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Estimating invariant dimensions in V2

Haruo Hosoya, Kota S Sasaki, Izumi Ohzawa
2013 BMC Neuroscience  
A modern approach to identify such invariant dimensions is a semi-automatic analysis of responses to a large number of randomly chosen stimuli; a well-known method is spike-triggered covariance analysis  ...  Previous work on hierarchical analysis of V2 used only a first-order analysis on top of a V1 population model and therefore was not capable of revealing invariant dimensions in V2 [3,4].  ...  Author details 1 Neural Information Analysis Laboratories, ATR International, Keihanna, Kyoto 619-0288, Japan. 2 Presto, Japan Science and Technology Agency, Chiyoda, Tokyo, Japan. 3 Graduate School of  ... 
doi:10.1186/1471-2202-14-s1-p302 fatcat:sp4tx6aldza6tpsgobnvofeu4m

An Overview of Bayesian Methods for Neural Spike Train Analysis

Zhe Chen
2013 Computational Intelligence and Neuroscience  
Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels.  ...  Some research challenges and opportunities for neural spike train analysis are discussed.  ...  Standard tuning curve or RF estimation methods include the spiketriggered average (STA) and spike-triggered covariance (STC). The Bayesian versions of the STA and STC have been proposed [91, 92] .  ... 
doi:10.1155/2013/251905 pmid:24348527 pmcid:PMC3855941 fatcat:nkst6mt3sfcqheuxheda3wq4wq

Variational Bayesian inference for point process generalized linear models in neural spike trains analysis

Zhe Chen, Fabian Kloosterman, Matthew A. Wilson, Emery N. Brown
2010 2010 IEEE International Conference on Acoustics, Speech and Signal Processing  
Point process generalized linear models (GLMs) have been widely used for neural spike trains analysis. Statistical inference for GLMs include maximum likelihood and Bayesian estimation.  ...  In this paper, we develop and apply VB inference for point process GLMs in neural spike train analysis. The hierarchical Bayesian framework allows us to tackle the variable selection problem.  ...  Let xt denote the input covariate at time t, yt denote the observed response variable, which equals to 1 if there is an event (spike) at time t and 0 otherwise.  ... 
doi:10.1109/icassp.2010.5495095 dblp:conf/icassp/ChenKWB10 fatcat:aebfb436qbffjdqkulmdynh3t4

Human group coordination in a sensorimotor task with neuron-like decision-making

Gerrit Schmid, Daniel A. Braun
2020 Scientific Reports  
We study four different computational models of the observed behavior, including a perceptron model, a reinforcement learning model, a Bayesian inference model and a Thompson sampling model that efficiently  ...  Changes in the spike triggered average. In general, the spike-triggered average (STA) is the average stimulus preceding a neural response.  ...  (b) Time dependency of the average spike triggered variance. The abscissa displays the ordinal spike number for each player.  ... 
doi:10.1038/s41598-020-64091-4 pmid:32427875 fatcat:sqegi7xcvjgkpgwbmmucdk37uy

Probabilistic Encoding Models for Multivariate Neural Data

Marcus A. Triplett, Geoffrey J. Goodhill
2019 Frontiers in Neural Circuits  
However, this characterization is complicated by the highly variable nature of neural responses, and thus usually requires probabilistic methods for analysis.  ...  Abbreviation Meaning MLE Maximum likelihood estimate STA Spike-triggered average LNP Linear-nonlinear-Poisson GLM Generalized linear model FA Factor analysis EM Expectation maximization  ...  Experimental parameters, spike history, and gain variables are often incorporated as covariates in the linear combination stage.  ... 
doi:10.3389/fncir.2019.00001 pmid:30745864 pmcid:PMC6360288 fatcat:vqnhoyvhwrdotkbqlrgkeoqu3m

Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods

Kamiar Rahnama Rad, Liam Paninski
2010 Network  
sensory and motor areas, the estimation of firing rates following spike-triggered covariance analyses, etc.  ...  Here we introduce methods based on Gaussian process nonparametric Bayesian techniques for estimating these two-dimensional rate maps.  ...  Finally, more abstract examples arise in the context of spike-triggered covariance analyses (Rust et al., 2005; Aguera y Arcas and Fairhall, 2003) .  ... 
doi:10.3109/0954898x.2010.532288 pmid:21138363 fatcat:4hqtotghmrhf5gsmknj2dyqwwi

Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data

Zhe Chen, David F Putrino, Soumya Ghosh, Riccardo Barbieri, Emery N Brown
2011 IEEE transactions on neural systems and rehabilitation engineering  
other algorithms, and this approach was then successfully applied to real spiking data recorded from the cat motor  ...  Algorithmic performances were compared using well-established goodness-of-fit measures in benchmark simulation studies, and the hierarchical Bayesian approach performed favorably when compared with the  ...  indices of trigger neurons.  ... 
doi:10.1109/tnsre.2010.2086079 pmid:20937583 pmcid:PMC3044782 fatcat:ycusw6o6azhnzmm2c2k6gyonoe

Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study

Hideaki Shimazaki
2013 Journal of Physics, Conference Series  
Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition.  ...  In addition, the previous model does not include effects of past spiking activity of the neurons on the current state of ensemble activity.  ...  The width ∆ determines a permissible range of synchronous activity of neurons in this analysis.  ... 
doi:10.1088/1742-6596/473/1/012009 fatcat:sqezrhe435hovd7fxmaghsgfeq

Features and functions of nonlinear spatial integration by retinal ganglion cells

Tim Gollisch
2013 Journal of Physiology - Paris  
spike-triggered covariance, extensions of generalized linear models).  ...  structure of the signal preceding the spike.  ...  (D) Eigenvalue spectrum of the spike-triggered covariance analysis of this cell.  ... 
doi:10.1016/j.jphysparis.2012.12.001 pmid:23262113 fatcat:npxp5drrafgmzmkg34cefcsom4

Removal of Spurious Correlations Between Spikes and Local Field Potentials

Theodoros P. Zanos, Patrick J. Mineault, Christopher C. Pack
2011 Journal of Neurophysiology  
To address the issue of spike contamination, we devised a novel Bayesian spike removal algorithm and confirmed its effectiveness in simulations and by applying it to the electrophysiological data.  ...  Overall, these results show that careful preprocessing is necessary to remove spikes from LFP signals, but that when effective spike removal is used, spike-LFP correlations can potentially yield novel  ...  Bayesian spike removal (green line)].  ... 
doi:10.1152/jn.00642.2010 pmid:21068271 fatcat:tl4up7yh2naaxjyhq7ngkdt5tu

Fast, Scalable, Bayesian Spike Identification for Multi-Electrode Arrays

Jason S. Prentice, Jan Homann, Kristina D. Simmons, Gašper Tkačik, Vijay Balasubramanian, Philip C. Nelson, William Rowland Taylor
2011 PLoS ONE  
Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit.  ...  As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed.  ...  We applied the square root of the inverse of this covariance matrix to all data, and used the resulting ''spatially whitened'' data for all analysis.  ... 
doi:10.1371/journal.pone.0019884 pmid:21799725 pmcid:PMC3140468 fatcat:sls5iz3ld5bsdlt3x2ds675csu

Bayesian modelling and analysis of spatio-temporal neuronal networks

Fabio Rigat, Mathisca de Gunst, Jaap van Pelt
2006 Bayesian Analysis  
The results of our analysis summarized in tables 8 and 9 suggest that neuron 29 triggers the response of neurons (7, 11, 22) through its own higher propensity to fire, revealing over time a complex pattern  ...  Report 2005-040 Bayesian modelling and analysis of spatio-temporal neuronal networks ISSN 1389-2355 Introduction High-throughput technology currently generates measurements of the activity of the nervous  ... 
doi:10.1214/06-ba124 fatcat:3rnwaxogsrbnxokzxzzg2dpwfi

Assessing neuronal interactions of cell assemblies during general anesthesia

Zhe Chen, S. Vijayan, ShiNung Ching, G. Hale, F. J. Flores, M. A. Wilson, E. N. Brown
2011 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
We further apply the analysis to experimental spike train data recorded from rats' barrel cortex during both active behavior and isoflurane anesthesia conditions.  ...  A hierarchical Bayesian modeling approach combined with a variational Bayes (VB) algorithm is used for statistical inference.  ...  C be the indices of trigger neurons.  ... 
doi:10.1109/iembs.2011.6091036 pmid:22255259 pmcid:PMC3840037 dblp:conf/embc/0001VCHFWB11 fatcat:6hovpfvodngxjjzyny5rto7w3q

Convergence properties of three spike-triggered analysis techniques

Liam Paninski
2003 Network  
We analyse the convergence properties of three spike-triggered data analysis techniques.  ...  Next, we analyse a spike-triggered covariance method, variants of which have been recently exploited successfully by Bialek, Simoncelli and colleagues.  ...  the spike-triggered covariance matrix.  ... 
doi:10.1088/0954-898x_14_3_304 pmid:12938766 fatcat:tkumximgx5fplg6gdv6gmhfcea

Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains

Jonathan W. Pillow, Yashar Ahmadian, Liam Paninski
2011 Neural Computation  
Understanding how stimulus information is encoded in spike trains is a central problem in computational neuroscience.  ...  Moreover, we may use the likelihood of the observed spike trains under the model to perform optimal decoding.  ...  In particular, (de Ruyter van Steveninck and Bialek, 1988) computed the entropy of the spike-triggered Gaussian approximation discussed above, in order to quantify the informativeness of single spikes  ... 
doi:10.1162/neco_a_00058 pmid:20964538 fatcat:vwc4spp24bg6pbuv6q4y4aznhu
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