Spike triggered covariance in strongly correlated gaussian stimuli.
Many biological systems perform computations on inputs that have very large dimensionality. Determining the relevant input combinations for a particular computation is often key to understanding its function. A common way to find the relevant input dimensions is to examine the difference in variance...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
|
| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC3764020?pdf=render |
| _version_ | 1819078000390438912 |
|---|---|
| author | Johnatan Aljadeff Ronen Segev Michael J Berry Tatyana O Sharpee |
| author_facet | Johnatan Aljadeff Ronen Segev Michael J Berry Tatyana O Sharpee |
| author_sort | Johnatan Aljadeff |
| collection | DOAJ |
| description | Many biological systems perform computations on inputs that have very large dimensionality. Determining the relevant input combinations for a particular computation is often key to understanding its function. A common way to find the relevant input dimensions is to examine the difference in variance between the input distribution and the distribution of inputs associated with certain outputs. In systems neuroscience, the corresponding method is known as spike-triggered covariance (STC). This method has been highly successful in characterizing relevant input dimensions for neurons in a variety of sensory systems. So far, most studies used the STC method with weakly correlated Gaussian inputs. However, it is also important to use this method with inputs that have long range correlations typical of the natural sensory environment. In such cases, the stimulus covariance matrix has one (or more) outstanding eigenvalues that cannot be easily equalized because of sampling variability. Such outstanding modes interfere with analyses of statistical significance of candidate input dimensions that modulate neuronal outputs. In many cases, these modes obscure the significant dimensions. We show that the sensitivity of the STC method in the regime of strongly correlated inputs can be improved by an order of magnitude or more. This can be done by evaluating the significance of dimensions in the subspace orthogonal to the outstanding mode(s). Analyzing the responses of retinal ganglion cells probed with [Formula: see text] Gaussian noise, we find that taking into account outstanding modes is crucial for recovering relevant input dimensions for these neurons. |
| first_indexed | 2024-12-21T19:06:08Z |
| format | Article |
| id | doaj.art-1ff3e0dbbd98422e9b8965514150f503 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-21T19:06:08Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-1ff3e0dbbd98422e9b8965514150f5032022-12-21T18:53:20ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582013-01-0199e100320610.1371/journal.pcbi.1003206Spike triggered covariance in strongly correlated gaussian stimuli.Johnatan AljadeffRonen SegevMichael J BerryTatyana O SharpeeMany biological systems perform computations on inputs that have very large dimensionality. Determining the relevant input combinations for a particular computation is often key to understanding its function. A common way to find the relevant input dimensions is to examine the difference in variance between the input distribution and the distribution of inputs associated with certain outputs. In systems neuroscience, the corresponding method is known as spike-triggered covariance (STC). This method has been highly successful in characterizing relevant input dimensions for neurons in a variety of sensory systems. So far, most studies used the STC method with weakly correlated Gaussian inputs. However, it is also important to use this method with inputs that have long range correlations typical of the natural sensory environment. In such cases, the stimulus covariance matrix has one (or more) outstanding eigenvalues that cannot be easily equalized because of sampling variability. Such outstanding modes interfere with analyses of statistical significance of candidate input dimensions that modulate neuronal outputs. In many cases, these modes obscure the significant dimensions. We show that the sensitivity of the STC method in the regime of strongly correlated inputs can be improved by an order of magnitude or more. This can be done by evaluating the significance of dimensions in the subspace orthogonal to the outstanding mode(s). Analyzing the responses of retinal ganglion cells probed with [Formula: see text] Gaussian noise, we find that taking into account outstanding modes is crucial for recovering relevant input dimensions for these neurons.http://europepmc.org/articles/PMC3764020?pdf=render |
| spellingShingle | Johnatan Aljadeff Ronen Segev Michael J Berry Tatyana O Sharpee Spike triggered covariance in strongly correlated gaussian stimuli. PLoS Computational Biology |
| title | Spike triggered covariance in strongly correlated gaussian stimuli. |
| title_full | Spike triggered covariance in strongly correlated gaussian stimuli. |
| title_fullStr | Spike triggered covariance in strongly correlated gaussian stimuli. |
| title_full_unstemmed | Spike triggered covariance in strongly correlated gaussian stimuli. |
| title_short | Spike triggered covariance in strongly correlated gaussian stimuli. |
| title_sort | spike triggered covariance in strongly correlated gaussian stimuli |
| url | http://europepmc.org/articles/PMC3764020?pdf=render |
| work_keys_str_mv | AT johnatanaljadeff spiketriggeredcovarianceinstronglycorrelatedgaussianstimuli AT ronensegev spiketriggeredcovarianceinstronglycorrelatedgaussianstimuli AT michaeljberry spiketriggeredcovarianceinstronglycorrelatedgaussianstimuli AT tatyanaosharpee spiketriggeredcovarianceinstronglycorrelatedgaussianstimuli |