Biomedical Engineering Reference
In-Depth Information
500 ms
a
b
c
40
0
1.8
1.8
1.8
0.8
0.8
0.8
−100
0
100
−100
0
100
−100
0
100
Time shift (ms)
d
e
f
40
0
3.3
3.3
3.3
0.5
0.5
0.5
−100
0
100
−100
0
100
−100
0
100
Time shift (ms)
Figure 12.1
Spike trains correlated by common input. Each panel includes 20 computer-
generated spike trains. Each row represents one neuron and each small, vertical
line one spike. Neurons were modeled as leaky integrate-and-fire units disconnected
from each other but driven by synaptic conductances that co-fluctuated across neu-
rons. Continuous traces superimposed on the rasters are firing rates, averaged over all
neurons, obtained by smoothing the spike trains with a Gaussian function with s=10
ms. Plots below the rasters are cross-correlation histograms averaged over multiple
distinct pairs of units.
These were based on longer spike trains that included the
segments shown.
tances gives rise to the sharp peak in the histogram of Figure 12.1a.
Figure 12.1b was generated using the same correlation values, but the excitatory
signals
g
E
(
varied more slowly (in addition, their magnitude was adjusted so that
similar output rates were produced). The characteristic time at which
g
E
(
t
)
varies is
its correlation time. Below we describe this quantity more accurately; for the mo-
ment the crucial point is that in Figure 12.1 the correlation time of
g
E
(
t
)
corresponds
to the time constant of excitatory synapses, t
E
. This is essentially the duration of
a unitary synaptic event. In Figure 12.1a the synaptic time constants for excitation
and inhibition were both equal to 2 ms. In Figure 12.1b t
I
stayed the same but t
E
was increased to 20 ms. As can be seen in the raster, this changed the postsynaptic
t
)
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