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Nick Alonso

I’m a researcher studying machine learning and computational neuroscience. I currently work at a startup in the bay area and recently graduated from UC, Irvine with a PhD in cognitive science. For more information see my CV, about page, or github.

Email: nalonso2@uci.edu
LinkedIn
CV

News

  • 3/9 New blog post ‘The Meta-Problem Test for AI Consciousness‘.
  • 2/22 Just presented by paper ‘Understanding and Improving Optimization in Predictive Coding Networks’ at AAAI-2024, which was selected for a talk.
  • 11/06 Just defended my dissertation “An Energy-based Approach to Learning and Memory in Artificial Neural Networks”!
  • 9/30 New article in the online magazine ‘The Gradient’ on AI consciousness (link)
  • 7/27 New article preprint “A Sparse Quantized Hopfield Network for Online-Continual Memory” (link)

Papers

Alonso, N., & Krichmar, J. (Forthcoming). A Sparse Quantized Hopfield Network for Online-Continual Memory. Nature Communications.

Alonso, N., Krichmar, J., & Neftci, E. (Forthcoming). Understanding and Improving Optimization in Predictive Coding Networks. Proceedings of the AAAI conference on artificial intelligence (Selected for oral presentation)

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).