<p>The signal, in terms of the covariance between the neural response and the stimulus is shown as a function of the cell number in a heterogeneous population based on the responses of all cells in the data set. The inset shows the distribution of the signal over the different eigenvectors of the neural spike-count correlation matrix (with uniform correlation coefficient of 0.2), as a function of the rank of their eigenvalue, in percent. Note that the first eigenvector corresponds to the uniform vector (cf <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081660#pone-0081660-g013" target="_blank">Figure 13</a>). The signal and the correlation matrix were estimated using 10,000 repetitions for every stimulus value .</p
<p><b>A</b>: Spike-train variances (black) and (gray) of excitatory and inhibitory neurons. <b>B</...
<p>(A) Edge weight estimated by mean partial correlation. Fluorescence traces of a neuron pair (i, j...
<p>(A) Distribution of correlation coefficients for pairs of neurons in the network. For the example...
<p>The signal, in terms of the covariance between the neural response and the stimulus is shown as a...
<p>The correlation coefficients matrix was computed for heterogeneous pseudo-population of neurons ...
<p>Neural responses for a population of 30 neurons containing all the cells in our data set were gen...
<p>(A) One over the Mean Square Error of OLE estimation, plotted as function of population size for ...
<p>(A) Eigenvalue spectrum of the spike count correlation matrix of a heterogeneous pseudo-populati...
<p>Pseudo-population responses of neurons were generated based on the response distribution of each...
<p>(A) The spectrum of the correlation matrix of a homogenous pseudo-population of 30 neurons, with ...
<p><b>A.</b> The eigenvalues of the signal correlation matrix for populations of 8 simultaneously re...
International audienceHow much information does a neural population convey about a stimulus? Answers...
<p>We consider signal encoding in a population of 20 neurons, each of which has a different dependen...
<p>Spectra of for increasing dataset size in the case of strongly correlated Gaussian noise (A,B) a...
<p>(A) Inputs are plotted when projected on the eigenvectors corresponding to the largest eigenvalue...
<p><b>A</b>: Spike-train variances (black) and (gray) of excitatory and inhibitory neurons. <b>B</...
<p>(A) Edge weight estimated by mean partial correlation. Fluorescence traces of a neuron pair (i, j...
<p>(A) Distribution of correlation coefficients for pairs of neurons in the network. For the example...
<p>The signal, in terms of the covariance between the neural response and the stimulus is shown as a...
<p>The correlation coefficients matrix was computed for heterogeneous pseudo-population of neurons ...
<p>Neural responses for a population of 30 neurons containing all the cells in our data set were gen...
<p>(A) One over the Mean Square Error of OLE estimation, plotted as function of population size for ...
<p>(A) Eigenvalue spectrum of the spike count correlation matrix of a heterogeneous pseudo-populati...
<p>Pseudo-population responses of neurons were generated based on the response distribution of each...
<p>(A) The spectrum of the correlation matrix of a homogenous pseudo-population of 30 neurons, with ...
<p><b>A.</b> The eigenvalues of the signal correlation matrix for populations of 8 simultaneously re...
International audienceHow much information does a neural population convey about a stimulus? Answers...
<p>We consider signal encoding in a population of 20 neurons, each of which has a different dependen...
<p>Spectra of for increasing dataset size in the case of strongly correlated Gaussian noise (A,B) a...
<p>(A) Inputs are plotted when projected on the eigenvectors corresponding to the largest eigenvalue...
<p><b>A</b>: Spike-train variances (black) and (gray) of excitatory and inhibitory neurons. <b>B</...
<p>(A) Edge weight estimated by mean partial correlation. Fluorescence traces of a neuron pair (i, j...
<p>(A) Distribution of correlation coefficients for pairs of neurons in the network. For the example...