I’m a PhD student studying machine learning and computational neuroscience at University of California, Irvine. My research uses ideas from neuroscience to improve learning in artificial neural networks and uses ideas from machine learning to develop theories of learning and memory in the brain. For more information see my CV, about page, or github.
University of California, Irvine
Cognitive Science Department
Alonso, N., Krichmar, J., & Neftci, E. (2023). Understanding and Improving Optimization in Predictive Coding Networks. arXiv preprint arXiv:2305.13562.
Alonso, N., Millidge, B., Krichmar, J., & Neftci, E. O. (2022). A Theoretical Framework for Inference Learning. Advances in Neural Information Processing Systems, 35, 37335-37348.
Wang, F., Alonso, N., & Teeter, C. (2022). Combining Spike Time Dependent Plasticity (STDP) and Backpropagation (BP) for Robust and Data Efficient Spiking Neural Networks (SNN) (No. SAND2022-16962). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States).
Alonso, N., & Neftci, E. (2021). Tightening the Biological Constraints on Gradient-Based Predictive Coding.
In International Conference on Neuromorphic Systems 2021 (pp. 1-9).