Spike Sorting

15 papers with code • 0 benchmarks • 0 datasets

Spike sorting is a class of techniques used in the analysis of electrophysiological data. Spike sorting algorithms use the shape(s) of waveforms collected with one or more electrodes in the brain to distinguish the activity of one or more neurons from background electrical noise.

Most implemented papers

Short and Sparse Deconvolution --- A Geometric Approach

qingqu06/sparse_deconvolution ICLR 2020

Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring motifs in signals with spatial or temporal structure.

An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions

ramintoosi/ROSS Scientific Reports 2021

Here, we develop an automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions to address these distortions and instabilities.

Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders

alexdelitzas/fcdae-neural-signal-denoising 18 Sep 2021

Extracellular recordings are severely contaminated by a considerable amount of noise sources, rendering the denoising process an extremely challenging task that should be tackled for efficient spike sorting.

Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation

timothyzh/mwscas2022spikesortingcriteria 13 May 2022

Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities.

Edge computing on TPU for brain implant signal analysis

rokaijano/spike-on-edge Neural Networks 2023

The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner.