The investigation of nonlinear processes in brain systems based on the oscillatory-wave approach is currently one of the most consequential domains of researching signal generation and processing mechanisms in the brain. These investigations may offer insights into various phenomena, such as the mechanism of associative memory, based on network models of biologically relevant neurons. A neural network architecture is proposed in this paper to solve applied problems, such as signal filtering, processing, and recognition of information images. Mechanisms for incomplete fragments were implemented, enabling the extraction and complete restoration of objects from memory. Each neuron in this neural network comprises a Hodgkin–Huxley biophysical model with a Mainen modification, the dynamics of which most plausibly reproduce the processes in the neural cells of the brain. In the research on individual neurons, a constant external current with varying amplitudes was applied. The study determined the average periods of self-oscillations and identified parameter values for the Andronov–Hopf bifurcation. In particular, a stable limit cycle is created and destroyed depending on different scenarios when the bias current is increased or decreased. A stable limit cycle arises via the Andronov–Hopf bifurcation and is terminated via the saddle-node bifurcation on the cycle. The study identified parameter regions whereby two neurons with excitatory and inhibitory synaptic connections synchronize. The neurons were all in self-oscillating mode and exhibited a stable limit cycle in the phase space. A three-layer neural network was constructed, comprising of a reference neuron, sensory, control, and interneurons. The control and intermediate layer connections are arranged based on the Hebbian learning rule. Areas of excitation for various types of neurons were found. Experiments were conducted using the neural network architecture to distinguish binary information patterns encoded through signal phase. Excitatory and inhibitory synaptic connections were employed to encode the images stored in memory. The dynamic patterns are determined through the in-phase or anti-phase mode of phase locking, i.e., phase synchronization relative to the global rhythm. Before reaching the output layer of neurons, the signals are summed on the interneuron layer with the signal of the reference neuron, resulting in the filtering out of a certain information segment (in-phase or anti-phase). The network accurately recognized the patterns after parameter adjustments were made. Areas of neuron phase synchronization were discovered, enabling control of network activity modes. The confirmation of the viability of phase capture and pattern recognition modes using Hodgkin–Huxley–Mainen neurons was achieved. In total, as a result of the implementation of the spike neural network modeling project: 1) two-parameter diagrams of regions of neuronal excitation in various modes are calculated; 2) the effect of excitatory and inhibitory coupling in synaptic currents is used to represent the input signal; 3) an abstract mathematical algorithm for calculating the Hebbian matrix has been transferred to the implementation of synaptic currents between layers of neurons; 4) the effect of phase clusters is used to represent the output pattern; 5) the neurons’ topology in the visual-cerebral department served as a working model for recognizing graphic images through the Hopfield network on a spike neural network, using the Hebbian rule.

The causes and mechanisms of most mental disorders remain unclear. While stress is known to trigger depressive and anxiety disorders, the role of genetically determined individual differences in forming vulnerability to post-stress neurochemical disorders, including those that affect neuroplasticity, remains unresolved. Early studies suggest that acute and chronic stress lead to elevated glucocorticoid levels in the bloodstream and alter the expression of glucocorticoid and mineralocorticoid receptors. Additionally, both forms of stress can decrease the levels of brain-derived neurotrophic factor (BDNF), which plays a role in neuronal growth, development, and synapse building [1]. However, current studies insufficiently explain the complete mechanism of the stress response as they only explore variations in post-stress alteration of gene expression for proteins related to differing nervous system functional characteristics. The strains of rats from the Biocollection of the Pavlov Institute of Physiology, RAS, are a convenient model for studying the impact of genetically determined nervous system features on stress reactivity. The strains, selected based on the threshold of nervous system excitability [2], include HT (high threshold of excitability, low excitability) and LT (low threshold of excitability, high excitability) strains. These strains display contrasting responses to extended emotional and painful stress and exhibit unique changes in brain structures implicated in emotional regulation and stress reactivity at the molecular, cellular, and epigenetic levels [2]. The aim of this study is to examine the mRNA levels of genes encoding glucocorticoid and mineralocorticoid receptors (NR3C1, NR3C2) and brain-derived neurotrophic factor (BDNF) in the hippocampus and blood of rat strains that exhibit contrasting nervous system excitability. The study investigates interstrain variations in animals under non-stressful conditions and changes in response to prolonged exposure to emotional and painful stimuli. The experiment utilized 96 animals, with 6 animals in both the control and experimental groups at each time point. The stress model utilized was long-term emotional and painful stress as per K. Gecht [2]. The study assessed the expression of genes for glucocorticoid receptors and neurotrophic factor by isolating RNA from the hippocampus and blood of rats of two strains decapitated at 1, 7, 24, and 60 days post-stress. Gene expression analysis was conducted through real-time PCR and underwent further data processing using the ΔΔCt method. The statistical tests employed included the Kruskal–Wallis and Mann–Whitney tests. The analysis of the expression of the observed genes in intact animals demonstrates that highly excitable LT rats have a significantly lower level of expression of the nr3c2 gene, which encodes the mineralocorticoid receptor in the hippocampus, when compared to the low excitable HT strain. When studying the short-term and long-term effects of stress on the expression of the observed genes (nr3c1, nr3c2, bdnf), a decline was observed in the level of bdnf mRNA in the hippocampus of the LT strain 60 days after stress. The study suggests that the genetically determined characteristics of highly excitable animals affect the weakened adaptive abilities of their nervous system. One of the markers of this weakening is the reduced level of mRNA for the nr3c2 gene in the hippocampus under normal conditions, as well as a decrease in the expression of the bdnf gene in response to stress.

Spreading depolarization (SD) is a wave of intense neuroglial depolarization that is widely recognized as a component of the acute brain response to various types of injury. Clinical studies conducted on patients with different types of stroke and traumatic brain injury which used intracranial recordings of cortical activity revealed a frequent incidence of cortical SD [1]. Site-specific intracerebral microinjections of drugs in experimental animals, or functional stereotactic surgery in patients, cause damage to both the neocortex and subcortical structures in the local area. Currently, most studies on SD are limited to the neocortex, but research shows that SD can be triggered in nearly all brain structures, albeit with varying levels of effectiveness [2]. The underlying mechanisms that render deep brain structures more vulnerable to SD remain unclear. Our recent research found that local injury to the amygdala can also lead to SD, although with a reduced likelihood compared to the neocortex [3]. The amygdala, a structure located in the temporal lobe, is highly susceptible to brain injury and plays a significant role in emotional behavior [4]. Specifically, it serves as a pivotal hub for fear memory and post-traumatic stress disorder pathogenesis. The post-injury neurobehavioral impairments are attributed to the limbic structure. Patients suffering from traumatic brain injury and stroke often exhibit cognitive dysfunction, generalized anxiety disorder, posttraumatic stress disorder, and other neuropsychiatric states. The role of SD in the pathogenesis of the behavioral deficit and its underlying mechanisms are not well understood. In this study, we examined the impact of amygdala micro-injury and associated SD on fear memory in rats. Neuronal tissue damage was induced using the standard method of intracerebral injection of substances through a thin 30 G needle inserted into a guide cannula (23 G) previously implanted in the amygdala. We used the classical Pavlovian fear conditioning paradigm to assess animal behavior. Twenty-four hours following the acquisition of fear, fear memory was evaluated during the first test. An hour after the first test, bilateral microinjury of the amygdala was performed. The influence of amygdala microinjury on aversive memory was assessed during the second test 24 hours later. Following the second test, rats underwent a two-day fear extinction process one to two days later. We found that bilateral micro-injury to the amygdala resulted in bilateral SD, unilateral SD, or no SD. Neither the injury nor the SD induced significant changes in fear conditioning during test 2, but they did affect subsequent fear extinction. The effect depended on whether the SD was triggered by the injury. If no SD occurred, fear extinction was disrupted, and rats exhibited high levels of freezing in response to sound. However, if bilateral SD was triggered by amygdala damage, fear memory was extinguished successfully and rapidly. The results of the present study suggest a strong involvement of SD induced by amygdala micro-injury in fear memory extinction. This information is crucial in comprehending the fundamental mechanisms behind post-traumatic stress disorder and exploring novel therapeutic approaches for treating this condition. The discoveries could be valuable in designing and interpreting experiments that involve local intracerebral microinjection.

The objective detection of sleepiness through physiological indices is crucial for ensuring transportation and industrial safety. As gadgets become more commonplace for measuring heart rate, it is important to identify valid indices that correlate with the level of sleepiness. Research indicates that time-domain and frequency-domain heart rate indices align with levels of alertness and sleepiness [e.g., 1, 2]. The aim of this paper is to identify heart rate indices that correlate with varying degrees of evening sleepiness in vivo, whether low or high. The data analyzed in this paper was obtained from 32 recordings from the SSDD (Subjective Sleepiness Dynamics Dataset) collected at the UNN Cyberpsychology Laboratory since 2022. The experiment was designed in the following manner: participants put on a heart rhythm sensor (Polar H10) at 7:40 PM in their homes and connected it to a smartphone app. Participants then completed a self-reported questionnaire in the electronic system. Starting at 8:00 PM, and every 30 minutes thereafter, the participant recorded data on the subjective level of sleepiness via the Karolinska (KSS) and Stanford (SSS) sleepiness scales up until the time of the participant’s bedtime. The selection of 32 recordings met specific criteria: all participants went to bed between 10:30 and 11:00 PM and completed both the KSS and SSS on time at each time point (08:00, 08:30, 09:00, 09:30, and 10:00 PM). For each time point, an integral sleepiness score was calculated according to the formula: sl=(KSS/10+SSS/7)/2. The 4-minute rhythmogram recordings corresponding to the time points of KSS and SSS filling were chosen for analysis. Data were processed in Jupyter Notebook. Heart rhythm indices, including time-domain, frequency-domain, and nonlinear indices, were computed for each time point from the rhythmogram — NN-interval sequence — using the “hrv-analysis” package. The study employed Student’s t-test to compare indices for varying levels of sleepiness based on integral scores, where “low level” was defined as sl 0.45 and “high level” was defined as sl 0.55. Additionally, the Pearson criterion was used to evaluate the correlation between sl and heart rate indices at each time interval. The indices responsible for the variability of the NN-interval sequence, namely NNi_50 (p=0.027), NNi_20 (p=0.007), and pNNi_20 (p=0.024), exhibited lower values during periods of “low” sleepiness (N=54) as compared to “high” sleepiness (N=58). Moreover, we observed that the autonomic balance index, i.e., the ratio of the power of the heart rate variability spectrum in the low-frequency band to that in the high-frequency band, was higher during ‘high’ sleepiness (p=0.015). Analysis revealed that correlations between integral sleepiness score and heart rate indices were present only for the 08:30 PM time point. Correlations of sl with the NN-interval range (R=–0.388; p=0.028), the severity of sympathetic regulatory circuit activity (R=–0.383; p=0.031), and the nonlinear cardiovagal index (R=–0.359; p=0.043) were found. Thus, it can be inferred that increased sleepiness is associated with decreased heart rate variability indices, along with decreased functioning of the autonomic nervous system overall.

Extreme events are typically defined as rare or unpredictable events that deviate significantly from typical behavior. Despite this, objective criteria for extreme events have yet to be established. Rareness may be characterized by certain scales or spatial and temporal boundaries, while intensity is an indication of an event’s potential to cause a significant change. One of the most prominent occurrences of extreme events in both neuroscience and medicine is in the case of epileptic seizures [1]. In speech synthesis, vocoder networks like WaveNet [2] generate audio. The model is a multi-layer convolutional neural network that functions as a causal filter and doesn’t predict the future. Due to this quality, the vocoder may have potential in time series prediction. Audio time series can be regarded as a dynamic system characterized by unpredictable switching regimes. For instance, transitioning from one letter to another can result in significant deviations in amplitude, similar to extreme events. This network receives r previous input counts known as a receptive field, and uses them to predict the next sample. The network is tree-like in structure, with exponentially increasing distances between subsequent layers of inputs. This is a necessary feature since the receptive field r is usually quite large, on the order of one or two thousand. Without this exponential increase in distance, the number of layers would depend linearly on r. Recurrent neural networks pose a challenge in optimizing the loss function when predicting time series sequences, as they tend to predict samples very similar to the previous one, causing the network to converge towards the mode. However, in a convolutional network, the output to the model will be longer due to the large receptive field. In the case of sound analysis, for instance, multiple oscillations occur within a given timeframe and the network does not elevate any specific sample. The study used artificial data generated from two coupled Hidmarsh–Rose neurons with chemical synaptic couplings. The observed variable was determined by the biological significance of the system, specifically the total membrane potential. The results exhibited extreme events across various coupling parameter values. Based on prior research [3], a numerical standard was selected for the events. The WaveNet vocoder model exhibits a 91% accuracy rate and 82% recall rate when forecasting extreme events of the same width as the prediction. It is noteworthy that recall is crucial in the forecast of extreme events since it identifies instances where the model predicted falsely that an extreme event would not occur.

According to the International League Against Epilepsy, epilepsy is a chronic condition of the brain that is characterized by a predisposition to epileptic seizures along with related neurobiological, cognitive, psychological, and social consequences. This is a general definition that does not take into account the diversity of epilepsies, including different aetiology, symptoms and mechanisms of epileptogenesis, making the development of a unified disease model challenging. The modeling framework addresses this challenge by selecting a distinct type of epilepsy and its associated manifestations, including electrophysiological, morphological (mainly neurodegeneration), and behavioral aspects [1]. In this study, a status epilepticus model is used with intrahippocampal kainic acid administration to reproduce electrophysiological activity and neurodegeneration. The process of reproducing two aspects simultaneously brings the kainate model closer to the disease called “epilepsy”. Neuroinflammation is a neurobiological process that is associated with a chronic brain condition that is characterized by a persistent susceptibility to epileptic seizures. Specifically, neuroinflammation is the response of the central nervous system (CNS) to various stimuli, including stroke, trauma, infection, autoimmune diseases, stress, and hyperexcitability of the neural network resulting from epileptic seizures. This response involves brain cells, specifically activated microglia and astrocytes, as well as neurons and brain vasculature cells, biosynthesizing and releasing molecules with inflammatory properties [2]. Currently, the study of neuroinflammation in relation to various pathological conditions includes an examination of the influence of the endocannabinoid system (ECS) [3]. However, research on epilepsy primarily focused on the ECS’s effects on network neuronal activity through CB1-mediated changes in synapse function (both excitatory and inhibitory), with only a limited number of studies exploring the interactions between the ECS and neuroinflammation [4]. This study analyzed neuroinflammatory dynamics after kainate administration and the effect of exogenous endocannabinoid receptor modulators on these dynamics. Neuroinflammation was evaluated through the measurement of expression levels of pro- and anti-inflammatory cytokines (IL1b, Il6, Cx3cl1, Ccl2, Tgfb1, Zc3h12a, Tnfa) in various areas including the ipsilateral ventral hippocampus, contralateral dorsal and ventral hippocampuses, neocortex, dura mater, cerebral and hippocampal meninges (undivided arachnoid and pia maters). Expression was quantified using quantitative PCR at 3 and 24 hours following convulsant injection. The study showed that seizures induced by kainate resulted in swift neuroinflammation development in the hippocampus, which resolved nearly entirely after 24 hours. A unique pattern of neuroinflammation was detected in the neocortex, with minor alterations at 3 hours and more pronounced modifications in the expression of inflammatory genes at 24 hours. Using the intrahippocampal kainate administration model, this study was the first to show a significantly delayed neuroinflammatory response in the neocortex compared to the hippocampus across a broad range of genes. Both activation of the cannabinoid CB1 and CB2 receptors and inhibition of the cannabinoid CB1 receptor increased neuroinflammation. However, cannabinoid receptor activation showed a predominantly proinflammatory effect in the neocortex, while CB1 receptor inhibition had a stronger effect in the hippocampus. Our findings indicate that cannabinoid receptor modulators regulate the kainate-induced neuroinflammatory response in the neocortex and hippocampus differently. Moreover, the well-known anti-inflammatory effect of cannabinoids is evident only within a certain range of cannabinoid concentrations and the timing of drug administration. In some cases, however, cannabinoids may have the opposite effect of increasing neuroinflammation.

In the field of neuronal signal transduction research, astroglia have become increasingly important. Astrocytes, or astroglial cells, are recognized as the third element in synaptic transmission regulation. This complex is conventionally known as a tripartite synapse. A multitude of mathematical models are being developed to investigate the function of astroglia in the tripartite synapse. Neurobiological experiments were carried out to confirm the dynamics observed in the previously proposed mean field activity mathematical model [1]. The experiments focused on evaluating the effect of optogenetic activation of astrocytes on synaptic transmission in C57BL/6 inbred mouse hippocampal slices. One month prior to the experiments, the AAV GFAP ChR2 eYFP virus was injected into the lateral ventricles of the brains of the experimental mice. This procedure was crucial for the expression of astrocyte-specific photosensitive channels, also known as channel rhodopsin. The patch-clamp method was used to conduct experiments on both the experimental mice and a control group of mice that did not receive virus injections. Spontaneous neuronal activity (local field potentials) and synaptic currents (GABA currents) were simultaneously recorded in the experiments. After activating astrocytes that expressed the photosensitive channel, an increase in GABAergic currents became evident during the transmission of synaptic signals between neurons. This suggests that astrocytes likely modulate synaptic transmission by releasing a gliotransmitter into the synaptic cleft. Thus, the study confirmed the presence of mean field activity patterns in a phenomenological model that describes the dynamics of a population of neurons. This model was developed based on the Tsodyks–Markram model and considers the primary attributes of neuron-glial interaction through a tripartite synapse. The model includes the short-term synaptic plasticity of the Tsodyks–Markram model and the astrocyte potential of synaptic transmission. The activation of astrocytes results in a diverse range of dynamic modes that describe various patterns of network activity under the mean field approach framework. Currently, multiple experimental hypotheses exist regarding astrocytes’ release of a gliotransfer into the synaptic cleft and its specific type. Alternatively, a complex cascade of sequential activation of glutamateergic and GABAergic receptors may occur. Additional experimental work is necessary to assess the pharmacological contribution of channels and transporters involved in modulating synaptic transmission during astrocyte optogenetic activation. The obtained results will refine the mathematical model of mean field neuronal activity to increase its biological plausibility. The methodology used involves identifying sparse nonlinear dynamical systems from data by solving for the system’s equations of motion [2]. These equations are reconstructed from noisy measurement data, allowing for a more accurate representation of the dynamic system. The only assumption regarding the arrangement of a dynamical system is that there exist only a handful of significant factors regulating the dynamics, leading to equations that are sparse within the realm of potential functions. Sparse regression is used to ascertain the minimum number of terms required in dynamic equations for precise data representation. This strategy enables the construction of mathematical models that are both as accurate as feasible, and maximally uncomplicated, eliminating the need for retraining. Notably, this method is suitable for parameterized systems and systems subjected to external influences or changes over time. The two approaches were used to forecast the dynamics of the mean field of a neural population. The accuracy of the forecast was subsequently evaluated.

Secretory sphingomyelinases are released in response to various types of stress stimuli, including inflammation [1]. Excessive activity and release of these enzymes can be pathological and accelerate age-related neurological changes, neuroinflammation, neurodegenerative disorders, and muscle dysfunction [1–3]. In contrast, sphingomyelinases were detected in areas close to exocytosis sites and dendritic spines, and their inhibition reduced synaptic transmission at both the presynaptic and postsynaptic levels in hippocampal synapses. Deficiency of acid sphingomyelinase results in neurodegenerative Niemann–Pick disease type A. Knockouts of neutral sphingomyelinases in model mice cause motor defects and disorders that resemble the Alzheimer’s disease phenotype [1, 2]. These findings suggest the existence of a mechanism regulating synaptic transmission, which is dependent on sphingomyelinases. In this study, we examined the impact of neutral sphingomyelinase on neurotransmission in mouse diaphragm muscles using microelectrode recordings of postsynaptic responses, exo-endocytic FM dyes, and fluorescent probes sensitive to membrane properties. The researchers discovered that adding sphingomyelinase at a low concentration (0.01 u/ml) for 15 minutes causes disruption of the lipid packaging of selectively synaptic membranes. This disruption leads to ceramide accumulation, a decrease in staining with Alexa Fluor 488 cholera toxin subunit B conjugate (a marker of GM1 ganglioside cluster), and a shift to the green part of the F2N12S fluorescence spectrum (an indicator of the expansion of the lipid-disordered phase). Increased fluorescence of 22-NBD-cholesterol, indicating an increase in membrane fluidity, and enhanced incorporation of exogenous ceramide into the outer monolayer were observed in the synaptic membranes. Increasing sphingomyelinase concentration to 0.1 u/ml intensifies changes in synaptic membranes and leads to corresponding alterations in membrane properties in muscle fiber plasmalemma. Subsequent experiments used a synapse-specific effect on membrane properties by applying sphingomyelinase at a low concentration for 15 minutes. The application of sphingomyelinase did not alter the amplitude and temporal parameters of miniature postsynaptic responses or the resting membrane potential. Additionally, sphingomyelinase (0.01 u/ml) did not change spontaneous secretion and evoked neurotransmitter release in response to single stimuli. However, the activity of sphingomyelinase led to a notable increase in the release of neurotransmitters and the rate of exocytotic unloading of FM dye from synaptic vesicles during nerve stimulation at 10, 20, and 70 Hz frequencies. The effect of sphingomyelinase on neurotransmission potentiation was irreversible and more significant at 10 Hz activity. Experiments involving intermittent nerve stimulation with short bursts of 60 stimuli each at 10 or 20 Hz frequencies, along with brief 0.5-second rest intervals, indicated that sphingomyelinase elevated the short-term facilitation of neurotransmitter release at the onset of each subsequent episode at 10 Hz (but not at 20 Hz) stimulation. Experiments with a combination of electrophysiological detection of evoked postsynaptic responses, monitoring of exocytotic release of the FM1-43 dye, and the use of FM1-43 fluorescence quencher that penetrates through the fusion pores have demonstrated that a significant portion of synaptic vesicles release the neurotransmitter through the transient fusion pore (kiss-and-run mechanism) during high-frequency (70 Hz) activity [4, 5]. Treatment with sphingomyelinase inhibited the neurotransmitter release pathway. As a consequence, the majority of neurosecretion events went through exocytosis, completely incorporating the vesicular membrane into the presynaptic membrane. Sphingomyelin in the membranes of presynaptic and synaptic vesicles is believed to have varying functions in regulating neurotransmitter release. To investigate this theory, we stimulated the exo-endocytosis processes of synaptic vesicles using sphingomyelinase, which provided access to the synaptic vesicle membranes. Under these circumstances, the enhancing impact of sphingomyelinase on neurotransmitter release and FM1-43 exocytosis rate were notably inhibited. Based on the obtained data, we hypothesize that sphingomyelinase disrupts the integrity of lipid rafts and selectively forms ceramide accumulations in synaptic membranes within the muscles at low concentrations. This ultimately leads to an increase in the mobilization of synaptic vesicles towards exocytosis sites during 10–70 Hz activity. Presumably, the synaptic vesicles from a distinct pool, which predominantly mediates the neurotransmission at a frequency of 10 Hz, are the most actively involved in exocytosis under these circumstances. Sphingomyelinase modifies membrane properties and can inhibit the release of neurotransmitters through the fusion pore during high-frequency activity, redirecting exocytosis towards the complete fusion pathway. Hydrolysis of sphingomyelin in both plasma and synaptic vesicle membranes reduces the stimulatory effects of sphingomyelinase, indicating a contrasting role of sphingomyelin in presynaptic and vesicular membranes for controlling neurotransmitter release [5].

The study of the molecular mechanisms underlying plastic processes in the nervous system is of great interest in modern neuroscience. It is important to understand that epigenetic modifications, which are crucial for the development and cellular differentiation, can also be involved in plastic processes in the adult nervous system. In our early work, we provided evidence that the expression of important memory-related genes, such as Prkcz and Prkci, can be regulated epigenetically [1]. In the current study, we extended the previous work to the systemic level by applying the RNA sequencing approach to evaluate global changes in the expression patterns of various genes during the induction of epigenetic rearrangements. Rat cortical neuron cultures were incubated with one of the nonselective histone deacetylase inhibitors (trichostatin A, TSA; sodium butyrate, NaB) to change the level of epigenetic regulation. Next, the total RNA was extracted and subjected to RNA-Seq libraries preparation and subsequent NGS-sequencing. Bioinformatics analysis of transcriptomic data revealed substantial overlapping of differentially expressed genes (DEGs) in NaB-treated and TSA-treated groups, indicating that different histone deacetylase (HDAC) inhibitors induce transcriptional changes in primary neuron cultures through common regulatory pathways irrespective of chemical structure of applied inhibitor. We found that histone deacetylase blockade is accompanied by a transition from proliferative processes to cellular differentiation. Gene Ontology (GO) analysis of DEGs datasets revealed that the upregulated genes were engaged in cell differentiation and specialization, tissue and embryonic morphogenesis, and the development of various peripheral tissues and organs. On the contrary, genes that reduce the expression under induction of epigenetic rearrangements were involved in biological processes associated with cell proliferation and, most interestingly, the specialization of various brain cells (neurons, astrocytes, oligodendrocytes). It was shown that the expression of a number of glial markers typical for astrocytes and oligodendrocytes was significantly reduced after application of HDAC inhibitors, which was also confirmed by quantitative PCR using specific primer pairs on selected target genes. During the data analysis, we also found a significant decrease in the expression of various neuronal markers associated with the cytoskeleton, the organization of pre- and postsynaptic endings, synaptic transmission. It is known that fine-tuning of various processes in the central nervous system is due to the production of different isoforms of proteins from the same gene due to the process of alternative splicing of the resulting mRNA. According to the literature, epigenetic rearrangements create a certain environment for the regulation of alternative splicing of different genes [2, 3]. It is shown that production of alternative isoforms can play an important role in various plastic processes [4, 5]. Therefore, using IsoformSwitchAnalyzeR and DEXSeq packages we analyzed the possibility of alternative splicing during the induction of epigenetic rearrangements in rat cortical neuron cultures by evaluating the abundance of various transcripts based on exon usage. We found that some glial genes and a large number of neuronal genes, especially those associated with postsynaptic organization and cell communication, were alternatively spliced after application of histone deacetylase inhibitors. Inhibition of HDAC activity in cortical neuron cultures mainly affected the choice of alternative transcription starts (ATSS) and terminators (ATTS), and to a lesser extent alternative splicing of exons. Obtained results were selectively confirmed by the quantitative PCR using specific pairs of primers for individual exons of different transcript isoforms. Thus, within this study, it was found that histone deacetylases play an important role in the specialization of various brain cells, and the suppression of their activity affects the expression and alternative splicing of various glial and neuronal marker genes. We do not exclude that global transcriptome changes caused by alternative splicing will lead to qualitative rearrangements of the neuron network, and this is the direction of future research.

Networks of phase oscillators have become a widely established paradigmatic model for studying emergent collective behavior across several real-world systems, including neuronal networks, populations of chemical oscillators, and power grids. The Kuramoto model, involving one-dimensional or two-dimensional phase oscillators, demonstrates the potential for networks to showcase exceptional collective dynamics. This encompasses various outcomes such as full, partial, explosive, and asymmetry-induced synchronization, clusters, chimeras, solitary states, and generalized splay states. Notably, increasing all-to-all coupling in the classical Kuramoto model induces full synchronization as the most probable outcome and dominant rhythm. Kuramoto networks with repulsive coupling usually display splay, generalized, and cluster splay states, but the conditions under which a certain rhythm can arise and prevail are not entirely understood. Equally important for connecting Kuramoto networks to practical physical systems is understanding the function of higher-order coupling terms. These terms display a Fourier decomposition of a general 2π-periodic interaction function [1]. Previous studies have demonstrated that the inclusion of higher-order terms in the classical Kuramoto model of oscillators with all-to-all attractive coupling can lead to multiple synchronous states and switching between synchronization clusters. However, the impact of higher-order coupling modes on rhythm generation in repulsive networks remains unexplored. In this work, we present significant progress in addressing the critical issue related to repulsive Kuramoto–Sakaguchi networks of phase oscillators with phase-lagged first-order and higher-order coupling. We demonstrate that weakly repulsive networks of even and odd numbers of oscillators with first-order coupling are dominated by two-cluster and three-cluster splay states, respectively. The three-cluster splay states consist of two distinct coherent clusters and one solitary oscillator. These tripod states can be considered a fusion of a two-body chimera and a solitary state. We have dubbed these patterns of three oscillators as “Cyclops states” in reference to the Greek mythological giant with a single eye. The solitary oscillator and synchronous clusters respectively represent the Cyclops’ eye and shoulders. We present a remarkable discovery that the inclusion of higher-order coupling modes leads to worldwide stability of cyclops states across almost the entire range of the phase-lag parameter controlling repulsion [2]. Beyond the Kuramoto oscillators, we demonstrate the robust presence of this effect in networks of canonical theta-neurons with adaptive coupling. Furthermore, our results provide insight into identifying dominant rhythms within repulsive physical and biological networks.