Title: Spontaneous and stimulus-induced coherent states of dynamically balanced neuronal networks,
Abstract: How the information microscopically processed by individual neurons is
integrated and used in organising the macroscopic behaviour of an animal is a
central question in neuroscience. Coherence of dynamics over different scales
has been suggested as a clue to the mechanisms underlying this integration.
Balanced excitation and inhibition amplify microscopic fluctuations to a
macroscopic level and may provide a mechanism for generating coherent dynamics
over the two scales. Previous theories of brain dynamics, however, have been
restricted to cases in which population-averaged activities have been
constrained to constant values, that is, to cases with no macroscopic degrees
of freedom. In the present study, we investigate balanced neuronal networks
with a nonzero number of macroscopic degrees of freedom coupled to microscopic
degrees of freedom. In these networks, amplified microscopic fluctuations drive
the macroscopic dynamics, while the macroscopic dynamics determine the
statistics of the microscopic fluctuations. We develop a novel type of
mean-field theory applicable to this class of interscale interactions, for
which an analytical approach has previously been unknown. Irregular macroscopic
rhythms similar to those observed in the brain emerge spontaneously as a result
of such interactions. Microscopic inputs to a small number of neurons
effectively entrain the whole network through the amplification mechanism.
Neuronal responses become coherent as the magnitude of either the balanced
excitation and inhibition or the external inputs is increased. Our mean-field
theory successfully predicts the behaviour of the model. Our numerical results
further suggest that the coherent dynamics can be used for selective read-out
of information. In conclusion, our results show a novel form of neuronal
information processing that bridges different scales, and advance our
understanding of the brain.