Publications

A full list. For the most-cited and most-recent picks, see selected publications on the home page or my Google Scholar profile.

Pre-prints

P. Urbina-Rodriguez, Z. Fountas, F. E. Rosas, J. Wang, A. I. Luppi, H. Bou-Ammar, M. Shanahan, P. A. M. Mediano, “A Brain-like Synergistic Core in LLMs Drives Behaviour and Learning”, arXiv, 2026. (pdf)
J. Ma, J. Wang, Z. Fountas, “Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs”, arXiv, 2025. (pdf)
H. Hazard, Z. Fountas, M. A. Benfeghoul, A. Oomerjee, J. Wang, H. Bou-Ammar, “SuRe: Surprise-Driven Prioritised Replay for Continual LLM Learning”, arXiv, 2025. (pdf)
F. Wieser, M. Benfeghoul, H. Bou-Ammar, J. Wang, Z. Fountas, “Subjective Depth and Timescale Transformers: Learning Where and When to Compute”, arXiv, 2025. (pdf)
M. Benfeghoul, T. Delgado, A. Oomerjee, H. Bou-Ammar, J. Wang, Z. Fountas, “Untangling Component Imbalance in Hybrid Linear Attention Conversion Methods”, arXiv, 2025. (pdf)
M. Benfeghoul, U. Zahid, Q. Guo, Z. Fountas, “When in Doubt, Think Slow: Iterative Reasoning with Latent Imagination”, arXiv, 2023. (pdf)
U. Zahid, Q. Guo, K. Friston, Z. Fountas, “Curvature-Sensitive Predictive Coding with Approximate Laplace Monte Carlo”, arXiv, 2023. (pdf)
A. Zakharov, Q. Guo, Z. Fountas, “Long-horizon Video Prediction Using a Dynamic Latent Hierarchy”, arXiv, 2022. (pdf)
A. Mariola, Z. Fountas, L. Barnett, W. Roseboom, “Event Segmentation in Continuous, Naturalistic Videos”, PsyArXiv, 2022. (pdf)
O. Miksik et al., “Building Proactive Voice Assistants: When and How (not) to Interact”, arXiv, 2020. (pdf)
Z. Fountas, M. Shanahan, “Assessing Selectivity in the Basal Ganglia: The 'Gearbox' Hypothesis”, bioRxiv, 2017. (pdf)

Journal articles

U. Zahid, Q. Guo, Z. Fountas, “Predictive Coding as a Neuromorphic Alternative to Backpropagation: A Critical Evaluation”, Neural Computation 35(12): 1881–1909, 2023. (arxiv pdf)
M. T. Sherman, Z. Fountas, A. K. Seth, W. Roseboom, “Trial-by-trial Predictions of Subjective Time from Human Brain Activity”, PLoS Computational Biology 18(7): e1010223, 2022. (html)
Z. Fountas, A. Sylaidi, K. Nikiforou, A. Seth, M. Shanahan, W. Roseboom, “A Predictive Processing Model of Episodic Memory and Time Perception”, Neural Computation 34(7): 1501–1544, 2022. (biorxiv version)
T. Wilschut, F. Sense, M. van der Velde, Z. Fountas, S. Maass, H. van Rijn, “Benefits of Adaptive Learning Transfer from Typing-Based Learning to Speech-Based Learning”, Frontiers in Artificial Intelligence, 2021. (link)
A. Loffler, A. Sylaidi, Z. Fountas, P. Haggard, “A Hierarchical Attractor Network Model of Perceptual versus Intentional Decision Updates”, Nature Communications, 2021. (html)
N. Sajid, E. Holmes, T. M. Hope, Z. Fountas, C. J. Price, K. J. Friston, “Simulating Lesion-Dependent Functional Recovery Mechanisms”, Scientific Reports, 2021. (html)
M. Suárez-Pinilla, K. Nikiforou, Z. Fountas, A. Seth, W. Roseboom, “Perceptual Content, Not Physiological Signals, Determines Perceived Duration When Viewing Dynamic, Natural Scenes”, Collabra: Psychology, 2019. (link)
W. Roseboom, Z. Fountas, K. Nikiforou, D. Bhowmik, M. Shanahan, A. Seth, “Activity in Perceptual Classification Networks as a Basis for Human Subjective Time Perception”, Nature Communications, 2019. (pdf)
Z. Fountas, M. Shanahan, “The Role of Cortical Oscillations in a Spiking Neural Network Model of the Basal Ganglia”, PLOS ONE, 2017. (link)
D. Gamez, Z. Fountas, A. K. Fidjeland, “A Neurally Controlled Computer Game Avatar with Human-like Behavior”, IEEE Transactions on Computational Intelligence and AI in Games, March 2013.

Book chapters

R. Vertegaal, T. Merritt, S. Greenberg, A. P. Tarun, Z. Li, Z. Fountas, “Interactive Inference: A Neuromorphic Theory of Human-Computer Interaction”, 2025.
Z. Fountas, A. Zakharov, “Bayesian Sense of Time in Biological and Artificial Brains”, in Time and Science (R. Lestienne & P. Harris, eds.), World Scientific, 2022. (arXiv link)

Conference proceedings

A. Oomerjee, Z. Fountas, H. Bou-Ammar, J. Wang, “Bottlenecked Transformers: Periodic KV Cache Consolidation for Generalised Reasoning”, accepted in ICLR 2026.
Z. Fountas, M. A. Benfeghoul, A. Oomerjee, F. Christopoulou, G. Lampouras, H. Bou-Ammar, J. Wang, “Human-like Episodic Memory for Infinite Context LLMs”, ICLR 2025. (pdf)
U. Zahid, Q. Guo, Z. Fountas, “Sample as You Infer: Predictive Coding with Langevin Dynamics”, ICML 2024. (pdf)
N. Sajid, P. Tigas, Z. Fountas, Q. Guo, A. Zakharov, L. Da Costa, “Modelling Non-Reinforced Preferences Using Selective Attention”, CoLLAs Workshop, 2022. (pdf)
W. Roseboom, A. Seth, M. Sherman, Z. Fountas, “The Perception of Time in Humans, Brains and Machines”, 13th Symposium of the BIAL Foundation. (PsyArXiv)
A. Zakharov, Q. Guo, Z. Fountas, “Variational Predictive Routing with Nested Subjective Timescales”, ICLR 2022. (pdf, project page)
N. Sajid, P. Tigas, A. Zakharov, Z. Fountas, K. Friston, “Exploration and Preference Satisfaction Trade-off in Reward-free Learning”, URL Workshop ICML, 2021. (pdf)
A. Zakharov, M. Crosby, Z. Fountas, “Episodic Memory for Learning Subjective-Timescale Models”, URL Workshop ICML, 2021. (pdf)
T. Wilschut, M. van der Veide, F. Sense, Z. Fountas, H. van Rijn, “Translating a Typing-Based Adaptive Learning Model to Speech-Based L2 Vocabulary Learning”, ACM UMAP 2021.
Z. Fountas, N. Sajid, P. A. M. Mediano, K. Friston, “Deep Active Inference Agents Using Monte-Carlo Methods”, NeurIPS 2020. (pdf and supplementary)
C. Bao, Z. Fountas, T. Olugbade, N. Bianchi-Berthouze, “Multimodal Data Fusion based on the Global Workspace Theory”, ACM ICMI 2020. (link)
J. Yamada, J. Shawe-Taylor, Z. Fountas, “Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning”, IJCNN 2020. (arXiv)
M. Sherman, Z. Fountas, A. K. Seth, W. Roseboom, “The Accumulation of Salient Changes in Visual Cortex Predicts Subjective Time”, CCN 2019, Berlin. (pdf)
Z. Fountas, K. Nikiforou, D. Bhowmik, M. Shanahan, W. Roseboom, A. Seth, “Clockless Biologically-Plausible Architecture for Temporal Perception Using Convolutional Neural Networks”, CCN 2017. (pdf)
J. C. Farah, C. Kaplanis, C. Snowden, L. Milic, Z. Fountas, P. A. M. Mediano, “Implementation of Attentional Bistability in a Computational Model of the Dragonfly Visual System”, CCN 2017. (pdf)
Z. Fountas, M. Shanahan, “GPU-based Fast Parameter Optimization for Phenomenological Spiking Neural Models”, IJCNN 2015, Killarney. (link)
Z. Fountas, M. Shanahan, “Phase Offset Between Slow Oscillatory Cortical Inputs Influences Competition in a Model of the Basal Ganglia”, IJCNN 2014, Beijing. (pdf)
Z. Fountas, M. Shanahan, “A Cognitive Neural Architecture as a Robot Controller”, in Biomimetic and Biohybrid Systems, Springer, 2013, pp. 371–373.
Z. Fountas, D. Gamez, A. K. Fidjeland, “A Neuronal Global Workspace for Human-like Control of a Computer Game Character”, IEEE CIG 2011, Seoul. (pdf)

Theses & technical reports

“Action Selection in the Rhythmic Brain: The Role of the Basal Ganglia and Tremor”, PhD thesis, Imperial College London, 2016. (link)
“Spiking Neural Networks for Human-like Avatar Control in a Simulated Environment”, MSc thesis, Imperial College London, 2011. (pdf)
“RoboCupRescue 2009 — Robot League Team P.A.N.D.O.R.A. (Greece)”, RoboCup World Championship and Symposium, Graz, 2009. (TDP/pdf)
“RoboCupRescue 2008 — Robot League Team P.A.N.D.O.R.A. (Greece)”, RoboCup World Championship and Symposium, Suzhou, 2008. (TDP/pdf)

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