| LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning S Franklin, T Madl, S D’Mello, J Snaider IEEE Transactions on Autonomous Mental Development, 1, 2013 | 169 | 2013 |
| The timing of the cognitive cycle T Madl, BJ Baars, S Franklin PloS one 6 (4), e14803, 2011 | 93 | 2011 |
| Computational cognitive models of spatial memory in navigation space: A review T Madl, K Chen, D Montaldi, R Trappl Neural Networks 65, 18-43, 2015 | 62 | 2015 |
| A LIDA cognitive model tutorial S Franklin, T Madl, S Strain, U Faghihi, D Dong, S Kugele, J Snaider, ... Biologically Inspired Cognitive Architectures 16, 105-130, 2016 | 40 | 2016 |
| Bayesian Integration of Information in Hippocampal Place Cells T Madl, S Franklin, K Chen, D Montaldi, R Trappl PLoS ONE, e89762, 2014 | 26 | 2014 |
| A LIDA-based model of the attentional blink T Madl, S Franklin ICCM 2012 proceedings, 283-288, 2012 | 20 | 2012 |
| Towards real-world capable spatial memory in the LIDA cognitive architecture RT Tamas Madl, Stan Franklin, Ke Chen, Daniela Montaldi Biologically Inspired Cognitive Architectures, 2016 | 17 | 2016 |
| Spatial Working Memory in the LIDA Cognitive Architecture T Madl, S Franklin, K Chen, R Trappl ICCM 2013, 2013 | 16 | 2013 |
| Deep machine learning application to the detection of preclinical neurodegenerative diseases of aging MJ Summers, T Madl, AE Vercelli, G Aumayr, DM Bleier, L Ciferri DigiCult: Scientific Journal on Digital Cultures 2 (2), 9-24, 2017 | 14 | 2017 |
| Constrained Incrementalist Moral Decision Making for a Biologically Inspired Cognitive Architecture T Madl, S Franklin A Construction Manual for Robots' Ethical Systems 1, 2015 | 14 | 2015 |
| Network analysis of heart beat intervals using horizontal visibility graphs T Madl Computing in Cardiology, 2016 | 13 | 2016 |
| Safe Semi-Supervised Learning of Sum-Product Networks M Trapp, T Madl, R Peharz, F Pernkopf, R Trappl Uncertainty in Artificial Intelligence, 2017 | 12 | 2017 |
| Exploring the structure of spatial representations T Madl, S Franklin, K Chen, R Trappl, D Montaldi PloS one 11 (6), e0157343, 2016 | 12 | 2016 |
| Structure inference in sum-product networks using infinite sum-product trees M Trapp, R Peharz, M Skowron, T Madl, F Pernkopf, R Trappl NIPS Workshop on Practical Bayesian Nonparametrics, 2016 | 12 | 2016 |
| A computational cognitive framework of spatial memory in brains and robots T Madl, S Franklin, K Chen, R Trappl Cognitive Systems Research 47, 147-172, 2018 | 8 | 2018 |
| Continuity and the Flow of Time - A Cognitive Science Perspective T Madl, S Franklin, J Snaider, U Faghihi Philosophy and Psychology of Time 1, 2016 | 8 | 2016 |
| Deep neural heart rate variability analysis T Madl NIPS 2016 Workshop on Machine Learning for Health (ML4HC), 2016 | 4 | 2016 |
| Bayesian mechanisms in spatial cognition: Towards real-world capable computational cognitive models of spatial memory T Madl The University of Manchester, Manchester, UK, 2016 | 1 | 2016 |
| Smartphone-based paroxysmal atrial fibrillation monitoring with robust generalization T Madl, D Madl arXiv preprint arXiv:1711.10862, 2017 | | 2017 |
| Deep machine learning detection of preclinical neurodegenerative diseases L Ciferri, MJ Summers, T Madl, A Vercelli, G Aumayr, GL Colombo World Health Organization, 2017 | | 2017 |
Stan FranklinProfessor Emeritus of Computer Science, The University of MemphisAdresse e-mail validée de memphis.edu
