HIPPIE: a generative model for electrophysiological analysis across species, technologies, and modalities

Abstract

Neuronal classification from extracellular electrophysiological recordings is challenging due to intrinsic waveform variability, noise, and technical differences across experiments, technologies, and species. We introduce HIPPIE (High-dimensional Interpretation of Physiological Patterns In Intercellular Electrophysiology), a deep learning framework that combines self-supervised pretraining on unlabeled datasets with supervised fine-tuning to classify neurons from extracellular recordings. Using conditional convolutional joint autoencoders, HIPPIE learns technology-adjusted representations of waveforms and spiking dynamics. Here we show, across mouse, rat, and macaque recordings, that HIPPIE classifies cell types competitively with existing methods while additionally supporting generative analyses that discriminative models cannot perform, including counterfactual decoding of electrophysiological signals under changed experimental conditioning, cross-species latent interpolation, and a cross-modal analysis revealing that spike-timing modalities and waveform morphology encode largely independent dimensions of neuronal identity. HIPPIE is available as both a Python package and a coding-free web application, providing a unified framework for multimodal neuronal classification across technologies, experimental conditions, and species. This presents HIPPIE, a deep-learning model that classifies neuron types from electrophysiological recordings across mice, rats and macaques, and can generate and translate signals between species or recording technologies.