| 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 | 233 | 2013 |
| The timing of the cognitive cycle T Madl, BJ Baars, S Franklin PloS one 6 (4), e14803, 2011 | 127 | 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 | 98 | 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 | 67 | 2016 |
| Bayesian Integration of Information in Hippocampal Place Cells T Madl, S Franklin, K Chen, D Montaldi, R Trappl PLoS ONE, e89762, 2014 | 34 | 2014 |
| A LIDA-based model of the attentional blink T Madl, S Franklin ICCM 2012 proceedings 283, 2012 | 22 | 2012 |
| Network analysis of heart beat intervals using horizontal visibility graphs T Madl Computing in Cardiology, 2016 | 21 | 2016 |
| 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 | 21 | 2016 |
| 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 DigitCult-Scientific Journal on Digital Cultures 2 (2), 9-24, 2017 | 20 | 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 | 20 | 2015 |
| 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 | 19 | 2018 |
| Spatial Working Memory in the LIDA Cognitive Architecture T Madl, S Franklin, K Chen, R Trappl ICCM 2013, 2013 | 19 | 2013 |
| Safe Semi-Supervised Learning of Sum-Product Networks M Trapp, T Madl, R Peharz, F Pernkopf, R Trappl Uncertainty in Artificial Intelligence, 2017 | 17 | 2017 |
| Exploring the structure of spatial representations T Madl, S Franklin, K Chen, R Trappl, D Montaldi PloS one 11 (6), e0157343, 2016 | 15 | 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 | 14 | 2016 |
| 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 | 9 | 2016 |
| Deep neural heart rate variability analysis T Madl NIPS 2016 Workshop on Machine Learning for Health (ML4HC), 2016 | 6 | 2016 |
| The Timing of the Cognitive Cycle M Tamas, B Bernard, F Stan PLos ONE, 4, 2011 | 2 | 2011 |
| Automated Disease Classification Using Whole Genome Sequencing (WGS) and Whole Transcriptome Sequencing (WTS) Data with Transparent Artificial Intelligence (AI) N Nadarajah, EP Coyotl, J Golden, S Hutter, T Madl, M Meggendorfer, ... Blood 138, 275, 2021 | 1 | 2021 |
| Bayesian mechanisms in spatial cognition: Towards real-world capable computational cognitive models of spatial memory T Madl PQDT-UK & Ireland, 2016 | 1 | 2016 |
Stan FranklinProfessor Emeritus of Computer Science, The University of MemphisVerified email at memphis.edu
