Positive Time Lags Research Articles

Seismic Attenuation Extraction From Traffic Signals Recorded by a Single Seismic Station

AbstractSeismic ambient noise contains rich information about the physical properties of the critical zone. From the motor vehicle noise, we extract the daily seismic attenuation by quantifying the linear relationship between the frequency and amplitude ratio of paired instantaneous spectra. After verifying the reliability of the proposed method, we apply it to seismic ambient noise data collected at three urban sites. The estimated attenuation is compared against three environmental variables: rainfall, temperature, and traffic volume. The results show that the estimated seismic attenuation correlates most strongly with precipitation with positive time lags, suggesting a high attenuation sensitivity to changes in soil moisture and groundwater system. Furthermore, differences in attenuation‐precipitation correlations indicate that near‐surface physical properties may vary significantly due to local site conditions. Our statistical method extracts reliable near‐surface information from highly complex urban ambient noise data, achieving unprecedented spatial and temporal resolution compared to existing ambient noise imaging methods.

Optimization of Mismatched Filters With Asymmetric Side-Lobe Shape for Short-Range MIMO Radars

This paper presents the design of signal compression and separation filters with asymmetric responses for multiple-input multiple-output (MIMO) radars that use pulses longer than the maximum return time of the reflected signal. Such radars suffer from direct penetration of the transmitted signal to the receiver, appearing as a very strong stationary reflection in the zero range. After pulse compression, the far-side lobes of this crosstalk may compete with the reflections of distant weak targets and limit the maximum radar range. Hence, in addition to the maximum suppression of the direct transmitted signal penetration to the receiver, the far-side lobe reduction of this crosstalk with positive time lags is desirable. Asymmetric response filters allow optimization to be focused on the region where the maximum suppression of crosstalk between the transmitted signal and the receiver is desired. Extreme suppression was achieved by applying lower demand in other areas and other parameters. In this paper, the problem is described in more detail along with the mathematical apparatus by which asymmetric filter outputs can be achieved.

Mutual information resonances in delay-coupled limit cycle and quasi-cycle brain rhythms.

We elucidate how coupling delays and noise impact phase and mutual information relationships between two stochastic brain rhythms. This impact depends on the dynamical regime of each PING-based rhythm, as well as on network heterogeneity and coupling asymmetry. The number of peaks at positive and negative time lags in the delayed mutual information between the two bi-directionally communicating rhythms defines our measure of flexibility of information sharing and reflects the number of ways in which the two networks can alternately lead one another. We identify two distinct mechanisms for the appearance of qualitatively similar flexible information sharing. The flexibility in the quasi-cycle regime arises from the coupling delay-induced bimodality of the phase difference distribution, and the related bimodal mutual information. It persists in the presence of asymmetric coupling and heterogeneity but is limited to two routes of information sharing. The second mechanism in noisy limit cycle regime is not induced by the delay. However, delay-coupling and heterogeneity enable communication routes at multiple time lags. Noise disrupts the shared compromise frequency, allowing the expression of individual network frequencies which leads to a slow beating pattern. Simulations of an envelope-phase description for delay-coupled quasi-cycles yield qualitatively similar properties as for the full system. Near the bifurcation from in-phase to out-of-phase behaviour, a single preferred phase difference can coexist with two information sharing routes; further, the phase laggard can be the mutual information leader, or vice versa. Overall, the coupling delay endows a two-rhythm system with an array of lead-lag relationships and mutual information resonances that exist in spite of the noise and across the Hopf bifurcation. These beg to be mapped out experimentally with the help of our predictions.

An Empirical Mode Decomposition Approach to Assess the Strength of Heart Period-Systolic Arterial Pressure Variability Interactions.

This work proposes an empirical mode decomposition (EMD) method to assess the strength of the interactions between heart period (HP) and systolic arterial pressure (SAP) variability. EMD was exploited to decompose the original series (OR) into its first, and fastest, intrinsic mode function (IMF1) and the residual (RES) computed by subtracting the IMF1 from OR. EMD procedure was applied to both HP and SAP variability series. Then, the cross correlation function (CCF) was computed over OR, IMF1 and RES series derived from HP and SAP variability in 13 healthy subjects (age 27±8 yrs, 5 males) at rest in supine position (REST) and during head-up tilt (TILT). The first CCF maximum at negative time lags and the first CCF minimum at positive time lags were taken respectively as indexes of the coupling along the feedback baroreflex and feedforward mechanical arms of HP-SAP closed-loop control. Results showed that the coupling strength is increased during TILT on both arms and this result holds on REST. At REST the coupling along both arms was stronger when computed over IMF1 because interactions were mainly governed by respiration. This result was not observed during TILT possibly due to the reduced importance of respiration compared to other mechanisms acting at slower time scales. The proposed method allowed the investigation of the strength of HP-SAP variability interactions according to the time scales of the physiological mechanisms.The proposed EMD-based method allows the quantification of the strength of the HP-SAP closed-loop variability interactions according to the different time scales of respiration and slower baroreflex-mediated reflexes.

Looking for the underlying cause of black hole X-ray variability in GRMHD simulations

ABSTRACT Long-term observations have shown that black hole X-ray binaries exhibit strong, aperiodic variability on time-scales of a few milliseconds to seconds. The observed light curves display various characteristic features like a lognormal distribution of flux and a linear rms–flux relation, which indicate that the underlying variability process is stochastic in nature. It is also thought to be intrinsic to accretion. This variability has been modelled as inward propagating fluctuations of mass accretion rate, although the physical process driving the fluctuations remains puzzling. In this work, we analyse five exceptionally long-duration general relativistic magnetohydrodynamic (GRMHD) simulations of optically thin, geometrically thick, black hole accretion flows to look for hints of propagating fluctuations in the simulation data. We find that the accretion profiles from these simulations do show evidence for inward propagating fluctuations below the viscous frequency by featuring strong radial coherence and positive time lags when comparing smaller to larger radii, although these time lags are generally shorter than the viscous time-scale and are frequency-independent. Our simulations also support the notion that the fluctuations in $\dot{M}$ build up in a multiplicative manner, as the simulations exhibit linear rms–mass flux relations, as well as lognormal distributions of their mass fluxes. When combining the mass fluxes from the simulations with an assumed emissivity profile, we additionally find broad agreement with observed power spectra and time lags, including a recovery of the frequency dependency of the time lags.

Dynamics of corticospinal motor control during overground and treadmill walking in humans.

Increasing evidence suggests cortical involvement in the control of human gait. However, the nature of corticospinal interactions remains poorly understood. We performed time-frequency analysis of electrophysiological activity acquired during treadmill and overground walking in 22 healthy, young adults. Participants walked at their preferred speed (4.2, SD 0.4 km/h), which was matched across both gait conditions. Event-related power, corticomuscular coherence (CMC), and intertrial coherence (ITC) were assessed for EEG from bilateral sensorimotor cortices and EMG from the bilateral tibialis anterior (TA) muscles. Cortical power, CMC, and ITC at theta, alpha, beta, and gamma frequencies (4-45 Hz) increased during the double support phase of the gait cycle for both overground and treadmill walking. High beta (21-30 Hz) CMC and ITC of EMG was significantly increased during overground compared with treadmill walking, as well as EEG power in theta band (4-7 Hz). The phase spectra revealed positive time lags at alpha, beta, and gamma frequencies, indicating that the EEG response preceded the EMG response. The parallel increases in power, CMC, and ITC during double support suggest evoked responses at spinal and cortical populations rather than a modulation of ongoing corticospinal oscillatory interactions. The evoked responses are not consistent with the idea of synchronization of ongoing corticospinal oscillations but instead suggest coordinated cortical and spinal inputs during the double support phase. Frequency-band dependent differences in power, CMC, and ITC between overground and treadmill walking suggest differing neural control for the two gait modalities, emphasizing the task-dependent nature of neural processes during human walking. NEW & NOTEWORTHY We investigated cortical and spinal activity during overground and treadmill walking in healthy adults. Parallel increases in power, corticomuscular coherence, and intertrial coherence during double support suggest evoked responses at spinal and cortical populations rather than a modulation of ongoing corticospinal oscillatory interactions. These findings identify neurophysiological mechanisms that are important for understanding cortical control of human gait in health and disease.

Lagrangian velocity and acceleration statistics of fluid and inertial particles measured in pipe flow with 3D particle tracking velocimetry

Three-dimensional particle tracking velocimetry (3D-PTV) has been applied to particle-laden pipe flow at Reynolds number 10,300, based on the bulk velocity and the pipe diameter. The volume fraction of the inertial particles was equal to 1.4×10−5. Lagrangian velocity and acceleration statistics were determined both for tracers and for inertial particles with Stokes number equal to 2.3, based on the particle relaxation time and the viscous time scale. The decay of Lagrangian velocity and acceleration correlation functions was measured both for the fluid and for the dispersed phase at various radial positions. The decay of Lagrangian velocity correlations is faster for inertial particles than for flow tracers, whereas the decay of Lagrangian acceleration correlations is about 25% slower for inertial particles than for flow tracers. Further differences between inertial and tracer particles are found in velocity fluctuations evaluated for both positive and negative time lags. The asymmetry in time of velocity cross-correlations is more pronounced for inertial particles. Quadrant analysis revealed another difference still near the wall: ejection and sweep events are less frequent for inertial particles than for tracers.

S-wave velocity images of the Dead Sea Basin provided by ambient seismic noise

Based on passive seismic interferometry applied to ambient seismic noise recordings between station pairs belonging to a small-scale array, we have obtained shear wave velocity images of the uppermost materials that make up the Dead Sea Basin. We extracted empirical Green’s functions from cross-correlations of long-term recordings of continuous data, and measured inter-station Rayleigh wave group velocities from the daily correlation functions for positive and negative correlation time lags in the 0.1–0.5Hz bandwidth. A tomographic inversion of the travel times estimated for each frequency is performed, allowing the laterally varying 3-D surface wave velocity structure below the array to be retrieved. Subsequently, the velocity-frequency curves are inverted to obtain S-wave velocity images of the study area as horizontal depth sections and longitude- and latitude-depth sections. The results, which are consistent with other previous ones, provide clear images of the local seismic velocity structure of the basin. Low shear velocities are dominant at shallow depths above 3.5km, but even so a spit of land with a depth that does not exceed 4km is identified as a salt diapir separating the low velocities associated with sedimentary infill on both sides of the Lisan Peninsula. The lack of low speeds at the sampling depth of 11.5km implies that there are no sediments and therefore that the basement is near 10–11km depth, but gradually decreasing from south to north. The results also highlight the bowl-shaped basin with poorly consolidated sedimentary materials accumulated in the central part of the basin. The structure of the western margin of the basin evidences a certain asymmetry both whether it is compared to the eastern margin and it is observed in north–south direction. Infill materials down to ∼8km depth are observed in the hollow of the basin, unlike what happens in the north and south where they are spread beyond the western Dead Sea shore.

Cortical origin of Up state onsets and offsets in anesthetized rats

Slow rhythms (< 1 Hz) where Up states of sustained neural activity alternate with almost quiescent Down states, are similarly expressed by mammal brains during slow-wave-sleep (SWS) and under anesthesia [1]. Although much effort has been devoted to uncover the physiological origin of such slow oscillations (SO), it is still debated whether Up state onsets in this quasi-periodic bouncing of neural activity are driven by cortico-cortical synaptic interactions [1-3], or if they are due to the interplay between thalamic nuclei and cerebral cortex [4,5]. We addressed this issue probing neuronal and synaptic activity across all layers of primary visual cortex (V1) of rats, which showed stable Up/Down SO when anesthetized with ketamine and medetomidine. During Up states, maximum multi-unit activity (MUA) was found at a depth of 880 ± 80 μm (mean ± SD, n = 14) from cortical surface, corresponding to upper layer 5. To be able to classify events as supra/infragranular and thus to accurately identify layer 4 position, we resorted to a classical current source density (CSD) analysis looking at the earliest synaptic activation following visual stimulation [6]. This, together with a principal component analysis (PCA), allowed us to characterize Up state onsets at high spatiotemporal resolution as a stereotyped sequence of events. Firstly, in deep layers (1260 ± 80 μm, mean ± SD, n = 14) a CSD sink appeared highlighting the arrival of synaptic input, always followed after 10 ms by a MUA onset at same depths. After a variable delay between 0 and 40 ms, MUA increased in upper layer 5 and propagated to supragranular layers. Interestingly, such upward spreading of activity from deep to more superficial layers was not altered by the inactivation of visual thalamus obtained injecting TTX in LGN (lateral geniculate nucleus). Downward transitions from Up to Down states, showed a different timing of MUA offsets across layers. We found both positive and negative time lags between MUA inactivation of upper layer 5 and deeper layers, highlighting only a weak correlation between these two events. In layer 5-6, CSD sinks followed with a rather precise delay of 50 ms MUA offsets at the same depth, supporting the hypothesis of a network whose preferred state is the quiescent one, despite a steady synaptic input continue to be received. Even for downward transitions the chain of events was unchanged injecting TTX in the thalamus. The collected evidence for both Up state initiation and termination unambiguously reserves for the thalamus a marginal role, pointing out cortico-cortical connectivity as the main drive for Up/Down SO. Such cortico-cortical chat takes place in a state-dependent manner.

Spatiotemporal neural interactions underlying continuous drawing movements as revealed by magnetoencephalography

Continuous and sequential movements are controlled by widely distributed brain regions. A series of studies have contributed to understanding the functional role of these regions in a variety of visuomotor tasks. However, little is known about the neural interactions underpinning continuous movements. In the current study, we examine the spatiotemporal neural interactions underlying continuous drawing movements and the association of them with behavioral components. We conducted an experiment in which subjects copied a pentagon continuously for ~45 s using an XY joystick, while neuromagnetic fluxes were recorded from their head using a 248-sensor whole-head magnetoencephalography (MEG) device. Each sensor time series was rendered stationary and non-autocorrelated by applying an autoregressive integrated moving average model and taking the residuals. We used the directional variability of the movement as a behavioral measure of the controls generated. The main objective of this study was to assess the relation between neural interactions and the variability of movement direction. That is, we divided the continuous recordings into consecutive periods (i.e., time-bins) of 51 steps duration and computed the pairwise cross-correlations between the prewhitened time series in each time-bin. The circular standard deviation of the movement direction within each time-bin provides an estimate of the directional variability of the 51-ms trajectory segment. We looked at the association between neural interactions and variability of movement direction, separately for each pair of sensors, by running a cross-correlation analysis between the strength of the MEG pairwise cross-correlations and the circular standard deviations. We identified two types of neuronal networks: in one, the neural interactions are correlated with the directional variability of the movement at negative time-lags (feedforward), and in the other, the neural interactions are correlated with the directional variability of the movement at positive time-lags (feedback). Sensors associated mostly with feedforward processes are distributed in the left hemisphere and the right occipital-temporal junction, whereas sensors related to feedback processes are distributed in the right hemisphere and the left cerebellar hemisphere. These results are in line with findings from a series of previous studies showing that specific brain regions are involved in feedforward and feedback control processes to plan, perform, and correct movements. Additionally, we looked at whether changes in movement direction modulate the neural interactions. Interestingly, we found a preponderance of sensors associated with changes in movement direction over the right hemisphere-ipsilateral to the moving hand. These sensors exhibit stronger coupling with the rest of the sensors for trajectory segments with high rather than low directional movement variability. We interpret these results as evidence that ipsilateral cortical regions are recruited for continuous movements when the curvature of the trajectory increases. To the best of our knowledge, this is the first study that shows how neural interactions are associated with a behavioral control parameter in continuous and sequential movements.

Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony?

Spike synchronization is thought to have a constructive role for feature integration, attention, associative learning, and the formation of bidirectionally connected Hebbian cell assemblies. By contrast, theoretical studies on spike-timing-dependent plasticity (STDP) report an inherently decoupling influence of spike synchronization on synaptic connections of coactivated neurons. For example, bidirectional synaptic connections as found in cortical areas could be reproduced only by assuming realistic models of STDP and rate coding. We resolve this conflict by theoretical analysis and simulation of various simple and realistic STDP models that provide a more complete characterization of conditions when STDP leads to either coupling or decoupling of neurons firing in synchrony. In particular, we show that STDP consistently couples synchronized neurons if key model parameters are matched to physiological data: First, synaptic potentiation must be significantly stronger than synaptic depression for small (positive or negative) time lags between presynaptic and postsynaptic spikes. Second, spike synchronization must be sufficiently imprecise, for example, within a time window of 5–10 ms instead of 1 ms. Third, axonal propagation delays should not be much larger than dendritic delays. Under these assumptions synchronized neurons will be strongly coupled leading to a dominance of bidirectional synaptic connections even for simple STDP models and low mean firing rates at the level of spontaneous activity.

Regressional modeling and forecasting of economic growth for Arkhangelsk region

The regression models of GRP, considering the impact of three main factors: investment in fixed assets, wages amount, and, importantly, the innovation factor –the expenditures for research and development, are constructed in this paper on the empirical data for Arkhangelsk region. That approach permits to evaluate explicitly the contribution of innovation to economic growth. Regression analysis is the main research instrument, all calculations areperformedin the Microsoft Excel. There were made meaningful conclusions regarding the potential of the region's GRP growth by various factors, including impacts of positive and negative time lags. Adequate and relevant models are the base for estimation and forecasting values of the dependent variable (GRP) and evaluating their confidence intervals. The invented method of research can be used in factor assessment and prediction of regional economic growth, including growth by expectations.

Toward Waveform Heliotomography: Observing Interactions of Helioseismic Waves with a Sunspot

We investigate how helioseismic waves that originate from effective point sources interact with a sunspot. These waves are reconstructed from observed stochastic wavefields on the Sun by cross-correlating photospheric Doppler-velocity signals. We select the wave sources at different locations relative to the sunspot, and investigate the p- and f-mode waves separately. The results reveal a complicated picture of waveform perturbations caused by the wave interaction with the sunspot. In particular, it is found that for waves originating from outside of the sunspot, p-mode waves travel across the sunspot with a small amplitude reduction and slightly higher speed, and wave amplitude and phase get mostly restored to the quiet-Sun values after passing the sunspot. The f-mode wave experiences some amplitude reduction passing through the sunspot, and the reduced amplitude is not recovered after that. The wave-propagation speed does not change before encountering the sunspot and inside the sunspot, but the wavefront becomes faster than the reference wave after passing through the sunspot. For waves originating from inside the sunspot umbra, both f- and p-mode waves show significant amplitude reductions and faster speed during all courses of propagation. A comparison of positive and negative time lags of cross-correlation functions shows an apparent asymmetry in the waveform changes for both the f- and p-mode waves. We suggest that the waveform variations of the helioseismic waves interacting with a sunspot found in this article can be used for developing a method of waveform heliotomography, similar to the waveform tomography of the Earth.

MODELING THE HARD STATES OF THREE BLACK HOLE CANDIDATES

Simultaneous multiwavelength observations were recently performed for three black hole candidates --- SWIFT J1753.5-0127, GRO J1655-40 and XTE J1720-318. In this paper we test the accretion-jet model originally proposed to XTE J1118+480 by investigating the hard state of these three sources using this model. The accretion flow in the model is composed of an inner hot accretion flow and an outer truncated thin disk. We find that the model satisfactorily explains the spectrum ranging from radio to X-rays, with the radio and X-ray spectra dominated by the synchrotron and thermal Comptonization emissions in the jet and the hot accretion flow respectively, while the infrared and optical being the sum of the emissions from the jet, hot accretion flow, and the truncated thin disk. Similar to the case of XTE J1118+480, the model can also explain, although only qualitatively in some cases, the observed timing features including QPO, and positive and negative time lags between the optical and X-ray emissions detected in SWIFT J1753.5-0127. The origin of the ejection events detected in XTE J1720-318 is also briefly discussed.

Supersoft AGNs and their relations to Galactic binaries

AbstractI review some basic results on AGN with supersoft X‐ray spectra and their relations to Galactic binaries in their soft high states. This paper is based on a talk given at the Supersoft Sources Workshop at ESTEC in May 2009. Given the length of the talk and the number of pages the review cannot be complete and is biased towards my personal view. I demonstrate that at high accretion rates supersoft AGNs and Galactic binaries share steep soft X‐ray spectra, that the X‐ray variability of supersoft AGNs is more pronounced compared to Galactic binaries in their high states, that the X‐ray variability of supersoft novae and supersoft AGNs is similar, and that in Galactic binaries mostly positive time lags are seen, while negative time lags are observed in some supersoft AGN (© 2010 WILEY‐VCH Verlag GmbH &amp; Co. KGaA, Weinheim)

From Neurons to Circuits: Linear Estimation of Local Field Potentials

Extracellular physiological recordings are typically separated into two frequency bands: local field potentials (LFPs) (a circuit property) and spiking multiunit activity (MUA). Recently, there has been increased interest in LFPs because of their correlation with functional magnetic resonance imaging blood oxygenation level-dependent measurements and the possibility of studying local processing and neuronal synchrony. To further understand the biophysical origin of LFPs, we asked whether it is possible to estimate their time course based on the spiking activity from the same electrode or nearby electrodes. We used "signal estimation theory" to show that a linear filter operation on the activity of one or a few neurons can explain a significant fraction of the LFP time course in the macaque monkey primary visual cortex. The linear filter used to estimate the LFPs had a stereotypical shape characterized by a sharp downstroke at negative time lags and a slower positive upstroke for positive time lags. The filter was similar across different neocortical regions and behavioral conditions, including spontaneous activity and visual stimulation. The estimations had a spatial resolution of approximately 1 mm and a temporal resolution of approximately 200 ms. By considering a causal filter, we observed a temporal asymmetry such that the positive time lags in the filter contributed more to the LFP estimation than the negative time lags. Additionally, we showed that spikes occurring within approximately 10 ms of spikes from nearby neurons yielded better estimation accuracies than nonsynchronous spikes. In summary, our results suggest that at least some circuit-level local properties of the field potentials can be predicted from the activity of one or a few neurons.

Characteristics of ambient seismic noise as a source for surface wave tomography

Interstation cross correlations of ambient seismic noise from 1 year of continuous data at periods between 6 and 50 s are used to study the origin of the ambient noise using stations located in Europe, southern Africa, Asia, and three regions within North America. The signal‐to‐noise ratios (SNR) of Rayleigh waves for positive and negative correlation time lags at periods of 8, 14, 25 and 50 s are used to determine the azimuthal distribution of strong ambient noise sources. Ambient noise in both the primary (10–20 s) and secondary microseism bands (5–10 s) comes dominantly from the directions of relatively nearby coastlines with stronger noise occurring in the Northern Hemisphere in northern winter and in the Southern Hemisphere in southern winter, consistent with the hypothesis that oceanic microseisms are generating this noise. The observed differences in the directivity of noise in the primary and secondary microseism bands are the consequence of propagation and attenuation, rather than the location of generation. At intermediate and long periods (&gt;20 s), there is much less seasonal variation in both signal strength and directivity. We argue that our results are explained most simply by near‐coastal sources rather than deep ocean sources at all periods. Although the dominant ambient noise sources are distributed inhomogeneously in azimuth, strong ambient noise emerges from most directions when using recordings that are 1 year in duration. Simulations illustrate that this is what ensures the accuracy of the empirical Green's functions and ambient noise tomography.

Operational Modal Analysis for Estimating the Dynamic Properties of a Stadium Structure during a Football Game

During a football game, the ambient vibrations at the roof of a football stadium were recorded. A very large data set consisting of 4 hours of data, sampled at 80 Hz, is available. By a data reduction procedure, the complete data set could be analysed at once in a very short time. The data set was also split in shorter segments corresponding to certain events before, during and after the game to investigate the influence of varying operational conditions on the dynamic properties.As the structural vibrations were caused by unmeasurable wind and crowd excitation, Operational Modal Analysis has to be applied to find the dynamic characteristics of the structure. The new operational PolyMAX parameter estimation method is used and compared with Stochastic Subspace Identification. Stochastic Subspace Identification requires the correlations between the responses as primary data, whereas PolyMAX operates on spectra or half spectra (i.e. the Fourier transform of the positive time lags of the correlation functions). The main advantage of PolyMAX is that it yields extremely clear stabilisation diagrams, making an automation of the parameter identification process rather straightforward. This enables a continuous monitoring of the dynamic properties of a structure.

Sleep-Related Neural Activity in a Premotor and a Basal-Ganglia Pathway of the Songbird

During singing, neurons in premotor nucleus RA (robust nucleus of the arcopallium) of the zebra finch produce complex temporal sequences of bursts that are recapitulated during sleep. RA receives input from nucleus HVC via the premotor pathway, and also from the lateral magnocellular nucleus of the anterior nidopallium (LMAN), part of a basal ganglia-related circuit essential for vocal learning. We explore the propagation of sleep-related spike patterns in these two pathways and their influences on RA activity. We promote sleep in head-fixed birds by injections of melatonin and make single-neuron recordings from the three major classes of neurons in HVC: RA-projecting neurons, Area X-projecting neurons, and interneurons. We also record LMAN neurons that project to RA. In paired recordings, spike trains from identified HVC neuron types are strongly coherent with spike trains in RA neurons, whereas LMAN projection neurons on average exhibit only a weak coherency with neurons in HVC and RA. We further examine the relative roles of HVC and LMAN in generating RA burst sequences with reversible inactivation. Lidocaine inactivation of HVC completely abolishes bursting in RA, whereas inactivation of LMAN has no effect on burst rates in RA. In combination, our data suggest that in adult birds, RA burst sequences in sleep are driven via the premotor pathway from HVC. We present a simple generative model of spike trains in HVC, RA, and LMAN neurons that is able to qualitatively reproduce observed coherency functions. We propose that commonly observed coherency peaks at positive and negative time lags are caused by sequentially correlated HVC activity.

An Accretion‐Jet Model for Black Hole Binaries: Interpreting the Spectral and Timing Features of XTE J1118+480

Multiwavelength observations of the black hole X-ray binary XTE J1118+480 have offered abundant spectral and timing information about the source and have thus provided serious challenges to theoretical models. We propose a coupled accretion-jet model to interpret the observations. We model the accretion flow as an outer standard thin accretion disk truncated at a transition radius by an inner hot accretion flow. The accretion flow accounts for the observed UVand X-ray emission, but it substantially underpredicts the radio and infrared fluxes, even after we allow for nonthermal electrons in the hot flow. We attribute the latter components to a jet. We model the jet emission by means of the internal shock scenario, which is widely employed for gamma-ray bursts. In our accretion-jet model of XTE J1118+480, the jet dominates the radio and infrared emission, the thin disk dominates the UV emission, and the hot flow produces most of the X-ray emission. The optical emission has contributions from all three components: jet, thin disk, and hot flow. The model qualitatively accounts for timing features, such as the intriguing positive and negative time lags between the optical and X-ray emission and the wavelengthdependent variability amplitude. Subject headingg accretion, accretion disks — black hole physics — ISM: jets and outflows — stars: individual (XTE J1118+480) — X-rays: stars