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HIPPIE: A MULTIMODAL DEEP LEARNING MODEL FOR ELECTROPHYSIOLOGICAL CLASSIFICATION OF NEURONS
Jesus Gonzalez Ferrerand 4 co-authors
University of California Santa Cruz
FENS Forum 2026 (2026)
Barcelona, Spain
Board PS02-07PM-568
Presentation
Date TBA
Board: PS02-07PM-568
Event Information
Poster Board
PS02-07PM-568
Poster
View posterAbstract
Extracellular electrophysiological recordings present unique computational challenges for neuronal classification due to noise, technical variability, and batch effects across experimental systems. We introduce HIPPIE (High-dimensional Interpretation of Physiological Patterns In Extracellular recordings), 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 robust, technology-adjusted representations of waveforms and spiking dynamics. This model can be applied to electrophysiological classification and clustering across diverse biological cultures and technologies. We validated HIPPIE on both in vivo mouse recordings and in vitro brain slices, where it demonstrated superior performance over other unsupervised methods in cell-type discrimination and aligned closely with anatomically defined classes. Its latent space organizes neurons along electrophysiological gradients, while enabling batch and individual corrected alignment of recordings across experiments. HIPPIE establishes a general framework for systematically decoding neuronal diversity in native and engineered systems.
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