A growing body of experimental evidence suggests that there is a complex interaction between stimulus-driven and spontaneous activity in the cortex . In primary sensory cortices of awake behaving animals, multi-electrode recordings show neural responses during periods when the neural population is driven by a sensory stimulus, as well as during periods of quite-wakefulness, when no sensory stimulus is provided to the animal. During quite-wakefulness, the population activity can take the form of characteristic synchronized bursts of spiking activity, or up-states, interspersed by periods of silence, or down-states. Why does such activity occur and what could be its functional role? Previous mechanistic explanations have proposed that spontaneous activity is due to a dynamic interplay of local excitation, adaptation and inhibition . Another mechanistic explanation is that "up" states could be triggered by modulations from distal areas, either top-down or subcortical, like a "gate" opening and closing . However, the computational role of spontaneous activity remains a mystery.
We propose that up-states appear in E/I networks that are optimised
for error correction. In recent work, we showed that E/I balance can
be understood as a form of optimal predictive coding: by maintaining a
tight balance, a population of neurons monitors its own coding error
. We find that when such networks are inactive, noise can induce
neural spiking. The error correcting mechanisms of the network then
respond to these noise-induced spikes by producing additional spikes
in an attempt to correct the error. This error-correcting response
appears as up-state of spiking activity. The network remains in an
up-state, propagating the error signal throughout the network, until
it manages to resolve the noise-induced error with an appropriate
spike-train. Once the error has been resolved, the network activity
returns to a down-state. According to this computational view,
up-states are an attempt to correct errors induced by noise. Finally,
we investigate the properties of these up-states, such as the
distribution of the duration of up-states and the distribution of time
epochs between up-states (inter-burst intervals) and we explore the
E/I network parameters that determine the shape of this distribution.
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