Nicolas Perez
-
- Lab member: 2018-
Nicolas Perez is a PhD student working on understanding how spiking neural networks can use heterogeneous neuron properties to carry out multiresolution processing of sensory data.
Videos
-
Neural heterogeneity promotes robust learningTalk / 2021
Talk on neural heterogeneity by Dan Goodman.
Publications
Note that only publications as part of the Neural Reckoning group are included here (see external publications below for full list).
2021
-
Perez-Nieves N, Goodman DFM (2021)
Sparse spiking gradient descent.
Advances in Neural Information Processing Systems -
Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFM (2021)
Neural heterogeneity promotes robust learning.
Nature Communications
2019
-
Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFM (2019)
Advantages of heterogeneity of parameters in spiking neural network training.
Cognitive Computational Neuroscience - + 1 conference paper
External publications
This is a short preview of the publications from other sources (ORCID, Semantic Scholar). Note that publications from work done outside the Neural Reckoning group are included in this list.
2023
2021
- Nicolas Perez Nieves, Dan F. M. Goodman (2021)
Sparse Spiking Gradient Descent
Neural Information Processing Systems - D. Mguni, et al. (2021)
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
International Conference on Learning Representations - Nicolas Perez Nieves, Yaodong Yang, Oliver Slumbers, D. Mguni, Jun Wang (2021)
Modelling Behavioural Diversity for Learning in Open-Ended Games
International Conference on Machine Learning
2020