CUSP: Complex spike sorting from multi-electrode array recordings with U-net sequence-to-sequence prediction.

ABSTRACT:

Background

Complex spikes (CSs) in cerebellar Purkinje cells convey unique signals complementary to Simple spike (SS) action potentials, but are infrequent and variable in waveform. Their variability and low spike counts, combined with recording artifacts such as electrode drift, make automated detection challenging.

New method

We introduce CUSP (CS sorting via U-net Sequence Prediction), a fully automated deep learning framework for CS sorting in high-density multi-electrode array recordings. CUSP uses a U-Net architecture with hybrid self-attention inception blocks to integrate local field potential and action potential signals and outputs CS event probabilities in a sequence-to-sequence manner. Detected events are clustered and paired with concurrently detected SSs to rec

SUBMITTER: Bao C 

PROVIDER: S-EPMC12757777 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature