  In randomly connected networks of pulse-coupled elements a time-dependent
input signal can be buffered over a short time. We studied the signal buffering
properties in simulated networks as a function of the networks state,
characterized by both the Lyapunov exponent of the microscopic dynamics and the
macroscopic activity derived from mean-field theory. If all network elements
receive the same signal, signal buffering over delays comparable to the
intrinsic time constant of the network elements can be explained by macroscopic
properties and works best at the phase transition to chaos. However, if only 20
percent of the network units receive a common time-dependent signal, signal
buffering properties improve and can no longer be attributed to the macroscopic
dynamics.
