Input Connections Research Articles

ФУНКЦИОНАЛЬНО-СТОИМОСТНОЙ АНАЛИЗ ДЛЯ СИСТЕМЫ МЕНЕДЖМЕНТА КАЧЕСТВА

The article deals with the urgent task of improving the functional cost analysis in the interests of introducing a quality management system, since the traditional understanding of this analysis does not meet all the requirements of quality management. To solve the problem, a system-object approach was used, which allows presenting the system as an element "Node-Function-Object" (functional object), taking into account a wider range of input and output connections. As a result of the study, an improved method of functional cost analysis has been developed that meets the requirements of ISO 9001:2015 and allows more accurate accounting of the costs necessary to obtain the final product. An example of the decomposition of the production process by means of a system-object approach is given, which made it possible to present the production process in more detail and adequately, in accordance with the requirements of the quality management system and the real input and output links of the processes performed by functional objects.

Stacked Input-Output High Step-Up Buck-Boost DC-DC Converter with Coupled Inductor and Voltage Multiplier

This paper presents three high voltage gain DC-DC converters based on stacked input and output connection of nonisolated buck-boost converters with coupled inductor and voltage multiplier cells. Besides that, an empirical approach for the coupled inductor winding is proposed to show how to reduce the leakage inductance. In this way, the proposed converter achieves high voltage gain, low voltage, and current stress across the components without using an auxiliary clamp circuit, and they present a low component count. Besides that, this paper presents the evaluation of MPPT, modeling, and control of the converters. To validate the theoretical evaluations, three prototypes were built and evaluated experimentally, considering input power of 200 W, input voltage of 37.4 V, output voltage of 400 V, and switching frequency of 50 kHz. Finally, it should be noted that the maximum efficiency achieved is 97.53% at nominal input power.

Sequential addition of neuronal stem cell temporal cohorts generates a feed-forward circuit in the Drosophila larval nerve cord.

How circuits self-assemble starting from neuronal stem cells is a fundamental question in developmental neurobiology. Here, we addressed how neurons from different stem cell lineages wire with each other to form a specific circuit motif. In Drosophila larvae, we combined developmental genetics (twin-spot mosaic analysis with a repressible cell marker, multi-color flip out, permanent labeling) with circuit analysis (calcium imaging, connectomics, network science). For many lineages, neuronal progeny are organized into subunits called temporal cohorts. Temporal cohorts are subsets of neurons born within a tight time window that have shared circuit-level function. We find sharp transitions in patterns of input connectivity at temporal cohort boundaries. In addition, we identify a feed-forward circuit that encodes the onset of vibration stimuli. This feed-forward circuit is assembled by preferential connectivity between temporal cohorts from different lineages. Connectivity does not follow the often-cited early-to-early, late-to-late model. Instead, the circuit is formed by sequential addition of temporal cohorts from different lineages, with circuit output neurons born before circuit input neurons. Further, we generate new tools for the fly community. Our data raise the possibility that sequential addition of neurons (with outputs oldest and inputs youngest) could be one fundamental strategy for assembling feed-forward circuits.

Excessive local host-graft connectivity in aging and amyloid-loaded brain.

Transplantation is a clinically relevant approach for brain repair, but much remains to be understood about influences of the disease environment on transplant connectivity. To explore the effect of amyloid pathology in Alzheimer’s disease (AD) and aging, we examined graft connectivity using monosynaptic rabies virus tracing in APP/PS1 mice and in 16- to 18-month-old wild-type (WT) mice. Transplanted neurons differentiated within 4 weeks and integrated well into the host visual cortex, receiving input from the appropriate brain regions for this area. Unexpectedly, we found a prominent several-fold increase in local inputs, in both amyloid-loaded and aged environments. State-of-the-art deep proteome analysis using mass spectrometry highlights complement system activation as a common denominator of environments promoting excessive local input connectivity. These data therefore reveal the key role of the host pathology in shaping the input connectome, calling for caution in extrapolating results from one pathological condition to another.

The investigation of the signal radiation mechanism of differentGSG‐pads connection methods

AbstractGSG‐PADs are mostly used structures in Integrated Circuits design. As the operating frequency increases, the coupling effect between the input and output GSG‐PADs is enhanced significantly. This causes signal energy leakage and impacts on the performance of adjacent devices in the system. Beyond microwave frequency, the coupling effect between the input and output of the GSG‐PADs is not only negligible but also varies in magnitude with respect to the connection methods of the GSG‐PADs in the circuit. Therefore, investigating the signal radiation properties of different GSG‐PADs connection methods is essential. Hence, three structures with different input and output ground PAD connection methods have been experimented. Based on the theoretical analysis of the electromagnetic field distribution between the pad network, a corresponding equivalent circuit model has been developed for the three structures. To validate the proposed model, the test structures were fabricated in 0.18 μm CMOS process and measured up to 67 GHz. The root‐mean‐square error (RMSE) of the magnitude of return loss and that of insertion loss are less than 0.013 and 0.003, respectively, when the measurement result is compared with the performance results of the proposed model. Finally, establish the complete model of the DUT and test pads to further verify the accuracy of the proposed two‐port GSG‐PADs model. Hence, the findings of the investigation can provide a theoretical basis for optimizing related layout in integrated circuit design.

Macroscale connections of the mouse lateral preoptic area and anterior lateral hypothalamic area.

The macroscale neuronal connections of the lateral preoptic area (LPO) and the caudally adjacent lateral hypothalamic area anterior region (LHAa) were investigated in mice by anterograde and retrograde axonal tracing. Both hypothalamic regions are highly and diversely connected, with connections to >200 gray matter regions spanning the forebrain, midbrain, and rhombicbrain. Intrahypothalamic connections predominate, followed by connections with the cerebral cortex and cerebral nuclei. A similar overall pattern of LPO and LHAa connections contrasts with substantial differences between their input and output connections. Strongest connections include outputs to the lateral habenula, medial septal and diagonal band nuclei, and inputs from rostral and caudal lateral septal nuclei; however, numerous additional robust connections were also observed. The results are discussed in relation to a current model for the mammalian forebrain network that associates LPO and LHAa with a range of functional roles, including reward prediction, innate survival behaviors (including integrated somatomotor and physiological control), and affect. The present data suggest a broad and intricate role for LPO and LHAa in behavioral control, similar in that regard to previously investigated LHA regions, contributing to the finely tuned sensory‐motor integration that is necessary for behavioral guidance supporting survival and reproduction.

Nociceptive and Opioidergic Cell-types, Circuitry, and Function in the Cingulate Cortex

Pain is an unpleasant emotional experience driven by the transformation of sensory neural signals into affective-cognitive information in cortical regions, including the anterior cingulate cortex (ACC). Opioid action in the ACC ameliorates aspects of the aversive quality of pain through mu opioid receptors (MOR). We hypothesized that MOR-expressing and functionally nociceptive ACC neurons represents a crucial neural circuit cell-type to pain affect and pathological attention to chronic pain. Using single nuclei RNA sequencing of murine ACC, we found that three cell-subtypes of Slc17a7 glutamatergic neurons were the most transcriptionally active to noxious stimuli (identified by weighted expression of 139 immediate early genes (IEGs)) and differential gene expression analysis between nuclei from uninjured and chronic neuropathic pain mice. These cell-subtypes all co-expressed Oprm1 with single genetic identifiers—Otof, Figf, Npr3—that we spatially resolved to label layer-specific ensembles in L2/3 IT, L5 ET, and L5 PT neurons, respectively. To gain genetic access to ACC nociceptive-Oprm1 ensembles, we developed an intersectional approach combining genetic elements of IEGs and Oprm1 with retrograde viral recombinases to drive expression of circuit mapping tools and optogenetic actuators with activity-, molecular-, and projection-dependency. We are beginning to mimic opioid analgesia with optical inhibition of ACC MOR+ nociceptive cell-types with an iC++ opsin, while capturing pain-related behaviors with a novel deep-learning system for unbiased pose-estimation of nocifensive behaviors. Our unpublished work will provide additional insight into the input connectivity from subcortical structures and the output projection map of the nociceptive ACC cell-types. Key among these is a possible reciprocal circuit between a subpopulation of medial basolateral amygdalar nociceptive cells and ACC L2 Otof+ cells. Identifying the local cortical and brain-wide structure of specific cell-type networks underlying opioid analgesia can aid the development of circuit targeted treatments with improved selectivity, safety, and lowered addiction liabilities. Grant support from 1DP2GM140923-01 - Harnessing cortical neuromodulation to disrupt pain perception Whitehall Foundation Award in Neurobiology - Integrative cortical circuits encoding attention to pain 1 F32 DA053099-01 (Nora McCall, Corder Lab postdoc) - Modulating pain through cortical endogenous opioid circuits.

ArSMART: An Improved SMART NoC Design Supporting Arbitrary-Turn Transmission

SMART NoC, which transmits unconflicted flits to distant processing elements (PEs) in one cycle through the express bypass, is a high-performance NoC design proposed recently. However, if contention occurs, flits with low priority would not only be buffered but also could not fully utilize bypass. Although there exist several routing algorithms that decrease contentions by rounding busy routers and links, they cannot be directly applicable to SMART since it lacks the support for arbitrary-turn (i.e., the number and direction of turns are free of constraints) routing. Thus, in this article, to minimize contentions and further utilize bypass, we propose an improved SMART NoC, called ArSMART, in which the arbitrary-turn transmission is enabled. Specifically, ArSMART divides the whole NoC into multiple clusters where the route computation is conducted by the cluster controller and the data forwarding is performed by the bufferless reconfigurable router. Since the long-range transmission in SMART NoC needs to bypass the intermediate arbitration, to enable this feature, we directly configure the input and output ports connection rather than applying hop-by-hop table-based arbitration. To further explore the higher communication capabilities, effective adaptive routing algorithms that are compatible with ArSMART are proposed. The route computation overhead, one of the main concerns for adaptive routing algorithms, is hidden by our carefully designed control mechanism. Compared with the state-of-the-art SMART NoC, the experimental results demonstrate an average reduction of 40.7% in application schedule length and 29.7% in energy consumption.

Diversified physiological sensory input connectivity questions the existence of distinct classes of spinal interneurons

SummaryThe spinal cord is engaged in all forms of motor performance but its functions are far from understood. Because network connectivity defines function, we explored the connectivity of muscular, tendon, and tactile sensory inputs among a wide population of spinal interneurons in the lower cervical segments. Using low noise intracellular whole cell recordings in the decerebrated, non-anesthetized cat in vivo, we could define mono-, di-, and trisynaptic inputs as well as the weights of each input. Whereas each neuron had a highly specific input, and each indirect input could moreover be explained by inputs in other recorded neurons, we unexpectedly also found the input connectivity of the spinal interneuron population to form a continuum. Our data hence contrasts with the currently widespread notion of distinct classes of interneurons. We argue that this suggested diversified physiological connectivity, which likely requires a major component of circuitry learning, implies a more flexible functionality.

Orientation and direction tuning align with dendritic morphology and spatial connectivity in mouse visual cortex

The functional properties of neocortical pyramidal cells (PCs), such as direction and orientation selectivity in visual cortex, predominantly derive from their excitatory and inhibitory inputs. For layer 2/3 (L2/3) PCs, the detailed relationship between their functional properties and how they sample and integrate information across cortical space is not fully understood. Here, we study this relationship by combining functional invivotwo-photon calcium imaging, invitro functional circuit mapping, and dendritic reconstruction of the same L2/3 PCs in mouse visual cortex. Our work reveals direct correlations between dendritic morphology and functional input connectivity and the orientation as well as direction tuning of L2/3 PCs. First, the apical dendritic tree is elongated along the postsynaptic preferred orientation, considering the representation of visual space in the cortex as determined by its retinotopic organization. Additionally, sharply orientation-tuned cells show a less complex apical tree compared with broadly tuned cells. Second, in direction-selective L2/3 PCs, the spatial distribution of presynaptic partners is offset from the soma opposite to the preferred direction. Importantly, although the presynaptic excitatory and inhibitory input distributions spatially overlap on average, the excitatory input distribution is spatially skewed along the preferred direction, in contrast to the inhibitory distribution. Finally, the degree of asymmetry is positively correlated with the direction selectivity of the postsynaptic L2/3 PC. These results show that the dendritic architecture and the spatial arrangement of excitatory and inhibitory presynaptic cells of L2/3 PCs play important roles in shaping their orientation and direction tuning.

Research on Advanced Control Method of Multiple Photovoltaic Strings Input Type Recognition

In order to decrease the performance effect of a single string in part of a photovoltaic (PV) array and increase the adaptive PV power generation, independent multiple series-connected PV strings are usually adopted in PV system projects to implement the input configuration. Besides, the conversion circuits with multipath inputs are required to be designed for the corresponding inverters to carry out the control algorithms. Since independent and parallel-connected modes exist for the PV string input in actual engineering projects, it is of great significance to effectively identify different PV inputs automatically for inverter inputs. In order to solve the problems of high hardware circuit requirements and complicated control algorithms in most of the existing control methods, a multiple PV string input type recognition method is proposed. Through the proposed software control algorithm, to realize automatically, accurately and quickly identify the external input connection type of the different photovoltaic strings under the external condition changes. The proposed method is free from increasing the inverter hardware cost, and it enhances the capability of identification and judgment. Moreover, experiments and engineering applications are implemented to verify the proposed control method.

Putting Together Pieces of the Lateral Septum: Multifaceted Functions and Its Neural Pathways.

The lateral septum (LS) is implicated as a hub that regulates a variety of affects, such as reward, feeding, anxiety, fear, sociability, and memory. However, it remains unclear how the LS, previously treated as a structure of homogeneity, exhibits such multifaceted functions. Emerging evidence suggests that different functions of the LS are mediated largely by its diverse input and output connections. It has also become clear that the LS is a heterogeneous region, where its dorsal and ventral poles play dissociable and often opposing roles. This functional heterogeneity can often be explained by distinct dorsal and ventral hippocampal inputs along the LS dorsoventral axis, as well as antagonizing connections between LS subregions. Similarly, outputs from LS subregions to respective downstream targets, such as hypothalamic, preoptic, and tegmental areas, also account for this functional heterogeneity. In this review, we provide an updated perspective on LS subregion classification, connectivity, and functions. We also identify key questions that have yet to be addressed in the field.

Computation, wiring, and plasticity in synaptic clusters.

Synaptic clusters on dendrites are extraordinarily compact computational building blocks. They contribute to key local computations through biophysical and biochemical signaling that utilizes convergence in space and time as an organizing principle. However, these computations can only arise in very special contexts. Dendritic cluster computations, their highly organized input connectivity, and the mechanisms for their formation are closely linked, yet these have not been analyzed as parts of a single process. Here, we examine these linkages. The sheer density of axonal and dendritic arborizations means that there are far more potential connections (close enough for a spine to reach an axon) than actual ones. We see how dendritic clusters draw upon electrical, chemical, and mechano-chemical signaling to implement the rules for formation of connections and subsequent computations. Crucially, the same mechanisms that underlie their functions also underlie their formation.

Research on Resources and Environment of Vietnam’s Higher Education System in Accordance with U21 Ranking of National Higher Education Systems

The network of research universities - Universitas 21 (U21) has developed a global ranking which examines 24 measures of performance indicators across four areas: Resources, Environment, Connectivity, and Output (called U21 Ranking of National Higher Education Systems) to assess the national higher education systems. In which, Resources and Environment are input variables, Connectivity and Output are outcomes. This paper will study Vietnam's policy environment and resources, corresponding to two input measures of the ranking: Resources and Environment. The paper also reviews the experiences of some countries, the current situation of resources, and the impact of the policies on national higher education compared to Vietnam. The authors have proposed six solutions on Resource and Environment to improve the position of the Vietnamese higher education system with the expectation that the higher education system of Vietnam will create positive changes and be in the Top 50 best national higher education systems of U21 Ranking.

Transcriptomic encoding of sensorimotor transformation in the midbrain.

Sensorimotor transformation, a process that converts sensory stimuli into motor actions, is critical for the brain to initiate behaviors. Although the circuitry involved in sensorimotor transformation has been well delineated, the molecular logic behind this process remains poorly understood. Here, we performed high-throughput and circuit-specific single-cell transcriptomic analyses of neurons in the superior colliculus (SC), a midbrain structure implicated in early sensorimotor transformation. We found that SC neurons in distinct laminae expressed discrete marker genes. Of particular interest, Cbln2 and Pitx2 were key markers that define glutamatergic projection neurons in the optic nerve (Op) and intermediate gray (InG) layers, respectively. The Cbln2+ neurons responded to visual stimuli mimicking cruising predators, while the Pitx2+ neurons encoded prey-derived vibrissal tactile cues. By forming distinct input and output connections with other brain areas, these neuronal subtypes independently mediated behaviors of predator avoidance and prey capture. Our results reveal that, in the midbrain, sensorimotor transformation for different behaviors may be performed by separate circuit modules that are molecularly defined by distinct transcriptomic codes.

Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission.

Simple SummaryInformation processing in the brain takes places at multiple stages, each of which is a local network of neurons. The long-range connections between these network stages are sparse and do not change over time. Thus, within each stage information arrives at a sparse subset of input neurons and must be routed to a sparse subset of output neurons. In this theoretical work, we investigate how networks achieve this routing in a self-organized manner without losing information. We show that biologically inspired self-organization entails that input information is distributed to all neurons in the network by strengthening many synapses in the local networks. Thus, after successful self-organization, input information can be read out and decoded from a small number of outputs. We also show that this way of self-organization can still be more energy efficient than creating more long-range in- and output connections.Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes into a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure that rapidly transmits stimulus information despite sparse input and output connectivity.

Freshwater input and ocean connectivity affect habitats and trophic ecology of fishes in Arctic coastal lagoons

Freshwater input and ocean connectivity affect habitats and trophic ecology of fishes in Arctic coastal lagoons

Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature

Ocean data timeseries are vital for a diverse range of stakeholders (ranging from government, to industry, to academia) to underpin research, support decision making, and identify environmental change. However, continuous monitoring and observation of ocean variables is difficult and expensive. Moreover, since oceans are vast, observations are typically sparse in spatial and temporal resolution. In addition, the hostile ocean environment creates challenges for collecting and maintaining data sets, such as instrument malfunctions and servicing, often resulting in temporal gaps of varying lengths. Neural networks (NN) have proven effective in many diverse big data applications, but few oceanographic applications have been tested using modern frameworks and architectures. Therefore, here we demonstrate a “proof of concept” neural network application using a popular “off-the-shelf” framework called “TensorFlow” to predict subsurface ocean variables including dissolved oxygen and nutrient (nitrate, phosphate, and silicate) concentrations, and temperature timeseries and show how these models can be used successfully for gap filling data products. We achieved a final prediction accuracy of over 96% for oxygen and temperature, and mean squared errors (MSE) of 2.63, 0.0099, and 0.78, for nitrates, phosphates, and silicates, respectively. The temperature gap-filling was done with an innovative contextual Long Short-Term Memory (LSTM) NN that uses data before and after the gap as separate feature variables. We also demonstrate the application of a novel dropout based approach to approximate the Bayesian uncertainty of these temperature predictions. This Bayesian uncertainty is represented in the form of 100 monte carlo dropout estimates of the two longest gaps in the temperature timeseries from a model with 25% dropout in the input and recurrent LSTM connections. Throughout the study, we present the NN training process including the tuning of the large number of NN hyperparameters which could pose as a barrier to uptake among researchers and other oceanographic data users. Our models can be scaled up and applied operationally to provide consistent, gap-free data to all data users, thus encouraging data uptake for data-based decision making.

Design of Fuzzy Ensemble Architecture Realized With the Aid of FCM-Based Fuzzy Partition and NN With Weighted LSE Estimation

Neural networks (NNs) with least square error (LSE) estimation form a certain type of single hidden layer feed-forward NNs. In this class of networks, the input connections (weights) and the biases of hidden neurons are generated randomly and fixed after being generated. The output connections are estimated by the LSE method rather than the back-propagation method. The random generation of the input connection weights and the hidden biases results in the larger number of hidden neurons to assure the quality of classification performance. To reduce the number of neurons in the hidden layer while maintaining the classification performance, we apply a “divide and conquer” strategy in this article. In other words, we divide an overall input space into several subspaces by using information granulation technique (Fuzzy C-Means clustering algorithm) and determine the local decision boundaries among related subspaces. A decision boundary defined in the input space can be considered as being composed of several decision boundaries defined in subspaces that form the entire input space. For the decision boundaries defined in the subspaces, their nonlinearity becomes lower in comparison with the one being encountered when considering the entire input space. Through the weighted LSE estimation instead of using the LSE estimation method, the connections of several NNs can be estimated without interfering with each other. After estimating the weights, the decision boundaries defined in the related subspaces are merged to a single decision boundary by using fuzzy ensemble technique. Several machine learning datasets and one real world application dataset are used to evaluate and validate the proposed fuzzy ensemble classifier. Based on the experimental results, the proposed classifier shows better classification performance when compared with the performance of some selected classifiers.

Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network.

Brain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA’s face activation and revealed a hierarchical network for the face processing of rFFA.