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In vitro neuronal culture is an important research platform in cellular and network neuroscience. However, neurons cultured on a homogeneous scaffold form dense, randomly connected networks and display excessively synchronized activity; this phenomenon has limited their applications in network‐level studies, such as studies of neuronal ensembles, or coordinated activity by a group of neurons. Herein, polydimethylsiloxane‐based microfluidic devices are developed to create small neuronal networks exhibiting a hierarchically modular structure resembling the connectivity observed in the mammalian cortex. The strength of intermodular coupling is manipulated by varying the width and height of the microchannels that connect the modules. Neuronal activity recording via calcium imaging shows that the spontaneous activity in networks with smaller microchannels (2.2–5.5 µm2) has lower synchrony and exhibits a threefold variety of neuronal ensembles. Optogenetic stimulation demonstrates that a reduction in intermodular coupling enriches evoked neuronal activity patterns and that repeated stimulation induces plasticity in neuronal ensembles in these networks. These findings suggest that cell engineering technologies based on microfluidic devices enable in vitro reconstruction of the intricate dynamics of neuronal ensembles, thus providing a robust platform for studying neuronal ensembles in a well‐defined physicochemical environment. [ABSTRACT FROM AUTHOR]

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Reservoir computing and predictive coding together yield a computational model for exploring how neuronal dynamics in the mammalian cortex underpin temporal signal processing. Here, we construct an in-silico model of biological neuronal networks grown on microelectrode arrays and explore their computing capabilities through a sine wave prediction task in a reservoir-based predictive coding framework. Our results show that the time interval between stimulation pulses is a critical determinant of task performance. Additionally, under a fixed feedback latency, pulse amplitude modulation is a favorable encoding scheme for input signals. These findings provide practical guidelines for future implementation of the model in biological experiments. [ABSTRACT FROM AUTHOR]

Epilepsy is a common neurological disorder, affecting over 65 million people worldwide. Unfortunately, despite resective surgery, over 30% of patients with drug-resistant epilepsy continue to experience seizures. Retrospective studies considering connectivity using intracranial electrocorticography (ECoG) obtained during neuromonitoring have shown that treatment failure is likely driven by failure to consider critical components of the seizure network, an idea first formally introduced in 2002. However, current studies only capture snapshots in time, precluding the ability to consider seizure network development. Over the past few years, multiwell microelectrode arrays have been increasingly used to study neuronal networks in vitro. As such, we sought to develop a novel in vitro MEA seizure model to allow for study of seizure networks. Specifically, we used 4-aminopyridine (4-AP) to capture hyperexcitable activity, and then show increased network changes after 2 days of chronic treatment. We characterize network changes using functional connectivity measures and a novel technique using dimensionality reduction. We find that 4-AP successfully captures persistently elevated mean firing rate and significant changes in underlying connectivity patterns. We believe this affords a robust in vitro seizure model from which longitudinal network changes can be studied, laying groundwork for future studies exploring seizure network development. [ABSTRACT FROM AUTHOR]

Cannabis use and cannabis use disorders have taken on a new social significance as a result of partial legalization. In 2021 a total of 4.5 million adults (8.8%) in Germany used the drug. The number of users as well as problematic use have risen in the last decade. Cannabis products with a high delta-9-tetrahydrocannabinol (THC) content and their regular use lead to changes in cannabinoid receptor distribution in the brain and to modifications in the structure and functionality of relevant neuronal networks. The consequences of cannabinoid use are particularly in the psychological functioning and can include intoxication, harmful use, dependence with withdrawal symptoms and cannabis-induced mental disorders. Changes in the diagnostics between ICD-10 and ICD-11 are presented. Interdisciplinary S3 guidelines on cannabis-related disorders are currently being developed and will be finalized shortly. [ABSTRACT FROM AUTHOR]

Biologically detailed models of brain circuitry are challenging to build and simulate due to the large number of neurons, their complex interactions, and the many unknown physiological parameters. Simplified mathematical models are more tractable, but harder to evaluate when too far removed from neuroanatomy/physiology. We propose that a multiscale model, coarse-grained (CG) while preserving local biological details, offers the best balance between biological realism and computability. This paper presents such a model. Generally, CG models focus on the interaction between groups of neurons--here termed "pixels"--rather than individual cells. In our case, dynamics are alternately updated at intra- and interpixel scales, with one informing the other, until convergence to equilibrium is achieved on both scales. An innovation is how we exploit the underlying biology: Taking advantage of the similarity in local anatomical structures across large regions of the cortex, we model intrapixel dynamics as a single dynamical system driven by "external" inputs. These inputs vary with events external to the pixel, but their ranges can be estimated a priori. Precomputing and tabulating all potential local responses speed up the updating procedure significantly compared to direct multiscale simulation. We illustrate our methodology using a model of the primate visual cortex. Except for local neuron-to-neuron variability (necessarily lost in any CG approximation) our model reproduces various features of large-scale network models at a tiny fraction of the computational cost. These include neuronal responses as a consequence of their orientation selectivity, a primary function of visual neurons. [ABSTRACT FROM AUTHOR]

Gi-coupled receptors, particularly cannabinoid receptors (CBRs), are considered perspective targets for treating brain pathologies, including epilepsy. However, the precise mechanism of the anticonvulsant effect of the CBR agonists remains unknown. We have found that WIN 55,212-2 (a CBR agonist) suppresses the synchronous oscillations of the intracellular concentration of Ca2+ ions (epileptiform activity) induced in the neurons of rat hippocampal neuron-glial cultures by bicuculline or NH4Cl. As we have demonstrated, the WIN 55,212-2 effect is mediated by CB1R receptors. The agonist suppresses Ca2+ inflow mediated by the voltage-gated calcium channels but does not alter the inflow mediated by NMDA, AMPA, and kainate receptors. We have also found that phospholipase C (PLC), protein kinase C (PKC), and G-protein-coupled inwardly rectifying K+ channels (GIRK channels) are involved in the molecular mechanism underlying the inhibitory action of CB1R activation against epileptiform activity. Thus, our results demonstrate that the antiepileptic action of CB1R agonists is mediated by different intracellular signaling cascades, including non-canonical PLC/PKC-associated pathways. [ABSTRACT FROM AUTHOR]

In this article, we aim to study how astrocytes control electrophysiological activity during neuronal network formation. We used a combination of spike/burst analysis, classification of spike waveforms based on various signal properties and tools from information theory to demonstrate how astrocytes modulate the electrical activity of neurons using microelectrode array (MEA) signals. We cultured rat primary cortical neurons and astrocytes on 60-electrode MEAs with different neuron/astrocyte ratios ranging from ‘pure’ neuronal cultures to co-cultures containing 50% neurons and 50% astrocytes. Our results show that astrocytes desynchronize the network and reduce predictability in the signals and affect the repolarization phase of the action potentials. Our work highlights that it is crucial to go beyond standard MEA analysis to assess how astrocytes control neuronal cultures and investigate any dysfunction that could potentially result in neuronal hyperactivity.

Background and objective: Hydronephrosis is essential in the diagnosis of renal colic. We automated the detection of hydronephrosis from ultrasound images to standardize the therapy and reduce the misdiagnosis of renal colic. Methods: Anonymously collected ultrasound images of human kidneys, both normal and hydronephrotic, were preprocessed for neural networks. Six “state of the art” models were trained and cross-validated for the detection of hydronephrosis, and two convolutional networks were used for kidney segmentation. In the testing phase, performance metrics included true positives, true negatives, false positives, false negatives, accuracy, and F1 score, while the evaluation of the segmentation task involved accuracy, precision, dice, jaccard, recall, and ASSD. Key findings and limitations: A total of 523 sonographic kidney images (423 nonhydronephrotic and 100 hydronephrotic) were collected from three different ultrasound devices. After training on this dataset, all models were used to evaluate 200 new ultrasound kidney images (142 nonhydronephrotic and 58 hydronephrotic kidneys). The highest validation accuracy (98.5%) was achieved by the AlexNet model (GoogLeNet 97%, AlexNet_v2 96%, ResNet50 96%, ResNet101 97.5%, and ResNet152 95%). The deeplabv3_resnet50 and deeplabv3_resnet101 reached a dice coefficient of 94.74% and 94.48%, respectively, on the task of automated kidney segmentation. The study is limited by analyzing only hydronephrosis, but this specific focus enabled high detection accuracy. Conclusions and clinical implications: We show that our automated ultrasound deep learning model can be trained and used to interpret and segmentate ultrasound images from different sources with high accuracy. This method will serve as an automated tool in the diagnostic algorithm of acute renal failure in the future. Patient summary: Hydronephrosis is crucial in the diagnosis of renal colic. Recent advances in artificial intelligence allow automated detection of hydronephrosis in ultrasound images with high accuracy. These methods will help standardize the diagnosis and treatment renal colic.

Over the past decades, spatiotemporal dynamics has received considerable attention in many fields, such as biology, chemistry and neuroscience. This paper reveals the spiral wave dynamics in a two-dimensional modified FitzHugh–Nagumo neuronal network. The network is composed of interacting neurons with nearest-neighbor connections. Time delays are introduced into the couplings among neurons. Numerical simulations are performed and interesting and abundant spatiotemporal patterns are obtained, such as spiral waves. It is shown that the time delays can give rise to the generation, transition and modulation of the spiral waves. Moreover, external periodic excitations are applied to regulate the spiral waves and different types of waves are observed, such as target wave and traveling wave. [ABSTRACT FROM AUTHOR]

Climate change, encompassing the greenhouse effect, is a scientifically acknowledged fact. Factors such as population increase and limited resources for economic growth warrant consideration. This paper aims to develop a new approach to explore the relationship between the greenhouse effect (including climate change) and economic growth and the social/welfare state and find if the government really focus on the reduction of the greenhouse or is marketing. The objective is to develop a study employing linear regression, neural networks, and other statistical tools to elucidate these relationships. The data comprise figures for the human development index (HDI), the greenhouse effect, the GDP, and environmental indicators. The method used will be a parametric workout about the variables that affect the greenhouse gas emissions, the relationship between it and the HDI, and finally, will apply a prediction of greenhouse effects incorporating a neural network. Since 2020, in European Union countries, and especially in new members, focus has been placed on the HDI rather than on the reduction in the greenhouse effect. On the other hand, neural networks allow advances that enable the European Union to focus on climate change, with large investments planned until 2030 because the reduction in greenhouse gases can be effectively lowered when the countries' expenditures are focused on environmental protection, including enhancing biodiversity. [ABSTRACT FROM AUTHOR]

Neurons build vast gap junction-coupled networks (GJ-nets) that are permeable to ions or small molecules, enabling lateral signaling. Herein, we investigate (1) the effect of blinding diseases on GJ-nets in mouse retinas and (2) the impact of electrical stimulation on GJ permeability. GJ permeability was traced in the acute retinal explants of blind retinal degeneration 1 (rd1) mice using the GJ tracer neurobiotin. The tracer was introduced via the edge cut method into the GJ-net, and its spread was visualized in histological preparations (fluorescent tagged) using microscopy. Sustained stimulation was applied to modulate GJ permeability using a single large electrode. Our findings are: (1) The blind rd1 retinas displayed extensive intercellular coupling via open GJs. Three GJ-nets were identified: horizontal, amacrine, and ganglion cell networks. (2) Sustained stimulation significantly diminished the tracer spread through the GJs in all the cell layers, as occurs with pharmaceutical inhibition with carbenoxolone. We concluded that the GJ-nets of rd1 retinas remain coupled and functional after blinding disease and that their permeability is regulatable by sustained stimulation. These findings are essential for understanding molecular signaling in diseases over coupled networks and therapeutic approaches using electrical implants, such as eliciting visual sensations or suppressing cortical seizures. [ABSTRACT FROM AUTHOR]

In the theory of gene networks, the mathematical apparatus that uses dynamical systems is fruitfully used. The same is true for the theory of neural networks. In both cases, the purpose of the simulation is to study the properties of phase space, as well as the types and the properties of attractors. The paper compares both models, notes their similarities and considers a number of illustrative examples. A local analysis is carried out in the vicinity of critical points and the necessary formulas are derived. [ABSTRACT FROM AUTHOR]

The sheet-metal-forming process is crucial in manufacturing various products, including pipes, cans, and containers. Despite its significance, controlling this complex process is challenging and may lead to defects and inefficiencies. This study introduces a novel approach to monitor the sheet-metal-forming process, specifically focusing on the rolling of cans in the oil-and-gas sector. The methodology employed in this work involves the application of temporal-signal-processing and artificial-intelligence (AI) techniques for monitoring and optimizing the manufacturing process. Temporal-signal-processing techniques, such as Markov transition fields (MTFs), are utilized to transform time series data into images, enabling the identification of patterns and anomalies. synamic time warping (DTW) aligns time series data, accommodating variations in speed or timing across different rolling processes. K-medoids clustering identifies representative points, characterizing distinct phases of the rolling process. The results not only demonstrate the effectiveness of this framework in monitoring the rolling process but also lay the foundation for the practical application of these methodologies. This allows operators to work with a simpler characterization source, facilitating a more straightforward interpretation of the manufacturing process. [ABSTRACT FROM AUTHOR]

Simple Summary: Patients with gliomas experience worsened quality of life due to epileptic attacks. Over 80% of patients are susceptible to seizures, significantly hindering their treatment and well-being. No cause of epilepsy associated with glioma has been identified; however, some mutations are known to contribute to seizures in various situations. The development of a tumour creates advantageous conditions for abnormal neuronal activity to occur. Identifying a therapeutic target is a significant undertaking that numerous studies are endeavouring to undertake. We investigate the potential correlation of pathophysiological processes in the development of both gliomas and epilepsy, aiming to identify any shared factors. Furthermore, we propose that the mutational profile of different types of gliomas may have an impact on the occurrence or absence of seizures. We conclude that spontaneous neuronal activity arises from both the tumour microenvironment and blood ingress into neuronal networks resulting from the breakdown of the blood–brain barrier. Approximately 30% of glioma patients are able to survive beyond one year postdiagnosis. And this short time is often overshadowed by glioma-associated epilepsy. This condition severely impairs the patient's quality of life and causes great suffering. The genetic, molecular and cellular mechanisms underlying tumour development and epileptogenesis remain incompletely understood, leading to numerous unanswered questions. The various types of gliomas, namely glioblastoma, astrocytoma and oligodendroglioma, demonstrate distinct seizure susceptibility and disease progression patterns. Patterns have been identified in the presence of IDH mutations and epilepsy, with tumour location in cortical regions, particularly the frontal lobe, showing a more frequent association with seizures. Altered expression of TP53, MGMT and VIM is frequently detected in tumour cells from individuals with epilepsy associated with glioma. However, understanding the pathogenesis of these modifications poses a challenge. Moreover, hypoxic effects induced by glioma and associated with the HIF-1a factor may have a significant impact on epileptogenesis, potentially resulting in epileptiform activity within neuronal networks. We additionally hypothesise about how the tumour may affect the functioning of neuronal ion channels and contribute to disruptions in the blood–brain barrier resulting in spontaneous depolarisations. [ABSTRACT FROM AUTHOR]

Resistive switching devices are candidates to be used as weight for the future hardware realization of neuronal networks. Herein, these resistive switches base on oxygen vacancy migration and can be toggled between different resistance states. Internal processes lead to a variability over time in these states. To understand this, a dynamical model is developed, which links the internal physical mechanism to the current response. Thereby, the electronic current is calculated by tunneling processes over oxygen vacancies. The model allows to understand the physical transport properties and the current–voltage dependence. Integrating ionic hopping processes clarifies the variability in the electronic conduction. The derived model does not require empirical input parameters and only depends on the properties of the materials. This allows to use literature values and independently compare them to electrical measurements. The model predicts the experiments correctly. Thereupon, the physical properties of the conduction are investigated with respect to the transport over discrete energy levels, which change under the hopping of oxygen vacancies. Afterwards, the current level and variability are simulated for increasing oxygen vacancy concentrations to investigate analog switching, which is required for neuronal network layers. Finally, the variability is tested with multiple interacting cells in a neuronal layer. [ABSTRACT FROM AUTHOR]

N‐acetylneuraminic acid (sialic acid) is present in large quantities in the brain and plays a crucial role in brain development, learning, and memory formation. How sialic acid contributes to brain development is not fully understood. The purpose of this study was to determine the effects of reduced sialylation on network formation in human iPSC‐derived neurons (iNeurons). Using targeted mass spectrometry and antibody binding, we observed an increase in free sialic acid and polysialic acid during neuronal development, which was disrupted by treatment of iNeurons with a synthetic inhibitor of sialic acid biosynthesis. Sialic acid inhibition disturbed synapse formation and network formation on microelectrode array (MEA), showing short but frequent (network) bursts and an overall lower firing rate, and higher percentage of random spikes. This study shows that sialic acid is necessary for neuronal network formation during human neuronal development and provides a physiologically relevant model to study the role of sialic acid in patient‐derived iNeurons. [ABSTRACT FROM AUTHOR]

As a bioinspired sensor with high temporal resolution, the spiking cameras have enormous potential in real applications, yet it remains largely unknown how to improve their performance. In this study, we showed that the critical dynamics resulting from balanced excitatory and inhibitory synaptic currents, as well as the heterogeneity in the resting potentials of neurons, can maximize the dynamic range of the neuronal networks jointly. We then built a spiking camera to demonstrate that its performance, characterized by the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), could be optimized if the critical dynamics and the heterogeneity were introduced in the neuronal networks of the spiking camera's core modules. We argue that criticality and heterogeneity can serve as basic optimization principles of brain-inspired computing systems to improve their performance. [ABSTRACT FROM AUTHOR]

A recent experimental study showed that inhibitory autapses favor firing synchronization of parvalbumin interneurons in the neocortex during gamma oscillations. In the present paper, to provide a comprehensive and deep understanding to the experimental observation, the influence of inhibitory autapses on synchronization of interneuronal network gamma oscillations is theoretically investigated. Weak, middle, and strong synchronizations of a globally inhibitory coupled network composed of Wang–Buzsáki model without autapses appear at the bottom-left, middle, and top-right of the parameter plane with the conductance (gsyn) and the decay constant (τsyn) of inhibitory synapses taken as the x-axis and y-axis, respectively. After introducing inhibitory autapses, the border between the strong and middle synchronizations in the (gsyn, τsyn) plane moves to the top-right with increasing the conductance (gaut) and the decay constant (τaut) of autapses, due to that interspike interval of the single neuron becomes longer, leading to that larger τsyn is needed to ensure the strong synchronization. Then, the synchronization degree of middle and strong synchronizations around the border in the (gsyn, τsyn) plane decreases, while of strong synchronization in the remaining region remains unchanged. The synchronization degree of weak synchronization increases with increasing τaut and gaut, due to that the inhibitory autaptic current becomes strong and long to facilitate synchronization. The enhancement of weak synchronization modulated by inhibitory autapses is also simulated in the random, small-world, and scale-free networks, which may provide explanations to the experimental observation. These results present complex dynamics of synchronization modulated by inhibitory autapses, which needs future experimental demonstrations. [ABSTRACT FROM AUTHOR]

Tetramethylenedisulfotetramine (tetramine or TETS), a potent convulsant, triggers abnormal electrical spike activity (ESA) and synchronous Ca2+ oscillation (SCO) patterns in cultured neuronal networks by blocking gamma-aminobutyric acid (GABAA) receptors. Murine hippocampal neuronal/glial cocultures develop extensive dendritic connectivity between glutamatergic and GABAergic inputs and display two distinct SCO patterns when imaged with the Ca2+ indicator Fluo-4: Low amplitude SCO events (LASE) and High amplitude SCO events (HASE) that are dependent on TTX-sensitive network electrical spike activity (ESA). Acute TETS (3.0 µM) increased overall network SCO amplitude and decreased SCO frequency by stabilizing HASE and suppressing LASE while increasing ESA. In multielectrode arrays, TETS also increased burst frequency and synchronicity. In the presence of TETS (3.0 µM), the clinically used anticonvulsive perampanel (0.1-3.0 µM), a noncompetitive AMPAR antagonist, suppressed all SCO activity, whereas the GABAA receptor potentiator midazolam (1.0-30 µM), the current standard of care, reciprocally suppressed HASE and stabilized LASE. The neuroactive steroid (NAS) allopregnanolone (0.1-3.0 µM) normalized TETS-triggered patterns by selectively suppressing HASE and increasing LASE, a pharmacological pattern distinct from its epimeric form eltanolone, ganaxolone, alphaxolone, and XJ-42, which significantly potentiated TETS-triggered HASE in a biphasic manner. Cortisol failed to mitigate TETS-triggered patterns and at >1 µM augmented them. Combinations of allopregnanolone and midazolam were significantly more effective at normalizing TETS-triggered SCO patterns, ESA patterns, and more potently enhanced GABA-activated Cl- current, than either drug alone.

Monte Carlo (MC) simulations of X-ray image devices require splitting the simulation into two parts (i.e. the generation of x-rays and the actual imaging). The X-ray production remains unchanged for repeated imaging and can thus be stored in phase space (PhS) files and used for subsequent MC simulations. Especially for medical images these dedicated PhS files require a large amount of data storage, which is partly why Generative Adversarial Networks (GANs) were recently introduced. We enhanced the approach by a conditional GAN to model multiple energies using one network. This study compares the use of PhSs, GANs, and conditional GANs as photon source with measurements.An X-ray -based imaging system (i.e. ImagingRing) was modelled in this study. half-value layers (HVLs), focal spot, and Heel effect were measured for subsequent comparison. MC simulations were performed with GATE-RTion v1.0 considering the geometry and materials of the imaging system with vendor specific schematics. A traditional GAN model as well as the favourable conditional GAN was implemented for PhS generation.Results of the MC simulation were in agreement with the measurements regarding HVL, focal spot, and Heel effect. The conditional GAN performed best with a non-saturated loss function with R1 regularisation and gave similarly results as the traditional GAN approach.GANs proved to be superior to the PhS approach in terms of data storage and calculation overhead. Moreover, a conditional GAN enabled an energy interpolation to separate the network training process from the final required X-ray energies.

The sensory and cognitive abilities of the mammalian neocortex are underpinned by intricate columnar and laminar circuits formed from an array of diverse neuronal populations. One approach to determining how interactions between these circuit components give rise to complex behavior is to investigate the rules by which cortical circuits are formed and acquire functionality during development. This review summarizes recent research on the development of the neocortex, from genetic determination in neural stem cells through to the dynamic role that specific neuronal populations play in the earliest circuits of neocortex, and how they contribute to emergent function and cognition. While many of these endeavors take advantage of model systems, consideration will also be given to advances in our understanding of activity in nascent human circuits. Such cross-species perspective is imperative when investigating the mechanisms underlying the dysfunction of early neocortical circuits in neurodevelopmental disorders, so that one can identify targets amenable to therapeutic intervention.

Gi-coupled receptors, particularly cannabinoid receptors (CBRs), are considered perspective targets for treating brain pathologies, including epilepsy. However, the precise mechanism of the anticonvulsant effect of the CBR agonists remains unknown. We have found that WIN 55,212-2 (a CBR agonist) suppresses the synchronous oscillations of the intracellular concentration of Ca2+ ions (epileptiform activity) induced in the neurons of rat hippocampal neuron-glial cultures by bicuculline or NH4Cl. As we have demonstrated, the WIN 55,212-2 effect is mediated by CB1R receptors. The agonist suppresses Ca2+ inflow mediated by the voltage-gated calcium channels but does not alter the inflow mediated by NMDA, AMPA, and kainate receptors. We have also found that phospholipase C (PLC), protein kinase C (PKC), and G-protein-coupled inwardly rectifying K+ channels (GIRK channels) are involved in the molecular mechanism underlying the inhibitory action of CB1R activation against epileptiform activity. Thus, our results demonstrate that the antiepileptic action of CB1R agonists is mediated by different intracellular signaling cascades, including non-canonical PLC/PKC-associated pathways.

Resistive switching devices are candidates to be used as weight for the future hardware realization of neuronal networks. Herein, these resistive switches base on oxygen vacancy migration and can be toggled between different resistance states. Internal processes lead to a variability over time in these states. To understand this, a dynamical model is developed, which links the internal physical mechanism to the current response. Thereby, the electronic current is calculated by tunneling processes over oxygen vacancies. The model allows to understand the physical transport properties and the current–voltage dependence. Integrating ionic hopping processes clarifies the variability in the electronic conduction. The derived model does not require empirical input parameters and only depends on the properties of the materials. This allows to use literature values and independently compare them to electrical measurements. The model predicts the experiments correctly. Thereupon, the physical properties of the conduction are investigated with respect to the transport over discrete energy levels, which change under the hopping of oxygen vacancies. Afterwards, the current level and variability are simulated for increasing oxygen vacancy concentrations to investigate analog switching, which is required for neuronal network layers. Finally, the variability is tested with multiple interacting cells in a neuronal layer.

Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems’ dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations—signs of instability—in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change. [ABSTRACT FROM AUTHOR]

Introduction: Glass coverslips are used as a substrate since Harrison's initial nerve cell culture experiments in 1910. In 1974, the first study of brain cells seeded onto polylysine (PL) coated substrate was published. Usually, neurons adhere quickly to PL coating. However, maintaining cortical neurons in culture on PL coating for a prolonged time is challenging. Methods: A collaborative study between chemical engineers and neurobiologists was conducted to find a simple method to enhance neuronal maturation on poly-D-lysine (PDL). In this work, a simple protocol to coat PDL efficiently on coverslips is presented, characterized, and compared to a conventional adsorption method. We studied the adhesion and maturation of primary cortical neurons with various morphological and functional approaches, including phase contrast microscopy, immunocytochemistry, scanning electron microscopy, patch clamp recordings, and calcium imaging. Results: We observed that several parameters of neuronal maturation are influenced by the substrate: neurons develop more dense and extended networks and synaptic activity is enhanced, when seeded on covalently bound PDL compared to adsorbed PDL. Discussion: Hence, we established reproducible and optimal conditions enhancing maturation of primary cortical neurons in vitro. Our method allows higher reliability and yield of results and could also be profitable for laboratories using PL with other cell types. [ABSTRACT FROM AUTHOR]

Summary: Models for human brain-oriented research are often established on primary cultures from rodents, which fails to recapitulate cellular specificity and molecular cues of the human brain. Here we investigated whether neuronal cultures derived from human induced pluripotent stem cells (hiPSCs) feature key advantages compared with rodent primary cultures. Using calcium fluorescence imaging, we tracked spontaneous neuronal activity in hiPSC-derived, human, and rat primary cultures and compared their dynamic and functional behavior as they matured. We observed that hiPSC-derived cultures progressively changed upon development, exhibiting gradually richer activity patterns and functional traits. By contrast, rat primary cultures were locked in the same dynamic state since activity onset. Human primary cultures exhibited features in between hiPSC-derived and rat primary cultures, although traits from the former predominated. Our study demonstrates that hiPSC-derived cultures are excellent models to investigate development in neuronal assemblies, a hallmark for applications that monitor alterations caused by damage or neurodegeneration.

The cumulation of cultural codes forms and structures the integral matrix of information capacity - the cognitive thesaurus (CT) of the psyche. The dissociation of the parameters of the current balance of somatic/mental homeostasis initiates a compensatory need. The "need" extracts from the CT (actualizes) the "image" of the cultural code - a representable informational equivalent of the "need" ("dominant"). Being the content of consciousness at the time of relevance, "dominant" acquires the properties of a resonant operator, a dominant focus, forming control mental constructs. Actualization of the "dominant" initiates the activity of the "action result acceptor." For the actual dominant, the "action result acceptor" is an indicator/corrector, a frequency matching censor (resonance): perceptual dynamics of operational images of external presentation ("code key") to the parameters of the "dominant" (code). Decoding the physical parameters of the "code" provides the potential to regulate the resonant impact on the arsenal of "cultural codes" of CT through moderation of the "code key." The CT, through the "action result acceptor" (the extra-conscious regulator of the actual autonoetic forms of mental activity), programs and formats the somatic/psychic continuum of the individual. The integral matrix of the CT, having a location outside the conscious sector of the psyche, including the imperative corrector of the perceived sector of the psyche associated with the emotional register, determining/correcting the main vectors of the regulation of the life continuum, is meaningfully close to the philosophical/psychological criteria of the main cognitive regulator of the person activity, defined as the soul.

Reservoir computing is a machine learning paradigm that transforms the transient dynamics of high-dimensional nonlinear systems for processing time-series data. Although the paradigm was initially proposed to model information processing in the mammalian cortex, it remains unclear how the nonrandom network architecture, such as the modular architecture, in the cortex integrates with the biophysics of living neurons to characterize the function of biological neuronal networks (BNNs). Here, we used optogenetics and calcium imaging to record the multicellular responses of cultured BNNs and employed the reservoir computing framework to decode their computational capabilities. Micropatterned substrates were used to embed the modular architecture in the BNNs. We first show that the dynamics of modular BNNs in response to static inputs can be classified with a linear decoder and that the modularity of the BNNs positively correlates with the classification accuracy. We then used a timer task to verify that BNNs possess a short-term memory of several 100 ms and finally show that this property can be exploited for spoken digit classification. Interestingly, BNN-based reservoirs allow categorical learning, wherein a network trained on one dataset can be used to classify separate datasets of the same category. Such classification was not possible when the inputs were directly decoded by a linear decoder, suggesting that BNNs act as a generalization filter to improve reservoir computing performance. Our findings pave the way toward a mechanistic understanding of information representation within BNNs and build future expectations toward the realization of physical reservoir computing systems based on BNNs. [ABSTRACT FROM AUTHOR]

Monte Carlo (MC) simulations of X-ray image devices require splitting the simulation into two parts (i.e. the generation of x-rays and the actual imaging). The X-ray production remains unchanged for repeated imaging and can thus be stored in phase space (PhS) files and used for subsequent MC simulations. Especially for medical images these dedicated PhS files require a large amount of data storage, which is partly why Generative Adversarial Networks (GANs) were recently introduced. We enhanced the approach by a conditional GAN to model multiple energies using one network. This study compares the use of PhSs, GANs, and conditional GANs as photon source with measurements. An X-ray -based imaging system (i.e. ImagingRing) was modelled in this study. half-value layers (HVLs), focal spot, and Heel effect were measured for subsequent comparison. MC simulations were performed with GATE-RTion v 1.0 considering the geometry and materials of the imaging system with vendor specific schematics. A traditional GAN model as well as the favourable conditional GAN was implemented for PhS generation. Results of the MC simulation were in agreement with the measurements regarding HVL, focal spot, and Heel effect. The conditional GAN performed best with a non-saturated loss function with R1 regularisation and gave similarly results as the traditional GAN approach. GANs proved to be superior to the PhS approach in terms of data storage and calculation overhead. Moreover, a conditional GAN enabled an energy interpolation to separate the network training process from the final required X-ray energies. [ABSTRACT FROM AUTHOR]

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IntroductionThe current deployment of the fifth generation (5G) of wireless communications raises new questions about the potential health effects of exposure to radiofrequency (RF) fields. So far, most of the established biological effects of RF have been known to be caused by heating. We previously reported inhibition of the spontaneous electrical activity of neuronal networks in vitro when exposed to 1.8 GHz signals at specific absorption rates (SAR) well above the guidelines. The present study aimed to assess the effects of RF fields at 3.5 GHz, one of the frequencies related to 5G, on neuronal activity in-vitro. Potential differences in the effects elicited by continuous-wave (CW) and 5G-modulated signals were also investigated.MethodsSpontaneous activity of neuronal cultures from embryonic cortices was recorded using 60-electrode multi-electrode arrays (MEAs) between 17 and 27 days in vitro. The neuronal cultures were subjected to 15 min RF exposures at SAR of 1, 3, and 28 W/kg.ResultsAt SAR close to the guidelines (1 and 3 W/kg), we found no conclusive evidence that 3.5 GHz RF exposure impacts the activity of neurons in vitro. On the contrary, CW and 5G-modulated signals elicited a clear decrease in bursting and total firing rates during RF exposure at high SAR levels (28 W/kg). Our experimental findings extend our previous results, showing that RF, at 1.8 to 3.5 GHz, inhibits the electrical activity of neurons in vitro at levels above environmental standards.

In the theory of gene networks, the mathematical apparatus that uses dynamical systems is fruitfully used. The same is true for the theory of neural networks. In both cases, the purpose of the simulation is to study the properties of phase space, as well as the types and the properties of attractors. The paper compares both models, notes their similarities and considers a number of illustrative examples. A local analysis is carried out in the vicinity of critical points and the necessary formulas are derived.