Learning to continually learn S Beaulieu, L Frati, T Miconi, J Lehman, KO Stanley, J Clune, N Cheney ECAI 2020, 992-1001, 2020 | 179 | 2020 |
Differentiable plasticity: training plastic neural networks with backpropagation T Miconi, K Stanley, J Clune International Conference on Machine Learning, 3559-3568, 2018 | 178 | 2018 |
Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks T Miconi Elife 6, e20899, 2017 | 157 | 2017 |
Defining and simulating open-ended novelty: requirements, guidelines, and challenges W Banzhaf, B Baumgaertner, G Beslon, R Doursat, JA Foster, B McMullin, ... Theory in Biosciences 135, 131-161, 2016 | 123 | 2016 |
Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity T Miconi, A Rawal, J Clune, KO Stanley arXiv preprint arXiv:2002.10585, 2020 | 90 | 2020 |
Evolution and complexity: The double-edged sword T Miconi Artificial life 14 (3), 325-344, 2008 | 56 | 2008 |
Why coevolution doesn’t “work”: superiority and progress in coevolution T Miconi European Conference on Genetic Programming, 49-60, 2009 | 37 | 2009 |
An improved system for artificial creatures evolution T Miconi, A Channon Proceedings of Artificial Life X, 255-261, 2006 | 37 | 2006 |
The impossibility of" fairness": a generalized impossibility result for decisions T Miconi arXiv preprint arXiv:1707.01195, 2017 | 33 | 2017 |
A virtual creatures model for studies in artificial evolution T Miconi, A Channon 2005 IEEE Congress on Evolutionary Computation 1, 565-572, 2005 | 33 | 2005 |
Evosphere: evolutionary dynamics in a population of fighting virtual creatures T Miconi 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on …, 2008 | 32 | 2008 |
Brain-inspired learning in artificial neural networks: a review S Schmidgall, R Ziaei, J Achterberg, L Kirsch, S Hajiseyedrazi, ... APL Machine Learning 2 (2), 2024 | 31 | 2024 |
A feedback model of attention explains the diverse effects of attention on neural firing rates and receptive field structure T Miconi, R VanRullen PLoS computational biology 12 (2), e1004770, 2016 | 29 | 2016 |
In silicon no one can hear you scream: Evolving fighting creatures T Miconi European Conference on Genetic Programming, 25-36, 2008 | 29 | 2008 |
When evolving populations is better than coevolving individuals: The blind mice problem T Miconi IJCAI, 647-652, 2003 | 29 | 2003 |
Spontaneous emergence of fast attractor dynamics in a model of developing primary visual cortex T Miconi, JL McKinstry, GM Edelman Nature communications 7 (1), 13208, 2016 | 24 | 2016 |
There's Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task T Miconi, L Groomes, G Kreiman Cerebral Cortex, 2015 | 23 | 2015 |
The gamma slideshow: object-based perceptual cycles in a model of the visual cortex T Miconi, R VanRullen Frontiers in Human Neuroscience 4, 205, 2010 | 22 | 2010 |
Estimating q (s, s’) with deep deterministic dynamics gradients A Edwards, H Sahni, R Liu, J Hung, A Jain, R Wang, A Ecoffet, T Miconi, ... International Conference on Machine Learning, 2825-2835, 2020 | 21 | 2020 |
Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworks T Miconi arXiv preprint arXiv:2107.01729, 2021 | 19* | 2021 |