Spiking Activity Of Neurons Research Articles

Triggering a Closed-Loop Stimulation by Neuron Spiking Activity

Triggering a Closed-Loop Stimulation by Neuron Spiking Activity

Fast gradient-free activation maximization for neurons in spiking neural networks

Elements of neural networks, both biological and artificial, can be described by their selectivity for specific cognitive features. Understanding these features is important for understanding the inner workings of neural networks. For a living system, such as a neuron, whose response to a stimulus is unknown and not differentiable, the only way to reveal these features is through a feedback loop that exposes it to a large set of different stimuli. The properties of these stimuli should be varied iteratively in order to maximize the neuronal response. To utilize this feedback loop for a biological neural network, it is important to run it quickly and efficiently in order to reach the stimuli that maximizes certain neurons’ activation with the least number of iterations possible. Here we present a framework with an efficient design for such a loop. We successfully tested it on an artificial spiking neural network (SNN), which is a model that simulates the asynchronous spiking activity of neurons in living brains. Our optimization method for activation maximization is based on the low-rank Tensor Train decomposition of the discrete activation function. The optimization space is the latent parameter space of images generated by SN-GAN or VQ-VAE generative models. To our knowledge, this is the first time that effective AM has been applied to SNNs. We track changes in the optimal stimuli for artificial neurons during training and show that highly selective neurons can form already in the early epochs of training and in the early layers of a convolutional spiking network. This formation of refined optimal stimuli is associated with an increase in classification accuracy. Some neurons, especially in the deeper layers, may gradually change the concepts they are selective for during learning, potentially explaining their importance for model performance. The source code of our framework, MANGO (for Maximization of neuronal Activation via Non-Gradient Optimization) is available on GitHub (https://github.com/iabs-neuro/mango)

CNSC-64. BEYOND THE LOCAL TUMOR MICROENVIRONMENT: CHARACTERIZING CHANGES TO NEURONAL ACTIVITY INDUCED BY PEDIATRIC HIGH-GRADE GLIOMA

Abstract Bidirectional electrochemical signaling between neurons and tumor cells is emerging as a critical regulator of pediatric high-grade glioma (HGG) pathology. Neuron-tumor interactions are known to induce local neuronal hyperactivity. However, recent work tracing the projections of these neuron-tumor networks revealed that HGG neuronal recruitment extends beyond the local tumor microenvironment (TME). These findings suggest that tumor-induced hyperexcitability may spread beyond the local TME and drive brain-wide circuit-level changes. To elucidate how non-tumor innervated brain regions are affected by TME hyperactivity we first characterized neuronal hyperactivity induced by a cortical patient-derived pediatric high-grade cell line in vitro on human iPSC-derived neurons. In vitro calcium imaging experiments demonstrated an increase in the number of calcium fluctuations in glioma co-cultured neurons. In parallel, multi-electrode recordings revealed increased population spiking activity and amplitude in neurons co-cultured with glioma cells. We then xenografted these HGG cells into the prefrontal cortex of mice and performed whole brain-clearing and cFos staining on tumor-bearing vs saline-injected control samples. As cFos is an immediate early gene activated by neuronal activity, we utilized the density of cFos expression as a readout-out of neuronal activation in our atlas-aligned segmented brain section analyses. We expectedly observed increased cFos expression within the ipsilateral primary and secondary motor region, which collectively comprise the local TME. Interestingly, we additionally observed elevated cFos in non-tumor bearing brain regions on both the ipsilateral and contralateral hemispheres, largely in layer 2/3 cortical neurons. These neurons form extensive cortico-cortical projections, and we coincidingly observed increased cFos expression in known cortical projection targets of the prefrontal xenograft location, such as somatosensory, visual, and retrosplenial cortices. Our findings suggest that tumor-induced neuronal hyperactivity extends beyond the local TME in a brain-wide manner and potentially follows established neuronal circuits, rendering these connected regions vulnerable to alterations in neuronal activity.

Intermittent rate coding and cue-specific ensembles support working memory.

Persistent, memorandum-specific neuronal spiking activity has long been hypothesized to underlie working memory1,2. However, emerging evidence suggests a potential role for 'activity-silent' synaptic mechanisms3-5. This issue remains controversial because evidence for either view has largely relied eitheron datasets that fail to capture single-trial population dynamics or onindirect measures of neuronal spiking. We addressed this controversyby examining the dynamics of mnemonic information on single trials obtained from large, local populations of lateral prefrontal neurons recorded simultaneously in monkeys performing a working memory task. Here we show that mnemonic information does not persist in the spiking activity of neuronal populations during memory delays, but instead alternates between coordinated 'On' and 'Off' states. At the level of single neurons, Off states are drivenby both a loss of selectivity for memoranda and a return of firing rates to spontaneous levels. Further exploiting the large-scale recordings used here, we show that mnemonic information is available in the patterns of functional connections among neuronal ensembles during Off states. Our results suggest that intermittent periods of memorandum-specific spiking coexist with synaptic mechanisms to support working memory.

Understanding novel neuromodulation pathways in tDCS: brain stem recordings in rats during trigeminal nerve direct current stimulation

tDCS is widely assumed to cause neuromodulation via the electric field in the cortex acting directly on cortical neurons. However, recent evidence suggests that tDCS may indirectly influence brain activity through cranial nerve pathways, notably the trigeminal nerve, but these neuromodulatory pathways remain unexplored. To investigate the first stages in this potential pathway we developed an animal model to study the effect of trigeminal nerve direct current stimulation (TN-DCS) on neuronal activity in the principal sensory nucleus (NVsnpr) and the mesencephalic nucleus of the trigeminal nerve (MeV). We conducted experiments on twenty-four male Sprague Dawley rats (n = 10 NVsnpr, n = 10 MeV during anodic stimulation, and n = 4 MeV during cathodic stimulation). DC stimulation, ranging from 0.5 to 3 mA, targeted the trigeminal nerve’s marginal branch. Concurrently, single-unit electrophysiological recordings were obtained using a 32-channel silicon probe, encompassing three 1-min intervals: pre, during, and post-stimulation. Xylocaine trigeminal nerve blockage served as a control. TN-DCS increased neuronal spiking activity in both NVsnpr and MeV, returning to baseline during the post-stimulation phase. The 3 mA DC stimulation of the blocked trigeminal nerve failed to induce increased spiking activity in the trigeminal nuclei. These findings provide empirical support for trigeminal nuclei modulation via TN-DCS, suggesting the cranial nerve pathways could play a role in mediating the tDCS effects in humans.

Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential.

As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics. Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity. A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity. Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.

Spike Count Analysis for MultiPlexing Inference (SCAMPI).

Understanding how neurons encode multiple simultaneous stimuli is a fundamental question in neuroscience. We have previously introduced a novel theory of stochastic encoding patterns wherein a neuron's spiking activity dynamically switches among its constituent single-stimulus activity patterns when presented with multiple stimuli (Groh et al., 2024). Here, we present an enhanced, comprehensive statistical testing framework for such " multiplexing " or " code juggling ". Our new approach evaluates whether dual-stimulus responses can be accounted for as mixtures of Poissons either anchored to or bounded by single-stimulus benchmarks. Our enhanced framework improves upon previous methods in two key ways. First, it introduces a stronger set of foils for multiplexing, including an "overreaching" category that captures overdispersed activity patterns unrelated to the single-stimulus benchmarks, reducing false detection of multiplexing/code-juggling. Second, it detects faster fluctuations - i.e. at sub-trial timescales - that would have been overlooked before. We utilize a Bayesian inference framework, considering the hypothesis with the highest posterior probability as the winner, and employ predictive recursion marginal likelihood method for the involving nonparametric density estimation. Reanalysis of previous findings confirms the general observation of "code juggling" and indicates that such juggling may well occur on faster timescales than previously suggested. We further confirm that juggling is more prevalent in (a) the inferotemporal face patch system for combinations of face stimuli than for faces and non-face objects; and (b) the primary visual cortex for distinct vs fused objects.

Purkinje cells in Crus I and II encode the visual stimulus and the impending choice as monkeys learn a reinforcement based visuomotor association task.

Visuomotor association involves linking an arbitrary visual cue to a well-learned movement. Transient inactivation of Crus I/II impairs primates' ability to learn new associations and delays motor responses without affecting the kinematics of the movement. The simple spikes of Purkinje cells in the Crus regions signal cognitive errors as monkeys learn to associate specific fractal stimuli with movements of the left or right hand. Here we show that as learning progresses, the simple spike activity of individual neurons becomes more selective for stimulus-response associations, with selectivity developing closer to the appearance of visual stimuli. Initially, most neurons respond to both associations, irrespective of the identity of the stimulus and the associated movement, but as learning advances, more neurons distinguish between specific stimulus-hand associations. Using a linear decoder, it was found that in early learning stages, the visual stimulus can be decoded only when the choice can also be decoded. As learning improves, the visual stimulus is decoded earlier than the choice. A simple model can replicate the observed simple spike signals and the monkeys' behavior in both the early and late learning stages.

Functional segregation and dynamic integration of the corticotectal descending signal in rat

The superior colliculus (SC) receives inputs from various brain regions in a layer- and radial subregion-specific manner, but whether the SC exhibits subregion-specific dynamics remains unclear. To address this issue, we recorded the spiking activity of single SC neurons while photoactivating cortical areas in awake head-fixed Thy1-ChR2 rats. We classified 309 neurons that responded significantly into 8 clusters according to the response dynamics. Among them, neurons with monophasic excitatory responses (7–12 ms latency) that returned to baseline within 20 ms were commonly observed in the optic and intermediate gray layers of centromedial and centrolateral SC. In contrast, neurons with complex polyphasic responses were commonly observed in the deep layers of the anterolateral SC. Cross-correlation analysis suggested that the complex pattern could be only partly explained by an internal circuit of the deep gray layer. Our results indicate that medial to centrolateral SC neurons simply relay cortical activity, whereas neurons in the deep layers of the anterolateral SC dynamically integrate inputs from the cortex, SNr, CN, and local circuits. These findings suggest a spatial gradient in SC integration, with a division of labor between simple relay circuits and those integrating complex dynamics.

Localized and Long-Lasting Adaptation in Dragonfly Target-Detecting Neurons.

Some visual neurons in the dragonfly (Hemicordulia tau) optic lobe respond to small, moving targets, likely underlying their fast pursuit of prey and conspecifics. In response to repetitive targets presented at short intervals, the spiking activity of these "small target motion detector" (STMD) neurons diminishes over time. Previous experiments limited this adaptation by including intertrial rest periods of varying durations. However, the characteristics of this effect have never been quantified. Here, using extracellular recording techniques lasting for several hours, we quantified both the spatial and temporal properties of STMD adaptation. We found that the time course of adaptation was variable across STMD units. In any one STMD, a repeated series led to more rapid adaptation, a minor accumulative effect more akin to habituation. Following an adapting stimulus, responses recovered quickly, though the rate of recovery decreased nonlinearly over time. We found that the region of adaptation is highly localized, with targets displaced by ∼2.5° eliciting a naive response. Higher frequencies of target stimulation converged to lower levels of sustained response activity. We determined that adaptation itself is a target-tuned property, not elicited by moving bars or luminance flicker. As STMD adaptation is a localized phenomenon, dependent on recent history, it is likely to play an important role in closed-loop behavior where a target is foveated in a localized region for extended periods of the pursuit duration.

Spiking Laguerre Volterra networks—predicting neuronal activity from local field potentials

Objective. Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations. Approach. Here, we fill this gap by proposing novel spiking Laguerre–Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics. Main results. The proposed networks were applied on extracellular microelectrode recordings of Parkinson’s Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features. Significance. These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

Cerebellar nuclei cells produce distinct pathogenic spike signatures in mouse models of ataxia, dystonia, and tremor.

The cerebellum contributes to a diverse array of motor conditions, including ataxia, dystonia, and tremor. The neural substrates that encode this diversity are unclear. Here, we tested whether the neural spike activity of cerebellar output neurons is distinct between movement disorders with different impairments, generalizable across movement disorders with similar impairments, and capable of causing distinct movement impairments. Using in vivo awake recordings as input data, we trained a supervised classifier model to differentiate the spike parameters between mouse models for ataxia, dystonia, and tremor. The classifier model correctly assigned mouse phenotypes based on single-neuron signatures. Spike signatures were shared across etiologically distinct but phenotypically similar disease models. Mimicking these pathophysiological spike signatures with optogenetics induced the predicted motor impairments in otherwise healthy mice. These data show that distinct spike signatures promote the behavioral presentation of cerebellar diseases.

Local activation of CB1 receptors by synthetic and endogenous cannabinoids dampens burst firing mode of reticular thalamic nucleus neurons in rats under ketamine anesthesia.

The reticular thalamic nucleus (RTN) is a thin shell that covers the dorsal thalamus and controls the overall information flow from the thalamus to the cerebral cortex through GABAergic projections that contact thalamo-cortical neurons (TC). RTN neurons receive glutamatergic afferents fibers from neurons of the sixth layer of the cerebral cortex and from TC collaterals. The firing mode of RTN neurons facilitates the generation of sleep-wake cycles; a tonic mode or desynchronized mode occurs during wake and REM sleep and a burst-firing mode or synchronized mode is associated with deep sleep. Despite the presence of cannabinoid receptors CB1 (CB1Rs) and mRNA that encodes these receptors in RTN neurons, there are few works that have analyzed the participation of endocannabinoid-mediated transmission on the electrical activity of RTN. Here, we locally blocked or activated CB1Rs in ketamine anesthetized rats to analyze the spontaneous extracellular spiking activity of RTN neurons. Our results show the presence of a tonic endocannabinoid input, since local infusion of AM 251, an antagonist/inverse agonist, modifies RTN neurons electrical activity; furthermore, local activation of CB1Rs by anandamide or WIN 55212-2 produces heterogeneous effects in the basal spontaneous spiking activity, where the main effect is an increase in the spiking rate accompanied by a decrease in bursting activity in a dose-dependent manner; this effect is inhibited by AM 251. In addition, previous activation of GABA-A receptors suppresses the effects of CB1Rs on reticular neurons. Our results show that local activation of CB1Rs primarily diminishes the burst firing mode of RTn neurons.

Somatodendritic orientation determines tDCS-induced neuromodulation of Purkinje cell activity in awake mice.

Transcranial direct-current stimulation (tDCS) of the cerebellum is a promising non-invasive neuromodulatory technique being proposed for the treatment of neurological and neuropsychiatric disorders. However, there is a lack of knowledge about how externally applied currents affect neuronal spiking activity in cerebellar circuits in vivo. We investigated how Cb-tDCS affects the firing rate of Purkinje cells (PC) and non-PC in the mouse cerebellar cortex to understand the underlying mechanisms behind the polarity-dependent modulation of neuronal activity induced by tDCS. Mice (n = 9) were prepared for the chronic recording of LFPs to assess the actual electric field gradient imposed by Cb-tDCS in our experimental design. Single-neuron extracellular recording of PCs in awake (n = 24) and anesthetized (n = 27) mice was combined with juxtacellular recordings and subsequent staining of PC with neurobiotin under anesthesia (n = 8) to correlate their neuronal orientation with their response to Cb-tDCS. Finally, a high-density Neuropixels recording system was used to demonstrate the relevance of neuronal orientation during the application of Cb-tDCS in awake mice (n = 6). In this study, we observe that Cb-tDCS induces a heterogeneous polarity-dependent modulation of the firing rate of Purkinje cells (PC) and non-PC in the mouse cerebellar cortex. We demonstrate that the apparently heterogeneous effects of tDCS on PC activity can be explained by taking into account the somatodendritic orientation relative to the electric field. Our findings highlight the need to consider neuronal orientation and morphology to improve tDCS computational models, enhance stimulation protocol reliability, and optimize effects in both basic and clinical applications.

A population of neurons selective for human voice in the monkey brain

Many animals can extract useful information from the vocalizations of other species. Neuroimaging studies have evidenced areas sensitive to conspecific vocalizations in the cerebral cortex of primates, but how these areas process heterospecific vocalizations remains unclear. Using fMRI-guided electrophysiology, we recorded the spiking activity of individual neurons in the anterior temporal voice patches of two macaques while they listened to complex sounds including vocalizations from several species. In addition to cells selective for conspecific macaque vocalizations, we identified an unsuspected subpopulation of neurons with strong selectivity for human voice, not merely explained by spectral or temporal structure of the sounds. The auditory representational geometry implemented by these neurons was strongly related to that measured in the human voice areas with neuroimaging and only weakly to low-level acoustical structure. These findings provide new insights into the neural mechanisms involved in auditory expertise and the evolution of communication systems in primates.

Exact Analysis of the Subthreshold Variability for Conductance-Based Neuronal Models with Synchronous Synaptic Inputs.

The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state, neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically, we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects postspiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime yields realistic subthreshold variability (voltage variance ≃4-9 mV2) only when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that, without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.

Blood pressure pulsations modulate central neuronal activity via mechanosensitive ion channels.

The transmission of the heartbeat through the cerebral vascular system causes intracranial pressure pulsations. We discovered that arterial pressure pulsations can directly modulate central neuronal activity. In a semi-intact rat brain preparation, vascular pressure pulsations elicited correlated local field oscillations in the olfactory bulb mitral cell layer. These oscillations did not require synaptic transmission but reflected baroreceptive transduction in mitral cells. This transduction was mediated by a fast excitatory mechanosensitive ion channel and modulated neuronal spiking activity. In awake animals, the heartbeat entrained the activity of a subset of olfactory bulb neurons within ~20 milliseconds. Thus, we propose that this fast, intrinsic interoceptive mechanism can modulate perception-for example, during arousal-within the olfactory bulb and possibly across various other brain areas.

Effects of kappa-opioid agonist U-50488 and p38 MAPK inhibitor SB203580 on the spike activity of pyramidal neurons in the basolateral amygdala

Introduction: Kappa-opioid receptor (KOR) signaling in the basolateral amygdala (BLA) underlies KOR agonist-induced aversion. In this study, we aimed to understand the individual and combined effects of KOR agonist U-50488 and p38 MAPK inhibitor SB203580 on the spiking activity of pyramidal neurons in the BLA to shed light on the complex interplay between KORs, the p38 MAPK, and neuronal excitability. Materials and Methods: Electrophysiological experiments were performed using the patch-clamp technique in the whole-cell configuration. Rat brain slices containing the amygdala were prepared, and pyramidal neurons within the BLA were visually patched and recorded in the current clamp mode. The neurons were identified by their accommodation properties and neural activity signals were amplified and analyzed. Using local perfusion, we obtained three dose-response curves for: (a) U-50488 (0.001–10 μM); (b) U-50488 (0.001–10 μM) in the presence of SB203580 (1 μM); and (c) U-50488 (0.01–10 μM) in the presence of SB203580 (5 μM). Results: After the application of U-50488, pyramidal neurons had a higher action potential firing rate in response to a current injection than control neurons (p<0.001). The dose-dependent curves we obtained indicate that the combination of U-50488 and SB203580 results in non-competitive antagonism. This conclusion is supported by the observed change in the curve’s slope with reduction in the maximum effect of U-50488. Thus, it can be assumed that the increase in spike activity of pyramidal neurons of the amygdala is mediated through the beta-arrestin pathway. When this pathway is blocked, the spike activity reverts to its baseline level. Conclusion: Our study found that the KOR agonist-induced spiking activity of the BLA pyramidal neurons is mediated by the beta-arrestin pathway and can be suppressed by the application of the p38 MAPK inhibitor SB203580.

Electrophysiological effects of kappa-opioid analgesic, RU-1205, using machine learning methods

The study is focused to the investigation of a new kappa-opioid agonist RU-1205, which exhibits an analgesic effect without causing dysphoric or aversive actions. It is assumed that this effects may be due to its functional selectivity, or the presence of an additional mechanism of action that involves blocking p38 mitogen-activated protein kinase (MAPK).The aim of the study was an experimental identification of RU-1205 mechanisms of action associated with the inhibition of MAPK p38 and functional selectivity for kappa opioid receptors.Materials and methods. The LFP activity was recorded in the male rats weighing 260–280 g (n=62) and implanted with chronic cortical and deep electrodes, after the intracerebroventricular administration of the well-studied reference substances: the selective kappa-opioid agonist U-50488 100 μg; the MAPK p38 blocker SB203580 1 μg; and the investigational compound RU-1205 at 350 μg. The weighted phase lag index (WPLI) was calculated. Subsequently, machine learning methods were employed to reduce the dimensionality and extract connectivity features using the principal component analysis method, then a signal classification was performed (models based on Gaussian processes). Using the local patch-clamp technique in the “whole-cell” configuration, the spike activity of pyramidal neurons in the basolateral amygdala was studied. Neurons were identified by their accommodation properties. After local perfusion of the test compounds, 3 dose-response curves were obtained for: (1) U-50488 at concentrations ranging from 0.001 to 10 μM; (2) combinations of U-50488 (0.001–10 μM) and RU-1205 (10 μM); and (3) the combinations of U-50488 (0.01–10 μM) and RU-1205 (100 μM).Results. The developed models made it possible to classify the compound RU-1205 as a “non-inhibitor” of MAPK p38 with a high probability. The results obtained were confirmed in patch-clamp experiments on acute brain slices where it was demonstrated that U-50488 statistically significantly increases the spike activity of pyramidal neurons of the basolateral amygdala (p <0.05), and RU-1205 interacts with U-50488, competitively suppressing its effect on the spike activity of neurons.Conclusion. The findings suggest that compound RU-1205 displays properties consistent with a functional kappa agonist activity and does not have a significant effect on MAPK p38. The study demonstrates the possibility of integrating electrophysiological measurements and advanced data analysis methods for a deep understanding of drug action and underscores the potential for further research in this area.

Influence of coupling topology and noise on the possibility of frequency tuning in ensembles of FitzHugh–Nagumo oscillators

Purpose. The study focuses on analyzing spike activity and synchronization in ensembles of FitzHugh-Nagumo neurons, both with and without external noise excitation. These networks can induce oscillations at different frequencies, depending on the excitability parameter of individual elements and the coupling strength between them. Additionally, variations in these parameters can lead to synchronization among the elements. The research investigates the dynamics of both a single-layer network, which includes a common element, and a three-layer network with an intermediate neuron-hub layer. Methods. To analyze the dynamics of the networks under investigation, we calculate the time-averaged spike frequencies of all elements. These frequencies are then averaged for each outer layer and compared with the frequency of the central element, as well as with each other in the case of a multilayer network. In order to assess the impact of coupling strength on the spike activity and synchronization of the network elements, we construct frequency distributions and frequency difference distributions in a plane of coupling strength coefficients. Results. It has been shown that small single-layer and three-layer networks of identical oscillators (FitzHugh-Nagumo neurons), with a simple coupling topology, can exhibit different spike activity in different parts of the system. In this case, the neurons transition to a self-oscillatory mode due to repulsive coupling between the elements. The research has established that in a single-layer network, a ring of elements can synchronize in frequency with the central element within a specific range of coupling strength values. In a three-layer system, layer synchronization can also be observed. Weak noise has minimal impact on the synchronization boundaries of all three layers, in terms of coupling parameters. However, as the noise intensity increases, this area decreases. At the same time, the noise leads to the emergence of a new synchronization region in which relay synchronization of the layers is observed in the absence of synchronization with the hub. Conclusion. The study explored the potential of exciting oscillations and achieving synchronization in single-layer and three-layer networks of coupled FitzHugh-Nagumo oscillators. The coupling strength between the elements varied in order to investigate its impact. Although the study only provided a broad understanding of the spike activity of excitable neurons in the two network models examined, it adequately demonstrated the crucial role of coupling in the spiking activity of these neurons.