Andrea Navas-Olive

Computer models & AI

PhD student

Andrea graduated in Physics in 2016 and obtained a Master degree in Computer Science in 2017, with a specialization in Machine Learning and Neuroinformatics. She is currently undergoing her PhD thesis in the Prida lab with a focus in understanding the dynamics of hippocampal oscillations.

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During this period, she has developed a computational model of the deep-superficial functionality on the CA1 hippocampal region, which successfully reproduced many of the biophysical and electrophysiological aspects of CA1 pyramidal cells recorded in vivo. Then, using machine learning she started exploiting deep neural networks to study relevant motifs underlying electrophysiological events during memory encoding and consolidation.

  • Further training, including Deep Learning Specialized Program from (120h), and Neuro-inspired Computation Course from IRCN (30h)
  • Teaching, Software analysis and design course from Computer Engineering degree EPS-UAM (90h), and Teacher Assistant at Neuromatch Academy Computational Neuroscience Summer School (90h)
  • Scientific diffusion and equality plan, including several talks around the country, and Award in the Three Minute Thesis Competition in Engineering and Architecture field.


  • A. Navas-Olive, et al. Multimodal determinants of phase-locked dynamics across deep-superficial hippocampal sublayers during theta oscillations. Nat Commun 11, 2217 (2020).Link to the paper
  • A. Sanchez-Aguilera, A. Navas-Olive, M Valero. Feedback and feedforward inhibition may resonate distinctly in the ripple symphony. J Neuroscience, 38 (2018).Link to the paper