| 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 | 113 | 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 | 105 | 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 | 38 | 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 | 36 | 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 | 30 | 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 | 24* | 2021 |
| 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 | 22 | 2020 |
| 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 | 21 | 2019 |
| 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 | 19* | 2022 |
| 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 | 19 | 2011 |
| 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 | 17 | 2020 |
| Spiking neural networks for human-like avatar control in a simulated environment Z Fountas Computing Science of Imperial College London, 1-60, 2011 | 17* | 2011 |
| 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 | 14 | 2021 |
| Episodic memory for subjective-timescale models A Zakharov, M Crosby, Z Fountas ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021 | 14* | 2021 |
| Variational Predictive Routing with Nested Subjective Timescales A Zakharov, Q Guo, Z Fountas International Conference on Learning Representations (ICLR), 2022 | 11 | 2022 |
| Advances in neural information processing systems Z Fountas, N Sajid, P Mediano, K Friston, H Larochelle, M Ranzato, ... Curran, Red Hook, NY 33, 11662-11675, 2020 | 11 | 2020 |
| 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 | 11 | 2014 |
| 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 | 10 | 2020 |
| Predictive Coding as a Neuromorphic Alternative to Backpropagation: A Critical Evaluation U Zahid, Q Guo, Z Fountas Neural Computation 35 (12), 1881-1909, 2023 | 9 | 2023 |
| 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 | 8 | 2015 |
Murray ShanahanImperial College London / DeepMindVerified email at imperial.ac.uk
