Research

My research interests include theoretical neuroscience, machine learning, and neural data analysis.

Theoretical neuroscience: Neuromodulation and Cellular Diversity

Much of my ongoing research is about building a connectionist framework for understanding neuromodulation. I am also interested in the roles of cell types in neural circuits.

  1. A role for cortical interneurons as adversarial discriminators
    Ari S Benjamin and Konrad P Kording
    PLOS Computational Biology, 2023
    DOI
  2. Walking the Weight Manifold: a Topological Approach to Conditioning Inspired by Neuromodulation
    Ari S Benjamin, Kyle Daruwalla, Christian Pehle, and Anthony M Zador
    arXiv preprint arXiv:2505.22994, 2025
    arXiv

Machine learning for single-cell data analysis

I am currently developing machine learning methods to analyze single-cell RNA sequencing data, focusing on how cellular diversity influences neural circuit function.

Stay tuned!

    Neural Network Theory

    I study the theoretical foundations of neural networks, exploring how learning algorithms shape representations and how network architecture affects computational capacity. This work bridges machine learning theory with neuroscience to understand both artificial and biological learning systems.

    1. Measuring and regularizing networks in function space
      Ari S Benjamin, David Rolnick, and Konrad Kording
      In ICLR, 2019
      arXiv
    2. Shared visual illusions between humans and artificial neural networks
      Ari Benjamin, Cheng Qiu, Ling-Qi Zhang, Konrad Kording, and Alan Stocker
      In 2019 Conference on Cognitive Computational Neuroscience, 2019
      DOI
    3. Efficient neural codes naturally emerge through gradient descent learning
      Ari S Benjamin, Ling-Qi Zhang, Cheng Qiu, Alan A Stocker, and Konrad P Kording
      Nature Communications, 2022
      DOI
    4. Continual learning with the neural tangent ensemble
      Ari Benjamin, Christian-Gernot Pehle, and Kyle Daruwalla
      Advances in Neural Information Processing Systems, 2024
      arXiv

    Machine learning for analyzing neural recordings

    I am currently developing machine learning methods to analyze neural recordings, focusing on how cellular diversity influences neural circuit function.

    1. The roles of supervised machine learning in systems neuroscience
      Joshua I Glaser, Ari S Benjamin, Roozbeh Farhoodi, and Konrad P Kording
      Progress in neurobiology, 2019
      DOI
    2. Modern machine learning as a benchmark for fitting neural responses
      Ari S Benjamin, Hugo L Fernandes, Tucker Tomlinson, Pavan Ramkumar, Chris VerSteeg, and 3 more authors
      Frontiers in computational neuroscience, 2018
      DOI
    3. Machine learning for neural decoding
      Joshua I Glaser, Ari S Benjamin, Raeed H Chowdhury, Matthew G Perich, Lee E Miller, and 1 more author
      eneuro, 2020
      DOI

    Past work

    Bio-inspired materials science and molecular dynamics

    Before I transitioned to neuroscience, I worked on bio-inspired materials science and molecular dynamics simulations. I was interested in self-assembly and how molecular interactions can lead to complex structures. The common thread that connected these interests was looking at nature in terms of its function, as might an engineer, as well as an interest in complex systems.

    1. Regulating ion transport in peptide nanotubes by tailoring the nanotube lumen chemistry
      Luis Ruiz, Ari Benjamin, Matthew Sullivan, and Sinan Keten
      The journal of physical chemistry letters, 2015
      DOI
    2. Polymer conjugation as a strategy for long-range order in supramolecular polymers
      Ari Benjamin and Sinan Keten
      The Journal of Physical Chemistry B, 2016
      DOI