| Domain adaptation in reinforcement learning via latent unified state representation J Xing, T Nagata, K Chen, X Zou, E Neftci, JL Krichmar Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 10452 …, 2021 | 51 | 2021 |
| CARLsim 6: an open source library for large-scale, biologically detailed spiking neural network simulation L Niedermeier, K Chen, J Xing, A Das, J Kopsick, E Scott, N Sutton, ... 2022 International Joint Conference on Neural Networks (IJCNN), 1-10, 2022 | 45 | 2022 |
| Neurorobots as a means toward neuroethology and explainable AI K Chen, T Hwu, HJ Kashyap, JL Krichmar, K Stewart, J Xing, X Zou Frontiers in Neurorobotics 14, 570308, 2020 | 27 | 2020 |
| Robust resting-state dynamics in a large-scale spiking neural network model of area CA3 in the mouse hippocampus JD Kopsick, C Tecuatl, K Moradi, SM Attili, HJ Kashyap, J Xing, K Chen, ... Cognitive Computation 15 (4), 1190-1210, 2023 | 18 | 2023 |
| Neuroevolution of a recurrent neural network for spatial and working memory in a simulated robotic environment X Zou, E Scott, A Johnson, K Chen, D Nitz, K De Jong, J Krichmar Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021 | 8 | 2021 |
| Differential spatial representations in hippocampal CA1 and subiculum emerge in evolved spiking neural networks K Chen, A Johnson, EO Scott, X Zou, KA De Jong, DA Nitz, JL Krichmar 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 7 | 2021 |
| Cortical motion perception emerges from dimensionality reduction with evolved spike-timing-dependent plasticity rules K Chen, M Beyeler, JL Krichmar Journal of Neuroscience 42 (30), 5882-5898, 2022 | 3 | 2022 |
| What can computer vision learn from visual neuroscience? Introduction to the special issue K Chen, HJ Kashyap, JL Krichmar, X Li Biological Cybernetics 117 (4), 297-298, 2023 | 1 | 2023 |
| A Computational Investigation of Cortical Motion Perception and Hippocampal Spatial Memory K Chen University of California, Irvine, 2023 | | 2023 |
| MSTd-like response properties emerge from evolving STDP and homeostatic parameters in a Spiking Neural Network (SNN) model K Chen, M Beyeler, J Krichmar | | |