Title: On the ground state of spiking network activity in mammalian cortex,
Abstract: Electrophysiological recordings of spiking activity are limited to a small
number of neurons. This spatial subsampling has hindered characterizing even
most basic properties of collective spiking in cortical networks. In
particular, two contradictory hypotheses prevailed for over a decade: the first
proposed an asynchronous irregular state, the second a critical state. While
distinguishing them is straightforward in models, we show that in experiments
classical approaches fail to correctly infer network dynamics because of
subsampling. Deploying a novel, subsampling-invariant estimator, we find that
in vivo dynamics do not comply with either hypothesis, but instead occupy a
narrow "reverberating" state consistently across multiple mammalian species and
cortical areas. A generic model tuned to this reverberating state predicts
single neuron, pairwise, and population properties. With these predictions we
first validate the model and then deduce network properties that are
challenging to obtain experimentally, like the network timescale and strength
of cortical input.