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Dependent Dirichlet Process Spike Sorting
2008
Neural Information Processing Systems
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Our approach is to augment a known time-varying Dirichlet process that ties together a sequence of infinite Gaussian mixture models, one per action potential waveform observation, with an interspike-interval-dependent ...
In this paper we propose a new incremental spike sorting model that automatically eliminates refractory period violations, accounts for action potential waveform drift, and can handle "appearance" and ...
The GPUDPM is a time dependent Dirichlet process (DDP) mixture model formulated in the Chinese restaurant process (CRP) sampling representation of a Dirichlet process mixture model (DPM). ...
dblp:conf/nips/GasthausWGT08
fatcat:dbcm3wdejjdsjdrqpi5oq5b6wy
A nonparametric Bayesian alternative to spike sorting
2008
Journal of Neuroscience Methods
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In lieu of sorting neural data to produce a single best spike train, we estimate a probabilistic model of spike trains given the observed data. ...
The analysis of extra-cellular neural recordings typically begins with careful spike sorting and all analysis of the data then rests on the correctness of the resulting spike trains. ...
This is possible to do by leveraging the dependent Dirichlet process work of Srebro and Roweis (2005) and Griffin and Steel (2006) . ...
doi:10.1016/j.jneumeth.2008.04.030
pmid:18602697
pmcid:PMC3880746
fatcat:j2r3oyt6pnfqlgwqcayr6bdvpe
On the Analysis of Multi-Channel Neural Spike Data
2011
Neural Information Processing Systems
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Dictionary learning is implemented via the beta-Bernoulli process, with spike sorting performed via the dynamic hierarchical Dirichlet process (dHDP), with these two models coupled. ...
Nonparametric Bayesian methods are developed for analysis of multi-channel spike-train data, with the feature learning and spike sorting performed jointly. ...
Multi-Channel Dynamic hierarchical Dirichlet process We sort the spikes on the channels by clustering the {s (c) n }, and in this sense feature design (learning {D⇤ (c) }) and sorting are performed simultaneously ...
dblp:conf/nips/ChenCC11
fatcat:vq54pgrkwzau3a7j5pxe6iqznq
Firing rate estimation using infinite mixture models and its application to neural decoding
2017
Journal of Neurophysiology
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This method does not require spike sorting and thereby improves decoding accuracy dramatically. ...
In this method, they used kernel density estimation to estimate intensity functions of marked point processes. ...
Using marked point processes does not require spike sorting beforehand. Hence, this approach avoids many problems due to spike sorting and greatly improves decoding accuracy. ...
doi:10.1152/jn.00818.2016
pmid:28794199
fatcat:a5yt6y4b75frvl6o4bhvrgw2bi
Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning & Mixture Modeling
[article]
2013
arXiv
pre-print
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sorting"). ...
Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage ("frequentist") learning process. ...
Importantly, we learn these features for the specific task at hand: spike sorting (i.e., clustering). ...
arXiv:1304.0542v2
fatcat:j6b2s2wsrneq5jixradug4kf7e
Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling
2014
IEEE Transactions on Biomedical Engineering
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sorting"). ...
Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage learning process. ...
Importantly, we learn these features for the specific task at hand: spike sorting (i.e., clustering). ...
doi:10.1109/tbme.2013.2275751
pmid:23912463
fatcat:7ujrxp7lrbfvjibcepcz75eqfi
Clustering action potential spikes: Insights on the use of overfitted finite mixture models and Dirichlet process mixture models
[article]
2016
arXiv
pre-print
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Two such models, Overfitted Finite Mixture models (OFMs) and Dirichlet Process Mixture models (DPMs) are considered to provide insights for unsupervised clustering of complex, multivariate medical data ...
The modelling of action potentials from extracellular recordings, or spike sorting, is a rich area of neuroscience research in which latent variable models are often used. ...
Spike sorting refers to the collection of techniques suited to this purpose, encompassing the stages of AP detection, processing and classification. ...
arXiv:1602.01915v1
fatcat:m4wmffhyebfpndruwgsmu5hjuu
A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation
2016
Journal of Neuroscience Methods
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Specifically, we apply a hierarchical Dirichlet process-hidden Markov model (HDP-HMM) using two Bayesian inference methods, one based on Markov chain Monte Carlo (MCMC) and the other based on variational ...
Computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. ...
Use of soft-labeled spikes Thus far, we have assumed that all recorded ensemble spikes are sorted and clustered into single units. ...
doi:10.1016/j.jneumeth.2016.01.022
pmid:26854398
pmcid:PMC4801699
fatcat:aqgqsrkgzjg2rlc2ygwtl7h4eu
Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting
2020
Frontiers in Systems Neuroscience
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In this regard, the study reports a compatibility evaluation on algorithms previously employed in spike sorting as well as the algorithms yet to be investigated for application in sorting neural spikes ...
Deciphering useful information from electrophysiological data recorded from the brain, in-vivo or in-vitro, is dependent on the capability to analyse spike patterns efficiently and accurately. ...
Dirichlet process was explored by Gasthaus et al. (2009) . ...
doi:10.3389/fnsys.2020.00034
pmid:32714155
pmcid:PMC7340107
fatcat:5nyqimg45rghzidiadnebpmvz4
Neural Clustering Processes
[article]
2020
arXiv
pre-print
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As a scientific application, we present a novel approach to neural spike sorting for high-density multielectrode arrays. ...
This makes the methods a natural choice for nonparametric Bayesian models, such as Dirichlet process mixture models (DPMM), and their extensions. ...
Details of spike sorting using NCP Data preprocessing. ...
arXiv:1901.00409v4
fatcat:sizcw7cglbfnxniafnsiv2uojy
Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures
[article]
2023
arXiv
pre-print
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In this paper, we develop a bi-clustering method to cluster the neural spiking activity spatially and temporally, according to their low-dimensional latent structures. ...
Modern neural recording techniques allow neuroscientists to obtain spiking activity of multiple neurons from different brain regions over long time periods, which requires new statistical methods to be ...
The neural spiking activity is essentially a point process, and there are some methods for finding clusters in point process by, such as, Dirichlet mixture of Hawkes process (Xu and Zha, 2017) , mixture ...
arXiv:2309.02213v3
fatcat:tf56rn43vffqjjdqzbo7ou5ala
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation
[article]
2014
arXiv
pre-print
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Specifically, to analyze rat hippocampal ensemble spiking activity, we apply a hierarchical Dirichlet process-hidden Markov model (HDP-HMM) using two Bayesian inference methods, one based on Markov chain ...
Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. ...
Use of Soft-labeled Spikes Thus far, we have assumed that all recorded ensemble spikes are sorted and clustered into single units. ...
arXiv:1411.7706v1
fatcat:6sm4225smfajtofntnf566bgue
Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models
[article]
2023
arXiv
pre-print
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2022 pre-print arXiv:2201.05044v2 fatcat:pbkpzp7urzd6ja2yd7a4f6e6paOther Versions
This construction is similar to Bayesian nonparametric mixture models like the Dirichlet process mixture model (DPMM) in that the number of latent events (i.e. clusters) is a random variable, but the point ...
We demonstrate the potential of Neyman-Scott processes on a variety of applications including sequence detection in neural spike trains and event detection in document streams. ...
Previous work on dependent Dirichlet processes may offer some ways of addressing this limitation [Müller and Rodriguez, 2013, Ch. 5] . ...
arXiv:2201.05044v3
fatcat:b5546asfnbgmvonrqaj5ox55fy
YASS: Yet Another Spike Sorter
[article]
2017
biorxiv/medrxiv
pre-print
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Our clustering approach adapts a "coreset" approach for data reduction and uses efficient inference methods in a Dirichlet process mixture model framework to dramatically improve the scalability and reliability ...
This manuscript describes an efficient, reliable pipeline for spike sorting on dense multi-electrode arrays (MEAs), where neural signals appear across many electrodes and spike sorting currently represents ...
Our approach is based on the Dirichlet process Gaussian mixture model (DP-GMM), which first introduced to the spike sorting problem by [48] . There have been 23 . ...
doi:10.1101/151928
fatcat:obql2pzqqna5tn5js2tceqhmh4
Spatial information based OSort for real-time spike sorting using FPGA
2020
IEEE Transactions on Biomedical Engineering
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Spiking activity of individual neurons can be separated from the acquired multi-unit activity with spike sorting methods. ...
Processing the recorded high-dimensional neural data can take a large amount of time when performed on general-purpose computers. ...
RESULTS The proposed Spike Sorting and Processing blocks were developed using Vivado HLS 2018.3. ...
doi:10.1109/tbme.2020.2996281
pmid:32746008
fatcat:ky5olxxsk5f7dfucjjvh2hc4wa
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