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Robust and Memoryless Median Estimation for Real-Time Spike Detection

Burman, A.; Sole-Casals, J.; Lew, S. E.

2024-07-23 neuroscience
10.1101/2024.07.19.604279 bioRxiv
Show abstract

We propose a novel moving median estimator specifically designed for online detection of threshold crossings in multi-channel signals, such as extracellular neural recordings. This estimator offers two key advantages: a reduced sensitivity to outliers and the elimination of memory requirements for storing arrival times. Furthermore, its design facilitates parallel implementation on FPGAs, making it ideal for real-time processing of multi-channel recordings.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.