Oscillations and synchrony in a cortical neural network

In this paper, the oscillations and synchronization status of two different network connectivity patterns based on Izhikevich model are studied. One of the connectivity patterns is a randomly connected neuronal network, the other one is a small-world neuronal network. This Izhikevich model is a simple model which can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Detailed investigations reveal that by varying some key parameters, such as the connection weights of neurons, the external current injection, the noise of intensity and the neuron number, this neuronal network will exhibit various collective behaviors in randomly coupled neuronal network. In addition, we show that by changing the number of nearest neighbor and connection probability in small-world topology can also affect the collective dynamics of neuronal activity. These results may be instructive in understanding the collective dynamics of mammalian cortex.     

Control of neural synchrony using channelrhodopsin-2: a computational study

In this paper, we present an optical stimulation based approach to induce 1:1 in-phase synchrony in a network of coupled interneurons wherein each interneuron expresses the light sensitive protein channelrhodopsin-2 (ChR2). We begin with a transition rate model for the channel kinetics of ChR2 in response to light stimulation. We then define "functional optical time response curve (fOTRC)" as a measure of the response of a periodically firing interneuron (transfected with ChR2 ion channel) to a periodic light pulse stimulation. We specifically consider the case of unidirectionally coupled (UCI) network and propose an open loop control architecture that uses light as an actuation signal to induce 1:1 in-phase synchrony in the UCI network. Using general properties of the spike time response curves (STRCs) for Type-1 neuron model (Ermentrout, Neural Comput 8:979-1001, 1996) and fOTRC, we estimate the (open loop) optimal actuation signal parameters required to induce 1:1 in-phase synchrony. We then propose a closed loop controller architecture and a controller algorithm to robustly sustain stable 1:1 in-phase synchrony in the presence of unknown deviations in the network parameters. Finally, we test the performance of this closed-loop controller in a network of mutually coupled (MCI) interneurons.     

Decomposing neural synchrony: toward an explanation for near-zero phase-lag in cortical oscillatory networks

Background

Synchronized oscillation in cortical networks has been suggested as a mechanism for diverse functions ranging from perceptual binding to memory formation to sensorimotor integration. Concomitant with synchronization is the occurrence of near-zero phase-lag often observed between network components. Recent theories have considered the importance of this phenomenon in establishing an effective communication framework among neuronal ensembles.

Methodology/Principal Findings

Two factors, among possibly others, can be hypothesized to contribute to the near-zero phase-lag relationship: (1) positively correlated common input with no significant relative time delay and (2) bidirectional interaction. Thus far, no empirical test of these hypotheses has been possible for lack of means to tease apart the specific causes underlying the observed synchrony. In this work simulation examples were first used to illustrate the ideas. A quantitative method that decomposes the statistical interdependence between two cortical areas into a feed-forward, a feed-back and a common-input component was then introduced and applied to test the hypotheses on multichannel local field potential recordings from two behaving monkeys.

Conclusion/Significance

The near-zero phase-lag phenomenon is important in the study of large-scale oscillatory networks. A rigorous mathematical theorem is used for the first time to empirically examine the factors that contribute to this phenomenon. Given the critical role that oscillatory activity is likely to play in the regulation of biological processes at all levels, the significance of the proposed method may extend beyond systems neuroscience, the level at which the present analysis is conceived and performed.     

Influence of connection type on phase synchrony: analysis of a neural mass model

Empirical studies have demonstrated synchronized frontal and parietal electrophysiological signals at 22-34?Hz during a conjunctive visual search task and at 36-56?Hz during a pop-out visual search task. Bidirectional (conjunctive) versus unidirectional (pop-out) information transfer between neuronal populations is hypothesized to underly this difference in synchronization frequency. This study modeled the influence of connection type (i.e., unidirectional vs. bidirectional) on phase synchrony between two neural populations using a neural mass model. Phase-locking values (PLVs) were used as the measure of synchrony between populations. Consistent with the connectivity hypothesis, the model revealed greater PLVs at 22-34?Hz when the two populations were connected bidirectionally than unidirectionally, but greater PLVs at 34-52?Hz when connected unidirectionally than bidirectionally. The model suggests that inter-population connectivity also changes with bottom-up versus top-down control of attention.     

Menstrual synchrony

Several studies have now documented menstrual synchrony in human females. There is a broad consensus that the phenomenon mainly occurs in women who spend a significant amount of time together, such as close friends and coworkers, and that social contact rather than a similar environment plays an important role in mediating the effect. However, the mechanisms involved and the adaptive function of menstrual synchrony are not understood. There is some evidence that olfactory cues between females might underlie the effect. More research is needed before the precise mechanisms that regulate menstrual synchrony are elucidated. Cynthia Graham received her MSc in clinical psychology from Glasgow University and her PhD from McGill University. Her previous research has involved menstrual synchrony and the effects of oral contraceptives on premenstrual changes. Currently she is working on a World Health Organization project assessing the effects of steroidal contraceptives on well-being and sexuality.     

Oscillatory neurocomputing with ring attractors: a network architecture for mapping locations in space onto patterns of neural synchrony

Theories of neural coding seek to explain how states of the world are mapped onto states of the brain. Here, we compare how an animal''s location in space can be encoded by two different kinds of brain states: population vectors stored by patterns of neural firing rates, versus synchronization vectors stored by patterns of synchrony among neural oscillators. It has previously been shown that a population code stored by spatially tuned ‘grid cells’ can exhibit desirable properties such as high storage capacity and strong fault tolerance; here it is shown that similar properties are attainable with a synchronization code stored by rhythmically bursting ‘theta cells’ that lack spatial tuning. Simulations of a ring attractor network composed from theta cells suggest how a synchronization code might be implemented using fewer neurons and synapses than a population code with similar storage capacity. It is conjectured that reciprocal connections between grid and theta cells might control phase noise to correct two kinds of errors that can arise in the code: path integration and teleportation errors. Based upon these analyses, it is proposed that a primary function of spatially tuned neurons might be to couple the phases of neural oscillators in a manner that allows them to encode spatial locations as patterns of neural synchrony.     

Antibody-induced cell-cycle synchrony

Synergism between specific antibodies and cytotoxic drugs in killing tumor cells is an established phenomenon. When cells grown in a suspension culture are treated with antibody, a subsequent increase in susceptibility to cytosine arabinoside has been shown to be due to the induction of cell-cycle synchrony. This observation may lead to a novel way of investigating cell-cycle-dependent processes and also an aspect of tumor-antibody interactions that is not generally recognized.     

Oscillations and hippocampal-prefrontal synchrony

The hippocampus and medial prefrontal cortex subserve spatial working memory in rodents. Recent evidence has demonstrated functional interactions between these brain regions in the form of sychronization of oscillatory activity during behavior. The nature of this synchrony and its relationship to behavioral performance suggests an important role in the function of the hippocampal-prefrontal circuit.     

Decoupling through synchrony in neuronal circuits with propagation delays

The level of synchronization in distributed systems is often controlled by the strength of the interactions between individual elements. In brain circuits the connection strengths between neurons are modified under the influence of spike-timing-dependent plasticity (STDP) rules. Here we show that when recurrent networks with conduction delays exhibit population bursts, STDP rules exert a strong decoupling force that desynchronizes activity. Conversely, when activity in the network is random, the same rules can have a coupling and synchronizing influence. The presence of these opposing forces promotes the self-organization of spontaneously active neuronal networks to a state at the border between randomness and synchrony. The decoupling force of STDP may be engaged by the synchronous bursts occurring in the hippocampus during slow-wave sleep, leading to the selective erasure of information from hippocampal circuits as memories are established in neocortical areas.     

Estrus and pregnancy after synchrony with lutalyse in conjunction with Syncro-Mate-B.

Estrous response and pregnancy rates are decreased for cows given Syncro-Mate-B (SMB) during metestrus (Day 1 to 5 of an estrous cycle). Data indicate these decreases are due, in part, to retention of a functional corpus luteum (CL). Our objective was to determine whether PGF2alpha administered in conjunction with SMB would improve estrous response and pregnancy rates in metestrous cows with no detrimental effects to cows in other stages of the estrous cycle. Three hundred seventy-three suckled beef cows were observed for estrus for 21 d before SMB administration to determine stage of an estrous cycle. Blood samples were collected 14 and 7 d before treatment and at SMB administration. Serum was assayed for concentration of progesterone to verify stage of estrous cycle or noncyclicity. All cows received the standard SMB regime and were allotted by age and stage of cycle to one of two groups. Cows denoted SMB + L received 25 mg of PGF2alpha 8 d after implantation, whereas cows denoted SMB served as controls. On Day 10, SMB implants were removed and females were observed for subsequent estrus. At this time, calves were removed from their dams for 48 h. Artificial insemination was performed 12 hr after observation of a standing estrus. Timed insemination was performed at 48 hr after implant removal for cows not inseminated at 24 or 36 hr after implant removal. Interval to synchronized estrus (within 5 d of implant removal) was lengthened for metestrous cows compared to cows in other stages of the cycle irrespective of treatment (P < 0.001). Cows receiving PGF2alpha had a greater pregnancy rate at 5 d compared to controls (P = .0672). Interval to estrus, estrous response, and pregnancy rate to A1 at d 28 or end of breeding season were not affected by administration of PGF2alpha in conjunction with SMB when compared to the standard SMB protocol.     

Problems with dimensionless measurement models of synchrony in biological systems

Synchrony is surprisingly complex even in the simplest cases. One strategy for simplifying complex phenomena is to define a dimensionless measurement model with the aim of (1) finding order, (2) comparing complex phenomena, and (3) making decisions about statistical significance. However, a model is only as good as its assumptions. In this paper, several types of dimensionless measurement models of synchrony among biological states are evaluated using the preceding criteria. These dimensionless measurement models are found to be inadequate even in the simplest cases of N individuals cycling through k non-overlapping states. Moreover, independent of their adequacy as measures of synchrony, there is the additional problem of the applicability of biological-state measurement models to rhythmic biological processes. Biological states are often just quantized observations of the phases of rhythmic biological processes. With the help of a concrete example, it is shown that quantizing the phases of a process into discrete states can lead to serious errors. These conclusions do not imply that the study of synchrony in biological systems is intractable. There are statistical approaches for detecting synchrony in groups and researchers are making progress towards understanding the general mechanisms of rhythmic phenomena in biological systems. Am. J. Primatol. 41:65–85, 1997. © 1997 Wiley-Liss, Inc.     

Reproductive synchrony in captive macaques

Captive bonnet macaques housed at the California Primate Research Center reproduce seasonally. The chances of producing surviving infants were substantially reduced among females who conceived at the peak of the mating season compared with females whose conceptions were more isolated in time. Primiparous females and low ranking females were most consistently affected by the extent of reproductive synchrony. Behavioral data suggest that harassment of conceiving and pregnant females may have contributed to the correlation between the extent of reproductive synchrony and infant mortality as the levels of aggression toward females were highest during the months in which conceptions were most common.     

Thalamic circuitry and thalamocortical synchrony

The corticothalamic system has an important role in synchronizing the activities of thalamic and cortical neurons. Numerically, its synapses dominate the inputs to relay cells and to the gamma-amino butyric acid (GABA)ergic cells of the reticular nucleus (RTN). The capacity of relay neurons to operate in different voltage-dependent functional modes determines that the inputs from the cortex have the capacity directly to excite the relay cells, or indirectly to inhibit them via the RTN, serving to synchronize high- or low-frequency oscillatory activity respectively in the thalamocorticothalamic network. Differences in the alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) subunit composition of receptors at synapses formed by branches of the same corticothalamic axon in the RTN and dorsal thalamus are an important element in the capacity of the cortex to synchronize low-frequency oscillations in the network. Interactions of focused corticothalamic axons arising from layer VI cortical cells and diffuse corticothalamic axons arising from layer V cortical cells, with the specifically projecting core relay cells and diffusely projecting matrix cells of the dorsal thalamus, form a substrate for synchronization of widespread populations of cortical and thalamic cells during high-frequency oscillations that underlie discrete conscious events.     

Canonical functions for dispersal-induced synchrony

Two processes are universally recognized for inducing spatial synchrony in abundance: dispersal and correlated environmental stochasticity. In the present study we seek the expected relationship between synchrony and distance in populations that are synchronized by density-independent dispersal. In the absence of dispersal, synchrony among populations with simple dynamics has been shown to echo the correlation in the environment. We ask what functional form we may expect between synchrony and distance when dispersal is the synchronizing agent. We formulate a continuous-space, continuous-time model that explicitly represents the time evolution of the spatial covariance as a function of spatial distance. Solving this model gives us two simple canonical functions for dispersal-induced covariance in spatially extended populations. If dispersal is rare relative to birth and death, then covariances between nearby points will follow the dispersal distance distribution. At long distances, however, the covariance tails off according to exponential or Bessel functions (depending on whether the population moves in one or two dimensions). If dispersal is common, then the covariances will follow the mixture distribution that is approximately Gaussian around the origin and with an exponential or Bessel tail. The latter mixture results regardless of the original dispersal distance distribution. There are hence two canonical functions for dispersal-induced synchrony     

Population synchrony in small-world networks

Network topography ranges from regular graphs (linkage between nearest neighbours only) via small-world graphs (some random connections between nodes) to completely random graphs. Small-world linkage is seen as a revolutionary architecture for a wide range of social, physical and biological networks, and has been shown to increase synchrony between oscillating subunits. We study small-world topographies in a novel context: dispersal linkage between spatially structured populations across a range of population models. Regular dispersal between population patches interacting with density-dependent renewal provides one ecological explanation for the large-scale synchrony seen in the temporal fluctuations of many species, for example, lynx populations in North America, voles in Fennoscandia and grouse in the UK. Introducing a small-world dispersal kernel leads to a clear reduction in synchrony with both increasing dispersal rate and small-world dispersal probability across a variety of biological scenarios. Synchrony is also reduced when populations are affected by globally correlated noise. We discuss ecological implications of small-world dispersal in the frame of spatial synchrony in population fluctuations.