Synaptic Conductance Research Articles

PEDOT:PSS Wire: A Two-Terminal Synaptic Device for Operation in Electrolyte and Saline Solutions.

Synaptic devices, which are designed to emulate the synaptic functions of neurons, have recently gained attention as key elements in the development of neuromorphic hardware. To date, most synaptic devices utilizing conductive polymer materials, particularly poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), have been designed as three-terminal devices. Nevertheless, a recent study revealed that a single PEDOT:PSS wire can function as a two-terminal synaptic device through additional polymerization, which creates asymmetry in the wire diameter between the anode and cathode. Owing to its high biocompatibility, PEDOT is considered a promising candidate for use in clinical information-processing devices. However, previous studies examined the synaptic function of PEDOT:PSS only in PSS solutions. Therefore, the performance of PEDOT:PSS wires in other solutions, such as physiological saline solutions, remains unknown. Herein, we investigated the effects of operating environmental conditions (including phosphate-buffered saline (PBS)) on the synaptic functions of the asymmetric PEDOT:PSS wire. Our results indicate that the synaptic conductance change in the PEDOT:PSS wire occurred in all investigated aqueous electrolyte solutions. Moreover, we revealed the relationship between the synaptic conductance change behavior and the molecular properties of the electrolyte ions. Furthermore, the waveform of the conductance change can be controlled by adjusting the conditions for wire asymmetrization. These results demonstrate that the PEDOT:PSS wire exhibits a synaptic conductance change, yielding a waveform suitable for machine learning, even under wet conditions (i.e., in any electrolyte solution, including PBS). Therefore, PEDOT:PSS wire is a promising material for two-terminal synaptic devices applicable in clinical studies.

Circuit function is more robust to changes in synaptic than intrinsic conductances.

Circuit function results from both intrinsic conductances of network neurons and the synaptic conductances that connect them. In models of neural circuits, different combinations of maximal conductances can give rise to similar activity. We compared the robustness of a neural circuit to changes in their intrinsic versus synaptic conductances. To address this, we performed a sensitivity analysis on a population of conductance-based models of the pyloric network from the crustacean stomatogastric ganglion (STG). The model network consists of three neurons with nine currents: a sodium current (Na), three potassium currents (Kd, KCa, A-type), two calcium currents (CaS and CaT), a hyperpolarization-activated current (H), a non-voltage-gated leak current (leak), and a neuromodulatory current (MI). The model cells are connected by seven synapses of two types, glutamatergic and cholinergic. We produced one hundred models of the pyloric network that displayed similar activities with values of maximal conductances distributed over wide ranges. We evaluated the robustness of each model to changes in their maximal conductances. We found that individual models have different sensitivities to changes in their maximal conductances, both in their intrinsic and synaptic conductances. As expected the models become less robust as the extent of the changes increase. Despite quantitative differences in their robustness, we found that in all cases, the model networks are more sensitive to the perturbation of their intrinsic conductances than their synaptic conductances.

Fractional-order memristive dynamics in colloidal graphitic carbon nitride systems.

We report on the synthesis and characterization of a colloidal graphitic carbon nitride (g-C_{3}N_{4}) system exhibiting complex memfractance behavior. The g-C_{3}N_{4} colloid was prepared through thermal polymerization of urea, followed by dispersion in deionized water. X-ray diffraction and scanning electron microscopy confirmed the successful synthesis of g-C_{3}N_{4}. Electrical characterization revealed nonpinched hysteresis loops in current-voltage curves, indicative of memristive behavior with additional capacitive components. The device demonstrated stable resistive switching between high (∼50kΩ) and low (∼22kΩ) impedance states over 500 cycles, as well as synaptic plasticity-like conductance modulation. To capture these complex dynamics, we employed a generalized memfractance model that interpolates between memristive, memcapacitive, and second-order memristive elements. This model, employing fractional-order derivatives, accurately fitted the experimental data, revealing the device's memory effects. The emergence of memfractance in this colloidal system opens new avenues for neuromorphic computing and unconventional information processing architectures, leveraging the unique properties of liquid-state memory devices.

Inference technique for the synaptic conductances in rhythmically active networks and application to respiratory central pattern generation circuits.

Unraveling synaptic interactions between excitatory and inhibitory interneurons within rhythmic neural circuits, such as central pattern generation (CPG) circuits for rhythmic motor behaviors, is critical for deciphering circuit interactions and functional architecture, which is a major problem for understanding how neural circuits operate. Here we present a general method for extracting and separating patterns of inhibitory and excitatory synaptic conductances at high temporal resolution from single neuronal intracellular recordings in rhythmically active networks. These post-synaptic conductances reflect the combined synaptic inputs from the key interacting neuronal populations and can reveal the functional connectome of the active circuits. To illustrate the applicability of our analytic technique, we employ our method to infer the synaptic conductance profiles in identified rhythmically active interneurons within key microcircuits of the mammalian (mature rat) brainstem respiratory CPG and provide a perspective on how our approach can resolve the functional interactions and circuit organization of these interneuron populations. We demonstrate the versatility of our approach, which can be applied to any other rhythmic circuits where conditions allow for neuronal intracellular recordings.

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.

Noise Enhances Excitability of a Neuronal Population with Heterogeneous Excitatory Neurons

AbstractCortical excitability is sensitive to noise perturbations despite homeostatic regulation. However, modeling the effect of noise in a heterogeneous neuronal population is a mathematical challenge. Using a mean‐field model of a randomly connected excitatory population, we investigated how noise affects excitability at the resting state. Stable equilibrium analysis revealed that increasing noise intensity enhances excitability, indexed by synaptic conductance. Further, this enhancement disappeared when excitability is sufficiently high due to strong mean input current, but not influenced by the connection probability of the population. Finally, the mechanism of noise‐induced enhancement was explained by eigenvalues moving toward the imaginary axis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Emergence of phase clusters and coexisting states reveals the structure-function relationship.

The Brain Connectome Project has made significant strides in uncovering the structural connections within the brain on various levels. This has led to the question of how brain structure and function are related. Our research explores this relationship in an adaptive neural network in which synaptic conductance between neurons follows spike-time synaptic plasticity rules. By adjusting the plasticity boundary, the network exhibits diverse collective behaviors, including phase synchronization, phase locking, hierarchical synchronization (phase clusters), and coexisting states. Using graph theory, we found that hierarchical synchronization is related to the community structure, while coexisting states are related to the hierarchical self-organizing and core-periphery structure. The network evolves into several tightly connected modules, with sparsely intermodule connections resulting in the formation of phase clusters. In addition, the hierarchical self-organizing structure facilitates the emergence of coexisting states. The coexistence state promotes the evolution of the core-periphery structure. Our results point towards the equivalence between function and structure, with function emerging from structure, and structure being influenced by function in a complex dynamic process.

A Mathematical Model of Spontaneous Action Potential Based on Stochastics Synaptic Noise Dynamics in Non-Neural Cells

We developed a mathematical model to simulate the dynamics of background synaptic noise in non-neuronal cells. By employing the stochastic Ornstein–Uhlenbeck process, we represented excitatory synaptic conductance and integrated it into a whole-cell model to generate spontaneous and evoke cellular electrical activities. This single-cell model encompasses numerous biophysically detailed ion channels, depicted by a set of ordinary differential equations in Hodgkin–Huxley and Markov formalisms. Consequently, this approach effectively induced irregular spontaneous depolarizations (SDs) and spontaneous action potentials (sAPs), resembling electrical activity observed in vitro. The input resistance decreased significantly, while the firing rate of spontaneous action potentials increased. Moreover, alterations in the ability to reach the action potential threshold were observed. Background synaptic activity can modify the input/output characteristics of non-neuronal excitatory cells. Hence, suppressing these baseline activities could aid in identifying new pharmaceutical targets for various clinical diseases.

Synaptic Characteristics of Fully Depleted Silicon‐on‐Insulator Metal‐Oxide‐Semiconductor Field‐Effect Transistors and Synapse‐Neuron Arrayed Neuromorphic Hardware System

A fully depleted silicon‐on‐insulator (FDSOI) metal‐oxide‐semiconductor field‐effect transistors (MOSFETs) device is investigated as for an electronic synapse emulating the synaptic functions of the human brain with stable characteristics. Gate‐last processed FDSOI MOSFET with a high‐k/metal gate stack features a memory window of 103. Synaptic conductance is stably regulated by utilizing the FDSOI MOSFET, which offers the advantage of mitigating leakage current when compared to bulk Si MOSFET. Short‐ and long‐term plasticity are investigated by applying engineered pulse, verifying the long‐term synaptic properties of pattern recognition processes. With controllable synaptic conductance, the trade‐off between conductance change and linearity regarding the recognition rate is evaluated, attaining a recognition rate of 0.83. To verify the pre‐ and post‐synaptic weights within a real hardware‐based neuromorphic system, 5 × 6 FDSOI field‐effect transistor (FET) synapse array is interconnected to 10 × 10 leaky integrate‐and‐fire (LIF) neuron array. The synaptic plasticity of FDSOI MOSFET in post‐neurons following neuron firing in the neuron device is successfully demonstrated. These results indicate that FDSOI MOSFET devices could be applicable as synapse devices due to controllability and capability to realize signal transmissions and self‐learning processes simultaneously and used to mimic a synapse neuron network system by configuring a hardware system interconnected with the LIF neuron.

Linear and nonlinear integrate-and-fire neurons driven by synaptic shot noise with reversal potentials.

The steady-state firing rate and firing-rate response of the leaky and exponential integrate-and-fire models receiving synaptic shot noise with excitatory and inhibitory reversal potentials is examined. For the particular case where the underlying synaptic conductances are exponentially distributed, it is shown that the master equationfor a population of such model neurons can be reduced from an integrodifferential form to a more tractable set of three differential equations. The system is nevertheless more challenging analytically than for current-based synapses: where possible, analytical results are provided with an efficient numerical scheme and code provided for other quantities. The increased tractability of the framework developed supports an ongoing critical comparison between models in which synapses are treated with and without reversal potentials, such as recently in the context of networks with balanced excitatory and inhibitory conductances.

Rapid changes in synchronizability in conductance-based neuronal networks with conductance-based coupling.

Real neurons connect to each other non-randomly. These connectivity graphs can potentially impact the ability of networks to synchronize, along with the dynamics of neurons and the dynamics of their connections. How the connectivity of networks of conductance-based neuron models like the classical Hodgkin-Huxley model or the Morris-Lecar model impacts synchronizability remains unknown. One powerful tool to resolve the synchronizability of these networks is the master stability function (MSF). Here, we apply and extend the MSF approach to networks of Morris-Lecar neurons with conductance-based coupling to determine under which parameters and for which graphs the synchronous solutions are stable. We consider connectivity graphs with a constant non-zero row sum, where the MSF approach can be readily extended to conductance-based synapses rather than the more well-studied diffusive connectivity case, which primarily applies to gap junction connectivity. In this formulation, the synchronous solution is a single, self-coupled, or "autaptic" neuron. We find that the primary determining parameter for the stability of the synchronous solution is, unsurprisingly, the reversal potential, as it largely dictates the excitatory/inhibitory potential of a synaptic connection. However, the change between "excitatory" and "inhibitory" synapses is rapid, with only a few millivolts separating stability and instability of the synchronous state for most graphs. We also find that for specific coupling strengths (as measured by the global synaptic conductance), islands of synchronizability in the MSF can emerge for inhibitory connectivity. We verified the stability of these islands by direct simulation of pairs of neurons coupled with eigenvalues in the matching spectrum.

Asynchronous and Slow-Wave Oscillatory States in Connectome-Based Models of Mouse, Monkey and Human Cerebral Cortex

Thanks to the availability of connectome data that map connectivity between multiple brain areas, it is now possible to build models of whole-brain activity. At the same time, advances in mean-field techniques have led to biologically based population models that integrate biophysical features such as membrane conductances or synaptic conductances. In this paper, we show that this approach can be used in brain-wide models of mice, macaques, and humans.We illustrate this approach by showing the transition from wakefulness to sleep, simulated using multi-scale models, in the three species. We compare the level of synchrony between the three species and find that the mouse brain displays a higher overall synchrony of slow waves compared to monkey and human brains. We show that these differences are due to the different delays in axonal signal propagation between regions associated with brain size differences between the species. We also make the program code—which provides a set of open-source tools for simulating large-scale activity in the cerebral cortex of mice, monkeys, and humans—publicly available.

Computational study of associations between the synaptic conductance of STN and GPe and the development of Parkinson's disease.

There is evidence that the subthalamic nucleus (STN) and globus pallidus pars externa (GPe) involve in the development of Parkinson's disease, a neurodegenerative disorder characterized by motor and non-motor symptoms and loss of dopaminergic neurons in which the error index (EI) in firing patterns is widely used to address the related issues. Whether and how this interaction mechanism of STN and GPe affects EI in Parkinson's disease is uncertain. To account for this, we propose a kind of basal ganglia-thalamic network model associated with Parkinson's disease coupled with neurons, and investigate the effect of synaptic conductance of STN and GPe on EI in this network, as well as their internal relationship under EI as an index. The results show a relationship like a piecewise function between the error index and the slope of the state transition function of synaptic conductance from STN to GPe ( ) and from GPe to STN ( ). And there is an approximate negative correlation between EI and . Increasing and decreasing can improve the fidelity of thalamus information transmission and alleviate Parkinson's disease effectively. These obtained results can give some theoretical evidence that the abnormal synaptic releases of STN and GPe may be the symptoms of the development of Parkinson's disease, and further enrich the understanding of the pathogenesis and treatment mechanism of Parkinson's disease.

Synaptic Characteristics of Atomic Layer-Deposited Ferroelectric Lanthanum-Doped HfO2 (La:HfO2) and TaN-Based Artificial Synapses.

Analog synaptic devices have made significant advances based on various electronic materials that can realize the biological synapse properties of neuromorphic computing. Ferroelectric (FE) HfO2-based materials with nonvolatile and low power consumption characteristics are being studied as promising materials for application to analog synaptic devices. The gradual reversal of FE multilevel polarization results in precise changes in the channel conductance and allows analogue synaptic weight updates. However, there have been few studies of FE synaptic devices doped with La, Y, and Gd. Furthermore, an investigation of interface quality is also crucial to enhance the remnant polarization (Pr), synaptic conductance linearity, and reliability characteristics. In this study, we demonstrate improved FE and artificial synaptic characteristics using an atomic layer-deposited (ALD) lanthanum-doped HfO2 (La:HfO2) and TaN electrode in the structure of an FE thin-film transistor (ITO/IGZO/La:HfO2/TaN), where indium-tin oxide (ITO) and indium-gallium-zinc oxide (IGZO) were used as source/drain and channel materials, respectively. Improved Pr and lower surface roughness were achieved by doped HfO2 and ALD TaN thin films. This synaptic transistor shows long-term potentiation and long-term depression with 200 levels of conductance states, high linearity (Ap, 0.97; Ad, 0.86), high Gmax/Gmin (∼6.1), and low cycle-to-cycle variability. In addition, a pattern recognition accuracy higher than 90% was achieved in an artificial neural network simulation.

On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networks

Overall balance of excitation and inhibition in cortical networks is central to their functionality and normal operation. Such orchestrated co-evolution of excitation and inhibition is established through convoluted local interactions between neurons, which are organized by specific network connectivity structures and are dynamically controlled by modulating synaptic activities. Therefore, identifying how such structural and physiological factors contribute to establishment of overall balance of excitation and inhibition is crucial in understanding the homeostatic plasticity mechanisms that regulate the balance. We use biologically plausible mathematical models to extensively study the effects of multiple key factors on overall balance of a network. We characterize a network’s baseline balanced state by certain functional properties, and demonstrate how variations in physiological and structural parameters of the network deviate this balance and, in particular, result in transitions in spontaneous activity of the network to high-amplitude slow oscillatory regimes. We show that deviations from the reference balanced state can be continuously quantified by measuring the ratio of mean excitatory to mean inhibitory synaptic conductances in the network. Our results suggest that the commonly observed ratio of the number of inhibitory to the number of excitatory neurons in local cortical networks is almost optimal for their stability and excitability. Moreover, the values of inhibitory synaptic decay time constants and density of inhibitory-to-inhibitory network connectivity are critical to overall balance and stability of cortical networks. However, network stability in our results is sufficiently robust against modulations of synaptic quantal conductances, as required by their role in learning and memory. Our study based on extensive bifurcation analyses thus reveal the functional optimality and criticality of structural and physiological parameters in establishing the baseline operating state of local cortical networks.

Inhibitory artificial synapses based on photoelectric co-modulation of graphene/WSe2 van der Waals heterojunctions

Optical artificial synapses possess several advantages, including high bandwidth, strong interference immunity, and ultra-fast signal transmission, overcoming the limitations of electrically stimulated synapses. Among various functional materials, 2D materials exhibit exceptional optical and electrical properties. By utilizing van der Waals heterostructures formed by these materials through rational design, synaptic devices can mimic the information perception ability of biological systems. This lays the foundation for low-energy artificial vision systems and neuromorphic computing. This study introduces an inhibitory artificial synapse based on photoelectric co-modulation of graphene/WSe2 van der Waals heterojunctions. By synergistically applying gate voltage and light pulses, we simulate memory and logic functions observed in the brain’s visual cortex. We achieve the construction of inhibitory synapses, enabling properties such as postsynaptic current response, short-term and long-term plasticity, and paired-pulse facilitation. Additionally, we accomplish the inverse recovery of device conductivity through separate gate voltage stimulation. Through bidirectional modulation of the artificial synaptic conductance, we construct an artificial hardware neural network that achieves 92.5% accuracy in recognizing handwritten digital images from the MNIST dataset. The network also has good recognition accuracy for handwritten digital images with different standard deviation Gaussian noise applied and other datasets. Furthermore, we successfully mimic the neural behavior of aversive learning for alcohol withdrawal in alcoholic patients using the device properties. The promising capabilities of artificial synapses constructed through electrical and optical synergistic modulation make them suitable for wearable electronics and artificial vision systems.

Single-compartment model of a pyramidal neuron, fitted to recordings with current and conductance injection.

For single neuron models, reproducing characteristics of neuronal activity such as the firing rate, amplitude of spikes, and threshold potentials as functions of both synaptic current and conductance is a challenging task. In the present work, we measure these characteristics of regular spiking cortical neurons using the dynamic patch-clamp technique, compare the data with predictions from the standard Hodgkin-Huxley and Izhikevich models, and propose a relatively simple five-dimensional dynamical system model, based on threshold criteria. The model contains a single sodium channel with slow inactivation, fast activation and moderate deactivation, as well as, two fast repolarizing and slow shunting potassium channels. The model quantitatively reproduces characteristics of steady-state activity that are typical for a cortical pyramidal neuron, namely firing rate not exceeding 30Hz; critical values of the stimulating current and conductance which induce the depolarization block not exceeding 80mV and 3, respectively (both values are scaled by the resting input conductance); extremum of hyperpolarization close to the midpoint between spikes. The analysis of the model reveals that the spiking regime appears through a saddle-node-on-invariant-circle bifurcation, and the depolarization block is reached through a saddle-node bifurcation of cycles. The model can be used for realistic network simulations, and it can also be implemented within the so-called mean-field, refractory density framework.

Propagation of Oscillations in the Indirect Pathway of the Basal Ganglia.

Oscillatory signals propagate in the basal ganglia from prototypic neurons in the external globus pallidus (GPe) to their target neurons in the substantia nigra pars reticulata (SNr), internal pallidal segment, and subthalamic nucleus. Neurons in the GPe fire spontaneously, so oscillatory input signals can be encoded as changes in timing of action potentials within an ongoing spike train. When GPe neurons were driven by an oscillatory current in male and female mice, these spike-timing changes produced spike-oscillation coherence over a range of frequencies extending at least to 100 Hz. Using the known kinetics of the GPe→SNr synapse, we calculated the postsynaptic currents that would be generated in SNr neurons from the recorded GPe spike trains. The ongoing synaptic barrage from spontaneous firing, frequency-dependent short-term depression, and stochastic fluctuations at the synapse embed the input oscillation into a noisy sequence of synaptic currents in the SNr. The oscillatory component of the resulting synaptic current must compete with the noisy spontaneous synaptic barrage for control of postsynaptic SNr neurons, which have their own frequency-dependent sensitivities. Despite this, SNr neurons subjected to synaptic conductance changes generated from recorded GPe neuron firing patterns also became coherent with oscillations over a broad range of frequencies. The presynaptic, synaptic, and postsynaptic frequency sensitivities were all dependent on the firing rates of presynaptic and postsynaptic neurons. Firing rate changes, often assumed to be the propagating signal in these circuits, do not encode most oscillation frequencies, but instead determine which signal frequencies propagate effectively and which are suppressed.SIGNIFICANCE STATEMENT Oscillations are present in all the basal ganglia nuclei, include a range of frequencies, and change over the course of learning and behavior. Exaggerated oscillations are a hallmark of basal ganglia pathologies, and each has a specific frequency range. Because of its position as a hub in the basal ganglia circuitry, the globus pallidus is a candidate origin for oscillations propagating between nuclei. We imposed low-amplitude oscillations on individual globus pallidus neurons at specific frequencies and measured the coherence between the oscillation and firing as a function of frequency. We then used these responses to measure the effectiveness of oscillatory propagation to other basal ganglia nuclei. Propagation was effective for oscillation frequencies as high as 100 Hz.

Heterogeneity of voltage gated sodium current density between neurons decorrelates spiking and suppresses network synchronization in Scn1b null mouse models

Voltage gated sodium channels (VGSCs) are required for action potential initiation and propagation in mammalian neurons. As with other ion channel families, VGSC density varies between neurons. Importantly, sodium current (INa) density variability is reduced in pyramidal neurons of Scn1b null mice. Scn1b encodes the VGSC β1/ β1B subunits, which regulate channel expression, trafficking, and voltage dependent properties. Here, we investigate how variable INa density in cortical layer 6 and subicular pyramidal neurons affects spike patterning and network synchronization. Constitutive or inducible Scn1b deletion enhances spike timing correlations between pyramidal neurons in response to fluctuating stimuli and impairs spike-triggered average current pattern diversity while preserving spike reliability. Inhibiting INa with a low concentration of tetrodotoxin similarly alters patterning without impairing reliability, with modest effects on firing rate. Computational modeling shows that broad INa density ranges confer a similarly broad spectrum of spike patterning in response to fluctuating synaptic conductances. Network coupling of neurons with high INa density variability displaces the coupling requirements for synchronization and broadens the dynamic range of activity when varying synaptic strength and network topology. Our results show that INa heterogeneity between neurons potently regulates spike pattern diversity and network synchronization, expanding VGSC roles in the nervous system.

Scaling down synaptic strength results in changes to the pattern of fictive breathing in a model of the brainstem respiratory network: medullary and pontine sources

Opioids remain a primary vehicle for pain management prescribed by clinicians. However, opioid-induced respiratory depression (OIRD) can cause apneas and be fatal at elevated doses. Two sites of action for these opioids have been suggested in the brainstem, one in the pons and the other in the preBötzinger Complex of the medulla. Additionally, there is electrophysiological evidence suggesting that an inward-rectifying K + current is opened upon μ-opioid receptor (μOR) activation causing neuronal hyperpolarizations. Activation of μORs may cause depletion of glutamate release from excitatory neurons. Our previous simulation efforts have demonstrated the plausibility of presynaptic actions of opioids among inspiratory neurons of the medulla and pons in depressing inspiratory drive or eliciting apnea within specific and singular synaptic connections. Here, we tested the effects of diminishing the strength of excitatory synaptic currents among several different classes of respiratory neurons in a large-scale model of the brainstem respiratory network and whether it would replicate OIRD. We simulated the simultaneous actions of μOR on multiple presynaptic sites among the medullary inspiratory network. We began by scaling down the synaptic conductances of target populations of medullary inspiratory neurons. The core of the model is made up of neurons that produce the fundamental inspiratory rhythm (“I-Driver” neurons), as well as neurons that normally have an inspiratory decrementing (“I-Dec”) or inspiratory augmenting (“I-Aug”) firing pattern and various expiratory phase firing patterns (“E-Dec,” “E-Aug,” “E-Tonic”). I-Driver neurons are analogous to medullary preBötzinger Complex neurons that putatively express μORs. In our model, these neurons project back onto other I-Driver neurons to promote recurrent excitation but also project onto I-Aug and I-Dec populations. Decreasing the synaptic strength from I-Driver neurons to these populations collectively slows fictive breathing until extended apnea occurs. To further understand how this apnea develops we systematically scaled down the synaptic strengths of I-Driver to each of the other specific populations and found that the excitatory connection from I-Driver to I-Dec populations is most influential in producing apnea. However, we also found that excitatory drive from tonic, non-respiratory modulated (NRM) neurons in the pons to the medulla could influence this pattern by grading down synaptic strength. In addition to any direct hyperpolarizing effects of opioids on μOR-expressing neurons, these data support the plausibility that OIRD could arise from changes due to glutamate release. This work was supported by the National Institutes of Health grant 1R01HL155721-01, 1R01HL163008, and T32HL134621. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.