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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.

Seizures are characterized by hypersynchronization of neuronal networks. Understanding these networks could provide a critical window for therapeutic control of recurrent seizure activity, i.e., epilepsy. However, imaging seizure networks has largely been limited to microcircuits in vitro or small windows in vivo. Here, we combine fast confocal imaging of genetically encoded calcium indicator (GCaMP)-expressing larval zebrafish with local field potential (LFP) recordings to study epileptiform events at whole-brain and single-neuron levels in vivo. Using an acute seizure model (pentylenetetrazole, PTZ), we reliably observed recurrent electrographic ictal-like events associated with generalized activation of all major brain regions and uncovered a well-preserved anterior-to-posterior seizure propagation pattern. We also examined brain-wide network synchronization and spatiotemporal patterns of neuronal activity in the optic tectum microcircuit. Brain-wide and single-neuronal level analysis of PTZ-exposed and 4-aminopyridine (4-AP)-exposed zebrafish revealed distinct network dynamics associated with seizure and non-seizure hyperexcitable states, respectively. Neuronal ensembles, comprised of coactive neurons, were also uncovered during interictal-like periods. Taken together, these results demonstrate that macro- and micro-network calcium motifs in zebrafish may provide a greater understanding of epilepsy.

The unification of cultural codes forms the basic set of code symbols (image+sense), which are the basis for recognition, orientation, social cooperation, and response to external appeals. The result of unification is a stable system of deterministic goal-setting and behavioral equivalents. Cultural codes, as a derivative of upbringing, education, and cultural landscape, are the result of spatiotemporal transformation (from fields of reception to displacement from consciousness and compression) of organized information constructs - "dominants" encoded in the form of electro/magnetic patterns. "Dominant" in this message is a generalized definition of organized, hierarchical constructs (packages) of information carriers, forced out of the conscious levels of the psyche and continuing to circulate in closed, reverberant neural circuits. The "dominants" forced out of consciousness circulate through recurrent neural circuits, the activity of which is reduced to several stable states, performing the functions of information retention. The electrical and magnetic parameters of the repressed "dominants" are the basic potential of frequency resonance upon presentation of a perceptual/cognitive construct that is close in frequency parameters - a "code key" at the entrance to the psyche system. The basic principle of information exchange is the formation of the initial resonance potential: the arsenal of "dominants," the concentration of information in frequency electro-magnetic patterns. The interaction of the "information universe" with the arsenal of "dominants" is a resonant process of frequency coincidence of the patterns of the "code key" and the "dominant" displaced from consciousness, which results in actualization, the appearance of an object of resonance in the field of voluntary attention, in consciousness. The updated "dominant," as a "resonant operator," determines the dynamics of the system as a whole for the period of relevance. The phase transition in the socio/cultural environment is the result of changes in the hidden meanings of social frames. The dynamics of the perception of the transformation of frames under changing (appearing new) hidden meanings ["habituation">"acceptability">"acceptability”] determines the growth of the frustration potential associated with acquired needs. The transformation of basic cultural codes rebuilds the mechanism of reproduction of the ethnocultural matrix and congruent social structures.

Use of neural networks for calculating flow temperatures of dynamic dual-flow countercurrent heat exchanger is investigated. The optimum neural network architecture for the problem of dynamic heat exchanger modeling is chosen and the optimum numbers of neurons and layers in the network for minimizing standard error are obtained. [ABSTRACT FROM AUTHOR]

We consider a finite system of interacting point processes with memory of variable length modeling a finite but large network of spiking neurons with two different leakage mechanisms. Associated to each neuron there are two point processes, describing its successive spiking and leakage times. For each neuron, the rate of the spiking point process is an exponential function of its membrane potential, with the restriction that the rate takes the value 0 when the membrane potential is 0. At each spiking time, the membrane potential of the neuron resets to 0, and simultaneously, the membrane potentials of the other neurons increase by one unit. The leakage can be modeled in two different ways. In the first way, at each occurrence time of the leakage point process associated to a neuron, the membrane potential of that neuron is reset to 0, with no effect on the other neurons. In the second way, if the membrane potential of the neuron is strictly positive, at each occurrence time of the leakage point process associated to that neuron, its membrane potential decreases by one unit, with no effect on the other neurons. In both cases, the leakage point process of the neurons has constant rate. For both models, we prove that the system has a metastable behavior as the population size diverges. This means that the time at which the system gets trapped by the list of null membrane potentials suitably re-scaled converges to a mean one exponential random time. [ABSTRACT FROM AUTHOR]

The morphometry of the hip and pelvis can be evaluated in native radiographs. Artificial-intelligence-assisted analyses provide objective, accurate, and reproducible results. This study investigates the performance of an artificial intelligence (AI)-based software using deep learning algorithms to measure radiological parameters that identify femoroacetabular impingement and hip dysplasia. Sixty-two radiographs (124 hips) were manually evaluated by three observers and fully automated analyses were performed by an AI-driven software (HIPPO™, ImageBiopsy Lab, Vienna, Austria). We compared the performance of the three human readers with the HIPPO™ using a Bayesian mixed model. For this purpose, we used the absolute deviation from the median ratings of all readers and HIPPO™. Our results indicate a high probability that the AI-driven software ranks better than at least one manual reader for the majority of outcome measures. Hence, fully automated analyses could provide reproducible results and facilitate identifying radiographic signs of hip disorders. [ABSTRACT FROM AUTHOR]

A significant component of the repetitive dynamics during locomotion in vertebrates is generated within the spinal cord. The legged locomotion of mammals is most likely controled by a hierarchical, multi-layer spinal network structure, while the axial circuitry generating the undulatory swimming motion of animals like lamprey is thought to have only a single layer in each segment. Recent experiments have suggested a hybrid network structure in zebrafish larvae in which two types of excitatory interneurons (V2a-I and V2a-II) both make first-order connections to the brain and last-order connections to the motor pool. These neurons are connected by electrical and chemical synapses across segments. Through computational modeling and an asymptotic perturbation approach we show that this interleaved interaction between the two neuron populations allows the spinal network to quickly establish the correct activation sequence of the segments when starting from random initial conditions, as needed for a swimming spurt, and to reduce the dependence of the intersegmental phase difference (ISPD) of the oscillations on the swimming frequency. The latter reduces the frequency dependence of the waveform of the swimming motion. In the model the reduced frequency dependence is largely due to the different impact of chemical and electrical synapses on the ISPD and to the significant spike-frequency adaptation that has been observed experimentally in V2a-II neurons, but not in V2a-I neurons. Our model makes experimentally testable predictions and points to a benefit of the hybrid structure for undulatory locomotion that may not be relevant for legged locomotion. [ABSTRACT FROM AUTHOR]

A growing societal awareness is calling upon scientists to reconsider the use of animals in research, which stimulates the development of translational in vitro models. The physiological and architectural interactions between different cell types within an organ present a challenge to these models, particularly for a complex organ such as the brain. Thus far, in vitro brain models mostly consist of a single cell type and demonstrate little predictive value. Here, we present a co-culture of an epileptic human neocortical biopsy on a layer of human induced pluripotent stem cell (hiPSC)-derived cortical neurons. The activity of the cortical neurons was recorded by a 120-electrode multi-electrode array. Recordings were obtained at 0, 3, and 6 days after assembly and compared to those obtained from cortical neurons without a biopsy. On all three recording days, the hybrid model displayed a firing rate, burst behavior, number of isolated spikes, inter-spike interval, and network bursting pattern that aligns with the characteristics of an epileptic network as reported by others. Thus, this novel model may be a non-animal, translational alternative for testing new therapies up to six days after resection. [ABSTRACT FROM AUTHOR]

Modern computational solutions used in the reconstruction of the global neuronal network arrangement seem to be particularly valuable for research on neuronal disconnection in schizophrenia. However, the vast number of algorithms used in these analyses may be an uncontrolled source of result inconsistency. Our study aimed to verify to what extent the characteristics of the global network organization in schizophrenia depend on the inclusion of a given type of functional connectivity measure. Resting-state EEG recordings from schizophrenia patients and healthy controls were collected. Based on these data, two identical procedures of graph-theory-based network arrangements were computed twice using two different functional connectivity measures (phase lag index, PLI, and phase locking value, PLV). Two series of between-group comparisons regarding global network parameters calculated on the basis of PLI or PLV gave contradictory results. In many cases, the values of a given network index based on PLI were higher in the patients, and the results based on PLV were lower in the patients than in the controls. Additionally, selected network measures were significantly different within the patient group when calculated from PLI or PLV. Our analysis shows that the selection of FC measures significantly affects the parameters of graph-theory-based neuronal network organization and might be an important source of disagreement in network studies on schizophrenia. [ABSTRACT FROM AUTHOR]

[Objective] The transformation from RGB to Munsell color space is a relevant issue for different tasks, such as the identification of soil taxonomy, organic materials, rock materials, skin types, among others. This research aims to develop alternatives based on feedforward networks and the convolutional neural networks to predict the hue, value, and chroma in the Munsell soil-color charts (MSCCs) from RGB images. [Methodology] We used images of Munsell soil-color charts from 2000 and 2009 versions taken from Millota et al. (2018) to train and test the models. A division of 2856 images in 10% for testing, 20% for validation, and 70% for training was used to build the models. [Results] The best approach was the convolutional neural networks for classification with 93% of total accuracy of hue, value, and chroma combination; it comprises three CNN, one for the hue prediction, another for value prediction, and the last one for chroma prediction. However, the three best models show closeness between the prediction and real values according to the CIEDE2000 distance. The cases classified incorrectly with this approach had a CIEDE2000 average of 0.27 and a standard deviation of 1.06. [Conclusions] The models demonstrated better color recognition in uncontrolled environments than the Transformation of Centore, which is the classical method to transform from RGB to HVC. The results were promising, but the model should be tested with real images at different applications, such as soil real images, to classify the soil color.

Biological neural networks are well known for their capacity to process information with extremely low power consumption. Fields such as Artificial Intelligence, with high computational costs, are seeking for alternatives inspired in biological systems. An inspiring alternative is to implement hardware architectures that replicate the behavior of biological neurons but with the flexibility in programming capabilities of an electronic device, all combined with a relatively low operational cost. To advance in this quest, here we analyze the capacity of the HEENS hardware architecture to operate in a similar manner as an in vitro neuronal network grown in the laboratory. For that, we considered data of spontaneous activity in living neuronal cultures of about 400 neurons and compared their collective dynamics and functional behavior with those obtained from direct numerical simulations (in silico) and hardware implementations (in duris silico). The results show that HEENS is capable to mimic both the in vitro and in silico systems with high efficient-cost ratio, and on different network topological designs. Our work shows that compact low-cost hardware implementations are feasible, opening new avenues for future, highly efficient neuromorphic devices and advanced human–machine interfacing. • Efficient hardware replicates in vitro NNs with low-power and real-time operation. • In vitro NNs share similarities with in silico and in duris silico simulations. • In silico and in duris silico similarity makes the latter good to model in vitro data. [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.

In this paper, we study the nonlinear dynamics of a multiplex system consisting of neuronal networks each with an arbitrary number of FitzHugh-Nagumo neurons and intra-connections and delayed couplings. The network contains an autaptic connection formed by the axon of a neuron on its own soma or dendrites. The stability and instability of the network are determined and the existence of bifurcation is discussed. Then, the study turns to validate the theoretical analysis through numerical simulations. Abundant dynamical phenomena of the network are explored, such as coexisting multi-period oscillations and chaotic responses.

Abstract Background Spinal interneurons (INs) relay sensory and motor control information between the brain and body. When this relay circuitry is disrupted from injury or disease, it is devastating to patients due to the lack of native recovery in central nervous system (CNS) tissues. Obtaining a purified population of INs is necessary to better understand their role in normal function and as potential therapies in CNS. The ventral V0 (V0V) INs are excitatory neurons involved in locomotor circuits and are thus of interest for understanding normal and pathological spinal cord function. To achieve scalable amounts of V0V INs, they can be derived from pluripotent sources, such as mouse embryonic stem cells (mESCs), but the resultant culture is heterogenous, obscuring the specific role of V0V INs. This study generated a transgenic mESC line to enrich V0V INs from induced cultures to allow for a scalable, enriched population for future in vitro and in vivo studies. Methods The transgenic Evx1-PAC mESC line was created by CRISPR-Cas9-mediated insertion of puromycin-N-acetyltransferase (PAC) into the locus of V0V IN marker Evx1. Evx1 and PAC mRNA expression were measured by qPCR. Viability staining helped establish the selection protocol for V0V INs derived from Evx1-PAC mESCs inductions. Immunostaining was used to examine composition of selected inductions. Cultures were maintained up to 30 days to examine maturation by expression of mature/synaptic markers, determined by immunostaining, and functional activity in co-cultures with selected motor neurons (MNs) and V2a INs on microelectrode arrays (MEAs). Results V0V IN inductions were best selected with 4 µg/mL puromycin on day 10 to 11 and showed reduction of other IN populations and elimination of proliferative cells. Long-term selected cultures were highly neuronal, expressing neuronal nuclear marker NeuN, dendritic marker MAP2, pre-synaptic marker Bassoon, and glutamatergic marker VGLUT2, with some cholinergic VAChT-expressing cells. Functional studies on MEAs showed that co-cultures with MNs or MNs plus V2a INs created neuronal networks with synchronized bursting. Conclusions Evx1-PAC mESCs can be used to purify V0V IN cultures for largely glutamatergic neurons that can be used in network formation studies or for rodent models requiring transplanted V0V INs.

The human brain contains an estimated 100 billion neurons that must be systematically organized into functional neural circuits for it to function properly. These circuits range from short-range local signaling networks between neighboring neurons to long-range networks formed between various brain regions. Compelling converging evidence indicates that alterations in neural circuits arising from abnormalities during early neuronal development or neurodegeneration contribute significantly to the etiology of neurological disorders. Supporting this notion, efforts to identify genetic causes of these disorders have uncovered an over-representation of genes encoding proteins involved in the processes of neuronal differentiation, maturation, synaptogenesis and synaptic function. Fasciculation and elongation protein zeta-1, a Kinesin-1 adapter, has emerged as a key central player involved in many of these processes. Fasciculation and elongation protein zeta-1-dependent transport of synaptic cargoes and mitochondria is essential for neuronal development and synapse establishment. Furthermore, it acts downstream of guidance cue pathways to regulate axo-dendritic development. Significantly, perturbing its function causes abnormalities in neuronal development and synapse formation both in the brain as well as the peripheral nervous system. Mutations and deletions of the fasciculation and elongation protein zeta-1 gene are linked to neurodevelopmental disorders. Moreover, altered phosphorylation of the protein contributes to neurodegenerative disorders. Together, these findings strongly implicate the importance of fasciculation and elongation protein zeta-1 in the establishment of neuronal circuits and its maintenance.

When the two eyes are presented with highly distinct stimuli, the resulting visual percept generally switches every few seconds between the two monocular images in an irregular fashion, giving rise to a phenomenon known as binocular rivalry. While a host of theoretical studies have explored potential mechanisms for binocular rivalry in the context of evoked model dynamics in response to simple stimuli, here we investigate binocular rivalry directly through complex stimulus reconstructions based on the activity of a two-layer neuronal network model with competing downstream pools driven by disparate monocular stimuli composed of image pixels. To estimate the dynamic percept, we derive a linear input-output mapping rooted in the non-linear network dynamics and iteratively apply compressive sensing techniques for signal recovery. Utilizing a dominance metric, we are able to identify when percept alternations occur and use data collected during each dominance period to generate a sequence of percept reconstructions. We show that despite the approximate nature of the input-output mapping and the significant reduction in neurons downstream relative to stimulus pixels, the dominant monocular image is well-encoded in the network dynamics and improvements are garnered when realistic spatial receptive field structure is incorporated into the feedforward connectivity. Our model demonstrates gamma-distributed dominance durations and well obeys Levelt's four laws for how dominance durations change with stimulus strength, agreeing with key recurring experimental observations often used to benchmark rivalry models. In light of evidence that individuals with autism exhibit relatively slow percept switching in binocular rivalry, we corroborate the ubiquitous hypothesis that autism manifests from reduced inhibition in the brain by systematically probing our model alternation rate across choices of inhibition strength. We exhibit sufficient conditions for producing binocular rivalry in the context of natural scene stimuli, opening a clearer window into the dynamic brain computations that vary with the generated percept and a potential path toward further understanding neurological disorders.

This article provides a review of the increasingly detailed imaging literature on the neurodevelopment of joint attention. Many findings from this literature support and inform the hypothesis that the neurodevelopment of joint attention contributes to the functional development of neural systems for human social cognition. Joint attention begins to develop by 5 months of age and is tantamount to the ability to adopt a common perspective with another person. It involves a whole-brain system with nodes in the: (a) dorsal and medial frontal cortex, (b) orbital frontal/insula cortex, (c) anterior/posterior cingulate cortex, (d) superior temporal cortex, (e) precuneus/parietal cortex, and (f) amygdala and striatum. This system integrates triadic information processing about (a) self-attention/action, (b) information about others' attention/action during social interactions that involve, (c) coordinated attention as well as processing a common referent in space. The results of this new imaging literature have the potential to advance current models of social cognition and the social brain, which rarely consider the contribution of the cognitive neurodevelopment of joint attention. The new neuroscience of joint attention is also extremely valuable for clinical research on social-cognitive neurodevelopmental disorders. This is most clearly the case for autism spectrum disorder (ASD) because it is consistent with the hypothesis of substantial functional neurodevelopmental continuity between the preschool impairments of joint attention, and childhood theory of mind ability that characterizes the development of ASD.

Background: Small clusters comprising hundreds to thousands of neurons are an important level of brain architecture that correlates single neuronal properties to fulfill brain function, but the specific mechanisms through which this scaling occurs are not well understood. In this study, we developed an in vitro experimental platform of small neuronal circuits (islands) to probe the importance of structural properties for their development, physiology, and response to microtrauma. Methods: Primary cortical neurons were plated on a substrate patterned to promote attachment in clusters of hundreds of cells (islands), transduced with GCaMP6f, allowed to mature until 10–13 days in vitro (DIV), and monitored with Ca2+ as a non-invasive proxy for electrical activity. We adjusted two structural factors–island size and cellular density–to evaluate their role in guiding spontaneous activity and network formation in neuronal islands. Results: We found cellular density, but not island size, regulates of circuit activity and network function in this system. Low cellular density islands can achieve many states of activity, while high cellular density biases islands towards a limited regime characterized by low rates of activity and high synchronization, a property we summarized as “flexibility.” The injury severity required for an island to lose activity in 50% of its population was significantly higher in low-density, high flexibility islands. Conclusion: Together, these studies demonstrate flexible living cortical circuits are more resilient to microtrauma, providing the first evidence that initial circuit state may be a key factor to consider when evaluating the consequences of trauma to the cortex. [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., Competing Interests: We declare we have no competing interests., (© 2024 The Author(s).)

The paper addresses the imperative shift towards automation in welding processes, leveraging advanced technologies such as industrial robotic systems. Focusing on the reconstruction and classification of weld joints, it introduces a methodology for automatic trajectory determination. Utilizing a laser profilometer mounted on the robot, weld joints are reconstructed in three dimensions, and spurious data is filtered out through signal processing. A classification algorithm, integrating signal processing and artificial intelligence, accurately categorizes joint profiles, including V-joints and single bevel T-joints. The proposed intelligent and adaptive system enhances welding automation by analyzing point cloud data from laser scanning to optimize welding trajectories. This study establishes a foundational framework for further refinement and broader application in welding automation. Key Points•Introduction of a methodology for automated trajectory determination in welding processes.•Utilization of laser scanning and signal processing for reconstruction and classification of weld joints.•Implementation of an intelligent and adaptive system to optimize welding trajectories., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Haploinsufficiency of the CACNA1A gene, encoding the pore-forming α1 subunit of P/Q-type voltage-gated calcium channels, is associated with a clinically variable phenotype ranging from cerebellar ataxia, to neurodevelopmental syndromes with epilepsy and intellectual disability. To understand the pathological mechanisms of CACNA1A loss-of-function variants, we characterized a human neuronal model for CACNA1A haploinsufficiency, by differentiating isogenic induced pluripotent stem cell lines into glutamatergic neurons, and investigated the effect of CACNA1A haploinsufficiency on mature neuronal networks through a combination of electrophysiology, gene expression analysis, and in silico modeling. We observed an altered network synchronization in CACNA1A+/- networks alongside synaptic deficits, notably marked by an augmented contribution of GluA2-lacking AMPA receptors. Intriguingly, these synaptic perturbations coexisted with increased non-synaptically driven activity, as characterized by inhibition of NMDA and AMPA receptors on micro-electrode arrays. Single-cell electrophysiology and gene expression analysis corroborated this increased intrinsic excitability through reduced potassium channel function and expression. Moreover, we observed partial mitigation of the CACNA1A+/- network phenotype by 4-aminopyridine, a therapeutic intervention for episodic ataxia type 2. Positive modulation of KCa2 channels could reverse the CACNA1A+/- network electrophysiological phenotype. In summary, our study pioneers the characterization of a human induced pluripotent stem cell-derived neuronal model for CACNA1A haploinsufficiency, and has unveiled novel mechanistic insights. Beyond showcasing synaptic deficits, this neuronal model exhibited increased intrinsic excitability mediated by diminished potassium channel function, underscoring its potential as a therapeutic discovery platform with predictive validity., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Neurons differentiate mechanical stimuli force and rate to elicit unique functional responses, driving the need for further tools to generate various mechanical stimuli. Here, cell-internal nanomagnetic forces (iNMF) are introduced by manipulating internalized magnetic nanoparticles with an external magnetic field across cortical neuron networks in vitro. Under iNMF, cortical neurons exhibit calcium (Ca 2+ ) influx, leading to modulation of activity observed through Ca 2+ event rates. Inhibiting particle uptake or altering nanoparticle exposure time reduced the neuronal response to nanomagnetic forces, exposing the requirement of nanoparticle uptake to induce the Ca 2+ response. In highly active cortical networks, iNMF robustly modulates synchronous network activity, which is lasting and repeatable. Using pharmacological blockers, it is shown that iNMF activates mechanosensitive ion channels to induce the Ca 2+ influx. Then, in contrast to transient mechanically evoked neuronal activity, iNMF activates Ca 2+ -activated potassium (K Ca ) channels to stabilize the neuronal membrane potential and induce network activity shifts. The findings reveal the potential of magnetic nanoparticle-mediated mechanical stimulation to modulate neuronal circuit dynamics, providing insights into the biophysics of neuronal computation., (© 2024 Wiley‐VCH GmbH.)

BackgroundSmall clusters comprising hundreds to thousands of neurons are an important level of brain architecture that correlates single neuronal properties to fulfill brain function, but the specific mechanisms through which this scaling occurs are not well understood. In this study, we developed an in vitro experimental platform of small neuronal circuits (islands) to probe the importance of structural properties for their development, physiology, and response to microtrauma.MethodsPrimary cortical neurons were plated on a substrate patterned to promote attachment in clusters of hundreds of cells (islands), transduced with GCaMP6f, allowed to mature until 10–13 days in vitro (DIV), and monitored with Ca2+ as a non-invasive proxy for electrical activity. We adjusted two structural factors–island size and cellular density–to evaluate their role in guiding spontaneous activity and network formation in neuronal islands.ResultsWe found cellular density, but not island size, regulates of circuit activity and network function in this system. Low cellular density islands can achieve many states of activity, while high cellular density biases islands towards a limited regime characterized by low rates of activity and high synchronization, a property we summarized as “flexibility.” The injury severity required for an island to lose activity in 50% of its population was significantly higher in low-density, high flexibility islands.ConclusionTogether, these studies demonstrate flexible living cortical circuits are more resilient to microtrauma, providing the first evidence that initial circuit state may be a key factor to consider when evaluating the consequences of trauma to the cortex.

The integration and the modulation of nociceptive messages at an early stage of their processing in the dorsal horn (DH) of the spinal cord play a key role in the elaboration of pain perception at the cortical level. In this short viewpoint, we will discuss some aspects of the organization and of the plasticity of DH neuronal networks referring in particular to the contribution of our laboratory in this field. [ABSTRACT FROM AUTHOR]

Multi-recording techniques show evidence that neurons coordinate their firing forming ensembles and that brain networks are made by connections between ensembles. While "canonical" microcircuits are composed of interconnected principal neurons and interneurons, it is not clear how they participate in recorded neuronal ensembles: "groups of neurons that show spatiotemporal co-activation". Understanding synapses and their plasticity has become complex, making hard to consider all details to fill the gap between cellular-synaptic and circuit levels. Therefore, two assumptions became necessary: First, whatever the nature of the synapses these may be simplified by "functional connections". Second, whatever the mechanisms to achieve synaptic potentiation or depression, the resultant synaptic weights are relatively stable. Both assumptions have experimental basis cited in this review, and tools to analyze neuronal populations are being developed based on them. Microcircuitry processing followed with multi-recording techniques show temporal sequences of neuronal ensembles resembling computational routines. These sequences can be aligned with the steps of behavioral tasks and behavior can be modified upon their manipulation, supporting the hypothesis that they are memory traces. In vitro, recordings show that these temporal sequences can be contained in isolated tissue of histological scale. Sequences found in control conditions differ from those recorded in pathological tissue obtained from animal disease models and those recorded after the actions of clinically useful drugs to treat disease states, setting the basis for new bioassays to test drugs with potential clinical use. These findings make the neuronal ensembles theoretical framework a dynamic neuroscience paradigm. [ABSTRACT FROM AUTHOR]

We investigate the relationship between the synchronous transition and the power law behavior in spiking networks which are composed of inhibitory neurons and balanced by dc current. In the region of the synchronous transition, the avalanche size and duration distribution obey a power law distribution. We demonstrate the robustness of the power law for event sizes at different parameters and multiple time scales. Importantly, the exponent of the event size and duration distribution can satisfy the critical scaling relation. By changing the network structure parameters in the parameter region of transition, quasicriticality is observed, that is, critical exponents depart away from the criticality while still hold approximately to a dynamical scaling relation. The results suggest that power law statistics can emerge in networks composed of inhibitory neurons when the networks are balanced by external driving signal. [ABSTRACT FROM AUTHOR]

• Scalable network design of Hodgkin–Huxley neurons with heterogeneous dynamics. • Feedback structure for separation of neuron-network dynamics. • Model validation on common neuronal motifs and large-scale networks. We present a novel modular, scalable and adaptable modelling framework to accurately model neuronal networks composed of neurons with different dynamic properties and distinct firing patterns based on a control-inspired feedback structure. We consider three important classes of neurons: inhibitory Fast spiking neurons, excitatory regular spiking with adaptations neurons, and excitatory intrinsic bursting neurons. We also take into consideration two basic means of neuronal interconnection: electrical and chemical synapses. By separating the neuronal dynamics from the network dynamics, we have developed a fully flexible feedback structure that can be further augmented to incorporate additional types of neurons and/or synapses. We use an augmented version of the Hodgkin–Huxley model to describe the individual neuron dynamics and graph theory to define the network structure. We provide simulation results for small fundamental neuron motifs as well as bigger neuronal networks and we verify the accuracy, flexibility and scalability of the proposed method. Therefore, we provide the basis for a comprehensive modelling framework that is able to imitate the dynamics of individual neurons and neuronal networks and is able to replicate basic normal brain function. The structure of the proposed framework is ideal for applications of control and optimization methods both for modelling the effect of pharmacological substances as well as for modelling diseased neuron and network conditions. [ABSTRACT FROM AUTHOR]

Background: Obsessive-compulsive disorder (OCD) is a frequent mental disorder that leads to an enormous impairment in the quality of life. Cognitive-behavioral explanatory approaches are well established. Scientific research on the underlying neurobiology has increased in recent years. Objective: This article reviews current research findings and the etiopathophysiological considerations derived from them. Material and methods: An overview of the genetic, epigenetic, structural, functional, and neurochemical alterations in OCD is presented. Additionally, the possible organic causes that can trigger obsessive-compulsive symptoms are summarized. Results: With respect to OCD a moderate heritability is assumed. On a molecular level, genetic variants and epigenetic variations in the serotonergic, dopaminergic and glutamatergic systems in particular seem to play a role in the pathogenesis of the disease and affect the corresponding neurotransmission. Cortico-striatal-thalamo-cortical loops are neurochemically modulated, and predominance of the activity of the direct excitatory pathway is hypothesized in OCD. Recent research also provides evidence for the involvement of frontoparietal and frontolimbic networks. Obsessive-compulsive symptoms may also have different organic (e.g., immunological) causes. Conclusion: The neurobiology of OCD is partially understood and categorized in an integrative neurobiological model. For the rare secondary immunological causes the concept of "autoimmune OCD" has recently been proposed. The better understanding of the neurobiology of OCD might allow for individualized, personalized treatment approaches in the future. [ABSTRACT FROM AUTHOR]

We address the questions of identifying pairs of interacting neurons from the observation of their spiking activity. The neuronal network is modeled by a system of interacting point processes with memory of variable length. The influence of a neuron on another can be either excitatory or inhibitory. To identify the existence and the nature of an interaction we propose an algorithm based only on the observation of joint activity of the two neurons in successive time slots. This reduces the amount of computation and storage required to run the algorithm, thereby making the algorithm suitable for the analysis of real neuronal data sets. We obtain computable upper bounds for the probabilities of false positive and false negative detection. As a corollary we prove the consistency of the identification algorithm. [ABSTRACT FROM AUTHOR]

The Fitzhugh-Nagumo model (FN model), which is successfully employed in modeling the function of the so-called membrane potential, exhibits various formations in neuronal networks and rich complex dynamics. This work deals with the problem of control and synchronization of the FN reaction-diffusion model. The proposed control law in this study is designed to be uni-dimensional and linear law for the purpose of reducing the cost of implementation. In order to analytically prove this assertion, Lyapunov’s second method is utilized and illustrated numerically in one- and/or two-spatial dimensions.

Multi-recording techniques show evidence that neurons coordinate their firing forming ensembles and that brain networks are made by connections between ensembles. While “canonical” microcircuits are composed of interconnected principal neurons and interneurons, it is not clear how they participate in recorded neuronal ensembles: “groups of neurons that show spatiotemporal co-activation”. Understanding synapses and their plasticity has become complex, making hard to consider all details to fill the gap between cellular-synaptic and circuit levels. Therefore, two assumptions became necessary: First, whatever the nature of the synapses these may be simplified by “functional connections”. Second, whatever the mechanisms to achieve synaptic potentiation or depression, the resultant synaptic weights are relatively stable. Both assumptions have experimental basis cited in this review, and tools to analyze neuronal populations are being developed based on them. Microcircuitry processing followed with multi-recording techniques show temporal sequences of neuronal ensembles resembling computational routines. These sequences can be aligned with the steps of behavioral tasks and behavior can be modified upon their manipulation, supporting the hypothesis that they are memory traces. In vitro, recordings show that these temporal sequences can be contained in isolated tissue of histological scale. Sequences found in control conditions differ from those recorded in pathological tissue obtained from animal disease models and those recorded after the actions of clinically useful drugs to treat disease states, setting the basis for new bioassays to test drugs with potential clinical use. These findings make the neuronal ensembles theoretical framework a dynamic neuroscience paradigm.

The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models. [ABSTRACT FROM AUTHOR]

Having a formal model of neural networks can greatly help in understanding and verifying their properties, behavior, and response to external factors such as disease and medicine. In this paper, we adopt a formal model to represent neurons, some neuronal graphs, and their composition. Some specific neuronal graphs are known for having biologically relevant structures and behaviors and we call them archetypes. These archetypes are supposed to be the basis of typical instances of neuronal information processing. In this paper we study six fundamental archetypes (simple series, series with multiple outputs, parallel composition, negative loop, inhibition of a behavior, and contralateral inhibition), and we consider two ways to couple two archetypes: (i) connecting the output(s) of the first archetype to the input(s) of the second archetype and (ii) nesting the first archetype within the second one. We report and compare two key approaches to the formal modeling and verification of the proposed neuronal archetypes and some selected couplings. The first approach exploits the synchronous programming language Lustre to encode archetypes and their couplings, and to express properties concerning their dynamic behavior. These properties are verified thanks to the use of model checkers. The second approach relies on a theorem prover, the Coq Proof Assistant, to prove dynamic properties of neurons and archetypes. [ABSTRACT FROM AUTHOR]

In this paper, we study the nonlinear dynamics of a multiplex system consisting of neuronal networks each with an arbitrary number of FitzHugh-Nagumo neurons and intra-connections and delayed couplings. The network contains an autaptic connection formed by the axon of a neuron on its own soma or dendrites. The stability and instability of the network are determined and the existence of bifurcation is discussed. Then, the study turns to validate the theoretical analysis through numerical simulations. Abundant dynamical phenomena of the network are explored, such as coexisting multi-period oscillations and chaotic responses. [ABSTRACT FROM AUTHOR]

Selective attention is relevant for goal directed behavior as it allows people to attend to task-relevant target stimuli and to ignore task-irrelevant distractors. Attentional focus at encoding affects subsequent memory for target and distractor stimuli. Remembering selectively more targets than distractors represents memory selectivity. Brain imaging studies suggest that the superior parietal cortex is associated with the dorsal attentional network supporting top-down control of selective attention while the inferior parietal cortex is associated with the ventral attentional network supporting bottom-up attentional orienting. To investigate the roles of the dorsal and ventral networks in the effect of selective attention during encoding on long-term memory, we stimulated the left superior and the right inferior parietal cortex. Building on previous work, we applied transcranial direct current stimulation (tDCS) during a study phase where pictures and words were presented simultaneously and participants had to switch between a picture and a word decision. A subsequent recognition test assessed memory for target and distractor pictures and words. We hypothesized that a relative increase in activity in the dorsal network would boost selective attention while increased activity in the ventral network would impair selective attention. We also expected to find corresponding effects on memory. Enhanced selective attention should lead to higher memory selectivity, while impaired selective attention should lead to lower memory selectivity. Our results replicated that task switching reduced memory selectivity. However, we found no significant effects of tDCS. Thus, the present study questions the effectiveness of the present tDCS protocol for modulating attention during task switching and subsequent memory.

The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of spike trains recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of modules. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.