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 .
Knowledge about the collective dynamics of cortical spiking is very informative about the underlying coding principles. However, even most basic properties are not known with certainty, because their assessment is hampered by spatial subsampling, i.e. the limitation that only a tiny fraction of all neurons can be recorded simultaneously with millisecond precision. Building on a novel, subsampling-invariant estimator, we fit and carefully validate a minimal model for cortical spike propagation. The model interpolates between two prominent states: asynchronous and critical. We find neither of them in cortical spike recordings across various species , but instead identify a narrow reverberating regime. This approach enables us to predict yet unknown properties from very short recordings and for every circuit individually, including responses to minimal perturbations, intrinsic network timescales, and the strength of external input compared to recurrent activation - thereby informing about the underlying coding principles for each circuit, area, state and task .
R
Electrophysiological recordings of spiking activity are limited to a small number of neurons. This spatial subsampling has hindered characterizing
Knowledge about the collective dynamics of cortical spiking is very informative about the underlying coding principles. However,
R
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
are not known with certainty, because their assessment is hampered by spatial subsampling, i.e. the limitation that only a tiny fraction of all neurons can be recorded simultaneously with millisecond precision. Building on
R
find that in vivo dynamics do not comply with either hypothesis
fit and carefully validate a minimal model for cortical spike propagation. The model interpolates between two prominent states: asynchronous and critical. We find neither of them in cortical spike recordings across various species
R
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
identify a narrow reverberating regime. This approach enables us to predict yet unknown properties from very short recordings and for every circuit individually, including responses to minimal perturbations, intrinsic network timescales, and the strength of external input compared to recurrent activation - thereby informing about the underlying coding principles for each circuit, area, state and task