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 develop machine learning methods for single-cell RNA sequencing data, with a focus on how the cellular composition of a sample determines its population-level phenotype. In TissueFormer (BMC Bioinformatics, 2026), I introduced a transformer that predicts sample-level labels from groups of single cells while retaining single-cell resolution — applying it to predict COVID-19 severity from blood scRNA-seq and to identify cortical areas from mouse spatial transcriptomics.

  1. Tissueformer: extending single-cell foundation models to predict population-level phenotypes
    Ari S Benjamin and Anthony M Zador
    BMC Bioinformatics, 2026
    DOI

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
  5. An Introduction to Connectionist Theories of Semantic Cognition
    Ari S Benjamin, Anna-Lea Beyer, Marianne De Heer Kloots, Jaedong Hwang, Hajer Karoui, and 6 more authors
    In Analytical Connectionism School, 2026

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