EJ Chichilnisky — accepted AI-conference papers

2026

Learning Biophysical Models of Large-Scale Multineuronal Data To Enable Precise Neurostimulation

Amrith Lotlikar, Ian Tanoh, Praful Vasireddy, Andrew Lanpouthakoun, Michael A Sommeling, Ramandeep Vilkhu +6

ICML 2026spotlight

Multi-compartment Hodgkin–Huxley (HH) models provide a principled framework for predicting neural dynamics and responses to electrical stimulation. However, fitting HH biophysical parameters typically requires intracellular recordings, which are invasive and low-throughput, limiting the ability to c…

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2022

Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors

Eric Gene Wu, Nora Brackbill, Alexander Sher, Alan Litke, Eero P Simoncelli, EJ Chichilnisky

NeurIPS 2022accept

Visual information arriving at the retina is transmitted to the brain by signals in the optic nerve, and the brain must rely solely on these signals to make inferences about the visual world. Previous work has probed the content of these signals by directly reconstructing images from retinal activit…

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2018

Learning a neural response metric for retinal prosthesis

Nishal P Shah, Sasidhar Madugula, EJ Chichilnisky, Yoram Singer, Jonathon Shlens

ICLR 2018poster

Retinal prostheses for treating incurable blindness are designed to electrically stimulate surviving retinal neurons, causing them to send artificial visual signals to the brain. However, electrical stimulation generally cannot precisely reproduce normal patterns of neural activity in the retina.…

Cited by 7 Source ↗PDF ↗Scholar ↗

2017

Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses

Eleanor Batty, Josh Merel, Nora Brackbill, Alexander Heitman, Alexander Sher, Alan Litke +2

ICLR 2017poster

Developing accurate predictive models of sensory neurons is vital to understanding sensory processing and brain computations. The current standard approach to modeling neurons is to start with simple models and to incrementally add interpretable features. An alternative approach is to start with a m…

Cited by 93 Source ↗Scholar ↗