Fluctuations and synchronizations of neural activities during sleep: Neural basis of possible sleep functions?

Physiological and psychological evidence have been accumulated concerning the function of sleep in development and learning/memory. Many conceptual ideas have been proposed to elucidate the mechanisms underlying them. Sleep consists of a wide variety of physiological processes. It has not yet been clarified which processes are involved in development and learning/memory processes. We have found that single neuronal activity exhibits a slowly fluctuating rate of discharge during rapid eye movement (REM) sleep and a random low discharge rate during non-rapid eye movement (NREM) sleep. It is suggested that a structural change of the neural network attractor underlies this neuronal dynamics-alternation by mathematical modeling. Functional interpretation of the neuronal dynamics-alternation was provided in combination with the phase locking of ponto-geniculo-occipital (PGO)/pontine (P) wave to the hippocampal theta wave, each of which is known to be involved in learning/memory processes. More directly, by the long-term sensory deprivation, the dynamics of neural activity during sleep was found to progressively change in a non-monotonic way. This finding reveals a possible interaction between sleep and reorganization of neural network in the matured brain. Here, in addition to the related findings, we described our idea about how sleep contributes to the learning/memory processes and reorganization of neural network of the matured brain through characteristic neural activities during sleep.

     

Formation and Dynamics of Waves in a Cortical Model of Cholinergic Modulation

Acetylcholine (ACh) is a regulator of neural excitability and one of the neurochemical substrates of sleep. Amongst the cellular effects induced by cholinergic modulation are a reduction in spike-frequency adaptation (SFA) and a shift in the phase response curve (PRC). We demonstrate in a biophysical model how changes in neural excitability and network structure interact to create three distinct functional regimes: localized asynchronous, traveling asynchronous, and traveling synchronous. Our results qualitatively match those observed experimentally. Cortical activity during slow wave sleep (SWS) differs from that during REM sleep or waking states. During SWS there are traveling patterns of activity in the cortex; in other states stationary patterns occur. Our model is a network composed of Hodgkin-Huxley type neurons with a M-current regulated by ACh. Regulation of ACh level can account for dynamical changes between functional regimes. Reduction of the magnitude of this current recreates the reduction in SFA the shift from a type 2 to a type 1 PRC observed in the presence of ACh. When SFA is minimal (in waking or REM sleep state, high ACh) patterns of activity are localized and easily pinned by network inhomogeneities. When SFA is present (decreasing ACh), traveling waves of activity naturally arise. A further decrease in ACh leads to a high degree of synchrony within traveling waves. We also show that the level of ACh determines how sensitive network activity is to synaptic heterogeneity. These regimes may have a profound functional significance as stationary patterns may play a role in the proper encoding of external input as memory and traveling waves could lead to synaptic regularization, giving unique insights into the role and significance of ACh in determining patterns of cortical activity and functional differences arising from the patterns.     

Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle

Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP) takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca²⁺/calmodulin-dependent protein kinase II (pCaMKIIα) in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK) showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS) followed by a rebound during rapid-eye-movement sleep (REM). The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes synaptic restructuring.     

What are the mechanisms activating the sleep-active neurons located in the preoptic area?

The purpose of this review is to outline the mechanisms responsible for the induction and maintenance of slow-wave sleep (SWS, also named non–rapid eye movement or non-REM sleep). The latest hypothesis on the mechanisms by which cortical activity switches from an activated state during waking to a synchronised state during SWS is presented. It is proposed that the activated cortical state during waking is induced by the activity of multiple waking systems, including the serotonergic, noradrenergic, cholinergic and hypocretin systems located at different subcortical levels. In contrast, the neurons inducing SWS are mainly localized in the ventrolateral preoptic (VLPO) and median preoptic nuclei. These neurons use the inhibitory neurotransmitter gamma-aminobutyric acid (GABA). The notion that the switch from waking to SWS is due to the inhibition of the waking systems by the VLPO sleep-active neurons is introduced. At the onset of sleep, the sleep neurons are activated by the circadian clock localized in the suprachiasmatic nucleus and a powerful hypnogenic factor, adenosine, which progressively accumulates in the brain during waking.

     

Chaos and synchrony in a model of a hypercolumn in visual cortex

Neurons in cortical slices emit spikes or bursts of spikes regularly in response to a suprathreshold current injection. This behavior is in marked contrast to the behavior of cortical neurons in vivo, whose response to electrical or sensory input displays a strong degree of irregularity. Correlation measurements show a significant degree of synchrony in the temporal fluctuations of neuronal activities in cortex. We explore the hypothesis that these phenomena are the result of the synchronized chaos generated by the deterministic dynamics of local cortical networks. A model of a hypercolumn in the visual cortex is studied. It consists of two populations of neurons, one inhibitory and one excitatory. The dynamics of the neurons is based on a Hodgkin-Huxley type model of excitable voltage-clamped cells with several cellular and synaptic conductances. A slow potassium current is included in the dynamics of the excitatory population to reproduce the observed adaptation of the spike trains emitted by these neurons. The pattern of connectivity has a spatial structure which is correlated with the internal organization of hypercolumns in orientation columns. Numerical simulations of the model show that in an appropriate parameter range, the network settles in a synchronous chaotic state, characterized by a strong temporal variability of the neural activity which is correlated across the hypercolumn. Strong inhibitory feedback is essential for the stabilization of this state. These results show that the cooperative dynamics of large neuronal networks are capable of generating variability and synchrony similar to those observed in cortex. Auto-correlation and cross-correlation functions of neuronal spike trains are computed, and their temporal and spatial features are analyzed. In other parameter regimes, the network exhibits two additional states: synchronized oscillations and an asynchronous state. We use our model to study cortical mechanisms for orientation selectivity. It is shown that in a suitable parameter regime, when the input is not oriented, the network has a continuum of states, each representing an inhomogeneous population activity which is peaked at one of the orientation columns. As a result, when a weakly oriented input stimulates the network, it yields a sharp orientation tuning. The properties of the network in this regime, including the appearance of virtual rotations and broad stimulus-dependent cross-correlations, are investigated. The results agree with the predictions of the mean field theory which was previously derived for a simplified model of stochastic, two-state neurons. The relation between the results of the model and experiments in visual cortex are discussed.     

Changing excitation and inhibition in simulated neural networks: effects on induced bursting behavior

 The development of synchronous bursting in neuronal ensembles represents an important change in network behavior. To determine the influences on development of such synchronous bursting behavior we study the dynamics of small networks of sparsely connected excitatory and inhibitory neurons using numerical simulations. The synchronized bursting activities in networks evoked by background spikes are investigated. Specifically, patterns of bursting activity are examined when the balance between excitation and inhibition on neuronal inputs is varied and the fraction of inhibitory neurons in the network is changed. For quantitative comparison of bursting activities in networks, measures of the degree of synchrony are used. We demonstrate how changes in the strength of excitation on inputs of neurons can be compensated by changes in the strength of inhibition without changing the degree of synchrony in the network. The effects of changing several network parameters on the network activity are analyzed and discussed. These changes may underlie the transition of network activity from normal to potentially pathologic (e.g., epileptic) states. Received: 21 May 2002 / Accepted in revised form: 3 December 2002 / Published online: 7 March 2003 Correspondence to: P. Kudela (e-mail: pkudela@jhmi.edu) Acknowledgements. This research was supported by NIH grant NS 38958.     

Mouse Activity across Time Scales: Fractal Scenarios

In this work we devise a classification of mouse activity patterns based on accelerometer data using Detrended Fluctuation Analysis. We use two characteristic mouse behavioural states as benchmarks in this study: waking in free activity and slow-wave sleep (SWS). In both situations we find roughly the same pattern: for short time intervals we observe high correlation in activity - a typical 1/f complex pattern - while for large time intervals there is anti-correlation. High correlation of short intervals ( to : waking state and to : SWS) is related to highly coordinated muscle activity. In the waking state we associate high correlation both to muscle activity and to mouse stereotyped movements (grooming, waking, etc.). On the other side, the observed anti-correlation over large time scales ( to : waking state and to : SWS) during SWS appears related to a feedback autonomic response. The transition from correlated regime at short scales to an anti-correlated regime at large scales during SWS is given by the respiratory cycle interval, while during the waking state this transition occurs at the time scale corresponding to the duration of the stereotyped mouse movements. Furthermore, we find that the waking state is characterized by longer time scales than SWS and by a softer transition from correlation to anti-correlation. Moreover, this soft transition in the waking state encompass a behavioural time scale window that gives rise to a multifractal pattern. We believe that the observed multifractality in mouse activity is formed by the integration of several stereotyped movements each one with a characteristic time correlation. Finally, we compare scaling properties of body acceleration fluctuation time series during sleep and wake periods for healthy mice. Interestingly, differences between sleep and wake in the scaling exponents are comparable to previous works regarding human heartbeat. Complementarily, the nature of these sleep-wake dynamics could lead to a better understanding of neuroautonomic regulation mechanisms.     

Astrocyte- neuron interaction as a mechanism responsible for generation of neural synchrony: a study based on modeling and experiments

Neural synchronization is considered as an important mechanism for information processing. In addition, based on recent neurophysiologic findings, it is believed that astrocytes regulate the synaptic transmission of neuronal networks. Therefore, the present study focused on determining the functional contribution of astrocytes in neuronal synchrony using both computer simulations and extracellular field potential recordings. For computer simulations, as a first step, a minimal network model is constructed by connecting two Morris-Lecar neuronal models. In this minimal model, astrocyte-neuron interactions are considered in a functional-based procedure. Next, the minimal network is extended and a biologically plausible neuronal population model is developed which considers functional outcome of astrocyte-neuron interactions too. The employed structure is based on the physiological and anatomical network properties of the hippocampal CA1 area. Utilizing these two different levels of modeling, it is demonstrated that astrocytes are able to change the threshold value of transition from synchronous to asynchronous behavior among neurons. In this way, variations in the interaction between astrocytes and neurons lead to the emergence of synchronous/asynchronous patterns in neural responses. Furthermore, population spikes are recorded from CA1 pyramidal neurons in rat hippocampal slices to validate the modeling results. It demonstrates that astrocytes play a primary role in neuronal firing synchronicity and synaptic coordination. These results may offer a new insight into understanding the mechanism by which astrocytes contribute to stabilizing neural activities.     

Adaptation of the 24-h growth hormone profile to a state of sleep debt

In normal men, the majority of GH secretion occurs in a single large postsleep onset pulse that is suppressed during total sleep deprivation. We examined the impact of semichronic partial sleep loss, a highly prevalent condition, on the 24-h growth hormone profile. Eleven young men were studied after six nights of restricted bedtimes (0100-0500) and after 7 nights of extended bedtimes (2100-0900). Slow-wave sleep (SWS) was estimated as the duration of stages III and IV. Slow-wave activity (SWA) was calculated as electroencephalogram power density in the 0.5- to 3-Hz frequency range. During the state of sleep debt, the GH secretory pattern was biphasic, with both a presleep onset "circadian" pulse and a postsleep onset pulse. Postsleep onset GH secretion was negatively related to presleep onset secretion and tended to be positively correlated with the amount of concomitant SWA. When sleep was restricted, both SWS and SWA were increased during early sleep. Unexpectedly, the increase in SWA affected the second, rather than the first, SWA cycle, suggesting that presleep onset GH secretion may have limited SWA in the first cycle, possibly via an inhibition of central GH-releasing hormone activity. Thus neither the GH profile nor the distribution of SWA conformed with predictions from acute sleep deprivation studies, indicating that adaptation mechanisms are operative during chronic partial sleep loss.     

In vivo approach to the cellular mechanisms for sensory processing in sleep and wakefulness

1. The present review analyzes sensory processing during sleep and wakefulness from a single neuronal viewpoint. Our premises are that processing changes throughout the sleep–wakefulness cycle may be at least partially evidenced in single neurons by (a) changes in the phase locking of the response to the hippocampal theta rhythm, (b) changes in the discharge rate and firing pattern of the response to sound, and (c) changes in the effects of the neurotransmitters involved in the afferent and efferent pathways.2. The first part of our report is based on the hypothesis that the encoding of sensory information needs a timer in order to be processed and stored, and that the hippocampal theta rhythm could contribute to the temporal organization. We have demonstrated that the guinea pig's auditory and visual neuronal discharge exhibits a temporal relationship (phase locking) to the hippocampal theta waves during wakefulness and sleep phases.3. The concept that the neural network organization during sleep versus wakefulness is different and can be modulated by sensory signals and vice versa, and that the sensory input may be influenced by the CNS state, i.e., asleep or awake, is introduced. During sleep the evoked firing of auditory units increases, decreases, or remains similar to that observed during quiet wakefulness. However, there has been no auditory unit yet that stops firing as the guinea pig enters sleep. Approximately half of the cortical neurons studied did not change firing rate when passing into sleep while others increased or decreased. Thus, the system is continuously aware of the environment. We postulate that those neurons that changed their evoked firing during sleep are also related to still unknown sleep processes.4. Excitatory amino acid neurotransmitters participate in the synaptic transmission of the afferent and efferent pathways in the auditory system. In the inferior colliculus, however, the effects of glutamate's mediating the response to sound and the efferent excitation evoked by cortical stimulation failed to show differences in sleep and wakefulness.5. Considering that neonates and also infants spend most of the time asleep, the continuous arrival of sensory information to the brain during both sleep phases may serve to sculpt the brain by activity-dependent mechanisms of neural development, as has been postulated for wakefulness.     

Hippocampal memory consolidation during sleep: a comparison of mammals and birds

The transition from wakefulness to sleep is marked by pronounced changes in brain activity. The brain rhythms that characterize the two main types of mammalian sleep, slow‐wave sleep (SWS) and rapid eye movement (REM) sleep, are thought to be involved in the functions of sleep. In particular, recent theories suggest that the synchronous slow‐oscillation of neocortical neuronal membrane potentials, the defining feature of SWS, is involved in processing information acquired during wakefulness. According to the Standard Model of memory consolidation, during wakefulness the hippocampus receives input from neocortical regions involved in the initial encoding of an experience and binds this information into a coherent memory trace that is then transferred to the neocortex during SWS where it is stored and integrated within preexisting memory traces. Evidence suggests that this process selectively involves direct connections from the hippocampus to the prefrontal cortex (PFC), a multimodal, high‐order association region implicated in coordinating the storage and recall of remote memories in the neocortex. The slow‐oscillation is thought to orchestrate the transfer of information from the hippocampus by temporally coupling hippocampal sharp‐wave/ripples (SWRs) and thalamocortical spindles. SWRs are synchronous bursts of hippocampal activity, during which waking neuronal firing patterns are reactivated in the hippocampus and neocortex in a coordinated manner. Thalamocortical spindles are brief 7–14 Hz oscillations that may facilitate the encoding of information reactivated during SWRs. By temporally coupling the readout of information from the hippocampus with conditions conducive to encoding in the neocortex, the slow‐oscillation is thought to mediate the transfer of information from the hippocampus to the neocortex. Although several lines of evidence are consistent with this function for mammalian SWS, it is unclear whether SWS serves a similar function in birds, the only taxonomic group other than mammals to exhibit SWS and REM sleep. Based on our review of research on avian sleep, neuroanatomy, and memory, although involved in some forms of memory consolidation, avian sleep does not appear to be involved in transferring hippocampal memories to other brain regions. Despite exhibiting the slow‐oscillation, SWRs and spindles have not been found in birds. Moreover, although birds independently evolved a brain region—the caudolateral nidopallium (NCL)—involved in performing high‐order cognitive functions similar to those performed by the PFC, direct connections between the NCL and hippocampus have not been found in birds, and evidence for the transfer of information from the hippocampus to the NCL or other extra‐hippocampal regions is lacking. Although based on the absence of evidence for various traits, collectively, these findings suggest that unlike mammalian SWS, avian SWS may not be involved in transferring memories from the hippocampus. Furthermore, it suggests that the slow‐oscillation, the defining feature of mammalian and avian SWS, may serve a more general function independent of that related to coordinating the transfer of information from the hippocampus to the PFC in mammals. Given that SWS is homeostatically regulated (a process intimately related to the slow‐oscillation) in mammals and birds, functional hypotheses linked to this process may apply to both taxonomic groups.     

Sleep-enhancing effects of far-infrared radiation in rats

Unrestrained male rats continuously exposed to far-infrared radiation exhibited a significant increase in slow wave sleep (SWS) during the light period but not in the dark period. The change was largely due to the elevated occurrence of SWS episodes but not to the prolongation of their duration. Paradoxical sleep was not affected throughout the observation period except for a significant decrease at the end of the dark period. Thus the far-infrared radiation exerted a sleep modulatory effect closely related to the circadian activity-rest cycle.     

Effect of cold stimulation during slow-wave sleep on sleep cycle

To clarify the effect of cold stimulation during slow-wave sleep (SWS) on the sleep cycle, we conducted a sleep experiment. Five healthy males slept on a bedding system we developed to make the inside of bedding cooler. When the subject was sleeping deeply in the second and fourth SWS, the system cooled their bedding. When the subject's sleep condition shifted toward arousal, the cold air was stopped. As a result, all subjects’ sleep stage shifted to light sleep and reached arousal. After stopping stimulation, they immediately returned to the SWS at the first stimulation. But at the second stimulation, the sleep state did not return to the SWS.     

Neuronal Avalanches Differ from Wakefulness to Deep Sleep – Evidence from Intracranial Depth Recordings in Humans

Neuronal activity differs between wakefulness and sleep states. In contrast, an attractor state, called self-organized critical (SOC), was proposed to govern brain dynamics because it allows for optimal information coding. But is the human brain SOC for each vigilance state despite the variations in neuronal dynamics? We characterized neuronal avalanches – spatiotemporal waves of enhanced activity - from dense intracranial depth recordings in humans. We showed that avalanche distributions closely follow a power law – the hallmark feature of SOC - for each vigilance state. However, avalanches clearly differ with vigilance states: slow wave sleep (SWS) shows large avalanches, wakefulness intermediate, and rapid eye movement (REM) sleep small ones. Our SOC model, together with the data, suggested first that the differences are mediated by global but tiny changes in synaptic strength, and second, that the changes with vigilance states reflect small deviations from criticality to the subcritical regime, implying that the human brain does not operate at criticality proper but close to SOC. Independent of criticality, the analysis confirms that SWS shows increased correlations between cortical areas, and reveals that REM sleep shows more fragmented cortical dynamics.     

The ATP-cytokine-adenosine hypothesis: How the brain translates past activity into sleep

Tumor necrosis factor alpha (TNF), interleukin-1 beta (IL1), and other cytokines are involved in non-rapid eye movement sleep (NREM) regulation under physiological and inflammatory conditions. Brain levels of IL1 and TNF increase with prolonged wakefulness. Injection of exogenous IL1 or TNF, mimicking sleep loss, induces sleepiness, excess sleep, fatigue, poor cognition, and enhanced sensitivity to pain. These symptoms characterize the syndrome associated with sleep loss. Extracellular ATP released during neuro- and glio-transmission, acting via purine P2 receptors on glia, releases IL1 and TNF. This extracellular ATP mechanism may provide an index of activity used by the brain to keep track of prior wakefulness. Prolonged wakefulness is associated with enhanced neuronal activity. TNF and IL1, in turn, act on neurons to change their intrinsic properties and sensitivities to neurotransmitters and neuromodulators such as adenosine and glutamate. Such actions change network input–output properties (i.e. state shift). State oscillations, for instance, occur within cortical columns and are responsive to TNF. Sleep is thus viewed as a local usedependent process regulated in part by cytokines. Further, state oscillations are viewed as a fundamental process of any neuronal/glia network. To investigate these hypotheses we developed an in vitro neuronal/glia culture system exhibiting field potential oscillations and have mathematically modeled the local use-dependent view of sleep initiation. These views have profound implications for sleep pathologies and function.

     

A multivariate population density model of the dLGN/PGN relay

Using a population density approach we study the dynamics of two interacting collections of integrate-and-fire-or-burst (IFB) neurons representing thalamocortical (TC) cells from the dorsal lateral geniculate nucleus (dLGN) and thalamic reticular (RE) cells from the perigeniculate nucleus (PGN). Each population of neurons is described by a multivariate probability density function that satisfies a conservation equation with appropriately defined probability fluxes and boundary conditions. The state variables of each neuron are the membrane potential and the inactivation gating variable of the low-threshold Ca²⁺ current I_T. The synaptic coupling of the populations and external excitatory drive are modeled by instantaneous jumps in the membrane potential of postsynaptic neurons. The population density model is validated by comparing its response to time-varying retinal input to Monte Carlo simulations of the corresponding IFB network composed of 100 to 1000 cells per population. In the absence of retinal input, the population density model exhibits rhythmic bursting similar to the 7 to 14 Hz oscillations associated with slow wave sleep that require feedback inhibition from RE to TC cells. When the TC and RE cell potassium leakage conductances are adjusted to represent cholingergic neuromodulation and arousal of the network, rhythmic bursting of the probability density model may either persists or be eliminated depending on the number of excitatory (TC to RE) or inhibitory (RE to TC) connections made by each presynaptic cell. When the probability density model is stimulated with constant retinal input (10–100 spikes/sec), a wide range of responses are observed depending on cellular parameters and network connectivity. These include asynchronous burst and tonic spikes, sleep spindle-like rhythmic bursting, and oscillations in population firing rate that are distinguishable from sleep spindles due to their amplitude, frequency, or the presence of tonic spikes. In this context of dLGN/PGN network modeling, we find the population density approach using 2,500 mesh points and resolving membrane voltage to 0.7 mV is over 30 times more efficient than 1000-cell Monte Carlo simulations. Action Editor: David Golomb     

Age-regulated cycling metabolites are relevant for behavior

Circadian cycles of sleep:wake and gene expression change with age in all organisms examined. Metabolism is also under robust circadian regulation, but little is known about how metabolic cycles change with age and whether these contribute to the regulation of behavioral cycles. To address this gap, we compared cycling of metabolites in young and old Drosophila and found major age-related variations. A significant model separated the young metabolic profiles by circadian timepoint, but could not be defined for the old metabolic profiles due to the greater variation in this dataset. Of the 159 metabolites measured in fly heads, we found 17 that cycle by JTK analysis in young flies and 17 in aged. Only four metabolites overlapped in the two groups, suggesting that cycling metabolites are distinct in young and old animals. Among our top cyclers exclusive to young flies were components of the pentose phosphate pathway (PPP). As the PPP is important for buffering reactive oxygen species, and overexpression of glucose-6-phosphate dehydrogenase (G6PD), a key component of the PPP, was previously shown to extend lifespan in Drosophila, we asked if this manipulation also affects sleep:wake cycles. We found that overexpression in circadian clock neurons decreases sleep in association with an increase in cellular calcium and mitochondrial oxidation, suggesting that altering PPP activity affects neuronal activity. Our findings elucidate the importance of metabolic regulation in maintaining patterns of neural activity, and thereby sleep:wake cycles.     

Paradoxical (REM) sleep genesis: The switch from an aminergic–cholinergic to a GABAergic–glutamatergic hypothesis

In the middle of the last century, Michel Jouvet discovered paradoxical sleep (PS), a sleep phase paradoxically characterized by cortical activation and rapid eye movements and a muscle atonia. Soon after, he showed that it was still present in “pontine cats” in which all structures rostral to the brainstem have been removed. Later on, it was demonstrated that the pontine peri-locus coeruleus α (peri-LCα in cats, corresponding to the sublaterodorsal nucleus, SLD, in rats) is responsible for PS onset. It was then proposed that the onset and maintenance of PS is due to a reciprocal inhibitory interaction between neurons presumably cholinergic specifically active during PS localized in this region and monoaminergic neurons. In the last decade, we have tested this hypothesis with our model of head-restrained rats and functional neuroanatomical studies. Our results confirmed that the SLD in rats contains the neurons responsible for the onset and maintenance of PS. They further indicate that (1) these neurons are non-cholinergic possibly glutamatergic neurons, (2) they directly project to the glycinergic premotoneurons localized in the medullary ventral gigantocellular reticular nucleus (GiV), (3) the main neurotransmitter responsible for their inhibition during waking (W) and slow wave sleep (SWS) is GABA rather than monoamines, (4) they are constantly and tonically excited by glutamate and (5) the GABAergic neurons responsible for their tonic inhibition during W and SWS are localized in the deep mesencephalic reticular nucleus (DPMe). We also showed that the tonic inhibition of locus coeruleus (LC) noradrenergic and dorsal raphe (DRN) serotonergic neurons during sleep is due to a tonic GABAergic inhibition by neurons localized in the dorsal paragigantocellular reticular nucleus (DPGi) and the ventrolateral periaqueductal gray (vlPAG). We propose that these GABAergic neurons also inhibit the GABAergic neurons of the DPMe at the onset and during PS and are therefore responsible for the onset and maintenance of PS.     

The Sleep Cycle Modelled as a Cortical Phase Transition

We present a mean-field model of the cortex that attempts to describe the gross changes in brain electrical activity for the cycles of natural sleep. We incorporate within the model two major sleep modulatory effects: slow changes in both synaptic efficiency and in neuron resting voltage caused by the ∼90-min cycling in acetylcholine, together with even slower changes in resting voltage caused by gradual elimination during sleep of somnogens (fatigue agents) such as adenosine. We argue that the change from slow-wave sleep (SWS) to rapid-eye-movement (REM) sleep can be understood as a first-order phase transition from a low-firing, coherent state to a high-firing, desychronized cortical state. We show that the model predictions for changes in EEG power, spectral distribution, and correlation time at the SWS-to-REM transition are consistent not only with those observed in clinical recordings of a sleeping human subject, but also with the on-cortex EEG patterns recently reported by Destexhe et al. [J. Neurosci. 19(11), (1999) 4595–4608] for the sleeping cat.     

Stimulus and network dynamics collide in a ratiometric model of the antennal lobe macroglomerular complex

Time is considered to be an important encoding dimension in olfaction, as neural populations generate odour-specific spatiotemporal responses to constant stimuli. However, during pheromone mediated anemotactic search insects must discriminate specific ratios of blend components from rapidly time varying input. The dynamics intrinsic to olfactory processing and those of naturalistic stimuli can therefore potentially collide, thereby confounding ratiometric information. In this paper we use a computational model of the macroglomerular complex of the insect antennal lobe to study the impact on ratiometric information of this potential collision between network and stimulus dynamics. We show that the model exhibits two different dynamical regimes depending upon the connectivity pattern between inhibitory interneurons (that we refer to as fixed point attractor and limit cycle attractor), which both generate ratio-specific trajectories in the projection neuron output population that are reminiscent of temporal patterning and periodic hyperpolarisation observed in olfactory antennal lobe neurons. We compare the performance of the two corresponding population codes for reporting ratiometric blend information to higher centres of the insect brain. Our key finding is that whilst the dynamically rich limit cycle attractor spatiotemporal code is faster and more efficient in transmitting blend information under certain conditions it is also more prone to interference between network and stimulus dynamics, thus degrading ratiometric information under naturalistic input conditions. Our results suggest that rich intrinsically generated network dynamics can provide a powerful means of encoding multidimensional stimuli with high accuracy and efficiency, but only when isolated from stimulus dynamics. This interference between temporal dynamics of the stimulus and temporal patterns of neural activity constitutes a real challenge that must be successfully solved by the nervous system when faced with naturalistic input.