<p>(<b>A</b>) A comparison of numerically obtained excitatory-inhibitory cross-correlations to the approximation given by Eq. (26). (<b>B</b>) Mean and standard deviation for the distribution of correlation functions for excitatory-inhibitory pairs of cells. (Solid line – mean cross-correlation, shaded area – one standard deviation from the mean, calculated using bootstrapping in a single network realization). (<b>C</b>) Mean and standard deviation for the distribution of cross-correlation functions conditioned on cell type <i>and</i> first order connectivity for a reciprocally coupled excitatory-inhibitory pair of cells. (Solid line – mean cross-correlation function, shaded area – one standard deviation from the mean found by bootstrapping...
<p>The Eqn. 1 is used for calculating the correlation coefficients for each simulation setting separ...
<p>Number of synaptic inputs binomially distributed as , with connection probability . <b>A</b> Pop...
<p><b>A.</b> Probability of discrimination error for a 2-Pool model of a neural population, as a fun...
<p>Activity in a network of binary inhibitory neurons with synaptic amplitudes . Each neuron receiv...
Novel experimental techniques reveal the simultaneous activity of larger and larger numbers of neuro...
<div><p>Novel experimental techniques reveal the simultaneous activity of larger and larger numbers ...
<p><b>A</b> Auto-covariance of the mean activity of one of the excitatory populations (black). Cross...
<p>(A) Distribution of correlation coefficients for pairs of neurons in the network. For the example...
<p>(<b>A</b>) Some of the submotifs contributing to correlations in the all–to–all network. (<b>B</b...
<p>Each neuron in population receives randomly drawn excitatory inputs with weight , randomly dra...
<p><b>A</b>: Spike-train variances (black) and (gray) of excitatory and inhibitory neurons. <b>B</...
<p>Excitatory synapse (thick solid line, synaptic delay <i>δ</i> = 3 ms), inhibitory synapse (thin s...
<p>(<b>A</b>) The FFI circuit (left) can be decomposed into three submotifs. Equation (18) shows tha...
<p><b>A</b> Average pairwise cross-covariances from simulations (solid curves) and <a href="http://w...
<p><b>A.</b> Architecture of the large network of spiking neurons. <b>B.</b> Architecture of the net...
<p>The Eqn. 1 is used for calculating the correlation coefficients for each simulation setting separ...
<p>Number of synaptic inputs binomially distributed as , with connection probability . <b>A</b> Pop...
<p><b>A.</b> Probability of discrimination error for a 2-Pool model of a neural population, as a fun...
<p>Activity in a network of binary inhibitory neurons with synaptic amplitudes . Each neuron receiv...
Novel experimental techniques reveal the simultaneous activity of larger and larger numbers of neuro...
<div><p>Novel experimental techniques reveal the simultaneous activity of larger and larger numbers ...
<p><b>A</b> Auto-covariance of the mean activity of one of the excitatory populations (black). Cross...
<p>(A) Distribution of correlation coefficients for pairs of neurons in the network. For the example...
<p>(<b>A</b>) Some of the submotifs contributing to correlations in the all–to–all network. (<b>B</b...
<p>Each neuron in population receives randomly drawn excitatory inputs with weight , randomly dra...
<p><b>A</b>: Spike-train variances (black) and (gray) of excitatory and inhibitory neurons. <b>B</...
<p>Excitatory synapse (thick solid line, synaptic delay <i>δ</i> = 3 ms), inhibitory synapse (thin s...
<p>(<b>A</b>) The FFI circuit (left) can be decomposed into three submotifs. Equation (18) shows tha...
<p><b>A</b> Average pairwise cross-covariances from simulations (solid curves) and <a href="http://w...
<p><b>A.</b> Architecture of the large network of spiking neurons. <b>B.</b> Architecture of the net...
<p>The Eqn. 1 is used for calculating the correlation coefficients for each simulation setting separ...
<p>Number of synaptic inputs binomially distributed as , with connection probability . <b>A</b> Pop...
<p><b>A.</b> Probability of discrimination error for a 2-Pool model of a neural population, as a fun...