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Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference

https://doi.org/10.1101/656389
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-11135-9_7openalex-snapshot
Scaling Spike Detection and Sorting for Next-Generation Electrophysiology
inferredcites →https://doi.org/10.1007/s10827-011-0341-0openalex-snapshot
Dipole characterization of single neurons from their extracellular action potentials
inferredcites →https://doi.org/10.1007/s12021-020-09467-7openalex-snapshot
MEArec: A Fast and Customizable Testbench Simulator for Ground-truth Extracellular Spiking Activity
inferredcites →https://doi.org/10.1016/0165-0270(95)00085-2openalex-snapshot
Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex
inferredcites →https://doi.org/10.1016/j.bios.2004.08.011openalex-snapshot
High-density electrode array for imaging in vitro electrophysiological activity
inferredcites →https://doi.org/10.1016/j.brainresbull.2015.04.007openalex-snapshot
Past, present and future of spike sorting techniques
inferredcites →https://doi.org/10.1016/j.celrep.2017.02.038openalex-snapshot
Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays
inferredcites →https://doi.org/10.1016/j.conb.2019.02.007openalex-snapshot
Continuing progress of spike sorting in the era of big data
inferredcites →https://doi.org/10.1016/j.jneumeth.2004.09.004openalex-snapshot
Spike source localization with tetrodes
inferredcites →https://doi.org/10.1016/j.jneumeth.2005.04.002openalex-snapshot
Model-based source localization of extracellular action potentials
inferredcites →https://doi.org/10.1016/j.jneumeth.2014.05.037openalex-snapshot
Localising and classifying neurons from high density MEA recordings
inferredcites →https://doi.org/10.1016/j.jneumeth.2015.01.029openalex-snapshot
ViSAPy: A Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms
inferredcites →https://doi.org/10.1016/j.neuron.2008.10.033openalex-snapshot
All My Circuits: Using Multiple Electrodes to Understand Functioning Neural Networks
inferredcites →https://doi.org/10.1016/j.neuron.2017.08.030openalex-snapshot
A Fully Automated Approach to Spike Sorting
inferredcites →https://doi.org/10.1038/nature24636openalex-snapshot
Fully integrated silicon probes for high-density recording of neural activity
inferredcites →https://doi.org/10.1038/nn1233openalex-snapshot
Large-scale recording of neuronal ensembles
inferredcites →https://doi.org/10.1038/nn1323openalex-snapshot
Recording spikes from a large fraction of the ganglion cells in a retinal patch
inferredcites →https://doi.org/10.1038/s41592-018-0109-9openalex-snapshot
Inferring single-trial neural population dynamics using sequential auto-encoders
inferredcites →https://doi.org/10.1039/c5lc00133aopenalex-snapshot
High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels
inferredcites →https://doi.org/10.1101/101030openalex-snapshot
Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction
inferredcites →https://doi.org/10.1101/151928openalex-snapshot
YASS: Yet Another Spike Sorter
inferredcites →https://doi.org/10.1101/275818openalex-snapshot
Why not record from every electrode with a CMOS scanning probe?
inferredcites →https://doi.org/10.1103/physreve.99.032408openalex-snapshot
Ensemble inhibition and excitation in the human cortex: An Ising-model analysis with uncertainties
inferredcites →https://doi.org/10.1109/embc.2017.8036988openalex-snapshot
Localizing neuronal somata from Multi-Electrode Array in-vivo recordings using deep learning
inferredcites →https://doi.org/10.1109/iembs.2007.4352531openalex-snapshot
An Efficient Algorithm for Current Source Localization with Tetrodes
inferredcites →https://doi.org/10.1109/iembs.2008.4650341openalex-snapshot
The 3D position estimation of neurons in the hippocampus based on the multi-site multi-unit recordings with silicon tetrodes
inferredcites →https://doi.org/10.1109/isscc.2003.1234276openalex-snapshot
A 128 × 128 CMOS bio-sensor array for extracellular recording of neural activity
inferredcites →https://doi.org/10.1109/isscc.2016.7418072openalex-snapshot
22.7 A 966-electrode neural probe with 384 configurable channels in 0.13µm SOI CMOS
inferredcites →https://doi.org/10.1109/jssc.2003.819174openalex-snapshot
A 128 x 128 cmos biosensor array for extracellular recording of neural activity
inferredcites →https://doi.org/10.1109/jssc.2009.2035196openalex-snapshot
Switch-Matrix-Based High-Density Microelectrode Array in CMOS Technology
inferredcites →https://doi.org/10.1109/jssc.2014.2359219openalex-snapshot
A 1024-Channel CMOS Microelectrode Array With 26,400 Electrodes for Recording and Stimulation of Electrogenic Cells In Vitro
inferredcites →https://doi.org/10.1109/vlsic.2016.7573558openalex-snapshot
A microelectrode array with 8,640 electrodes enabling simultaneous full-frame readout at 6.5 kfps and 112-channel switch-matrix readout at 20 kS/s
inferredcites →https://doi.org/10.1111/j.1460-9568.2012.08249.xopenalex-snapshot
Localization of single‐cell current sources based on extracellular potential patterns: the spike CSD method
inferredcites →https://doi.org/10.1152/jn.00103.2016openalex-snapshot
Validating silicon polytrodes with paired juxtacellular recordings: method and dataset
inferredcites →https://doi.org/10.1152/jn.00210.2018openalex-snapshot
Combining biophysical modeling and deep learning for multielectrode array neuron localization and classification
inferredcites →https://doi.org/10.1152/jn.00515.2010openalex-snapshot
Three-dimensional localization of neurons in cortical tetrode recordings
inferredcites →https://doi.org/10.1152/jn.01023.2004openalex-snapshot
Polytrodes: High-Density Silicon Electrode Arrays for Large-Scale Multiunit Recording
inferredcites →https://doi.org/10.1152/jn.2000.84.1.390openalex-snapshot
Intracellular Features Predicted by Extracellular Recordings in the Hippocampus In Vivo
inferredcites →https://doi.org/10.1152/jn.2000.84.1.401openalex-snapshot
Accuracy of Tetrode Spike Separation as Determined by Simultaneous Intracellular and Extracellular Measurements
inferredcites →https://doi.org/10.1162/neco.1995.7.5.889openalex-snapshot
The Helmholtz Machine
inferredcites →https://doi.org/10.1162/neco.1997.9.6.1179openalex-snapshot
The NEURON Simulation Environment
inferredcites →https://doi.org/10.1371/journal.pone.0019884openalex-snapshot
Fast, Scalable, Bayesian Spike Identification for Multi-Electrode Arrays
inferredcites →https://doi.org/10.1523/jneurosci.4906-06.2007openalex-snapshot
Comparison of Recordings from Microelectrode Arrays and Single Electrodes in the Visual Cortex
inferredcites →https://doi.org/10.17863/cam.42246openalex-snapshot
Turing: A language for flexible probabilistic inference
inferredcites →https://doi.org/10.3389/fncel.2018.00481openalex-snapshot
Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina
inferredcites →https://doi.org/10.3389/fncir.2015.00044openalex-snapshot
The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex
inferredcites →https://doi.org/10.3389/fninf.2015.00028openalex-snapshot
Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays
inferredcites →https://doi.org/10.3389/fninf.2018.00092openalex-snapshot
Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0
inferredcites →https://doi.org/10.3389/fnins.2014.00423openalex-snapshot
Revealing neuronal function through microelectrode array recordings
inferredcites →https://doi.org/10.48550/arxiv.1401.4082openalex-snapshot
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
inferredcites →https://doi.org/10.48550/arxiv.1711.01846openalex-snapshot
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
inferredcites →https://doi.org/10.7554/elife.34518openalex-snapshot
A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo
inferredcites →https://doi.org/10.7554/elife.61834openalex-snapshot
SpikeInterface, a unified framework for spike sorting

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s13534-024-00395-yopenalex-snapshot
Unsupervised spike sorting for multielectrode arrays based on spike shape features and location methods
inferred← citeshttps://doi.org/10.1016/j.jneumeth.2024.110297openalex-snapshot
Localization of neurons from extracellular footprints
inferred← citeshttps://doi.org/10.1038/s41592-025-02614-5openalex-snapshot
DREDge: robust motion correction for high-density extracellular recordings across species
inferred← citeshttps://doi.org/10.1088/1741-2552/abf521openalex-snapshot
ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution
inferred← citeshttps://doi.org/10.1088/1741-2552/ac0f4aopenalex-snapshot
A data-driven spike sorting feature map for resolving spike overlap in the feature space
inferred← citeshttps://doi.org/10.1088/2516-1091/ac6b96openalex-snapshot
Spike sorting: new trends and challenges of the era of high-density probes
inferred← citeshttps://doi.org/10.1101/2021.11.05.467503openalex-snapshot
Three-dimensional spike localization and improved motion correction for Neuropixels recordings
inferred← citeshttps://doi.org/10.1101/2023.08.11.553023openalex-snapshot
DARTsort: A modular drift tracking spike sorter for high-density multi-electrode probes
inferred← citeshttps://doi.org/10.1101/2023.09.21.558869openalex-snapshot
Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes
inferred← citeshttps://doi.org/10.1101/2023.10.30.564831openalex-snapshot
Towards robust and generalizable representations of extracellular data using contrastive learning
inferred← citeshttps://doi.org/10.31219/osf.io/jhau2openalex-snapshot
Spike sorting: new trends and challenges of the era of high-density probes
inferred← citeshttps://doi.org/10.48550/arxiv.2003.08533openalex-snapshot
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking
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