Coupled variability in primary sensory areas and the hippocampus during spontaneous activity

Abstract

The cerebral cortex is an anatomically divided and functionally specialized structure. It includes distinct areas, which work on different states over time. The structural features of spiking activity in sensory cortices have been characterized during spontaneous and evoked activity. However, the coordination among cortical and sub-cortical neurons during spontaneous activity across different states remains poorly characterized. We addressed this issue by studying the temporal coupling of spiking variability recorded from primary sensory cortices and hippocampus of anesthetized or freely behaving rats. During spontaneous activity, spiking variability was highly correlated across primary cortical sensory areas at both small and large spatial scales, whereas the cortico-hippocampal correlation was modest. This general pattern of spiking variability was observed under urethane anesthesia, as well as during waking, slow-wave sleep and rapid-eye-movement sleep, and was unchanged by novel stimulation. These results support the notion that primary sensory areas are strongly coupled during spontaneous activity.

Subsampled Directed-Percolation Models Explain Scaling Relations Experimentally Observed in the Brain

“…Urethane is a well-established drug that provides spontaneous changes of brain states that resemble sleep state alternations (Clement et al, 2008 ). In the last decade, experimental preparations using urethane have helped elucidate questions concerning mechanisms and the functional relevance of state-dependent patterns of brain activity (Curto et al, 2009 ; Renart et al, 2010 ; Mochol et al, 2015 ; de Vasconcelos et al, 2017 ). The property to promote spontaneous change in the levels of spiking variability cannot be achieved in other anesthesia approaches, such as pentobarbital and isoflurane.…”

“…This experimental setup is known to yield spiking activity which is highly variable, ranging from very asynchronous to very synchronous population activity (Clement et al, 2008 ). These regimes can be characterized by different ranges of the coefficient of variation ( CV ) of the population firing rate (de Vasconcelos et al, 2017 ), which is thought of as a simple marker of cortical states (Harris and Thiele, 2011 ). By parsing the data according to levels of spiking variability, Fontenele et al ( 2019 ) found that the scaling relation Equation (4) was satisfied at an intermediate value of CV , suggesting a phase transition away from both the synchronized and desynchronized ends of the spiking variability spectrum.…”

Criticality between Cortical States

“…DOI: 10.1103/PhysRevLett.122.208101 It is well established that cortical activity exhibits a rich repertoire of dynamical states [1][2][3]. This knowledge, initially based on electroencephalographic recordings, later reached the spiking activity of large neuronal populations of both anesthetized and freely moving animals [4,5], in which the variability level has been used as a proxy of the cortical state [5][6][7][8]. However, only recently has the diversity of cortical states been systematically considered in studies of criticality [9,10].…”

“…Here we show that the signatures of criticality in vivo vary continuously across the cortical states, and that scaling relations predicted by the theory of critical phenomena can be used to determine a well-defined critical point. We quantified the variety of cortical states [4,5] in terms of the coefficient of variation (CV) [5,6] of the summed population activity in the primary visual cortex (V1) of urethane-anesthetized rats [8] (Fig. S1 [15]).…”

“…S4 [15]), within nonoverlapping 10 s windows. In that timescale, CV typically changes rapidly [4][5][6]8] In what follows, we sort results according to CV values and average over consecutive percentiles to obtain hCVi as a representative of a given spiking variability level ( Fig. S1 [15]).…”