  How the information microscopically processed by individual neurons is
integrated and used in organizing the behavior of an animal is a central
question in neuroscience. The coherence of neuronal dynamics over different
scales has been suggested as a clue to the mechanisms underlying this
integration. Balanced excitation and inhibition may amplify microscopic
fluctuations to a macroscopic level, thus providing a mechanism for generating
coherent multiscale dynamics. Previous theories of brain dynamics, however,
were restricted to cases in which inhibition dominated excitation and
suppressed fluctuations in the macroscopic population activity. In the present
study, we investigate the dynamics of neuronal networks at a critical point
between excitation-dominant and inhibition-dominant states. In these networks,
the microscopic fluctuations are amplified by the strong excitation and
inhibition to drive the macroscopic dynamics, while the macroscopic dynamics
determine the statistics of the microscopic fluctuations. Developing a novel
type of mean-field theory applicable to this class of interscale interactions,
we show that the amplification mechanism generates spontaneous, irregular
macroscopic rhythms similar to those observed in the brain. Through the same
mechanism, microscopic inputs to a small number of neurons effectively entrain
the dynamics of the whole network. These network dynamics undergo a
probabilistic transition to a coherent state, as the magnitude of either the
balanced excitation and inhibition or the external inputs is increased. Our
mean-field theory successfully predicts the behavior of this model.
Furthermore, we numerically demonstrate that the coherent dynamics can be used
for state-dependent read-out of information from the network. These results
show a novel form of neuronal information processing that connects neuronal
dynamics on different scales.
