fluctuations in Biological, Biophysical, & Biomedical Systems, volume 6602, Article No. 66020R, 2007 (Invited Paper).
    25. F. Martorell, M. D. McDonnell, D. Abbott and A. Rubio. SNDR enhancement in noisy sinusoidal signals by non-linear processing elements. In M. Macucci, L. K. J. Vandamme, C. Ciofi and M. B.Weissman, editors, Proc. SPIE Noise and Fluctuations in Circuits, Devices, and Materials, volume 6600, Article No. 660011, 2007.

    Areas of study and research

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