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.
Benchmarks
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Most implemented papers
Neural Clustering Processes
tensorflow/neural-structured-learning
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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
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
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
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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
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
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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
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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
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
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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
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NeurIPS 2019
Large-scale, high-density electrical recording and stimulation in primate retina were used as a lab prototype for an artificial retina.
