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

Neural Clustering Processes

tensorflow/neural-structured-learning ICML 2020

Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces.

High-dimensional cluster analysis with the Masked EM Algorithm

klusta-team/klustakwik 11 Sep 2013

Cluster analysis faces two problems in high dimensions: first, the `curse of dimensionality' that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data.

Fast and accurate spike sorting of high-channel count probes with KiloSort

cortex-lab/KiloSort NeurIPS 2016

Unlike previous algorithms that compress the data with PCA, KiloSort operates on the raw data which allows it to construct a more accurate model of the waveforms.

YASS: Yet Another Spike Sorter

paninski-lab/yass NeurIPS 2017

Spike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data.

Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders

ds2p/crsae 12 Jul 2018

We demonstrate the ability of CRsAE to recover the underlying dictionary and characterize its sensitivity as a function of SNR.

Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural Recording

tong-wu-umn/spike-compression-autoencoder 14 Sep 2018

The proposed model is built upon a deep compressive autoencoder (CAE) with discrete latent embeddings.

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference

colehurwitz/vae_spike_localization NeurIPS 2019

Determining the positions of neurons in an extracellular recording is useful for investigating functional properties of the underlying neural circuitry.

Short-and-Sparse Deconvolution -- A Geometric Approach

qingqu06/sparse_deconvolution 28 Aug 2019

This paper is motivated by recent theoretical advances, which characterize the optimization landscape of a particular nonconvex formulation of SaSD.

Spike Sorting using the Neural Clustering Process

yueqiw/ncp-sort NeurIPS Workshop Neuro_AI 2019

We present a novel approach to spike sorting for high-density multielectrode probes using the Neural Clustering Process (NCP), a recently introduced neural architecture that performs scalable amortized approximate Bayesian inference for efficient probabilistic clustering.

Efficient characterization of electrically evoked responses for neural interfaces

Chichilnisky-Lab/shah-neurips-2019 NeurIPS 2019

Large-scale, high-density electrical recording and stimulation in primate retina were used as a lab prototype for an artificial retina.