Uncalibrated Visual Servoing. - Michael W. Spratling; Roberto Cipolla (BMVC, 1996)

A self-organising neural network for modelling cortical development. - Michael W. Spratling; Gillian Hayes 001 (ESANN, 1998)

Learning sensory-motor cortical mappings without training. - Michael W. Spratling; Gillian Hayes 001 (ESANN, 1998)

Artificial ontogenesis : a connectionist model of development. - Michael W. Spratling (1999)

Learning Synaptic Clusters for Nonlinear Dendritic Processing. - Michael W. Spratling; Gillian M. Hayes (Neural Process. Lett., 2000)

Preintegration Lateral Inhibition Enhances Unsupervised Learning. - Michael W. Spratling; M. H. Johnson (Neural Comput., 2002)

Exploring the functional significance of dendritic inhibition in cortical pyramidal cells. - Michael W. Spratling; M. H. Johnson (Neurocomputing, 2003)

Neural coding strategies and mechanisms of competition. - Michael W. Spratling; M. H. Johnson (Cogn. Syst. Res., 2004)

Learning Viewpoint Invariant Perceptual Representations from Cluttered Images. - Michael W. Spratling (IEEE Trans. Pattern Anal. Mach. Intell., 2005)

Learning Image Components for Object Recognition. - Michael W. Spratling (J. Mach. Learn. Res., 2006)

Reconciling predictive coding and biased competition models of cortical function. - Michael W. Spratling (Frontiers Comput. Neurosci., 2008)

Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation. - Michael W. Spratling; Kris De Meyer; Raul Kompass (Comput. Intell. Neurosci., 2009)

Learning Posture Invariant Spatial Representations Through Temporal Correlations. - Michael W. Spratling (IEEE Trans. Auton. Ment. Dev., 2009)

Multiplicative Gain Modulation Arises Through Unsupervised Learning in a Predictive Coding Model of Cortical Function. - Kris De Meyer; Michael W. Spratling (Neural Comput., 2011)

Predictive coding accounts for V1 response properties recorded using reverse correlation. - Michael W. Spratling (Biol. Cybern., 2012)

Unsupervised Learning of Generative and Discriminative Weights Encoding Elementary Image Components in a Predictive Coding Model of Cortical Function. - Michael W. Spratling (Neural Comput., 2012)

Predictive coding as a model of the V1 saliency map hypothesis. - Michael W. Spratling (Neural Networks, 2012)

Image Segmentation Using a Sparse Coding Model of Cortical Area V1. - Michael W. Spratling (IEEE Trans. Image Process., 2013)

Classification using sparse representations: a biologically plausible approach. - Michael W. Spratling (Biol. Cybern., 2014)

A single functional model of drivers and modulators in cortex. - Michael W. Spratling (J. Comput. Neurosci., 2014)

Predictive Coding. - Michael W. Spratling (Encyclopedia of Computational Neuroscience, 2014)

A neural model of binocular saccade planning and vergence control. - Muhammad Wasif; Michael W. Spratling (Adapt. Behav., 2015)

Contour Detection in Colour Images Using a Neurophysiologically Inspired Model. - Qi Wang 019; Michael W. Spratling (Cogn. Comput., 2016)

A neural implementation of Bayesian inference based on predictive coding. - Michael W. Spratling (Connect. Sci., 2016)

A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms. - Toni Heidenreich; Michael W. Spratling (CoRR, 2016)

Predictive coding as a model of cognition. - Michael W. Spratling (Cogn. Process., 2016)

A neural implementation of the Hough transform and the advantages of explaining away. - Michael W. Spratling (Image Vis. Comput., 2016)

A simplified texture gradient method for improved image segmentation. - Qi Wang 019; Michael W. Spratling (Signal Image Video Process., 2016)

An Integrated System based on Binocular Learned Receptive Fields for Saccade-vergence on Visually Salient Targets. - Daniele Re; Agostino Gibaldi; Silvio P. Sabatini; Michael W. Spratling (VISIGRAPP (6: VISAPP), 2017)

A neural model for eye-head-arm coordination. - Muhammad Wasif; Michael W. Spratling (Adv. Robotics, 2017)

A Hierarchical Predictive Coding Model of Object Recognition in Natural Images. - Michael W. Spratling (Cogn. Comput., 2017)

A Neural Model of Coordinated Head and Eye Movement Control. - Muhammad Wasif; Michael W. Spratling (J. Intell. Robotic Syst., 2017)

Two collaborative filtering recommender systems based on sparse dictionary coding. - Ismail Emre Kartoglu; Michael W. Spratling (Knowl. Inf. Syst., 2018)

Contour detection refined by a sparse reconstruction-based discrimination method. - Qi Wang 019; Michael W. Spratling (Signal Image Video Process., 2018)

Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN. - Bo Gao; Michael W. Spratling (CoRR, 2020)

Target Propagation in Recurrent Neural Networks. - Nikolay Manchev; Michael W. Spratling (J. Mach. Learn. Res., 2020)

Explaining away results in accurate and tolerant template matching. - Michael W. Spratling (Pattern Recognit., 2020)

Registration Based Few-Shot Anomaly Detection. - Chaoqin Huang; Haoyan Guan; Aofan Jiang; Ya Zhang 002; Michael W. Spratling; Yanfeng Wang 001 (ECCV (24), 2022)

CobNet: Cross Attention on Object and Background for Few-Shot Segmentation. - Haoyan Guan; Michael W. Spratling (ICPR, 2022)

On the Biological Plausibility of Orthogonal Initialisation for Solving Gradient Instability in Deep Neural Networks. - Nikolay Manchev; Michael W. Spratling (ISCMI, 2022)

Shape-Texture Debiased Training for Robust Template Matching. - Bo Gao; Michael W. Spratling (Sensors, 2022)

More robust object tracking via shape and motion cue integration. - Bo Gao; Michael W. Spratling (Signal Process., 2022)

The Importance of Anti-Aliasing in Tiny Object Detection. - Jinlai Ning; Michael W. Spratling (ACML, 2023)

Data augmentation alone can improve adversarial training. - Lin Li 070; Michael W. Spratling (ICLR, 2023)

Rethinking the Backbone Architecture for Tiny Object Detection. - Jinlai Ning; Haoyan Guan; Michael W. Spratling (VISIGRAPP (5: VISAPP), 2023)

Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing. - Lin Li 070; Michael W. Spratling (CoRR, 2023)

When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review. - Maxime Fontana; Michael W. Spratling; Miaojing Shi (CoRR, 2023)

Comprehensive Assessment of the Performance of Deep Learning Classifiers Reveals a Surprising Lack of Robustness. - Michael W. Spratling (CoRR, 2023)

OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift. - Lin Li 070; Yifei Wang; Chawin Sitawarin; Michael W. Spratling (CoRR, 2023)

Understanding and combating robust overfitting via input loss landscape analysis and regularization. - Lin Li 070; Michael W. Spratling (Pattern Recognit., 2023)

Explaining away results in more robust visual tracking. - Bo Gao; Michael W. Spratling (Vis. Comput., 2023)

One Prompt Word is Enough to Boost Adversarial Robustness for Pre-Trained Vision-Language Models. - Lin Li 070; Haoyan Guan; Jianing Qiu; Michael W. Spratling (CVPR, 2024)

OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift. - Lin Li 070; Yifei Wang; Chawin Sitawarin; Michael W. Spratling (ICML, 2024)

Query semantic reconstruction for background in few-shot segmentation. - Haoyan Guan; Michael W. Spratling (Vis. Comput., 2024)

When Multitask Learning Meets Partial Supervision: A Computer Vision Review. - Maxime Fontana; Michael W. Spratling; Miaojing Shi (Proc. IEEE, 2024)

Learning multi-modal recurrent neural networks with target propagation. - Nikolay Manchev; Michael W. Spratling (Comput. Intell., 2024)

Optimizing Dense Visual Predictions Through Multi-Task Coherence and Prioritization. - Maxime Fontana; Michael W. Spratling; Miaojing Shi (WACV, 2025)

Filter competition results in more robust Convolutional Neural Networks. - Bo Gao; Michael W. Spratling (Neurocomputing, 2025)

A comprehensive assessment benchmark for rigorously evaluating deep learning image classifiers. - Michael W. Spratling (Neural Networks, 2025)

Robust shortcut and disordered robustness: Improving adversarial training through adaptive smoothing. - Lin Li 070; Michael W. Spratling (Pattern Recognit., 2025)

A margin-based replacement for cross-entropy loss. - Michael W. Spratling; Heiko H. Schütt (CoRR, 2025)

Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models. - Bo Gao; Michael W. Spratling (CoRR, 2025)

AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation. - Lin Li 070; Jianing Qiu; Michael W. Spratling (Int. J. Comput. Vis., 2025)

Few-Shot Anomaly Detection via Category-Agnostic Registration Learning. - Chaoqin Huang; Haoyan Guan; Aofan Jiang; Ya Zhang 002; Michael W. Spratling; Xinchao Wang; Yanfeng Wang 001 (IEEE Trans. Neural Networks Learn. Syst., 2025)