Input-driven oscillations in networs with excitatory and inhibitory neurons with dynamic synapses.
D Marinazzo, Bert Kappen and C.C. A.M. Gielen
Previous work has shown that networks of neurons with two coupled
layers of excitatory and inhibitory neurons can reveal oscillatory activity.
For example, B¨orgers and Kopell (2003) have shown that oscillations
occur when the excitatory neurons receive a sufficiently large input. A
constant drive to the excitatory neurons is sufficient for oscillatory activity.
Other studies (Doiron, Chacron, Maler, Longtin, & Bastian, 2003;
Doiron, Lindner, Longtin, Maler, & Bastian, 2004) have shown that networks
of neurons with two coupled layers of excitatory and inhibitory
neurons reveal oscillatory activity only if the excitatory neurons receive
correlated input, regardless of the amount of excitatory input. In this
study, we show that these apparently contradictory results can be explained
by the behavior of a single model operating in different regimes
of parameter space. Moreover, we show that adding dynamic synapses in
the inhibitory feedback loop provides a robust network behavior over a
broad range of stimulus intensities, contrary to that of previous models.
A remarkable property of the introduction of dynamic synapses is that
the activity of the network reveals synchronized oscillatory components
in the case of correlated input, but also reflects the temporal behavior
of the input signal to the excitatory neurons. This allows the network to
encode both the temporal characteristics of the input and the presence of
spatial correlations in the input simultaneously.