High Speed Monte Carlo Simulations on Vector Parallel Computers. - K. J. M. Moriarty; Sergiu Sanielevici; Thomas Trappenberg (Parallel Algorithms Appl., 1994)

Non-monotone network dynamics; preliminary results. - Thomas Trappenberg (ICNN, 1997)

Neural field model of oculomotor preparation and disengagement. - Thomas Trappenberg; S. Simpson; Raymond M. Klein; P. McMullen; Douglas P. Munoz; Michael Dorris (ICNN, 1997)

Dynamic Cooperation and Competition in a Network of Spiking Neurons. - Thomas Trappenberg (ICONIP, 1998)

Input variable selection using independent component analysis. - Andrew D. Back; Thomas Trappenberg (IJCNN, 1999)

Modeling time-varying processes by unfolding the time domain. - Lars Kindermann; Thomas Trappenberg (IJCNN, 1999)

A Classification Scheme for Applications with Ambiguous Data. - Thomas Trappenberg; Andrew D. Back (IJCNN (6), 2000)

Effective Size of Receptive Fields of Inferior Temporal Visual Cortex Neurons in Natural Scenes. - Thomas Trappenberg; Edmund T. Rolls; Simon M. Stringer (NIPS, 2001)

Selecting inputs for modeling using normalized higher order statistics and independent component analysis. - Andrew D. Back; Thomas Trappenberg (IEEE Trans. Neural Networks, 2001)

Self-organizing continuous attractor networks and motor function. - Simon M. Stringer; Edmund T. Rolls; Thomas Trappenberg; Ivan E. Tavares de Araújo (Neural Networks, 2003)

Self-organising continuous attractor networks with multiple activity packets, and the representation of space. - Simon M. Stringer; Edmund T. Rolls; Thomas Trappenberg (Neural Networks, 2004)

A multi-modular associator network for simple temporal sequence learning and generation. - Michael Lawrence; Thomas Trappenberg; Alan Fine (ESANN, 2005)

Coverage-performance estimation for classification with ambiguous data. - Thomas Trappenberg (ESANN, 2005)

Motivational modulation of endogenous inputs to the superior colliculus. - Jason Satel; Thomas Trappenberg; Raymond M. Klein (IJCNN, 2005)

Differences in the subthreshold dynamics of leaky integrate-and-fire and Hodgkin-Huxley neuron models. - Dominic I. Standage; Thomas Trappenberg (IJCNN, 2005)

A continuous attractor neural network model of divided visual attention. - Dominic I. Standage; Thomas Trappenberg; Raymond M. Klein (IJCNN, 2005)

Multi-packet regions in stabilized continuous attractor networks. - Thomas Trappenberg; Dominic I. Standage (Neurocomputing, 2005)

Modelling divided visual attention with a winner-take-all network. - Dominic I. Standage; Thomas Trappenberg; Raymond M. Klein (Neural Networks, 2005)

A Heuristic for Free Parameter Optimization with Support Vector Machines. - Matthew Boardman; Thomas Trappenberg (IJCNN, 2006)

Rapid learning and robust recall of long sequences in modular associator networks. - Michael Lawrence; Thomas Trappenberg; Alan Fine (Neurocomputing, 2006)

Input Variable Selection: Mutual Information and Linear Mixing Measures. - Thomas Trappenberg; Jie Ouyang; Andrew D. Back (IEEE Trans. Knowl. Data Eng., 2006)

The Trouble with Weight-Dependent STDP. - Dominic I. Standage; Thomas Trappenberg (IJCNN, 2007)

Computational consequences of experimentally derived spike-time and weight dependent plasticity rules. - Dominic I. Standage; Sajiya Jalil; Thomas Trappenberg (Biol. Cybern., 2007)

Modeling the integration of expectations in visual search with centre-surround neural fields. - Joshua P. Salmon; Thomas Trappenberg (Neural Networks, 2008)

Fundamentals of Computational Neuroscience (2. ed.). - Thomas Trappenberg (Oxford University Press, 2009)

Learning Intialized by Topologically Correct Representation. - Pitoyo Hartono; Thomas Trappenberg (SMC, 2009)

Top-Down Control of Learning in Biological Self-Organizing Maps. - Thomas Trappenberg; Pitoyo Hartono; Douglas Rasmusson (WSOM, 2009)

Detection of Mine-Like Objects Using Restricted Boltzmann Machines. - Warren A. Connors; Patrick C. Connor; Thomas Trappenberg (Canadian AI, 2010)

Overlap versus Imbalance. - Misha Denil; Thomas Trappenberg (Canadian AI, 2010)

Selective attention improves self-organization of cortical maps with multiple inputs. - Thomas Trappenberg; Aya Saito; Pitoyo Hartono (IJCNN, 2010)

Characterizing a Brain-Based Value-Function Approximator. - Patrick C. Connor; Thomas Trappenberg (Canadian AI, 2011)

Comparison of Learned versus Engineered Features for Classification of Mine Like Objects from Raw Sonar Images. - Paul Hollensen; Warren A. Connors; Thomas Trappenberg (Canadian AI, 2011)

Internal topographical structure in training autonomous robot. - Pitoyo Hartono; Thomas Trappenberg (SMC, 2011)

A Characterization of the Combined Effects of Overlap and Imbalance on the SVM Classifier - Misha Denil; Thomas Trappenberg (CoRR, 2011)

Internal representation of sensory information for training autonomous robot. - Pitoyo Hartono; Thomas Trappenberg (SCIS&ISIS, 2012)

Spatial Interactions in the Superior Colliculus Predict Saccade Behavior in a Neural Field Model. - Robert A. Marino; Thomas Trappenberg; Michael Dorris; Douglas P. Munoz (J. Cogn. Neurosci., 2012)

Biologically plausible feature selection through relative correlation. - Patrick C. Connor; Thomas Trappenberg (IJCNN, 2013)

Classificability-regulated self-organizing map using restricted RBF. - Pitoyo Hartono; Thomas Trappenberg (IJCNN, 2013)

Bubbles in the robot. - Thomas Trappenberg (IJCNN, 2013)

Improved Path Integration Using a Modified Weight Combination Method. - Warren A. Connors; Thomas Trappenberg (Cogn. Comput., 2013)

Visualizing Hierarchical Representation in a Multilayered Restricted RBF Network. - Pitoyo Hartono; Paul Hollensen; Thomas Trappenberg (ICANN, 2014)

Simulating oculomotor inhibition of return with a two-dimensional dynamic neural field model of the superior colliculus. - Jason Satel; Farzaneh Sheikhnezhad Fard; Zhiguo Wang; Thomas Trappenberg (Aust. J. Intell. Inf. Process. Syst., 2014)

An Architecture for Distinguishing between Predictors and Inhibitors in Reinforcement Learning. - Patrick C. Connor; Thomas Trappenberg (ICLR (Workshop Poster), 2014)

A Brief Introduction to Probabilistic Machine Learning and Its Relation to Neuroscience. - Thomas Trappenberg (Growing Adaptive Machines, 2014)

Detection of event-related potentials in individual subjects using support vector machines. - Hossein Parvar; Lauren D. Sculthorpe-Petley; Jason Satel; Rober Boshra; Ryan C. N. D'Arcy; Thomas Trappenberg (Brain Informatics, 2015)

Classifier with Hierarchical Topographical Maps as Internal Representation. - Pitoyo Hartono; Paul Hollensen; Thomas Trappenberg (ICLR (Workshop), 2015)

A biological mechanism for Bayesian feature selection: Weight decay and raising the LASSO. - Patrick C. Connor; Paul Hollensen; Olav E. Krigolson; Thomas Trappenberg (Neural Networks, 2015)

Modeling human target reaching with an adaptive observer implemented with dynamic neural fields. - Farzaneh Sheikhnezhad Fard; Paul Hollensen; Dietmar Heinke; Thomas Trappenberg (Neural Networks, 2015)

Learning-Regulated Context Relevant Topographical Map. - Pitoyo Hartono; Paul Hollensen; Thomas Trappenberg (IEEE Trans. Neural Networks Learn. Syst., 2015)

A Neural Field Approach to Obstacle Avoidance. - Chun Kwang Tan; Paul G. Plöger; Thomas Trappenberg (KI, 2016)

Impact of biased mislabeling on learning with deep networks. - Farzaneh Sheikhnezhad Fard; Paul Hollensen; Stuart McIlroy; Thomas Trappenberg (IJCNN, 2017)

In vivo classification of inflammation in blood vessels with convolutional neural networks. - Stuart McIlroy; Yoshimasa Kubo; Thomas Trappenberg; James Toguri; Christian Lehmann (IJCNN, 2017)

Mixing Habits and Planning for Multi-Step Target Reaching Using Arbitrated Predictive Actor-Critic. - Farzaneh Sheikhnezhad Fard; Thomas Trappenberg (IJCNN, 2018)

Words Are Not Temporal Sequences of Characters. - Michael Traynor; Thomas Trappenberg (IJCNN, 2018)

Supervised Versus Unsupervised Deep Learning Based Methods for Skin Lesion Segmentation in Dermoscopy Images. - Abder-Rahman Ali; Jingpeng Li 001; Thomas Trappenberg (Canadian AI, 2019)

Mitigating Overfitting Using Regularization to Defend Networks Against Adversarial Examples. - Yoshimasa Kubo; Thomas Trappenberg (Canadian AI, 2019)

Self-training for Cell Segmentation and Counting. - Junliang Luo; Sageev Oore; Paul Hollensen; Alan Fine; Thomas Trappenberg (Canadian AI, 2019)

Using a Deep CNN for Automatic Classification of Sleep Spindles: A Preliminary Study. - Francesco Usai; Thomas Trappenberg (Canadian AI, 2019)

A Deep Learning Based Approach to Skin Lesion Border Extraction With a Novel Edge Detector in Dermoscopy Images. - Abder-Rahman Ali; Jingpeng Li 001; Sally Jane O'Shea; Guang Yang 006; Thomas Trappenberg; Xujiong Ye (IJCNN, 2019)

Learning Adaptive Weight Masking for Adversarial Examples. - Yoshimasa Kubo; Michael Traynor; Thomas Trappenberg; Sageev Oore (IJCNN, 2019)

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study. - Peter Q. Lee; Alessandro Guida; Steve Patterson; Thomas Trappenberg; Chris V. Bowen; Steven D. Beyea; Jennifer Merrimen; Cheng Wang 041; Sharon E. Clarke (Comput. Medical Imaging Graph., 2019)

Skin cancer detection based on deep learning and entropy to detect outlier samples. - André G. C. Pacheco; Abder-Rahman Ali; Thomas Trappenberg (CoRR, 2019)

A Novel Model for Arbitration Between Planning and Habitual Control Systems. - Farzaneh Sheikhnezhad Fard; Thomas Trappenberg (Frontiers Neurorobotics, 2019)

Topographic representation adds robustness to supervised learning. - Pitoyo Hartono; Thomas Trappenberg (J. Intell. Fuzzy Syst., 2019)

On Out-of-Distribution Detection Algorithms with Deep Neural Skin Cancer Classifiers. - André G. C. Pacheco; Chandramouli Shama Sastry; Thomas Trappenberg; Sageev Oore; Renato A. Krohling (CVPR Workshops, 2020)

Learning dynamic weights for an ensemble of deep models applied to medical imaging classification. - André G. C. Pacheco; Thomas Trappenberg; Renato A. Krohling (IJCNN, 2020)

Representational Rényi Heterogeneity. - Abraham Nunes; Martin Alda; Timothy Bardouille; Thomas Trappenberg (Entropy, 2020)

Multiplicative Decomposition of Heterogeneity in Mixtures of Continuous Distributions. - Abraham Nunes; Martin Alda; Thomas Trappenberg (Entropy, 2020)

Logical Activation Functions: Logit-space equivalents of Boolean Operators. - Scott C. Lowe; Robert Earle; Jason d'Eon; Thomas Trappenberg; Sageev Oore (CoRR, 2021)

LogAvgExp Provides a Principled and Performant Global Pooling Operator. - Scott C. Lowe; Thomas Trappenberg; Sageev Oore (CoRR, 2021)

Label-free Monitoring of Self-Supervised Learning Progress. - Isaac Xu; Scott C. Lowe; Thomas Trappenberg (CCECE, 2022)

Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators. - Scott C. Lowe; Robert Earle; Jason d'Eon; Thomas Trappenberg; Sageev Oore (NeurIPS, 2022)

Hierarchical Multi-Label Classification with Missing Information for Benthic Habitat Imagery. - Isaac Xu; Benjamin Misiuk; Scott C. Lowe; Martin Gillis; Thomas Trappenberg; Craig J. Brown (IJCNN, 2024)

BenthicNet: A global compilation of seafloor images for deep learning applications. - Scott C. Lowe; Benjamin Misiuk; Isaac Xu; Shakhboz Abdulazizov; Amit R. Baroi; Alex C. Bastos; Merlin Best; Vicki Ferrini; Ariell Friedman; Deborah Hart; Ove Hoegh-Guldberg; Daniel Ierodiaconou; et al. (CoRR, 2024)

A Generalized Transformer-based Radio Link Failure Prediction Framework in 5G RANs. - Kazi Hasan; Thomas Trappenberg; Israat Haque 001 (CoRR, 2024)

An Information-Geometric Formulation of Pattern Separation and Evaluation of Existing Indices. - Harvey Wang; Selena Singh; Thomas Trappenberg; Abraham Nunes (Entropy, 2024)

Pupillometry for Arm and Hand Motor Intent Detection. - Shane Forbrigger; Thomas Trappenberg; Ya-Jun Pan 001 (ICORR, 2025)

Masked strategies for images with small objects. - Martin Gillis; Ming Hill; Paul Hollensen; Alan Fine; Thomas Trappenberg (CoRR, 2025)

Platelet enumeration in dense aggregates. - Martin Gillis; Yogeshwar Shendye; Paul Hollensen; Alan Fine; Thomas Trappenberg (CoRR, 2025)