Publications

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Books and Chapters

T.P. Trappenberg (2019) Fundamentals of Machine Learning, Oxford University Press, ISBN: 978-0-19-882804-4.

T.P. Trappenberg (2018) Deep Probabilistic Machine Learning for Intelligent Control, in Intelligent Control in Drying, A. Martynenko and A. Bück (eds.), CRC Press, ISBN 9781498732758

T.P. Trappenberg (2014) A Brief Introduction to Probabilistic Machine Learning and Its Relation to Neuroscience, in Growing Adaptive Machines, T. Kowaliw et al. (eds.), Studies in Computational Intelligence 557, DOI: 10.1007/978-3-642-55337-0_2, Springer

D. Standage and T. Trappenberg (2012) Cognitive Neuroscience, in The Cambridge Handbook to Cognitive Science, Keith Frankish and William Ramsey, Cambridge University Press, ISBN-10: 0521691907, ISBN-13: 978-0521691901

T.P. Trappenberg (2010) Fundamentals of Computational Neuroscience, 2nd edition, Oxford University Press, ISBN13: 9780199568413, ISBN10: 0199568413.

T.P. Trappenberg (2008) Decision making and population decoding with strongly inhibitory neural field models, in Computational Modelling in Behavioural Neuroscience: Closing the gap between neurophysiology and behaviour', Psychology Press, London, Dietmar Heinke & Eirini Mavritsaki (eds.).

T.P. Trappenberg (2005) Continuous attractor neural networks, in 'Recent Developments in Biologically Inspired Computing', Leandro Nunes de Castro & Fernando J. Von Zuben (eds.), IDEA Group Publishing, ISBN 1-59140-313-8.

T.P. Trappenberg (2002) Fundamentals of Computational Neuroscience, Oxford University Press, ISBN 0-19851-583-9/0-19851-582-0.

T.P Trappenberg (1992) Eigenschaften einiger Feldtheorien mit spontaner Symmetriebrechung auf endlichen Gittern, Ph.D Thesis, Verlag Shaker, Aachen 1992, ISBN 3-86111-193-4.

Papers

A. Nunes, T. Trappenberg, M. Alda (2019) We need an operational framework for heterogeneity in psychiatric research, J Psychiatry Neurosci 2020;45(1):3-6

J.A. Garry, T. Trappenberg, S. Beyea, and T. Bardouille (2019) Classification and Analysis of Minimally-Processed Data from a Large Magnetoencephalography Dataset using Convolutional Neural Networks, https://www.biorxiv.org/content/10.1101/846964v1

A Nunes et al. (2019)Prediction of Lithium Response using Clinical Data, Acta psychiatrica Scandinavica, 2019

F. Sheikhnezhad Fard and T. Trappenberg (2019) A novel model for arbitration between planning and habitual control systems, Frontiers in neurorobotics 13, 52

A.G.C. Pacheco, A.R. Ali, T. Trappenberg (2019) Skin cancer detection based on deep learning and entropy to detect outlier samples, arXiv preprint arXiv:1909.04525

A.R. Ali, J. Li, S.J. O'Shea, G. Yang, T Trappenberg, X Ye (2019) A Deep Learning Based Approach to Skin Lesion Border Extraction With a Novel Edge Detector in Dermoscopy Images, 2019 International Joint Conference on Neural Networks

Y. Kubo, M. Traynor, T. Trappenberg, S. Oore (2019) Learning Adaptive Weight Masking for Adversarial Examples, 2019 International Joint Conference on Neural Networks

F. Usai, T. Trappenberg (2019) Using a Deep CNN for Automatic Classification of Sleep Spindles: A Preliminary Study, Canadian Conference on Artificial Intelligence

Y. Kubo, T. Trappenberg (2019) Mitigating Overfitting Using Regularization to Defend Networks Against Adversarial Examples, Canadian Conference on Artificial Intelligence

A.R. Ali, J. Li, T. Trappenberg (2019) Supervised Versus Unsupervised Deep Learning Based Methods for Skin Lesion Segmentation in Dermoscopy Images, Canadian Conference on Artificial Intelligence

J. Luo, S. Oore, P. Hollensen, A. Fine, T. Trappenberg (2019) Self-training for cell segmentation and counting, Canadian Conference on Artificial Intelligence



P.Q. Lee, A.Guidab S. Patterson, T. Trappenberg, C. Bowen, S. D.Beyea, J. Merrimen, C. Wang, S. E. Clarke (2019) Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study. Computerized Medical Imaging and Graphics, 75, 14-23.

B. Coe, T. Trappenberg and D.P. Munoz (2019) Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus, Front. Syst. Neurosci., 13 February 2019 | https://doi.org/10.3389/fnsys.2019.00003

C. Hassall, P. Connor, T. Trappenberg, J. McDonald, and O. Krigolson (2018) Learning what matters: A neural explanation for the sparsity bias, International Journal of Psychophysiology,127, 62-72, Elsevier

A. Nunes et al. (2018) Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group, Molecular Psychiatry | https://www.nature.com/articles/s41380-018-0228-9

A. Nunes, T. Helson, L. Dixon, T. Trappenberg, and A. Keshen (2018) Characteristic impairments of goal-directed and habitual control in bulimia nervosa, bioRxiv, 348250, Cold Spring Harbor Laboratory

P. Hartono and T. Trappenberg (2018) Topographic representation adds robustness to supervised learning, Journal of Intelligent & Fuzzy Systems,,1-14, IOS Press

F. Fard and T. Trappenberg (2018), Mixing Habits and Planning for Multi-Step Target Reaching Using Arbitrated Predictive Actor-Critic, International Joint Conference on Neural Networks (IJCNN),1-8,

M. Traynor and T. Trappenberg, (2018) Words are not temporal sequences of characters,2018 International Joint Conference on Neural Networks (IJCNN),1-6,2018,IEEE

F. Fard and T. Trappenberg (2017) A novel model for arbitration between planning and habitual control systems, arXiv preprint arXiv:1712.02441

F. Fard, A. Nunes, and T. Trappenberg, Thomas (2017) An actor-critic with an internal model, Annual Conference on Cognitive Computational Neuroscience (CCN)

S. McIlroy, Y. Kubo, J. Toguri, C. Lehmann and T. Trappenberg (2017) In vivo Classification of Inflammation in Blood Vessels with Convolutional Neural Networks, International Joined Conference in Neural Networks 2017, Anchorage, USA

F. Fard, P. Hollensen, S. Mcilory and T. Trappenberg (2017) Impact of biased mislabeling on learning with deep networks. Joined Conference in Neural Networks 2017, Anchorage, USA

C. Tan, P. Plöger and T. Trappenberg (2016) A Neural Field Approach to Obstacle Avoidance. In: Friedrich G., Helmert M., Wotawa F. (eds) KI 2016: Advances in Artificial Intelligence. KI 2016. Lecture Notes in Computer Science, vol 9904: 69-87, Springer, Cham, https://link.springer.com/chapter/10.1007/978-3-319-46073-4_6

F. S. Fard, P. Hollensen, D. Heinke, and T. Trappenberg (2015) Modeling human target reaching with an adaptive observer implemented with dynamic neural fields, Neural Networks: 72, 1330. Special issue on Cognitive Robotics, http://authors.elsevier.com/a/1S9Jb3BBjKJNcU.

P. Connor, P. Hollensen, O. Krigolson, T. Trappenberg (2015) A biological mechanism for Bayesian feature selection: Weight decay and raising the LASSO, Neural Networks 67:121–130.

T. Trappenberg, P. Hartono, P. Hollensen (2015) Classifier with Hierarchical Topographical Maps as Internal Representation, International Conference on Learning Representations (ICLR 2015, workshop track)

L. Sculthorpe-Petley, C. Liu, S. Satel, T.P. Trappenberg, R. Boshra, R. D'Arcy (2015) A rapid event-related potential (ERP) method for point-of-care evaluation of brain function: Development of the Halifax Consciousness Scanner, Journal of Neuroscience Methods: 245

P. Hartono, P. Hollensen, T. Trappenberg (2015) Learning-Regulated Context Relevant Topographical Map, IEEE Transaction on Neural Networks and Learning Systems: 26(10)

J. Satel, Farzaneh S. Fard, Z. Wang and T.P. Trappenberg (2014) Simulating oculomotor inhibition of return with a two-dimensional dynamic neural field model of the superior colliculus Australian Journal of Intelligent Information Processing Systems 11/2014; 14(1):27-32.

H. Parvar, L. Sculthorpe-Petley, J. Satel, R. Boshra, R.C.N. D’Arcy, T.P. Trappenberg (2014) Detection of event-related potentials in individual subjects using support vector machines, Brain Informatics, DOI 10.1007/s40708-014-0006-7, November.

R Boshra, J Satel, L Sculthorpe-Petley, R D'Arcy, T Trappenberg (2014) Single subject statistical analyses of ERP data for applications in brain injury 24th Annual Meeting of the Canadian Society for Brain, Behaviour and ...

D. Standage, T. Trappenberg and G. Blohm (2014), Calcium-dependent calcium decay explains STDP in a dynamic model of hippocampal synapses, PLOS ONE, 9(1):e86248, 2014

P. Connor, V LoLordo and T. Trappenberg (2014), An Elemental Model of Retrospective Revaluation Without Within-Compound Associations, Learning & Behavior 42:22–38. (2014 Clifford T. Morgan Best Article Awards)

P. Hartono, T.P. Trappenberg, P. Hollensen (2014) Visualizing Hierarchical Representation in A Multi- layered Restricted RBF Network. International Conference on Artificial Neural Networks (ICANN 2014).

T. Trappenberg (2013), Bubbles in a Robot, International Joined Conference on Neural Networks IJCNN 2013.

P. Connor and T. Trappenberg (2013), Biologically Plausible Feature Reduction Through Relative Correlation, International Joined Conference on Neural Networks IJCNN 2013.

P. Hartono and T. Trappenberg (2013), Classificability-regulated Self-Organizing Map using Restricted RBF, International Joined Conference on Neural Networks IJCNN 2013.

W. Connors and T. Trappenberg (2013), Improved Path Integration Using A Modified Weight Combination Method, Cognitive Computation, March 2013.

T. Trappenberg and P. Hollensen (2013), Sparse coding and challenges for Bayesian models of the brain, Behavioral and Brain Sciences, 36(3), 232 - 233.

P. Connor , L. Mattina and T. Trappenberg (2011) Simulating hyperactivity in ADHD using reinforcement learning, Annual Conference of the Japanese Neural Network Society (JNNS) 2011.

P. Hollensen, P. Hartono and T. Trappenberg (2011) Topographic RBM as robot controller, Annual Conference of the Japanese Neural Network Society (JNNS) 2011.

A. Hoggarth, R. Rankaduwa, A. Fine and T. Trappenberg (2011) Temporal sequence learning and the hippocampus: A continuous attractor model of location based learning in sequential activation of place cells, Annual Conference of the Japanese Neural Network Society (JNNS) 2011.

R. Marino, T. Trappenberg, M. Dorris, D.Munoz (2011), Spatial Interactions in the Superior Colliculus Predict Saccade Behavior in a Neural Field Model, Journal of Cognitive Neuroscience, 24(2):315-36 [[1]].

Z. Wang, J. Satel, T. Trappenberg and R. Klein (2011), Aftereffects of Saccades Explored in a Dynamic Neural Field Model of the Superior Colliculus, Journal of Eye Movement Research 4(2):1, 1--16.

P. Connor and T. Trappenberg (2011), Characterizing a Brain-Based Value-Function Approximator, in Advances in Artificial Intelligence LNAI 2056, Eleni Stroulia and Stan Matwin (eds), Springer 2011.

P. Hollensen, W. Connors and T. Trappenberg (2011), Comparison of Learned Versus Engineered Features for Detection of Mine Like Objects from Raw Sonar Images, in Advances in Artificial Intelligence LNAI 2056, Eleni Stroulia and Stan Matwin (eds), Springer 2011.

P. Hartono and T. Trappenberg, Internal Topographical Structure in Training Autonomous Robot, IEEE SMC 2011.

P. Hartono and T. Trappenberg, Autonomous Robot with Internal Topological Representation, The 3rd Int. Conf. on Cognitive Neurodynamics (accepted).

P. Connor and T. Trappenberg (2011), A new functional role for lateral inhibition in the striatum: Pavlovian conditioning, Computational and System Neuroscience (COSYNE) 2011, [2]

J Satel, Z. Wang, T. Trappenberg and R. Klein (2011), Modeling inhibition of return as short-term depression of early sensory input to the superior colliculus, Vision Research 51 (2011) 987–996.

A Saito, T. Trappenberg and P. Hartono (2010), Relation between Topological Organization and Learning Ability of Neural Networks, Technical Report of IEICE.

T. Trappenberg, A Saito and P. Hartono (2010), Selective attention improves self-organization of cortical maps with multiple inputs, IJCNN 2010.

M. Denil and T. Trappenberg (2009), Overlap versus Imbalance, Lecture Notes in Computer Science 6085, AI2010, Atefeh Farzindar and Vlado Keselj (eds), 220-231, Springer.

W. Connors, P. Connor and T. Trappenberg (2010), Detection of Mine-Like Objects using Restricted Boltzmann Machines, Lecture Notes in Computer Science 6085, AI2010, Atefeh Farzindar and Vlado Keselj (eds), 362-365, Springer.

P. Hartono and T. Trappenberg (2009), Learning Intialized by Topologically Correct Representation, IEEE SMC09, 2723-2727.

J. Satel, T. Trappenberg and A. Fine (2009), Are binary synapses superior to graded weight representations in stochastic attractor networks? Cognitive Neurodynamics 3, 243-250.

T. Trappenberg, P. Hartono, and D. Rasmusson (2009), Top-down control of learning in biological self-organizing maps, Lecture Notes in Computer Science 5629, WSOM 2009, J. Principe and R. Miikkulainen (eds), 316-324, Springer.

J.P. Salmon, J. P. and T.P. Trappenberg (2008), Modeling the integration of expectations in visual search with centre-surround neural fields. Neural Networks, 21: 1476-1492.

T. Trappenberg (2008), Tracking population densities using dynamic neural fields with moderately strong inhibition, Cognitive Neurodynamics 2:171–177.

T. Trappenberg (2008), Dynamics of population decoding with strong inhibition, in Advances in Cognitive Neurodynamics, Wang, Rubin; Gu, Fanji; Shen, Enhua (Eds.), Springer.

S. Wu and T. Trappenberg (2008), Learning in sparse attractor networks with inhibition, in Advances in Cognitive Neurodynamics, Wang, Rubin; Gu, Fanji; Shen, Enhua (Eds.), Springer.

D. Standage, S. Jalil and T. Trappenberg (2007), Computational consequences of experimentally derived spike-time and weight dependent plasticity rules, Biological Cybernetics, Vol. 96, No. 6. (June 2007), pp. 615-623.

D. Standage and T. Trappenberg (2007), The trouble with weight-dependent STDP, IJCNN'07

M. Boardman, T. Trappenberg (2006), A Heuristic for Free Parameter Optimization with Support Vector Machines, WCCI 2006, 1337-1344. Source Code. Homepage

M. Lawrence , T. Trappenberg, A. Fine (2006) Rapid learning and robust recall of long sequences in modular associator networks, Neurocomputing , 69(7-9): 634-641.

T. Trappenberg, J. Ouyang, A. Back (2006) Input Variable Selection: Mutual Information and Linear Mixing Measures, IEEE Transactions on Knowledge & Data Engineering, Vol. 18, No. 1, pp. 37-46.

D. Standage, T. Trappenberg, R. Klein (2005) Modelling divided visual attention with a winner-take-all network, Neural Networks, 18, 5-6:620-627.

D. Standage, T. Trappenberg, R. Klein (2005) A Continuous Attractor Neural Network Model of Divided Visual Attention, IJCNN'05.

J. Satel, T. Trappenberg, R. Klein (2005) Motivational modulation of endogenous inputs to the superior colliculus, IJCNN'05

D. Standage, T. Trappenberg (2005) Differences in the subthreshold dynamics of leaky integrate-and-fire and Hodgkin-Huxley neuron models, IJCNN'05.

S. Stringer, E. Rolls, and T. Trappenberg (2005). Self-organizing continuous attractor network models of hippocampal spatial view cells. Neurobiology of Learning and Memory, 83: 79-92.

M. Lawrence, T. Trappenberg, A. Fine (2005) A multi-modular associator network for simple temporal sequence learning and generation, in Proceedings of ESANN'05, Michel Verlysen (ed), d-side, Belgium, 423-428.

T.P. Trappenberg (2005) Coverage-performance estimation for classification with ambiguous data, in Proceedings of ESANN'05, Michel Verlysen (ed), d-side, Belgium, 411-416.

T.P. Trappenberg and D.I. Standage (2005), Multi-packet regions in stabilized continuous attractor networks, Neurocomputing, 65-66:617-622.

Stringer S.M., Rolls E.T., Trappenberg T.P. (2004) Self-organizing continuous attractor networks with multiple activity packets, and the representation of space, Neural Networks 17(1):5-27.

Trappenberg, T.P. (2003), Why is our working memory so large?, Neural Information Processing – Letters and Reviews, Vol 1, No. 3, pp. 97-101.

Stringer S.M., Rolls E.T., Trappenberg T.P.,de Araujo I.E.T.(2003), Self-organizing continuous attractor networks and motor function, Neural Networks 16(2):161-182.

Hashemi S. and Trappenberg T.P. (2002), Using SVM for Classification in Datasets with Ambiguous Data, International Conference on Information Systems, Analysis and Synthesis SCI 2002.

Rolls E.T., Stringer S.M., Trappenberg T.P. (2002), A unified model of spatial and episodic memory, Proceedings B of the Royal Society 269:1087-1093.

Trappenberg T.P., Rolls E.T., and Stringer S.M. (2002) Effective size of receptive fields of inferior temporal visual cortex neurons in natural scenes, Neural Information Processing Systems NIPS 2001.

Stringer S.M., Trappenberg T.P., Rolls E.T. and Araujo I.E.T. (2002), Self-organising continuous attractor networks and path integration: One-dimensional models of head direction cells, Network: Computation in Neural Systems, 13:217-242.

Stringer S.M., Rolls E.T., Trappenberg T.P. and Araujo I.E.T. (2002), Self-organising continuous attractor networks and path integration: Two-dimensional models of place cells, Network: Computation in Neural Systems, 13:429-446.

Trappenberg T.P, Dorris M., Munoz D.P., and Klein R.M. (2001) A Model of saccade initiation based on the competitive integration of exogenous and endogenous signals in the superior colliculus, Journal of Cognitive Neuroscience 13:256-271.

Back A.D., Trappenberg T.P. (2001), Selecting Inputs For Modeling Using Normalized Higher Order Statistics and Independent Component Analysis, IEEE Transactions in Neural Networks, Vol. 12(3):612-617.

Trappenberg T.P., Back A.D. (2000) A classification scheme for applications with ambiguous data, International Joined Conference on Neural Networks IJCNN 2000.

Trappenberg T.P., Klein R.M. (1999) Generating Oculomotor and Neural Behavior in a Neural Field Model of the Superior Colliculus, Behavioral and Brain Science 22:700-701.

Back A.D., Trappenberg T.P. (1999), Input variable selection using independent component analysis, International Joined Conference on Neural Networks IJCNN 1999..

Kindermann L., Trappenberg T.P (1999) Modeling time-varying processes by unfolding the time domain, International Joined Conference on Neural Networks IJCNN 1999, (Best Paper Award)..

Trappenberg T.P. (1998) Dynamic cooperation and competition in a network of spiking neurons, International Conference on Neural and Intelligent Processing ICONIP 1998.

Trappenberg, T.P., Nakahara H., Hikosaka O., (1998) Modeling reward-dependent activity pattern of caudate neurons, International Conference on Artificial Neural Networks ICANN'98, Vol. 2: 973-978.

Trappenberg T.P., Simpson S., Klein R.M., Munoz D.P., Dorris M.C., McMullen P. (1997) Neural field model of oculomotor preparation and disengagement, International Conference on Neural Networks ICNN’97, Vol1:591-596.

Trappenberg T. (1997) Non-monotone network dynamics; preliminary results, International Conference on Neural Networks ICNN’97.

Margio C., Moriarty K.J.M., Plache B., Trappenberg T.P. (1994) Bank Conflict Resolution, Comput. Phys. Commun. 83(2-3):125-129.

Margio C., Moriarty K.J.M., Trappenberg T.P. (1994) Object Oriented Monte Carlo Simulations, Computers in Physics 9(5):540-545.

Moriarty K.J.M., Trappenberg T.P., Rebbi C. (1994) Development of massively parallel applications, Comput. Phys. Commun. 81(1-2):153-162.

Coley A.A., Trappenberg T.P. (1994) The Quark-Hadron Phase Transition, QCD Lattice Calculations and Inhomogeneous Big-Bang Nucleosynthesis, Phys. Rev. D 50.

Moriarty K.J.M., Sanielevici S., Trappenberg T.P. (1994), High-Speed Monte Carlo Simulations on Vector Parallel Computers, Parallel Algorithms and Applications 3.

Moriarty K.J.M., Trappenberg T.P. (1993) Programming Tools for Parallel Computers, Int. J. Mod. Phys. C, Vol. 4, No. 6. Heller M., Janes T., Margio C., Moriarty K.J.M., Plache B., Trappenberg T.P, (1994) Current development of OPTIMAX, High Performance Computing SS'94, Toronto, Canada.

Patton P.C., Moriarty K.J.M., Trappenberg T.P. (1994) Parallel programming environments for workstation clusters, High Performance Computing SS'94, Toronto, Canada.

Moriarty K.J.M., Sanielevici S., Trappenberg T.P, Kuba D.W (1993) An efficiently microtasked CRAY Y-MP C90 version of the Kuba-Moriarty SU(3) gauge theory simulation program, Comput. Phys. Commun. 76 (1993) 87.

Moriarty K.J.M., Sanielevici S., Sun K., Trappenberg T.P (1993) Object-Oriented programming applied to lattice gauge theory, Computers in Physics 7(5):560:569.

Moriarty K.J.M., Sanielevici S., Trappenberg T.P. (1993) Beginning to Experience High Performance Fortran, High Performance Computing SS'93, Calgary, Canada.

Frick C., Lin L., Montvay I., Münster G., Plagge M., Trappenberg T.P., Wittig H. (1993) Numerical simulation of heavy fermions in an SU(2) L x SU(2) R symmetric Yukawa model, Nucl. Phys. B397, 431.

Grossman B., Laursen M.L., Trappenberg T.P., Wiese U.-J. (1993) The confinement-deconfinement interface tension and the spectrum of the transfer matrix, Nucl. Phys. B396:584.

Frick C., Lin L., Montvay I., Münster G., Plagge M., Trappenberg T.P., Wittig H. (1993) Mass spectrum and bounds on the couplings in Yukawa models with mirror-fermions, Nucl. Phys. B (Proc. Suppl.) 30:647.

Grossman B., Laursen M.L., Trappenberg T., Wiese U.-J. (1993) The interface tension in quenched QCD at the critical temperature, Nucl. Phys. B (Proc. Suppl) 30:869.

Grossman B., Laursen M.L., Trappenberg T.P., Wiese U.-J. (1993) The confinement-deconfinement interface tension, wetting, and the spectrum of the transfer matrix, Nucl. Phys. B (Proc. Suppl) 30:865.

Trappenberg, T.P., (1992) The spectrum of the T-matrix and the surface tension in the SU(3) gauge theory, Int. J. of Mod. Phys. C 3:947, and in Dynamics of First Order Phase Transitions, H.J. Herrmann, W. Janke and F. Karsch (eds.), World Scientific, Singapore, 1992.

Grossman B., Laursen M.L., Trappenberg T.P., Wiese U.-J. (1992) A multicanonical algorithm for SU(3) pure Gauge Theory, Phys. Lett. B293:175.

Bock W., De A.K., Frick C., Jersak J., Trappenberg T.P. (1992) Spontaneous symmetry breaking on the lattice generated by Yukawa interaction, Nucl. Phys. B378:652.

Trappenberg T.P., Wiese U.-J. (1992) Z(3)-Instantons in Models for Wetting of Hot Gluons, Nucl. Phys. B372:703.

Bock W., De A.K., Frick C., Jansen K., Trappenberg T.P. (1992) Search for an upper bound of the renormalized Yukawa coupling, in a lattice fermion-Higgs model, Nucl. Phys. B371:683.

Barbour I. M., Bock W., Davies C.T.H., De A.K., Henty D., Smit J., Trappenberg T.P. (1992) The eigenvalue spectra in Z(2) x Z(2) and SU(2)L x SU(2)R fermion--Higgs models, Nucl. Phys. B368 (1992) 390.

Trappenberg T.P. (1992) Critical Exponents for Wetting of Hot Gluons, Proceedings of the workshop Effective field theories of the standard model, Dobogókö, Hungary, edited by U.-G. Meißner, World Scientific, Singapore.

Meyer-Ortmanns H., Trappenberg T.P. (1990) Surface Tension from Finite Volume Vacuum Tunneling in the 3D-Ising Model, J. Stat. Phys. 58:185.

Jansen K., Montvay I., Münster G., Trappenberg T.P., Wolff U. (1989) Broken Phase of the 4-Dimensional Ising Model in a Finite Volume, Nucl. Phys. B322:698.

Jansen K., Jersak J., Montvay I., Münster G., Trappenberg T.P., Wolff U. (1988) Vacuum Tunneling in the Four-Dimensional Ising Model, Phys. Lett. B213:203.

Abstracts (not complete)

T. Trappenberg (2003) Learning and phase transitions in centre-surround recurrent networks. Talk at 7th International Conference on Cognitive and Neural Systems, Boston.

T. Trappenberg (2002) Object recognition in natural scenes and attentional influences on the size of receptive fields of the inferior temporal visual cortex neurons, Talk at 6th International Conference on Cognitive and Neural Systems, Boston.

Simpson S.D., Klein R.M., Trappenberg T.P. (1998) Simulating oculomotor behavior within a neural field model. Poster presented at the Cognitive Neuroscience Society, San Francisco.

Nakahara H., Trappenberg T.P., Hikosaka O., Kawagoe R., Takikawa Y. (1998) Computational analysis on reward-modulated activities of caudate neurons. 28th Annual Meeting of Society for Neuroscience Abstracts. p1651. 649.5.

Trappenberg T.P. (1998) How does the mind wander around? Talk given at the International Conference on Neural Correlates of Consciousness, Bremen, Germany.

Simpson S., Trappenberg T.P., Klein R.M., McMullen P. (1997, March) Saccadic reaction time as a function of target location probability and the gap effect. Annual meeting of the Cognitive Neuroscience Society. Boston, Mass.

Presentations (not complete)

CoSMo2011

Siegburg2013

IEEE Autonomous Robotics Chapter 2013