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arXiv Machine Learning By Luca Ghafourpour, Cindy Zhao, Valentin Duruisseaux, Bahareh Tolooshams, Philip H. Wong, Costas A. Anastassiou, Anima Anandkumar

Capturing Membrane Dynamics and Spike Timing of Human Neurons at Scale using Neural Operators

Read the original on arXiv Machine Learning →

The paper introduces a neural-operator framework that learns a shared operator across multiple neurons of the same cell type, conditioned on biologically interpretable electrophysiological features. This approach scales biophysically realistic neuron models to large populations while preserving rich cellular dynamics and within-cell-type variability. Evaluation shows accurate reproduction of subthreshold membrane dynamics, spike waveforms, firing-rate responses, and spike timing across major cortical inhibitory neuron types, with predicted electrophysiological feature distributions closely matching detailed simulations and human recordings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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