Exploring the spectrum of dynamical regimes and timescales in spontaneous cortical activity

Abstract

Rhythms at slow (\1 Hz) frequency of alternating Up and Down states occur during slow-wave sleep states, under deep anaesthesia and in cortical slices of mammals maintained in vitro. Such spontaneous oscillations result from the interplay between network reverberations nonlinearly sustained by a strong synaptic coupling and a fatigue mechanism inhibiting the neurons firing in an activity-dependent manner. Varying pharmacologically the excitability level of brain slices we exploit the network dynamics underlying slow rhythms, uncovering an intrinsic anticorrelation between Up and Down state durations. Besides, a non-monotonic change of Down state duration is also observed, which shrinks the distribution of the accessible frequencies of the slow rhythms. Attractor dynamics with activity-dependent self-inhibition predicts a similar trend even when the system excitability is reduced, because of a stability loss of Up and Down states. Hence, such cortical rhythms tend to display a maximal size of the distribution of Up/Down frequencies, envisaging the location of the system dynamics on a critical boundary of the parameter space. This would be an optimal solution for the system in order to display a wide spectrum of dynamical regimes and timescales.

Stochastic transitions into silence cause noise correlations in cortical circuits

“…In contrast to previous network models of up/down transitions that, due to a strong fatigue mechanism (e.g., spike frequency adaptation), operate in the oscillatory regime (Fig. 3C, Top Right) (24,28,39), our model displays weak adaptation and relies on fluctuations to escape from the otherwise-stable attractors (26,27). Moreover, by increasing the external input I, plus optionally decreasing the adaptation strength β, the network becomes desynchronized (Fig.…”

“…To describe the fluctuations of r(t), we used a rate model with adaptation a(t) where the dynamics were given by the following (22,25,28): τ r dr dt = −rðtÞ + ϕðαrðtÞ − aðtÞ + IðtÞ − θÞ, τ a da dt = −aðtÞ + βrðtÞ, [2] where θ = 2 was the activation threshold and the external input I(t) = I + stim(t) + σξ(t) was composed of constant term I (range, 0-4 a.u. ), the stimulus step function (amplitude, 60 a.u.…”

“…Recurrent networks can also generate fast oscillations in the population activity, but, in a regime of low rates, typical of cortical circuits, average spike count correlations are negligible (21). Network models producing nonzero average correlations are those exhibiting up and down dynamics (22)(23)(24)(25)(26)(27)(28)(29). Most of these studies have focused on investigating the mechanisms underlying the slow oscillatory activity observed in cortical slices (30), under anesthesia (31, 32), or during slow-wave sleep (33).…”

Landau–Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization

“…This Hopf-bifurcation scenario has been extensively discussed in the literature (see e.g. (62)) and it is at the basis § Deviations from ρ * = 0 stem from the small but non-vanishing external driving h = 0.…”

Sleep, neuroengineering and dynamics

“…Stimulation, as well as learning, can effect on both the dynamics (Mattia and Sanchez-Vives 2012;Schütt and Claussen 2012) and on the plasticity (Clopath 2012;Vogt and Hofmann 2012). Vogt and Hofmann (2012) demonstrate modulatory effects of dopamine based on an underlying STDP learning mechanism.…”