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Mikio Aoi


We are interested in how populations of neurons coordinate their activity to implement computations. Specifically, we want to answer the question – What are the features of neural population activity that are important for understanding the dynamics of behavior? We approach this problem with two, parallel approaches:

  1. New data analysis tools providing useful, compact descriptions of neural activity vis-à-vis ever larger scales and complexity of data.
  2. New theoretical frameworks for understanding the computational significance of these statistical descriptions.

We engage in both of these approaches in close collaboration with experimentalists and theorists and a collaborative setting is part of the lab culture.

Select Publications

  • Aoi, M.C., Mante, V. & Pillow, J.W. Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making. Nat Neurosci 23, 1410–1420 (2020).
  • Aoi, M.C., Pillow, J.W. Model-based targeted dimensionality reduction for neuronal population data. Adv Neural Inf Process Syst. 2018;31:6690-6699.
  • Aoi, M.C., Pillow, J.W. Scalable Bayesian inference for high-dimensional neural receptive fields. bioRxiv. 2017.
  • Aoi, M., Lepage, K., Kramer, M. and Eden, U. Rate-adjusted spike–LFP coherence comparisons from spike-train statistics. Journal of Neuroscience Methods, 240, 141-153 (2015).


Professor Aoi received his PhD in Biomathematics at North Carolina State University, studying the regulatory mechanisms of cerebral blood flow. He did postdoctoral work in the Department of Mathematics and Statistics at Boston University as a part of the multi-institution Cognitive Rhythms Collaborative, and at Princeton Neuroscience Institute in the lab of Jonathan Pillow. His work spans the areas of computational neuroscience, machine learning, and statistics.

Mikio Aoi