Svetlana Postnova

Neural Synchronization at Tonic-to-Bursting Transitions

We studied the synchronous behavior of two electrically-coupled model neurons as a function of the coupling strength when the individual neurons are tuned to different activity patterns that ranged from tonic firing via chaotic activity to burst discharges. We observe asynchronous and various synchronous states such as out-of-phase, in-phase and almost in-phase chaotic synchronization. The highest variety of synchronous states occurs at the transition from tonic firing to chaos where the highest coupling strength is also needed for in-phase synchronization which is, essentially, facilitated towards the bursting range. This demonstrates that tuning of the neuron’s internal dynamics can have significant impact on the synchronous states especially at the physiologically relevant tonic-to-bursting transitions.

A Mathematical Model of Homeostatic Regulation of Sleep-Wake Cycles by Hypocretin/Orexin

We introduce a physiology-based mathematical model of sleep-wake cycles, suggesting a novel mechanism of homeostatic regulation of sleep. In this model, the homeostatic process is determined by the neuropeptide hypocretin/ orexin, which is a cotransmitter of the lateral hypothalamus. Hypocretin/ orexin neurons are silent during sleep and active during wakefulness. Firing of these neurons is sustained by reciprocal excitatory synaptic connections with local glutamate interneurons. This feedback loop has been simulated with a minimal but physiologically plausible model. It includes 2 simplified Hodgkin-Huxley type neurons that are connected via glutamate synapses, one of which additionally contains hypocretin/orexin as the functionally relevant cotransmitter. During the active state (wakefulness), the synaptic efficacy of hypocretin/orexin declines as a result of the ongoing firing. It recovers during the silent (sleep) state. We demonstrate that these homeostatic changes can account for typical alterations of sleep-wake transitions, for example, introduced by napping, sleep deprivation, or alarm clock. In combination with a circadian input, the model mimics the transitions between silent and firing states in agreement with sleep-wake cycles. These simulation results support the concept of state-dependent alterations of hypocretin/orexin effects as an important homeostatic process in sleep-wake regulation, although additional mechanisms can be involved.

Exploring Sleepiness and Entrainment on Permanent Shift Schedules in a Physiologically Based Model

The effects of permanent shift work on entrainment and sleepiness are examined using a mathematical model that combines a model of sleep-wake switch in the brain with a model of the human circadian pacemaker entrained by light and nonphotic inputs. The model is applied to 8-hour permanent shift schedules to understand the basic mechanisms underlying changes of entrainment and sleepiness. Average sleepiness is shown to increase during the first days on the night and evening schedules, that is, shift start times between 0000 to 0700 h and 1500 to 2200 h, respectively. After the initial increase, sleepiness decreases and stabilizes via circadian re-entrainment to the cues provided by the shifts. The increase in sleepiness until entrainment is achieved is strongly correlated with the phase difference between a circadian oscillator entrained to the ambient light and one entrained to the shift schedule. The higher this phase difference, the larger the initial increase in sleepiness. When entrainment is achieved, sleepiness stabilizes and is the same for different shift onsets within the night or evening schedules. The simulations reveal the presence of a critical shift onset around 2300 h that separates schedules, leading to phase advance (night shifts) and phase delay (evening shifts) of the circadian pacemaker. Shifts starting around this time take longest to entrain and are expected to be the worst for long-term sleepiness and well-being of the workers. Surprisingly, we have found that the circadian pacemaker entrains faster to night schedules than to evening ones. This is explained by the longer photoperiod on night schedules compared to evening. In practice, this phenomenon is difficult to see due to days off on which workers switch to free sleep-wake activity. With weekends, the model predicts that entrainment is never achieved on evening and night schedules unless the workers follow the same sleep routine during weekends as during work days. Overall, the model supports experimental observations, providing new insights into the mechanisms and allowing the examination of conditions that are not accessible experimentally.

Propagation effects of current and conductance noise in a model neuron with subthreshold oscillations

We have examined the effects of current and conductance noise in a single-neuron model which can generate a variety of physiologically important impulse patterns. Current noise enters the membrane equation directly while conductance noise is propagated through the activation variables. Additive Gaussian white noise which is implemented as conductance noise appears in the voltage equations as an additive and a multiplicative term. Moreover, the originally white noise is turned into colored noise. The noise correlation time is a function of the systems control parameters which may explain the different effects of current and conductance noise in different dynamic states. We have found the most significant, qualitative differences between different noise implementations in a pacemaker-like, tonic firing regime at the transition to chaotic burst discharges. This reflects a dynamic state of high physiological relevance.

A Physiologically Based Model of Orexinergic Stabilization of Sleep and Wake

The orexinergic neurons of the lateral hypothalamus (Orx) are essential for regulating sleep-wake dynamics, and their loss causes narcolepsy, a disorder characterized by severe instability of sleep and wake states. However, the mechanisms through which Orx stabilize sleep and wake are not well understood. In this work, an explanation of the stabilizing effects of Orx is presented using a quantitative model of important physiological connections between Orx and the sleep-wake switch. In addition to Orx and the sleep-wake switch, which is composed of mutually inhibitory wake-active monoaminergic neurons in brainstem and hypothalamus (MA) and the sleep-active ventrolateral preoptic neurons of the hypothalamus (VLPO), the model also includes the circadian and homeostatic sleep drives. It is shown that Orx stabilizes prolonged waking episodes via its excitatory input to MA and by relaying a circadian input to MA, thus sustaining MA firing activity during the circadian day. During sleep, both Orx and MA are inhibited by the VLPO, and the subsequent reduction in Orx input to the MA indirectly stabilizes sustained sleep episodes. Simulating a loss of Orx, the model produces dynamics resembling narcolepsy, including frequent transitions between states, reduced waking arousal levels, and a normal daily amount of total sleep. The model predicts a change in sleep timing with differences in orexin levels, with higher orexin levels delaying the normal sleep episode, suggesting that individual differences in Orx signaling may contribute to chronotype. Dynamics resembling sleep inertia also emerge from the model as a gradual sleep-to-wake transition on a timescale that varies with that of Orx dynamics. The quantitative, physiologically based model developed in this work thus provides a new explanation of how Orx stabilizes prolonged episodes of sleep and wake, and makes a range of experimentally testable predictions, including a role for Orx in chronotype and sleep inertia.

Adaptation to shift work: physiologically based modeling of the effects of lighting and shifts' start time.

Shift work has become an integral part of our life with almost 20% of the population being involved in different shift schedules in developed countries. However, the atypical work times, especially the night shifts, are associated with reduced quality and quantity of sleep that leads to increase of sleepiness often culminating in accidents. It has been demonstrated that shift workers’ sleepiness can be improved by a proper scheduling of light exposure and optimizing shifts timing. Here, an integrated physiologically-based model of sleep-wake cycles is used to predict adaptation to shift work in different light conditions and for different shift start times for a schedule of four consecutive days of work. The integrated model combines a model of the ascending arousal system in the brain that controls the sleep-wake switch and a human circadian pacemaker model. To validate the application of the integrated model and demonstrate its utility, its dynamics are adjusted to achieve a fit to published experimental results showing adaptation of night shift workers (n = 8) in conditions of either bright or regular lighting. Further, the model is used to predict the shift workers’ adaptation to the same shift schedule, but for conditions not considered in the experiment. The model demonstrates that the intensity of shift light can be reduced fourfold from that used in the experiment and still produce good adaptation to night work. The model predicts that sleepiness of the workers during night shifts on a protocol with either bright or regular lighting can be significantly improved by starting the shift earlier in the night, e.g.; at 21∶00 instead of 00∶00. Finally, the study predicts that people of the same chronotype, i.e. with identical sleep times in normal conditions, can have drastically different responses to shift work depending on their intrinsic circadian and homeostatic parameters.

Impulse pattern in bi-directionally coupled model neurons of different dynamics.

The effects of bi-directional gap junction coupling of two model neurons with subthreshold oscillations have been examined when the individual neurons are operating at different dynamical states either in the tonic or bursting firing mode. Our simulations indicate that intermediate coupling strengths mostly lead to highly variable, often chaotic impulse patterns whereas transition to completely synchronized activity at high coupling strengths is generally going along with transitions to regular limit cycle activity. The synchronized activity pattern, however, can be completely different from the original pattern of the uncoupled neurons.

A minimal physiologically based model of the HPA axis under influence of the sleep-wake cycles.

The hypothalamic-pituitary-adrenal axis (also called the HPA or stress axis) exhibits distinct circadian and ultradian rhythms in cortisol release that cannot be explained solely by the feedback loops from cortisol to the control systems in the paraventricular nucleus (PVN) and pituitary gland. The HPA axis is intimately connected with other brain functions. In particular, it is strongly affected by the sleep-wake cycles via direct and indirect effects of the circadian and homeostatic mechanisms. For example, the HPA axis has direct inputs from the master circadian clock in the suprachiasmatic nuclei (SCN), and from the various sleep-wake related neuronal populations, which themselves are under the effects of the circadian and homeostatic processes. In this paper a first step towards a physiologically based mathematical model of the HPA-axis under effects of the sleep-wake cycles is presented. This model accounts for 3 major characteristics of daily cortisol profile in the blood: i) abrupt increase of cortisol concentration in response to awakening, the so-called cortisol-awakening response (CAR); ii) reduced cortisol levels during daytime with underlying ultradian oscillations; and iii) suppression of cortisol release during sleep.

Real-Time simulations of synchronization in a conductance-based neuronal network with a digital FPGA hardware-core

A FPGA hardware core has been designed for real-time network simulations with up to 400 physiologically realistic, conductance-based neurons of the Hodgkin-Huxley type. A PC-FPGA interface allows easy parameter adjustment and on-line display of basic synchronization measures like field potentials, spike times or color-coded voltages of the complete array. Simulations of 20 ·20 gap-junction coupled 4-dimensional neurons reveal remarkable alterations of the synchronization states and impulse patterns during linearly increasing coupling strengths.

INTER-PATTERN TRANSITIONS IN A NOISY BURSTING CELL

This paper studies the deterministic and stochastic dynamics of a thermo-receptor model. Special emphasis is paid to the transition zones between the main spiking patterns. In these zones, noise can induce or suppress spike generation, allowing for a nonmonotoneous dependence of the mean firing rate on the noise intensity. We show how this peculiar dependence can be explained in terms of bifurcation phenomena in the underlying deterministic model.