Spike Synchronization Research Articles

The contribution of low contrast-preferring neurons to information representation in the primary visual cortex after learning.

Animals exhibit improved perception of lower-contrast visual objects after training. We explored this neuronal mechanism using multiple single-unit recordings from deep layers of the primary visual cortex (V1) of trained rats during orientation discrimination. We found that the firing rates of a subset of neurons increased by reducing luminance contrast, being at least above basal activities at low contrast. These low contrast–preferring neurons were rare during passive viewing without training or anesthesia after training. They fired more frequently in correct-choice than incorrect-choice trials. At single-neuron and population levels, they efficiently represented low-contrast orientations. Following training, in addition to generally enhanced excitation, the phase synchronization of spikes to beta oscillations at high contrast was stronger in putative inhibitory than excitatory neurons. The change in excitation-inhibition balance might contribute to low-contrast preference. Thus, low-contrast preference in V1 activity is strengthened in an experience-dependent manner, which may contribute to low-contrast visual discrimination.

Ripples reflect a spectrum of synchronous spiking activity in human anterior temporal lobe.

Direct brain recordings have provided important insights into how high-frequency activity captured through intracranial EEG (iEEG) supports human memory retrieval. The extent to which such activity is comprised of transient fluctuations that reflect the dynamic coordination of underlying neurons, however, remains unclear. Here, we simultaneously record iEEG, local field potential (LFP), and single unit activity in the human temporal cortex. We demonstrate that fast oscillations within the previously identified 80-120 Hz ripple band contribute to broadband high-frequency activity in the human cortex. These ripple oscillations exhibit a spectrum of amplitudes and durations related to the amount of underlying neuronal spiking. Ripples in the macro-scale iEEG are related to the number and synchrony of ripples in the micro-scale LFP, which in turn are related to the synchrony of neuronal spiking. Our data suggest that neural activity in the human temporal lobe is organized into transient bouts of ripple oscillations that reflect underlying bursts of spiking activity.

Experimental Epileptogenesis in a Cell Culture Model of Primary Neurons from Rat Brain: A Temporal Multi-Scale Study.

Understanding seizure development requires an integrated knowledge of different scales of organization of epileptic networks. We developed a model of “epilepsy-in-a-dish” based on dissociated primary neuronal cells from neonatal rat hippocampus. We demonstrate how a single application of glutamate stimulated neurons to generate spontaneous synchronous spiking activity with further progression into spontaneous seizure-like events after a distinct latency period. By computational analysis, we compared the observed neuronal activity in vitro with intracranial electroencephalography (EEG) data recorded from epilepsy patients and identified strong similarities, including a related sequence of events with defined onset, progression, and termination. Next, a link between the neurophysiological changes with network composition and cellular structure down to molecular changes was established. Temporal development of epileptiform network activity correlated with increased neurite outgrowth and altered branching, increased ratio of glutamatergic over GABAergic synapses, and loss of calbindin-positive interneurons, as well as genome-wide alterations in DNA methylation. Differentially methylated genes were engaged in various cellular activities related to cellular structure, intracellular signaling, and regulation of gene expression. Our data provide evidence that a single short-term excess of glutamate is sufficient to induce a cascade of events covering different scales from molecule- to network-level, all of which jointly contribute to seizure development.

Disparate insults relevant to schizophrenia converge on impaired spike synchrony and weaker synaptic interactions in prefrontal local circuits

Schizophrenia results from hundreds of known causes, including genetic, environmental, and developmental insults that cooperatively increase risk of developing the disease. In spite of the diversity of causal factors, schizophrenia presents with a core set of symptoms and brain abnormalities (both structural and functional) that particularly impact the prefrontal cortex. This suggests that many different causal factors leading to schizophrenia may cause prefrontal neurons and circuits to fail in fundamentally similar ways. The nature of convergent malfunctions in prefrontal circuits at the cell and synaptic levels leading to schizophrenia are not known. Here, we apply convergence-guided search to identify core pathological changes in the functional properties of prefrontal circuits that lie downstream of mechanistically distinct insults relevant to the disease. We compare the impacts of blocking NMDA receptors in monkeys and deleting a schizophrenia risk gene in mice on activity timing and effective communication in prefrontal local circuits. Although these manipulations operate through distinct molecular pathways and biological mechanisms, we found they produced convergent pathophysiological effects on prefrontal local circuits. Both manipulations reduced the frequency of synchronous (0-lag) spiking between prefrontal neurons and weakened functional interactions between prefrontal neurons at monosynaptic lags as measured by information transfer between the neurons. The two observations may be related, as reduction in synchronous spiking between prefrontal neurons would be expected to weaken synaptic connections between them via spike-timing-dependent synaptic plasticity. These data suggest that the link between spike timing and synaptic connectivity could comprise the functional vulnerability that multiple risk factors exploit to produce disease.

Impact of Purkinje Cell Simple Spike Synchrony on Signal Transmission from Flocculus.

Purkinje cells (PCs) in the cerebellar flocculus carry rate-coded information that ultimately drives eye movement. Floccular PCs lying nearby each other exhibit partial synchrony of their simple spikes (SS). Elsewhere in the cerebellum, PC SS synchrony has been demonstrated to influence activity of the PCs' synaptic targets, and some suggest it constitutes another vector for information transfer. We investigated in the cerebellar flocculus the extent to which the rate code and PC synchrony interact. One motivation for the study was to explain the cerebellar deficits in ataxic mice like tottering; we speculated that PC synchrony has a positive effect on rate code transmission that is lost in the mutants. Working in transgenic mice whose PCs express channelrhodopsin, we exploited a property of optogenetics to control PC synchrony: pulsed photostimulation engenders stimulus-locked spiking, whereas continuous photostimulation engenders spiking whose timing is unconstrained. We photoactivated flocculus PCs using pulsed stimuli with sinusoidally varying timing vs. continuous stimuli with sinusoidally varying intensity. Recordings of PC pairs confirmed that pulsed stimuli engendered greater PC synchrony. We quantified the efficiency of transmission of the evoked PC firing rate modulation from the amplitudes of firing rate modulation and eye movement. Rate code transmission was slightly poorer in the conditions that generated greater PC synchrony, arguing against our motivating speculation regarding the origin of ataxia in tottering. Floccular optogenetic stimulation prominently augmented a 250-300Hz local field potential oscillation, and we demonstrate relationships between the oscillation power and the evoked PC synchrony.

Learning hierarchically-structured concepts

We use a recently developed synchronous Spiking Neural Network (SNN) model to study the problem of learning hierarchically-structured concepts. We introduce an abstract data model that describes simple hierarchical concepts. We define a feed-forward layered SNN model, with learning modeled using Oja’s local learning rule, a well known biologically-plausible rule for adjusting synapse weights. We define what it means for such a network to recognize hierarchical concepts; our notion of recognition is robust, in that it tolerates a bounded amount of noise.Then, we present a learning algorithm by which a layered network may learn to recognize hierarchical concepts according to our robust definition. We analyze correctness and performance rigorously; the amount of time required to learn each concept, after learning all of the sub-concepts, is approximately O1ηkℓmaxlog(k)+1ɛ+blog(k), where k is the number of sub-concepts per concept, ℓmax is the maximum hierarchical depth, η is the learning rate, ɛ describes the amount of uncertainty allowed in robust recognition, and b describes the amount of weight decrease for “irrelevant” edges. An interesting feature of this algorithm is that it allows the network to learn sub-concepts in a highly interleaved manner. This algorithm assumes that the concepts are presented in a noise-free way; we also extend these results to accommodate noise in the learning process. Finally, we give a simple lower bound saying that, in order to recognize concepts with hierarchical depth two with noise-tolerance, a neural network should have at least two layers.The results in this paper represent first steps in the theoretical study of hierarchical concepts using SNNs. The cases studied here are basic, but they suggest many directions for extensions to more elaborate and realistic cases.

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.

Suppression of synchronous spiking in two interacting populations of excitatory and inhibitory quadratic integrate-and-fire neurons.

Collective oscillations and their suppression by external stimulation are analyzed in a large-scale neural network consisting of two interacting populations of excitatory and inhibitory quadratic integrate-and-fire neurons. In the limit of an infinite number of neurons, the microscopic model of this network can be reduced to an exact low-dimensional system of mean-field equations. Bifurcation analysis of these equations reveals three different dynamic modes in a free network: a stable resting state, a stable limit cycle, and bistability with a coexisting resting state and a limit cycle. We show that in the limit cycle mode, high-frequency stimulation of an inhibitory population can stabilize an unstable resting state and effectively suppress collective oscillations. We also show that in the bistable mode, the dynamics of the network can be switched from a stable limit cycle to a stable resting state by applying an inhibitory pulse to the excitatory population. The results obtained from the mean-field equations are confirmed by numerical simulation of the microscopic model.

Interneuron-specific gamma synchronization indexes cue uncertainty and prediction errors in lateral prefrontal and anterior cingulate cortex.

Inhibitory interneurons are believed to realize critical gating functions in cortical circuits, but it has been difficult to ascertain the content of gated information for well-characterized interneurons in primate cortex. Here, we address this question by characterizing putative interneurons in primate prefrontal and anterior cingulate cortex while monkeys engaged in attention demanding reversal learning. We find that subclasses of narrow spiking neurons have a relative suppressive effect on the local circuit indicating they are inhibitory interneurons. One of these interneuron subclasses showed prominent firing rate modulations and (35-45 Hz) gamma synchronous spiking during periods of uncertainty in both, lateral prefrontal cortex (LPFC) and anterior cingulate cortex (ACC). In LPFC, this interneuron subclass activated when the uncertainty of attention cues was resolved during flexible learning, whereas in ACC it fired and gamma-synchronized when outcomes were uncertain and prediction errors were high during learning. Computational modeling of this interneuron-specific gamma band activity in simple circuit motifs suggests it could reflect a soft winner-take-all gating of information having high degree of uncertainty. Together, these findings elucidate an electrophysiologically characterized interneuron subclass in the primate, that forms gamma synchronous networks in two different areas when resolving uncertainty during adaptive goal-directed behavior.

Epidural cerebellar stimulation drives widespread neural synchrony in the intact and stroke perilesional cortex

BackgroundCerebellar electrical stimulation has shown promise in improving motor recovery post-stroke in both rodent and human studies. Past studies have used motor evoked potentials (MEPs) to evaluate how cerebellar stimulation modulates ongoing activity in the cortex, but the underlying mechanisms are incompletely understood. Here we used invasive electrophysiological recordings from the intact and stroke-injured rodent primary motor cortex (M1) to assess how epidural cerebellar stimulation modulates neural dynamics at the level of single neurons as well as at the level of mesoscale dynamics.MethodsWe recorded single unit spiking and local field potentials (LFPs) in both the intact and acutely stroke-injured M1 contralateral to the stimulated cerebellum in adult Long-Evans rats under anesthesia. We analyzed changes in the firing rates of single units, the extent of synchronous spiking and power spectral density (PSD) changes in LFPs during and post-stimulation.ResultsOur results show that post-stimulation, the firing rates of a majority of M1 neurons changed significantly with respect to their baseline rates. These firing rate changes were diverse in character, as the firing rate of some neurons increased while others decreased. Additionally, these changes started to set in during stimulation. Furthermore, cross-correlation analysis showed a significant increase in coincident firing amongst neuronal pairs. Interestingly, this increase in synchrony was unrelated to the direction of firing rate change. We also found that neuronal ensembles derived through principal component analysis were more active post-stimulation. Lastly, these changes occurred without a significant change in the overall spectral power of LFPs post-stimulation.ConclusionsOur results show that cerebellar stimulation caused significant, long-lasting changes in the activity patterns of M1 neurons by altering firing rates, boosting neural synchrony and increasing neuronal assemblies’ activation strength. Our study provides evidence that cerebellar stimulation can directly modulate cortical dynamics. Since these results are present in the perilesional cortex, our data might also help explain the facilitatory effects of cerebellar stimulation post-stroke.

Synchronization in populations of electrochemical bursting oscillators with chaotic slow dynamics.

We investigate the synchronization of coupled electrochemical bursting oscillators using the electrodissolution of iron in sulfuric acid. The dynamics of a single oscillator consisted of slow chaotic oscillations interrupted by a burst of fast spiking, generating a multiple time-scale dynamical system. A wavelet analysis first decomposed the time series data from each oscillator into a fast and a slow component, and the corresponding phases were also obtained. The phase synchronization of the fast and slow dynamics was analyzed as a function of electrical coupling imposed by an external coupling resistance. For two oscillators, a progressive transition was observed: With increasing coupling strength, first, the fast bursting intervals overlapped, which was followed by synchronization of the fast spiking, and finally, the slow chaotic oscillations synchronized. With a population of globally coupled 25 oscillators, the coupling eliminated the fast dynamics, and only the synchronization of the slow dynamics can be observed. The results demonstrated the complexities of synchronization with bursting oscillations that could be useful in other systems with multiple time-scale dynamics, in particular, in neuronal networks.

Concomitant Processing of Choice and Outcome in Frontal Corticostriatal Ensembles Correlates with Performance of Rats.

The frontal cortex-basal ganglia network plays a pivotal role in adaptive goal-directed behaviors. Medial frontal cortex (MFC) encodes information about choices and outcomes into sequential activation of neural population, or neural trajectory. While MFC projects to the dorsal striatum (DS), whether DS also displays temporally coordinated activity remains unknown. We studied this question by simultaneously recording neural ensembles in the MFC and DS of rodents performing an outcome-based alternative choice task. We found that the two regions exhibited highly parallel evolution of neural trajectories, transforming choice information into outcome-related information. When the two trajectories were highly correlated, spike synchrony was task-dependently modulated in some MFC-DS neuron pairs. Our results suggest that neural trajectories concomitantly process decision-relevant information in MFC and DS with increased spike synchrony between these regions.

The Study for Synchronization between Two Coupled FitzHugh-Nagumo Neurons Based on the Laplace Transform and the Adomian Decomposition Method

The synchronization between two coupled FitzHugh-Nagumo (FHN) neurons with or without external current is studied by using the Laplace transform and the Adomian decomposition method. Different from other researches, the synchronization error system is expressed as sets of Volterra integral equations based on the convolution theorem in the Laplace transform. Then, it is easy to analytically obtain the conditions that synchronization errors disappear based on the successive approximation method in integral equation theorem, the correctness of which is verified by numerical simulations. Furthermore, the synchronous dynamics of the two coupled FHN neurons also can be written in the form of Volterra integral equations, which is more convenient to analytically solve by using the Adomian decomposition method. It is found that the occurrence of synchronization between the two FHN neurons only depends on the coupling strength and is irrelevant to the external current. Only synchronous rest state in the two FHN neurons without external current can be achieved, while synchronous spikes appear if the external current is not zero.

Rate and Temporal Coding of Regular and Irregular Pulse Trains in Auditory Midbrain of Normal-Hearing and Cochlear-Implanted Rabbits.

Although pitch is closely related to temporal periodicity, stimuli with a degree of temporal irregularity can evoke a pitch sensation in human listeners. However, the neural mechanisms underlying pitch perception for irregular sounds are poorly understood. Here, we recorded responses of single units in the inferior colliculus (IC) of normal hearing (NH) rabbits to acoustic pulse trains with different amounts of random jitter in the inter-pulse intervals and compared with responses to electric pulse trains delivered through a cochlear implant (CI) in a different group of rabbits. In both NH and CI animals, many IC neurons demonstrated tuning of firing rate to the average pulse rate (APR) that was robust against temporal jitter, although jitter tended to increase the firing rates for APRs ≥ 1280Hz. Strength and limiting frequency of spike synchronization to stimulus pulses were also comparable between periodic and irregular pulse trains, although there was a slight increase in synchronization at high APRs with CI stimulation. There were clear differences between CI and NH animals in both the range of APRs over which firing rate tuning was observed and the prevalence of synchronized responses. These results suggest that the pitches of regular and irregular pulse trains are coded differently by IC neurons depending on the APR, the degree of irregularity, and the mode of stimulation. In particular, the temporal pitch produced by periodic pulse trains lacking spectral cues may be based on a rate code rather than a temporal code at higher APRs.

Collective Synchronous Spiking in a Brain Network of Coupled Nonlinear Oscillators.

A network of propagating nonlinear oscillatory modes (waves) in the human brain is shown to generate collectively synchronized spiking activity (hypersynchronous spiking) when both amplitude and phase coupling between modes are taken into account. The nonlinear behavior of the modes participating in the network are the result of the nonresonant dynamics of weakly evanescent cortical waves that, as shown recently, adhere to an inverse frequency-wave number dispersion relation when propagating through an inhomogeneous anisotropic media characteristic of the brain cortex. This description provides a missing link between simplistic models of synchronization in networks of small amplitude phase coupled oscillators and in networks built with various empirically fitted models of pulse or amplitude coupled spiking neurons. Overall the phase-amplitude coupling mechanism presented in the Letter shows significantly more efficient synchronization compared to current standard approaches and demonstrates an emergence of collective synchronized spiking from subthreshold oscillations that neither phase nor amplitude coupling alone are capable of explaining.

Analysis of spiking synchrony in visual cortex reveals distinct types of top-down modulation signals for spatial and object-based attention.

The activity of a border ownership selective (BOS) neuron indicates where a foreground object is located relative to its (classical) receptive field (RF). A population of BOS neurons thus provides an important component of perceptual grouping, the organization of the visual scene into objects. In previous theoretical work, it has been suggested that this grouping mechanism is implemented by a population of dedicated grouping (“G”) cells that integrate the activity of the distributed feature cells representing an object and, by feedback, modulate the same cells, thus making them border ownership selective. The feedback modulation by G cells is thought to also provide the mechanism for object-based attention. A recent modeling study showed that modulatory common feedback, implemented by synapses with N-methyl-D-aspartate (NMDA)-type glutamate receptors, accounts for the experimentally observed synchrony in spike trains of BOS neurons and the shape of cross-correlations between them, including its dependence on the attentional state. However, that study was limited to pairs of BOS neurons with consistent border ownership preferences, defined as two neurons tuned to respond to the same visual object, in which attention decreases synchrony. But attention has also been shown to increase synchrony in neurons with inconsistent border ownership selectivity. Here we extend the computational model from the previous study to fully understand these effects of attention. We postulate the existence of a second type of G-cell that represents spatial attention by modulating the activity of all BOS cells in a spatially defined area. Simulations of this model show that a combination of spatial and object-based mechanisms fully accounts for the observed pattern of synchrony between BOS neurons. Our results suggest that modulatory feedback from G-cells may underlie both spatial and object-based attention.

Spontaneous, synchronous zinc spikes oscillate with neural excitability and calcium spikes in primary hippocampal neuron culture.

Zinc has been suggested to act as an intracellular signaling molecule due to its regulatory effects on numerous protein targets including enzymes, transcription factors, ion channels, neurotrophic factors, and postsynaptic scaffolding proteins. However, intracellular zinc concentration is tightly maintained at steady levels under natural physiological conditions. Dynamic changes in intracellular zinc concentration have only been detected in certain types of cells that are exposed to pathologic stimuli or upon receptor ligand binding. Unlike calcium, the ubiquitous signaling metal ion that can oscillate periodically and spontaneously in various cells, spontaneous zinc oscillations have never been reported. In this work, we made the novel observation that the developing neurons generated spontaneous and synchronous zinc spikes in primary hippocampal cultures using a fluorescent zinc sensor, FluoZin-3. Blocking of glutamate receptor-dependent calcium influx depleted the zinc spikes, suggesting that these zinc spikes were driven by the glutamate-mediated spontaneous neural excitability and calcium spikes that have been characterized in early developing neurons. Simultaneous imaging of calcium or pH together with zinc, we uncovered that a downward pH spike was evoked with each zinc spike and this transient cellular acidification occurred downstream of calcium spikes but upstream of zinc spikes. Our results suggest that spontaneous, synchronous zinc spikes were generated through calcium influx-induced cellular acidification, which liberates zinc from intracellular zinc binding ligands. Given that changes in zinc concentration can modulate activities of proteins essential for synapse maturation and neuronal differentiation, these zinc spikes might act as important signaling roles in neuronal development.

Levodopa affects spike and local field synchronisation in the pedunculopontine nucleus of a rat model of Parkinson's disease.

The pedunculopontine nucleus (PPN) undergoes significant anatomic and electrophysiological alterations in Parkinson’s disease (PD), severely impacting locomotion. However, the effect of 6-hydroxydopamine (6-OHDA) lesion and levodopa (L-DOPA) therapy on the relationships between spike activities and local field potential (LFP) within the PPN is not well-understood. Synchronisation between the spike activity of individual neurones and LFP of neuronal ensembles is a crucial problem in the pathogenesis of PD. In this study, LFP signals and spikes in the PPN of rats in control, lesioned, and L-DOPA groups were recorded synchronously with a multi-unit electrical signal acquisition system and analysed for their coherence value, spike-field coherence, and phase-lock relationship. The spike-LFP relationship in the PPN was markedly increased in specific frequency bands because of the 6-OHDA lesion but differed depending on the animal locomotion state and neuronal type. L-DOPA had a limited therapeutic effect on the 6-OHDA-induced increase in the coherence value. Our study demonstrates that the PPN spike-LFP relationship is involved in the pathogenesis of PD and is critical for the effects of L-DOPA, providing a basis for the clinical treatment of refractory PD symptoms.

A piezoelectric sensing neuron and resonance synchronization between auditory neurons under stimulus

• A new auditory neural circuit is presented. • A functional neuron model is obtained. • Two auditory neurons reach resonance synchronization without synapse coupling. A piezoelectric ceramic can convert external mechanical force and vibration into electric signals by producing distinct output voltage when certain deformation is induced. As a result, a piezoelectric ceramic in nonlinear circuit can be used as sensitive sensor for detecting external sound signals. In this paper, a piezoelectric ceramic is incorporated into a simple FithzHugh–Nagumo neural circuit for capturing and encoding external sound signals. The improved neural circuit and functional neuron models are obtained, and a variety of firing modes are reproduced in the neural activities by changing the external sound signals. For animals, two ears can receive and encode the external sound signals synchronously in effective way, and thus the synchronization between two auditory neurons. To explore the capacity of discernment and cooperation of two ears in the auditory system when external voice is applied, two piezoelectric sensing neurons (PSNs) are driven by the same external voice for detecting possible synchronization approach without any synapse coupling. It is found that two identical PSNs driven by the same periodical stimuli (external forces) can reach synchronous bursting, spiking, and periodical firings, respectively. In case of chaotic firing, the synchronization stability is dependent on the external forcing applied on the two PSNs. Furthermore, external additive noise is applied for considering stochastic forcing on the PSN, it is confirmed that two identical PSNs can reach kinds of synchronous firings, while some intermediate noise intensities seldom enhance the synchronization stability between two identical PSNs. The Hamilton energy in isolated PSN driven by external forcing is estimated, and this kind of non-coupling synchronization is explained as a kind of resonance synchronization. These results can be helpful to design functional auxiliary devices for those patients with hearing impairment.

Synchronous spiking associated with prefrontal high γ oscillations evokes a 5-Hz rhythmic modulation of spiking in locus coeruleus.

The brainstem noradrenergic locus coeruleus (LC) is reciprocally connected with the prefrontal cortex (PFC). Coupling between LC spiking and the depolarizing phase of slow (1-2 Hz) waves in PFC field potentials during sleep and anesthesia suggests that LC drives cortical state transition. Reciprocal LC-PFC connectivity should also allow interactions in the opposing (top-down) direction, but prior work has only studied prefrontal control over LC activity using electrical or optogenetic stimulation. Here, we describe the physiological characteristics of spontaneously occurring top-down LC-PFC interactions. We recorded LC multiunit activity (MUA) simultaneously with PFC single-unit and local field potential (LFP) activity in urethane-anesthetized rats. We observed cross-regional coupling between the phase of 5-Hz oscillations in LC-MUA and the power of PFC LFP 60-200 Hz high γ (hγ). Transient increases in PFC hγ power preceded peaks in the 5-Hz LC-MUA oscillation. Analysis of cross-regional transfer entropy demonstrated that the PFC hγ transients were predictive of a transient increase in LC-MUA. An ∼29 ms delay between these signals was consistent with the conduction velocity from the PFC to the LC. Finally, we showed that PFC hγ transients are associated with synchronized spiking of a subset (27%) of PFC single units. Our data suggest that PFC hγ transients may indicate the timing of the top-down excitatory input to LC, at least under conditions when LC neuronal population activity fluctuates rhythmically at 5 Hz. Synchronized PFC neuronal spiking that occurs during hγ transients may provide a previously unknown mode of top-down control over the LC.NEW & NOTEWORTHY The prefrontal cortex (PFC) is thought to control activity in the noradrenergic locus coeruleus (LC). Prior anatomical and prefrontal stimulation studies demonstrated the potential for PFC-LC interactions; however, it is unknown what types of PFC activity affect the LC. Here, we show that transient increases in PFC high γ power and associated changes in PFC unit-pair synchrony are a potential sign of top-down control over the LC.