Jack Lindsey

  • Program Years: 2019-2023
  • Academic Institution: Columbia University
  • Field of Study: Computational Neuroscience
  • Academic Advisor: Ashok Litwin-Kumar
  • Practicum(s):
    Sandia National Laboratories, New Mexico (2021)
  • Degree(s):
    B.S. Mathematics, and M.S. Computer Science, Stanford University, 2019

Current Status


J. Lindsey* and Elias B. Issa (2023). "Factorized visual representations in the primate visual system and deep neural networks." bioRxiv preprint.
J. Lindsey and A. Litwin-Kumar (2022). "Theory of systems memory consolidation via recall-gated plasticity." bioRxiv preprint.
J. Lindsey* & A. Litwin-Kumar. (2022). Action-modulated Midbrain Dopamine Activity Arises from Distributed Control Policies. NeurIPS 2022.
J. Lindsey* & J.B. Aimone (2022). Sequence Learning and Consolidation on Loihi using On-chip Plasticity. Neuro-inspired Computational Elements Conference, 70-72.
K.G.C. Mizes, J. Lindsey, G.S. Escola, & B. Olveczky (2022). Dissociating the contributions of sensorimotor striatum to automatic and visually-guided motor sequences. Under review.
G. Chen, B. Kang, J. Lindsey, S. Druckmann, & N. Li. (2021). Modularity and robustness of frontal cortical networks. Cell, 184(14), 3717-3730. Link
F. Li, J. Lindsey, …, & G. M. Rubin (2020). The connectome of the adult Drosophila mushroom body provides insights into function. eLife 9: e62576.
J. Lindsey* & A. Litwin-Kumar (2020). Learning to Learn with Feedback and Local Plasticity. NeurIPS proceedings.
J. Lindsey*, S. Ocko, S. Ganguli, & S. Deny (2019). A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs. ICLR proceedings (oral presentation).
S. Ocko, J. Lindsey*, S. Ganguli, & S. Deny (2018). The Emergence of Multiple Retinal Cell types through Efficient Coding of Natural Movies. NeurIPS proceedings.
M. Jain & J. Lindsey* (2018). A Neural Network Model of Complementary Learning Systems. CogSci 2018 proceedings (oral presentation).
M. Jain & J. Lindsey* (2018). Semiparametric Reinforcement Learning. ICLR, Workshop Track.
J. Lindsey* (2017). Pre-Training Attention Mechanisms. NeurIPS 2017 Workshop on Cognitively Informed Artificial Intelligence.


Department of Energy Computational Science Graduate Fellowship
Dean's Award for Academic Achievement, Stanford University
J.E. Wallace Sterling Award for Scholastic Achievement, Stanford University
Elected to Phi Beta Kappa
Hertz Foundation Fellowship Finalist
Rhodes Scholarship Finalist