Spike Synchronization Research Articles

Dopamine-induced Relaxation of Spike Synchrony Diversifies Burst Patterns in Cultured Hippocampal Networks

The intricate interplay of neurotransmitters orchestrates a symphony of neural activity in the hippocampus, with dopamine emerging as a key conductor in this complex ensemble. Despite numerous studies uncovering the cellular mechanisms of dopamine, its influence on hippocampal neural networks remains elusive. Combining in vitro electrophysiological recordings of rat embryonic hippocampal neurons, pharmacological interventions, and computational analyses of spike trains, we found that dopamine induces a relaxation in network synchrony. This relaxation expands the repertoire of burst dynamics within these hippocampal networks, a phenomenon notably absent under the administration of dopamine antagonists. Our study provides a thorough understanding of how dopamine signaling influences the formation of functional networks among hippocampal neurons.

Substantial gas enrichment in shales influenced by volcanism during the Ordovician–Silurian transition

The substantial gas enrichment in shales of the Ordovician–Silurian transition was associated with the development of the most favorable source rock. While organic matter enrichment has been linked to intensive volcanism, it remains a challenge to precisely evaluate the impact of the volcanism on substantial gas enrichment containing the largest gas storage capacity. This study focused on consecutive borehole shale samples from the Wufeng–Longmaxi formations during the Ordovician–Silurian transition in southern China. We conducted a comprehensive analysis, integrating the major geological volcanism with high-resolution analysis, including QEMSCAN, argon-ion SEM, thin-section examination, XRD mineralogy, TOC, Hg concentration, petrophysical properties and nanopore structure analysis (low-pressure CO2/N2 gas adsorption, porosity and permeability). The results link the significant shale gas enrichment in Wufeng–Longmaxi formations to intensive volcanism across the Ordovician–Silurian transition. We identified the most favorable shale intervals in the lower Longmaxi Formation, aligning with the peak period of volcanism. This period showed synchronous spikes in Hg, Hg/TOC, and TOC contents. Shale deposited during this favorable paleoenvironment exhibited the highest values of TOC, porosity, permeability, specific surface area, pore volume, maximum gas adsorption capacity, leading to the largest amount of gas content and substantial gas enrichment. Our work, therefore, provides new insights into identifying the most favorable shale gas resources. This knowledge assists in accurate predictions of the stratigraphic ‘sweet spot’ intervals for large shale gas storage capacity, providing crucial information for engineering explorations and developments in shale formations.

Cortical dynamics of perception as trains of coherent gamma oscillations, with the pulvinar as central coordinator

Synchronization of spikes carried by the visual streams is strategic for the proper binding of cortical assemblies, hence for the perception of visual objects as coherent units. Perception of a complex visual scene involves multiple trains of gamma oscillations, coexisting at each stage in visual and associative cortex. Here, we analyze how this synchrony is managed, so that the perception of each visual object can emerge despite this complex interweaving of cortical activations. After a brief review of structural and temporal facts, we analyze the interactions which make the oscillations coherent for the visual elements related to the same object. We continue with the propagation of these gamma oscillations within the sensory chain. The dominant role of the pulvinar and associated reticular thalamic nucleus as cortical coordinator is the common thread running through this step-by-step description. Synchronization mechanisms are analyzed in the context of visual perception, although the present considerations are not limited to this sense. A simple experiment is described, with the aim of assessing the validity of the elements developed here. A first set of results is provided, together with a proposed method to go further in this investigation.

Pulse Shape and Voltage-Dependent Synchronization in Spiking Neuron Networks.

Pulse-coupled spiking neural networks are a powerful tool to gain mechanistic insights into how neurons self-organize to produce coherent collective behavior. These networks use simple spiking neuron models, such as the θ-neuron or the quadratic integrate-and-fire (QIF) neuron, that replicate the essential features of real neural dynamics. Interactions between neurons are modeled with infinitely narrow pulses, or spikes, rather than the more complex dynamics of real synapses. To make these networks biologically more plausible, it has been proposed that they must also account for the finite width of the pulses, which can have a significant impact on the network dynamics. However, the derivation and interpretation of these pulses are contradictory, and the impact of the pulse shape on the network dynamics is largely unexplored. Here, I take a comprehensive approach to pulse coupling in networks of QIF and θ-neurons. I argue that narrow pulses activate voltage-dependent synaptic conductances and show how to implement them in QIF neurons such that their effect can last through the phase after the spike. Using an exact low-dimensional description for networks of globally coupled spiking neurons, I prove for instantaneous interactions that collective oscillations emerge due to an effective coupling through the mean voltage. I analyze the impact of the pulse shape by means of a family of smooth pulse functions with arbitrary finite width and symmetric or asymmetric shapes. For symmetric pulses, the resulting voltage coupling is not very effective in synchronizing neurons, but pulses that are slightly skewed to the phase after the spike readily generate collective oscillations. The results unveil a voltage-dependent spike synchronization mechanism at the heart of emergent collective behavior, which is facilitated by pulses of finite width and complementary to traditional synaptic transmission in spiking neuron networks.

Cell firing between ON alpha retinal ganglion cells and coupled amacrine cells in the mouse retina.

Gap junctions are channels that allow for direct transmission of electrical signals between cells. However, the ability of one cell to be impacted or controlled by other cells through gap junctions remains unclear. In this study, heterocellular coupling between ON α retinal ganglion cells (α-RGCs) and displaced amacrine cells (ACs) in the mouse retina was used as a model. The impact of the extent of coupling of interconnected ACs on the synchronized firing between coupled ON α-RGC-AC pair was investigated using the dopamine 1 receptor (D1R) antagonist-SCH23390 and agonist-SKF38393. It was observed that the synchronized firing between the ON α-RGC-ACs pairs was increased by the D1R antagonist SCH23390, whereas it was eradicated by the agonist SKF38393. Subsequently, the signaling drive was investigated by infecting coupled ON α-RGC-AC pairs with the channelrhodopsin-2(ChR2) mutation L132C engineered to enhance light sensitivities. The results demonstrated that the spikes of ON α-RGCs (without ChR2) could be triggered by ACs (with ChR2) through the gap junction, and vice versa. Furthermore, it was observed that ON α-RGCs stimulated with 3-10 Hz currents by whole cell patch could elicit synchronous spikes in the coupled ACs, and vice versa. This provided direct evidence that the firing of one cell could be influenced by another cell through gap junctions. However, this phenomenon was not observed between OFF α-RGC pairs. The study implied that the synchronized firing between ON α-RGC-AC pairs could potentially be affected by the coupling of interconnected ACs. Additionally, one cell type could selectively control the firing of another cell type, thereby forcefully transmitting information. The key role of gap junctions in synchronizing firing and driving cells between α-RGCs and coupled ACs in the mouse retina was highlighted.NEW & NOTEWORTHY This study investigates the role of gap junctions in transmitting electrical signals between cells and their potential for cell control. Using ON α retinal ganglion cells (α-RGCs) and amacrine cells (ACs) in the mouse retina, the researchers find that the extent of coupling between ACs affects synchronized firing. Bidirectional signaling occurs between ACs and ON α-RGCs through gap junctions.

Synchronous Measurements of Extracellular Action Potentials and Neurochemical Activity with Carbon Fiber Electrodes in Nonhuman Primates.

Measuring the dynamic relationship between neuromodulators, such as dopamine, and neuronal action potentials is imperative to understand how these fundamental modes of neural signaling interact to mediate behavior. We developed methods to measure concurrently dopamine and extracellular action potentials (i.e., spikes) in monkeys. Standard fast-scan cyclic voltammetric (FSCV) electrochemical (EChem) and electrophysiological (EPhys) recording systems are combined and used to collect spike and dopamine signals, respectively, from an array of carbon fiber (CF) sensors implanted in the monkey striatum. FSCV requires the application of small voltages at the implanted sensors to measure redox currents generated from target molecules, such as dopamine. These applied voltages create artifacts at neighboring EPhys measurement sensors which may lead to misclassification of these signals as physiological spikes. Therefore, simple automated temporal interpolation algorithms were designed to remove these artifacts and enable accurate spike extraction. We validated these methods using simulated artifacts and demonstrated an average spike recovery rate of 84.5%. We identified and discriminated cell type-specific units in the monkey striatum that were shown to correlate to specific behavioral task parameters related to reward size and eye movement direction. Synchronously recorded spike and dopamine signals displayed contrasting relations to the task variables, suggesting a complex relationship between these two modes of neural signaling. Future application of our methods will help advance our understanding of the interactions between neuromodulator signaling and neuronal activity, to elucidate more detailed mechanisms of neural circuitry and plasticity mediating behaviors in health and in disease.

Simple spike patterns and synaptic mechanisms encoding sensory and motor signals in Purkinje cells and the cerebellar nuclei

Whisker stimulation in awake mice evokes transient suppression of simple spike probability in crus I/II Purkinje cells. Here, we investigated how simple spike suppression arises synaptically, what it encodes, and how it affects cerebellar output. Invitro, monosynaptic parallel fiber (PF)-excitatory postsynaptic currents (EPSCs) facilitated strongly, whereas disynaptic inhibitory postsynaptic currents (IPSCs) remained stable, maximizing relative inhibitory strength at the onset of PF activity. Short-term plasticity thus favors the inhibition of Purkinje spikes before PFs facilitate. Invivo, whisker stimulation evoked a 2-6ms synchronous spike suppression, just 6-8ms (∼4 synaptic delays) after sensory onset, whereas active whisker movements elicited broadly timed spike rate increases that did not modulate sensory-evoked suppression. Firing in the cerebellar nuclei (CbN) inversely correlated with disinhibition from sensory-evoked simple spike suppressions but was decoupled from slow, non-synchronous movement-associated elevations of Purkinje firing rates. Synchrony thus allows the CbN to high-pass filter Purkinje inputs, facilitating sensory-evoked cerebellar outputs that can drive movements.

Spike synchrony as a measure of Gestalt structure

The function of spike synchrony is debatable: some researchers view it as a mechanism for binding perceptual features, others – as a byproduct of brain activity. We argue for an alternative computational role: synchrony can estimate the prior probability of incoming stimuli. In V1, this can be achieved by comparing input with previously acquired visual experience, which is encoded in plastic horizontal intracortical connections. V1 connectivity structure can encode the acquired visual experience in the form of its aggregate statistics. Since the aggregate statistics of natural images tend to follow the Gestalt principles, we can assume that V1 is more often exposed to Gestalt-like stimuli, and this is manifested in its connectivity structure. At the same time, the connectivity structure has an impact on spike synchrony in V1. We used a spiking model with V1-like connectivity to demonstrate that spike synchrony reflects the Gestalt structure of the stimulus. We conducted simulation experiments with three Gestalt laws: proximity, similarity, and continuity, and found substantial differences in firing synchrony for stimuli with varying degrees of Gestalt-likeness. This allows us to conclude that spike synchrony indeed reflects the Gestalt structure of the stimulus, which can be interpreted as a mechanism for prior probability estimation.

A prefrontal network model operating near steady and oscillatory states links spike desynchronization and synaptic deficits in schizophrenia.

Schizophrenia results in part from a failure of prefrontal networks but we lack full understanding of how disruptions at a synaptic level cause failures at the network level. This is a crucial gap in our understanding because it prevents us from discovering how genetic mutations and environmental risks that alter synaptic function cause prefrontal network to fail in schizophrenia. To address that question, we developed a recurrent spiking network model of prefrontal local circuits that can explain the link between NMDAR synaptic and 0-lag spike synchrony deficits we recently observed in a pharmacological monkey model of prefrontal network failure in schizophrenia. We analyze how the balance between AMPA and NMDA components of recurrent excitation and GABA inhibition in the network influence oscillatory spike synchrony to inform the biological data. We show that reducing recurrent NMDAR synaptic currents prevents the network from shifting from a steady to oscillatory state in response to extrinsic inputs such as might occur during behavior. These findings strongly parallel dynamic modulation of 0-lag spike synchrony we observed between neurons in monkey prefrontal cortex during behavior, as well as the suppression of this 0-lag spiking by administration of NMDAR antagonists. As such, our cortical network model provides a plausible mechanism explaining the link between NMDAR synaptic and 0-lag spike synchrony deficits observed in a pharmacological monkey model of prefrontal network failure in schizophrenia.

Coherent olfactory bulb gamma oscillations arise from coupling independent columnar oscillators.

Spike timing-based representations of sensory information depend on embedded dynamical frameworks within neuronal networks that establish the rules of local computation and interareal communication. Here, we investigated the dynamical properties of olfactory bulb circuitry in mice of both sexes using microelectrode array recordings from slice and in vivo preparations. Neurochemical activation or optogenetic stimulation of sensory afferents evoked persistent gamma oscillations in the local field potential. These oscillations arose from slower, GABA(A) receptor-independent intracolumnar oscillators coupled by GABA(A)-ergic synapses into a faster, broadly coherent network oscillation. Consistent with the theoretical properties of coupled-oscillator networks, the spatial extent of zero-phase coherence was bounded in slices by the reduced density of lateral interactions. The intact in vivo network, however, exhibited long-range lateral interactions that suffice in simulation to enable zero-phase gamma coherence across the olfactory bulb. The timing of action potentials in a subset of principal neurons was phase-constrained with respect to evoked gamma oscillations. Coupled-oscillator dynamics in olfactory bulb thereby enable a common clock, robust to biological heterogeneities, that is capable of supporting gamma-band spike synchronization and phase coding across the ensemble of activated principal neurons.NEW & NOTEWORTHY Odor stimulation evokes rhythmic gamma oscillations in the field potential of the olfactory bulb, but the dynamical mechanisms governing these oscillations have remained unclear. Establishing these mechanisms is important as they determine the biophysical capacities of the bulbar circuit to, for example, maintain zero-phase coherence across a spatially extended network, or coordinate the timing of action potentials in principal neurons. These properties in turn constrain and suggest hypotheses of sensory coding.

Dysfunction of specific auditory fibers impacts cortical oscillations, driving an autism phenotype despite near-normal hearing.

Autism spectrum disorder is discussed in the context of altered neural oscillations and imbalanced cortical excitation-inhibition of cortical origin. We studied here whether developmental changes in peripheral auditory processing, while preserving basic hearing function, lead to altered cortical oscillations. Local field potentials (LFPs) were recorded from auditory, visual, and prefrontal cortices and the hippocampus of BdnfPax2 KO mice. These mice develop an autism-like behavioral phenotype through deletion of BDNF in Pax2+ interneuron precursors, affecting lower brainstem functions, but not frontal brain regions directly. Evoked LFP responses to behaviorally relevant auditory stimuli were weaker in the auditory cortex of BdnfPax2 KOs, connected to maturation deficits of high-spontaneous rate auditory nerve fibers. This was correlated with enhanced spontaneous and induced LFP power, excitation-inhibition imbalance, and dendritic spine immaturity, mirroring autistic phenotypes. Thus, impairments in peripheral high-spontaneous rate fibers alter spike synchrony and subsequently cortical processing relevant for normal communication and behavior.

Electric spike emergence and synchronization during wet etching of overlapping low energy light ion microbeam-irradiated polymers

Electric spike emission was observed for the first time for thin polymer foils irradiated with low energy light ion microbeams at overlapping fluences, when placing them in between two vessels filled with etchant and distilled water, respectively, and applying a low sinusoidal transverse voltage. The various shapes of recorded spike types are described and examined. From their emergence that occurs after different derived etching times, and the inter-correlation of different spike parameters, we draw the first conclusions about their formation mechanism, behavior, and usefulness for technical applications.

Vasoactive intestinal peptide-expressing interneurons modulate the effect of behavioral state on cortical activity.

Animals live in a complex and changing environment with various degrees of behavioral demands. Behavioral states affect the activity of cortical neurons and the dynamics of neuronal populations, however not much is known about the cortical circuitry behind the modulation of neuronal activity across behavioral states. Here we show that a class of GABAergic inhibitory interneurons that express vasoactive intestinal peptide-expressing interneurons (VIP), namely VIP interneurons, play a key role in the circuits involved in the modulation of cortical activity by behavioral state, as reflected in the mice facial motion. We show that inhibition of VIP interneurons reduces the correlated activity between the behavioral state of the animal and the spiking of individual neurons. We also show that VIP inhibition during the quiet state decreases the synchronous spiking of the neurons but increases delta power and phase locking of spiking to the delta-band activity. Taken together our data show that VIP interneurons modulate the behavioral state-dependency of cortical activity across different time scales.

Scalable Multi-FPGA HPC Architecture for Associative Memory System.

Associative memory is a cornerstone of cognitive intelligence within the human brain. The Bayesian confidence propagation neural network (BCPNN), a cortex-inspired model with high biological plausibility, has proven effective in emulating high-level cognitive functions like associative memory. However, the current approach using GPUs to simulate BCPNN-based associative memory tasks encounters challenges in latency and power efficiency as the model size scales. This work proposes a scalable multi-FPGA high performance computing (HPC) architecture designed for the associative memory system. The architecture integrates a set of hypercolumn unit (HCU) computing cores for intra-board online learning and inference, along with a spike-based synchronization scheme for inter-board communication among multiple FPGAs. Several design strategies, including population-based model mapping, packet-based spike synchronization, and cluster-based timing optimization, are presented to facilitate the multi-FPGA implementation. The architecture is implemented and validated on two Xilinx Alveo U50 FPGA cards, achieving a maximum model size of 200×10 and a peak working frequency of 220 MHz for the associative memory system. Both the memory-bounded spatial scalability and compute-bounded temporal scalability of the architecture are evaluated and optimized, achieving a maximum scale-latency ratio (SLR) of 268.82 for the two-FPGA implementation. Compared to a two-GPU counterpart, the two-FPGA approach demonstrates a maximum latency reduction of 51.72× and a power reduction exceeding 5.28× under the same network configuration. Compared with the state-of-the-art works, the two-FPGA implementation exhibits a high pattern storage capacity for the associative memory task.

Self-pulsation and synchronization of optical neurons based on microrings

Driven by the growing research enthusiasm for neuromorphic photonics, optical neurons mimicking biological properties based on microresonant cavities and lasers are attracting increasing attention, in which spiking synchronization of couple networks consisting of optical neurons based on lasers has been systematically investigated, and the emergence of neural network with biological properties based on microrings is foreseen. In this paper, considering the importance of oscillatory synchronization between neurons to achieve the basic biological functions, we investigate the period of self-pulsation which is fundamental oscillation within microring as a function of input light power and detuning, and achieve self-pulsation period synchronization of two mutually coupled microrings. In addition, the self-pulsation synchronization in time series between the unidirectionally coupled driving microring and responding microring is achieved by adding delayed feedback and adjusting key parameters, and this synchronization still exists even with the inclusion of more responding microrings. Our work provides basic investigations of the self-pulsation period as well as synchronization properties between coupled optical neurons based on microrings, and would be helpful for different concepts of neuromorphic computing based on microring, including artificial neural networks (ANN), spike neural networks (SNN) and reservoir computing.

The Interplay of Common Noise and Finite Pulses on Biological Neurons

The response of neurons is highly sensitive to the stimulus. The stimulus can be associated with a direct injection in vitro experimentation (e.g., time dependent and independent inputs); or post-synaptic potentials resulting from the interaction of many neurons. A typical incoming stimulus resembles a noise which in principle can be described as a random variable. In computational neuroscience, the noise has been extensively studied for different setups. In this study, we investigate the effect of noisy inputs in a minimal network of two identical leaky integrate-and-fire (LIF) neurons interacting with finite pulses. In particular, we consider a Gaussian white noise as a standard function for stochastic modelling of neurons, while taking into account the pulse width as an elementary component for the signal transmission. By exploring the role of noise and finite pulses, the two neurons show a synchronous spiking behaviour characterized by fluctuations in the interspike intervals. Above some critical values the synchronous regime collapses onto asynchronous dynamics. The abrupt change in such dynamics is accompanied by a hysteresis, i.e., the coexistence of synchronous and asynchronous firing behaviour.

Bilateral and synchronous “dents de scie” spikes: A highly specific EEG pattern of young adult Dravet syndrome

BackgroundIn Dravet syndrome (DS), EEGs evolve over time. ObjectiveTo describe a peculiar EEG pattern in two adults with a de novo SCN1A gene mutation, in exon 5 (case 1) and 9 (case 2). MethodsTwo female patients underwent a prolonged video EEG (24 h) as part of their epilepsy assessment. ResultsIn both cases, the EEG showed a very peculiar and stereotypical pattern of bilateral synchronous spikes at about 5–6 Hz. This activity was present during wakefulness and highly activated at sleep onset and in NREM sleep, which could show nearly continuous spike activity. This activity dramatically decreased in REM sleep and after awakening. This pattern of “dents de scie” (sawtooth) spikes maintained the same morphology throughout the entire EEG recording. In both patients, the spikes were favored by passive eye closure. During wakefulness, the spikes could evolve into atypical absences while keeping the same “dents de scie” pattern. Neither patient had tonic or myoclonic seizures at the time of the EEG assessment. Both were moderately retarded, and neither one had a typical DS gait disorder. Previous EEG recordings of case 1 performed at 9.5 and 18.5 years showed spike-waves, but the morphology did not correspond to the EEG recording observed at 22 years. ConclusionsBoth patients have a similar electro-clinical phenotype. This “dents de scie” pattern appears to be very specific and could be pathognomonic in a subgroup of young adults with DS. Results of sleep EEG recording could be added to the diagnostic criteria for this syndrome.

Transsynaptic entrainment of cerebellar nuclear cells by alternating currents in a frequency dependent manner.

Transcranial alternating current stimulation (tACS) is a non-invasive neuromodulation technique that is being tested clinically for treatment of a variety of neural disorders. Animal studies investigating the underlying mechanisms of tACS are scarce, and nearly absent in the cerebellum. In the present study, we applied 10-400 Hz alternating currents (AC) to the cerebellar cortex in ketamine/xylazine anesthetized rats. The spiking activity of cerebellar nuclear (CN) cells was transsynaptically entrained to the frequency of AC stimulation in an intensity and frequency-dependent manner. Interestingly, there was a tuning curve for modulation where the frequencies in the midrange (100 and 150 Hz) were more effective, although the stimulation frequency for maximum modulation differed for each CN cell with slight dependence on the stimulation amplitude. CN spikes were entrained with latencies of a few milliseconds with respect to the AC stimulation cycle. These short latencies and that the transsynaptic modulation of the CN cells can occur at such high frequencies strongly suggests that PC simple spike synchrony at millisecond time scales is the underlying mechanism for CN cell entrainment. These results show that subthreshold AC stimulation can induce such PC spike synchrony without resorting to supra-threshold pulse stimulation for precise timing. Transsynaptic entrainment of deep CN cells via cortical stimulation could help keep stimulation currents within safety limits in tACS applications, allowing development of tACS as an alternative treatment to deep cerebellar stimulation. Our results also provide a possible explanation for human trials of cerebellar stimulation where the functional impacts of tACS were frequency dependent.

Transitions to synchronization induced by synaptic increasing in coupled tonic neurons with electrical synapses

Experimental evidence had suggested that the net synaptic strength of nervous system increases during wakefulness. By using a model of coupled tonic neurons, we studied transitions to synchronization induced by synaptic increasing. Our findings revealed that in the presence of electrical synapses, synaptic increasing initially leads to a transition from desynchronized tonic activity (DTA) to desynchronized bursting activity (DBA). This DBA then transitions towards bursting activity with burst synchronization (BABS), followed by bursting activity with spike synchronization (BASS). As the synaptic strength continues to increase, there is a transition from BASS to approximately synchronized tonic activity (ASTA) once the synaptic strength reaches a critical value. Finally, completely synchronized tonic activity (CSTA) emerges when the synaptic strength reaches another critical value. Notably, the values of these two critical synaptic strengths, for ASTA and CSTA respectively, depend solely on the absolute value of the second largest eigenvalue of network coupling matrix. Through numerical and analytical methods, we demonstrated that both of these critical values and the absolute value of the eigenvalue follow a power-law relationship with exponent −1. These results suggested that the transitions to synchronization driven by synaptic increasing are a multi-time-scale phenomenon consisting of six stages: DTA, DBA, BABS, BASS, ASTA, and CSTA. We also briefly discussed the meaningful implications of sleep deprivation and the further challenging investigations that arise from these findings.

Investigating the ability of astrocytes to drive neural network synchrony.

Recent experimental works have implicated astrocytes as a significant cell type underlying several neuronal processes in the mammalian brain, from encoding sensory information to neurological disorders. Despite this progress, it is still unclear how astrocytes are communicating with and driving their neuronal neighbors. While previous computational modeling works have helped propose mechanisms responsible for driving these interactions, they have primarily focused on interactions at the synaptic level, with microscale models of calcium dynamics and neurotransmitter diffusion. Since it is computationally infeasible to include the intricate microscale details in a network-scale model, little computational work has been done to understand how astrocytes may be influencing spiking patterns and synchronization of large networks. We overcome this issue by first developing an "effective" astrocyte that can be easily implemented to already established network frameworks. We do this by showing that the astrocyte proximity to a synapse makes synaptic transmission faster, weaker, and less reliable. Thus, our "effective" astrocytes can be incorporated by considering heterogeneous synaptic time constants, which are parametrized only by the degree of astrocytic proximity at that synapse. We then apply our framework to large networks of exponential integrate-and-fire neurons with various spatial structures. Depending on key parameters, such as the number of synapses ensheathed and the strength of this ensheathment, we show that astrocytes can push the network to a synchronous state and exhibit spatially correlated patterns.