Zafeirios Fountas
Zafeirios Fountas
Senior Research Scientist, Huawei Technologies, London
Verified email at - Homepage
Cited by
Cited by
Deep active inference agents using Monte-Carlo methods
Z Fountas, N Sajid, PAM Mediano, K Friston
Advances in Neural Information Processing Systems (NeurIPS) 33, 11662 - 11675, 2020
Activity in perceptual classification networks as a basis for human subjective time perception
W Roseboom, Z Fountas, K Nikiforou, D Bhowmik, M Shanahan, AK Seth
Nature communications 10 (1), 267, 2019
A neurally controlled computer game avatar with humanlike behavior
D Gamez, Z Fountas, AK Fidjeland
IEEE Transactions on Computational Intelligence and AI in Games 5 (1), 1-14, 2012
A predictive processing model of episodic memory and time perception
Z Fountas, A Sylaidi, K Nikiforou, AK Seth, M Shanahan, W Roseboom
Neural Computation 34 (7), 1501-1544, 2022
The role of cortical oscillations in a spiking neural network model of the basal ganglia
Z Fountas, M Shanahan
PLoS One 12 (12), e0189109, 2017
Exploration and preference satisfaction trade-off in reward-free learning
N Sajid, P Tigas, A Zakharov, Z Fountas, K Friston
ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021
Perceptual content, not physiological signals, determines perceived duration when viewing dynamic, natural scenes
M Suárez-Pinilla, K Nikiforou, Z Fountas, AK Seth, W Roseboom
Collabra: Psychology 5 (1), 55, 2019
A neuronal global workspace for human-like control of a computer game character
Z Fountas, D Gamez, AK Fidjeland
2011 IEEE Conference on Computational Intelligence and Games (CIG'11), 350-357, 2011
Building proactive voice assistants: When and how (not) to interact
O Miksik, I Munasinghe, J Asensio-Cubero, SR Bethi, ST Huang, S Zylfo, ...
arXiv preprint arXiv:2005.01322, 2020
Trial-by-trial predictions of subjective time from human brain activity
MT Sherman, Z Fountas, AK Seth, W Roseboom
PLOS Computational Biology 18 (7), e1010223, 2022
Evolution of a complex predator-prey ecosystem on large-scale multi-agent deep reinforcement learning
J Yamada, J Shawe-Taylor, Z Fountas
2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020
Spiking neural networks for human-like avatar control in a simulated environment
Z Fountas
Computing Science of Imperial College London, 1-60, 2011
Episodic memory for subjective-timescale models
A Zakharov, M Crosby, Z Fountas
ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021
Phase offset between slow oscillatory cortical inputs influences competition in a model of the basal ganglia
Z Fountas, M Shanahan
2014 International Joint Conference on Neural Networks (IJCNN), 2407-2414, 2014
Variational Predictive Routing with Nested Subjective Timescales
A Zakharov, Q Guo, Z Fountas
International Conference on Learning Representations (ICLR), 2022
Benefits of adaptive learning transfer from typing-based learning to speech-based learning
T Wilschut, F Sense, M van der Velde, Z Fountas, SC Maaß, H van Rijn
Frontiers in artificial intelligence 4, 780131, 2021
Multimodal data fusion based on the global workspace theory
C Bao, Z Fountas, T Olugbade, N Bianchi-Berthouze
Proceedings of the 2020 International Conference on Multimodal Interaction …, 2020
GPU-based fast parameter optimization for phenomenological spiking neural models
Z Fountas, M Shanahan
2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015
Translating a typing-based adaptive learning model to speech-based L2 vocabulary learning
T Wilschut, M van der Velde, F Sense, Z Fountas, H van Rijn
Proceedings of the 29th ACM conference on user modeling, Adaptation and …, 2021
Simulating lesion-dependent functional recovery mechanisms
N Sajid, E Holmes, TM Hope, Z Fountas, CJ Price, KJ Friston
Scientific Reports 11 (1), 7475, 2021
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