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Sliding window strategy for convolutional spike sorting with Lasso : Algorithm, theoretical guarantees and complexity [article]

Laurent Dragoni, Rémi Flamary, Karim Lounici, Patricia Reynaud-Bouret
2022 arXiv   pre-print
We also show under reasonable assumptions that the Lasso estimator retrieves the true time occurrences of the spikes with large probability.  ...  We rephrase this problem as a particular optimization problem : Lasso for convolutional models in high dimension.  ...  Conclusion In this paper we propose a novel sliding window working set algorithm that can solve exactly the large scale Lasso in spike sorting in an efficient way by exploiting the convolutional structure  ... 
arXiv:2110.15813v2 fatcat:iowzjd2bzjdudaehsofnf4yfiu

Short-and-Sparse Deconvolution – A Geometric Approach [article]

Yenson Lau, Qing Qu, Han-Wen Kuo, Pengcheng Zhou, Yuqian Zhang, John Wright
2019 arXiv   pre-print
Variants of this problem arise in applications such as image deblurring, microscopy, neural spike sorting, and more.  ...  Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring motifs in signals with spatial or temporal structure.  ...  We would like to thank Gongguo Tang, Shuyang Ling, Carlos Fernandez-Granda, Ruoxi Sun, Liam Paniski for fruitful discussions.  ... 
arXiv:1908.10959v2 fatcat:wwampdz2lre7hlqvcxk4ln3znu

Nonlinear decoding of natural images from large-scale primate retinal ganglion recordings [article]

Young Joon Kim, Nora Brackbill, Ella Batty, JinHyung Lee, Catalin Mitelut, William Tong, E.J. Chichilnisky, Liam Paninski
2020 bioRxiv   pre-print
Here, we present an efficient nonlinear decoding approach for inferring natural scene stimuli from the spiking activities of retinal ganglion cells (RGCs).  ...  Trained and validated on real retinal spike data from > 1000 simultaneously recorded macaque RGC units, the decoder demonstrates the necessity of nonlinear computations for accurate decoding of the fine  ...  Acknowledgments 476 We thank Eric Wu and Nishal Shah for helpful discussions.  ... 
doi:10.1101/2020.09.07.285742 fatcat:oig32my6undjlmgjhrtrmrg7li

Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings

Young Joon Kim, Nora Brackbill, Eleanor Batty, JinHyung Lee, Catalin Mitelut, William Tong, E. J. Chichilnisky, Liam Paninski
2021 Neural Computation  
Here, we present an efficient nonlinear decoding approach for inferring natural scene stimuli from the spiking activities of retinal ganglion cells (RGCs).  ...  Trained and validated on real retinal spike data from more than 1000 simultaneously recorded macaque RGC units, the decoder demonstrates the necessity of nonlinear computations for accurate decoding of  ...  Acknowledgments We thank Eric Wu and Nishal Shah for helpful discussions. References Bialek, W., de Ruyter van Steveninck, R., Rieke, F., & Warland, D. (1997) . Spikes: Exploring the neural code.  ... 
doi:10.1162/neco_a_01395 pmid:34411268 fatcat:vzdwmxecovhinow3d5gagwd4zq

Machine Learning of Spatiotemporal Bursting Behavior in Developing Neural Networks

Jewel YunHsuan Lee, Michael Stiber, Dong Si
2018 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
This communication presents an application of machine learning techniques to bridge the gap between microscopic and macroscopic behaviors and identify the small-scale activity that leads to large-scale  ...  It is relatively common to be faced with datasets containing many millions of neural spikes collected from tens of thousands of neurons.  ...  We wanted to collect simulated spike data with millisecond-scale resolution from large neural population (at least 10 4 cells) over long periods of development (days to weeks) to approximate the size and  ... 
doi:10.1109/embc.2018.8512358 pmid:30440408 fatcat:7bypllivwrfcxieoasu5dt2yii

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

Mike Davies, Andreas Wild, Garrick Orchard, Yulia Sandamirskaya, Gabriel A. Fonseca Guerra, Prasad Joshi, Philipp Plank, Sumedh R. Risbud
2021 Proceedings of the IEEE  
This is now changing with the advent of Intel's Loihi, a neuromorphic research processor designed to support a broad range of spiking neural networks with sufficient scale, performance, and features to  ...  For more information, see https://creativecommons.org/licenses/by/4.0/  ...  Previous results [13] , [53] demonstrated the efficiency of neuromorphic architectures, such as Loihi, for solving LASSO problems with LCA, especially the convolutional form of the problem. 3 Convolutional  ... 
doi:10.1109/jproc.2021.3067593 fatcat:krqdmy3u6jdvfl7btjglek5ag4

Deep Residual Auto-Encoders for Expectation Maximization-inspired Dictionary Learning [article]

Bahareh Tolooshams, Sourav Dey, Demba Ba
2020 arXiv   pre-print
In an application to recordings of electrical activity from the brain, we demonstrate that CRsAE learns realistic spike templates and speeds up the process of identifying spike times by 900x compared to  ...  The encoder can be interpreted either as a recurrent neural network or as a deep residual network, with two-sided ReLU non-linearities in both cases.  ...  The authors would also like to thank AWS for their generous support.  ... 
arXiv:1904.08827v2 fatcat:v6zrfyfdljfqzhyvlkclksghya

Sparse Spectro-Temporal Receptive Fields Based on Multi-Unit and High-Gamma Responses in Human Auditory Cortex

Rick L. Jenison, Richard A. Reale, Amanda L. Armstrong, Hiroyuki Oya, Hiroto Kawasaki, Matthew A. Howard, Manuel S. Malmierca
2015 PLoS ONE  
The contribution of local spiking activity to the highgamma power signal was factored out of the STRF using the GLM method, and this contribution was significant in 85 percent of the cases.  ...  Traditional methods for estimating STRFs from single-unit recordings, such as spike-triggered-averages, tend to be noisy and are less robust to other response signals such as local field potentials.  ...  Acknowledgments We thank Haiming Chen, Kirill Nourski, and Rachel Gold, for help with data collection and Carol Dizack for graphic art work.  ... 
doi:10.1371/journal.pone.0137915 pmid:26367010 pmcid:PMC4569421 fatcat:vj5euz35incfbhvsd3vrvvzxay

Active learning of cortical connectivity from two-photon imaging data

Martín A. Bertrán, Natalia L. Martínez, Ye Wang, David Dunson, Guillermo Sapiro, Dario Ringach, Thomas Wennekers
2018 PLoS ONE  
We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters.  ...  Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.  ...  Gregory Randall for important discussions during the early stages of this work. This work has been supported by NIH, NSF, and DoD. Author Contributions  ... 
doi:10.1371/journal.pone.0196527 pmid:29718955 pmcid:PMC5931643 fatcat:4rlzxvzsgfh57deqyhhmvc3aly

Accounting for network effects in neuronal responses using L1 regularized point process models

Ryan C Kelly, Robert E Kass, Matthew A Smith, Tai Sing Lee
2010 Advances in Neural Information Processing Systems  
We also found that the same spikes could be accounted for with the local field potentials, a surrogate measure of global network states.  ...  This suggests the activity of the surrounding neurons and global brain states can exert considerable influence on the activity of a neuron.  ...  The active set is the set of all coordinates with nonzero coefficients for which the coordinate descent is being performed.  ... 
pmid:22162918 pmcid:PMC3235005 fatcat:ph2ntmvce5frnnksntxx7igdqy

A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings

Jonathan W. Pillow, Jonathon Shlens, E. J. Chichilnisky, Eero P. Simoncelli, Bart Krekelberg
2013 PLoS ONE  
We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms.  ...  Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data.  ...  Acknowledgments We thank Christophe Pouzat for helpful comments and suggestions, Alan Litke, Alexander Sher, Matthew Grivich and Dumitru Petrusca for technical development and Greg Field, Martin Greschner  ... 
doi:10.1371/journal.pone.0062123 pmid:23671583 pmcid:PMC3643981 fatcat:3ltdtc232vfexh3hjkfkja74va

Active Learning of Cortical Connectivity from Two-Photon Imaging Data [article]

Martin Bertran, Natalia Martinez, Ye Wang, David Dunson, Guillermo Sapiro, Dario Ringach
2018 bioRxiv   pre-print
We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters.  ...  Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model  ...  Gregory Randall for important discussions during the early stages of 767 this work. This work has been supported by NIH, NSF, and DoD. 768 PLOS 43/47  ... 
doi:10.1101/268599 fatcat:ldnr2mtvr5hbra5t53dgbzvxge

Single-trial decoding of intended eye movement goals from lateral prefrontal cortex neural ensembles

Chadwick B. Boulay, Florian Pieper, Matthew Leavitt, Julio Martinez-Trujillo, Adam J. Sachs
2016 Journal of Neurophysiology  
We recorded neuronal spiking activity from microelectrode arrays implanted in area 8A of the LPFC of two adult macaques while they made visually guided saccades to one of eight targets in a center-out  ...  LPFC neural activity.  ...  For each feature set, we used lasso regression to predict saccade end point coordinates.  ... 
doi:10.1152/jn.00788.2015 pmid:26561608 pmcid:PMC4760465 fatcat:3l3cyyxkvrbhdp7j4au6e3wgiq

In-flight Novelty Detection with Convolutional Neural Networks [article]

Adam Hartwell, Felipe Montana, Will Jacobs, Visakan Kadirkamanathan, Andrew R Mills, Tom Clark
2021 arXiv   pre-print
This paper proposes that system output measurements, described by a convolutional neural network model of normality, are prioritised in real-time for the attention of preventative maintenance decision  ...  Due to the complexity of gas turbine engine time-varying behaviours, deriving accurate physical models is difficult, and often leads to models with low prediction accuracy and incompatibility with real-time  ...  Tensorflow: A sys- ral Networks: An Embedded Computing Per- tem for large-scale machine learning. In 12th spective. IEEE Access, 8:57967–57996, 2020.  ... 
arXiv:2112.03765v1 fatcat:sqmwgsn4ezdt7jtevawimjqt5q

Brain–Machine Interface Engineering

Justin C. Sanchez, José C. Principe
2007 Synthesis Lectures on Biomedical Engineering  
activity.  ...  The topics featured include analysis techniques for determining neural representation, modeling in motor systems, computing with neural spikes, and hardware implementation of neural interfaces.  ...  (2.17) Hence, the convolution output in the HatWT provides a feature set related with binning with different window widths.  ... 
doi:10.2200/s00053ed1v01y200710bme017 fatcat:jm6kaqyjurgddmssiru2fy435i
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