Literature DB >> 15661304

Extraction of the average and differential dynamical response in stimulus-locked experimental data.

A Sornborger1, T Yokoo, A Delorme, C Sailstad, L Sirovich.   

Abstract

In optical imaging experiments of primary visual cortex, visual stimuli evoke a complicated dynamics. Typically, any stimulus with sufficient contrast evokes a response. Much of the response is the same regardless of which stimulus is presented. For instance, when oriented drifting gratings are presented to the visual system, over 90% of the response is the same from orientation to orientation. Small differences may be seen, however, between the responses to different orientations. A problem in the analysis of optical measurements of the response to stimulus in cortical tissue is the distinction of the 'global' or 'non-specific' response from the 'differential' or 'stimulus-specific' response. This problem arises whenever the signal of interest is the difference in response to various stimuli and is evident in many kinds of uni- and multivariate data. To this end, we present enhancements to a frequency-based method that we previously introduced called the periodic stacking method. These enhancements allow us to separately estimate the dynamics of both the average signal across all stimuli (the 'global' response) and deviations from the average amongst the various stimuli (the 'stimulus-specific' response) evoked in response to a set of stimuli. We also discuss improvements in the signal-to-noise ratio, relative to standard trial averaging methods, that result from the data-adaptive smoothing in our method.

Entities:  

Mesh:

Year:  2005        PMID: 15661304     DOI: 10.1016/j.jneumeth.2004.06.012

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  7 in total

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Authors:  Andrew E Hudson; Nicholas D Schiff; Jonathan D Victor; Keith P Purpura
Journal:  Eur J Neurosci       Date:  2009-06-25       Impact factor: 3.386

2.  Dimensionally-reduced visual cortical network model predicts network response and connects system- and cellular-level descriptions.

Authors:  Louis Tao; Andrew T Sornborger
Journal:  J Comput Neurosci       Date:  2009-10-06       Impact factor: 1.621

3.  Improved dimensionally-reduced visual cortical network using stochastic noise modeling.

Authors:  Louis Tao; Jeremy Praissman; Andrew T Sornborger
Journal:  J Comput Neurosci       Date:  2011-08-27       Impact factor: 1.621

4.  A multivariate, multitaper approach to detecting and estimating harmonic response in cortical optical imaging data.

Authors:  A T Sornborger; T Yokoo
Journal:  J Neurosci Methods       Date:  2011-09-29       Impact factor: 2.390

5.  Bimodal modulation and continuous stimulation in optical imaging to map direction selectivity.

Authors:  M P Vanni; J Provost; C Casanova; F Lesage
Journal:  Neuroimage       Date:  2009-09-25       Impact factor: 6.556

6.  An algorithm for the analysis of temporally structured multidimensional measurements.

Authors:  Barak Blumenfeld
Journal:  Front Comput Neurosci       Date:  2010-01-27       Impact factor: 2.380

7.  Demixed principal component analysis of neural population data.

Authors:  Dmitry Kobak; Wieland Brendel; Christos Constantinidis; Claudia E Feierstein; Adam Kepecs; Zachary F Mainen; Xue-Lian Qi; Ranulfo Romo; Naoshige Uchida; Christian K Machens
Journal:  Elife       Date:  2016-04-12       Impact factor: 8.140

  7 in total

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