The balance between excitation and inhibition is critical in the physiology of the cerebral cortex. To understand the influence of inhibitory control on the emergent activity of the cortical network, inhibition was progressively blocked in a slice preparation that generates spontaneous rhythmic up states at a similar frequency to those occurring in vivo during slow-wave sleep or anesthesia. Progressive removal of inhibition induced a parametric shortening of up state duration and elongation of the down states, the frequency of oscillations decaying. Concurrently, a gradual increase in the network firing rate during up states occurred. The slope of transitions between up and down states was quantified for different levels of inhibition. The slope of upward transitions reflects the recruitment of the local network and was progressively increased when inhibition was decreased, whereas the speed of activity propagation became faster. Removal of inhibition eventually resulted in epileptiform activity. Whereas gradual reduction of inhibition induced linear changes in up/down states and their propagation, epileptiform activity was the result of a nonlinear transformation. A computational network model showed that strong recurrence plus activity-dependent hyperpolarizing currents were sufficient to account for the observed up state modulations and predicted an increase in activity-dependent hyperpolarization following up states when inhibition was decreased, which was confirmed experimentally.

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Key points We confirm that GABAB receptors (GABAB‐Rs) are involved in the termination of Up‐states; their blockade consistently elongates Up‐states. GABAB‐Rs also modulate Down‐states and the oscillatory cycle, thus having an impact on slow oscillation rhythm and its regularity. The most frequent effect of GABAB‐R blockade is elongation of Down‐states and subsequent decrease of oscillatory frequency, with an increased regularity. In a quarter of cases, GABAB‐R blockade shortened Down‐states and increased oscillatory frequency, changes that are independent of firing rates in Up‐states. Our computer model provides mechanisms for the experimentally observed dynamics following blockade of GABAB‐Rs, for Up/Down durations, oscillatory frequency and regularity. The time course of excitation, inhibition and adaptation can explain the observed dynamics of the network. This study brings novel insights into the role of GABAB‐R‐mediated slow inhibition on the slow oscillatory activity, which is considered the default activity pattern of the cortical network. Abstract Slow wave oscillations (SWOs) dominate cortical activity during deep sleep, anaesthesia and in some brain lesions. SWOs are composed of periods of activity (Up states) interspersed with periods of silence (Down states). The rhythmicity expressed during SWOs integrates neuronal and connectivity properties of the network and is often altered under pathological conditions. Adaptation mechanisms as well as synaptic inhibition mediated by GABAB receptors (GABAB‐Rs) have been proposed as mechanisms governing the termination of Up states. The interplay between these two mechanisms is not well understood, and the role of GABAB‐Rs controlling the whole cycle of the SWO has not been described. Here we contribute to its understanding by combining in vitro experiments on spontaneously active cortical slices and computational techniques. GABAB‐R blockade modified the whole SWO cycle, not only elongating Up states, but also affecting the subsequent Down state duration. Furthermore, while adaptation tends to yield a rather regular behaviour, we demonstrate that GABAB‐R activation desynchronizes the SWOs. Interestingly, variability changes could be accomplished in two different ways: by either shortening or lengthening the duration of Down states. Even when the most common observation following GABAB‐Rs blocking is the lengthening of Down states, both changes are expressed experimentally and also in numerical simulations. Our simulations suggest that the sluggishness of GABAB‐Rs to follow the excitatory fluctuations of the cortical network can explain these different network dynamics modulated by GABAB‐Rs.

The senescence-accelerated mouse prone 8 (SAMP8) model is characterized by accelerated, progressive cognitive decline as well as Alzheimer’s disease (AD)-like neurodegenerative changes, and resembles the etiology of multicausal, sporadic late-onset/age-related AD in humans. Our aim was to find whether these AD-like pathological features, together with the cognitive deficits present in the SAMP8 strain, are accompanied by disturbances in cortical network activity with respect to control mice (SAM resistance 1, SAMR1) and, if so, how the alterations in cortical activity progress with age. For this purpose, we characterized the extracellular spontaneous oscillatory activity in different regions of the cerebral cortex of SAMP8 and SAMR1 mice under ketamine anesthesia at 5 and 7 months of age. Under these conditions, slow oscillations and fast rhythms generated in the cortical network were recorded and different parameters of these oscillations were quantified and compared between SAMP8 and their control, SAMR1 mice. The average frequency of slow oscillations in SAMP8 mice was decreased with respect to the control mice at both studied ages. An elongation of the silent periods or Down states was behind the decreased slow oscillatory frequency while the duration of active or Up states remained stable. SAMP8 mice also presented increased cycle variability and reduced high frequency components during Down states. During Up states, the power peak in the gamma range was displaced towards lower frequencies in all the cortical areas of SAMP8 with respect to control mice suggesting that the spectral profile of SAMP8 animals is shifted towards lower frequencies. This shift is reminiscent to one of the principal hallmarks of electroencephalography (EEG) abnormalities in patients with Alzheimer’s disease, and adds evidence in support of the suitability of the SAMP8 mouse as a model of this disease. Although some of the differences between SAMP8 and control mice were emphasized with age, the evolution of the studied parameters as SAMR1 mice got older indicates that the SAMR1 phenotype tends to converge with that of SAMP8 animals. To our knowledge, this is the first systematic characterization of the cortical slow and fast rhythms in the SAMP8 strain and it provides useful insights about the cellular and synaptic mechanisms underlying the reported alterations.