On the ground state of spiking network activity in mammalian cortex
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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.
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