<p>While the network ISI distributions at low and high values of are quite well fitted by an exponential (shown as a solid black line), the network ISI at the critical threshold cannot be described by a single exponential (it strongly deviates from the exponential at intervals larger than 0.03 ms).</p
The distribution of time intervals between successive spikes generated by a neuronal cell --the inte...
<p>The solid line is a power law best fit of the data in the interval 1 ms 23 ms and gives exponen...
We study the scaling of fluctuations with the mean of traffic in complex networks using a model wher...
<p>Solid lines are power law best fits of the data in the intervals for the sizes and 1 ms 23 ms ...
Observed inter-spike-interval distributions of spike trains with limited input encoding, shown as n ...
(a) through (c) correspond to (a) through (c) in Fig 2. We try to avoid the dynamics shown in panel ...
<p>〈INSI〉 (panel A) and CV<sub>INSI</sub> (panel B) <i>vs</i> the real part ℜ<i>λ</i> of the dominan...
<p>The first peak is clustered around short ISIs – this defines the intervals within the bursts wher...
Spike arrays showing the last 150 time-steps of three different network states categorized by ISI st...
<p><b>A</b>: histograms of interspike interval (ISI) distribution for both the 6-state and the simpl...
<p>Distributions of closed triples corresponding to (a) , (b) , (c) , and (d) loops in the Epinions...
One-hundred networks with 500 different initial conditions each were generated with edge densities (...
<p>The distribution is plotted on the log scale on the -axis since the in-degree distribution was as...
<p>(A) Spike trains of excitatory neurons. (C) Firing rate distribution of excitatory neurons. (B,D)...
<p>The results for square networks are shown by solid lines with error bars obtained across ten simu...
The distribution of time intervals between successive spikes generated by a neuronal cell --the inte...
<p>The solid line is a power law best fit of the data in the interval 1 ms 23 ms and gives exponen...
We study the scaling of fluctuations with the mean of traffic in complex networks using a model wher...
<p>Solid lines are power law best fits of the data in the intervals for the sizes and 1 ms 23 ms ...
Observed inter-spike-interval distributions of spike trains with limited input encoding, shown as n ...
(a) through (c) correspond to (a) through (c) in Fig 2. We try to avoid the dynamics shown in panel ...
<p>〈INSI〉 (panel A) and CV<sub>INSI</sub> (panel B) <i>vs</i> the real part ℜ<i>λ</i> of the dominan...
<p>The first peak is clustered around short ISIs – this defines the intervals within the bursts wher...
Spike arrays showing the last 150 time-steps of three different network states categorized by ISI st...
<p><b>A</b>: histograms of interspike interval (ISI) distribution for both the 6-state and the simpl...
<p>Distributions of closed triples corresponding to (a) , (b) , (c) , and (d) loops in the Epinions...
One-hundred networks with 500 different initial conditions each were generated with edge densities (...
<p>The distribution is plotted on the log scale on the -axis since the in-degree distribution was as...
<p>(A) Spike trains of excitatory neurons. (C) Firing rate distribution of excitatory neurons. (B,D)...
<p>The results for square networks are shown by solid lines with error bars obtained across ten simu...
The distribution of time intervals between successive spikes generated by a neuronal cell --the inte...
<p>The solid line is a power law best fit of the data in the interval 1 ms 23 ms and gives exponen...
We study the scaling of fluctuations with the mean of traffic in complex networks using a model wher...