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Journal articles on the topic 'Complex conductance networks'
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Xiong, Kezhao, Zonghua Liu, Chunhua Zeng, and Baowen Li. "Thermal-siphon phenomenon and thermal/electric conduction in complex networks." National Science Review 7, no. 2 (September 2, 2019): 270–77. http://dx.doi.org/10.1093/nsr/nwz128.
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Abstract In past decades, a lot of studies have been carried out on complex networks and heat conduction in regular lattices. However, very little attention has been paid to the heat conduction in complex networks. In this work, we study (both thermal and electric) energy transport in physical networks rewired from 2D regular lattices. It is found that the network can be transferred from a good conductor to a poor conductor, depending on the rewired network structure and coupling scheme. Two interesting phenomena were discovered: (i) the thermal-siphon effect—namely the heat flux can go from a low-temperature node to a higher-temperature node and (ii) there exits an optimal network structure that displays small thermal conductance and large electrical conductance. These discoveries reveal that network-structured materials have great potential in applications in thermal-energy management and thermal-electric-energy conversion.
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López, Eduardo, Shai Carmi, Shlomo Havlin, Sergey V. Buldyrev, and H. Eugene Stanley. "Anomalous electrical and frictionless flow conductance in complex networks." Physica D: Nonlinear Phenomena 224, no. 1-2 (December 2006): 69–76. http://dx.doi.org/10.1016/j.physd.2006.09.031.
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Nykamp, Duane Q., and Daniel Tranchina. "A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Extension to Slow Inhibitory Synapses." Neural Computation 13, no. 3 (March 1, 2001): 511–46. http://dx.doi.org/10.1162/089976601300014448.
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A previously developed method for efficiently simulating complex networks of integrate-and-fire neurons was specialized to the case in which the neurons have fast unitary postsynaptic conductances. However, inhibitory synaptic conductances are often slower than excitatory ones for cortical neurons, and this difference can have a profound effect on network dynamics that cannot be captured with neurons that have only fast synapses. We thus extend the model to include slow inhibitory synapses. In this model, neurons are grouped into large populations of similar neurons. For each population, we calculate the evolution of a probability density function (PDF), which describes the distribution of neurons over state-space. The population firing rate is given by the flux of probability across the threshold voltage for firing an action potential. In the case of fast synaptic conductances, the PDF was one-dimensional, as the state of a neuron was completely determined by its transmembrane voltage. An exact extension to slow inhibitory synapses increases the dimension of the PDF to two or three, as the state of a neuron now includes the state of its inhibitory synaptic conductance. However, by assuming that the expected value of a neuron's inhibitory conductance is independent of its voltage, we derive a reduction to a one-dimensional PDF and avoid increasing the computational complexity of the problem. We demonstrate that although this assumption is not strictly valid, the results of the reduced model are surprisingly accurate.
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Narantsatsralt, Ulzii-Utas, and Sanggil Kang. "Social Network Community Detection Using Agglomerative Spectral Clustering." Complexity 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3719428.
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Community detection has become an increasingly popular tool for analyzing and researching complex networks. Many methods have been proposed for accurate community detection, and one of them is spectral clustering. Most spectral clustering algorithms have been implemented on artificial networks, and accuracy of the community detection is still unsatisfactory. Therefore, this paper proposes an agglomerative spectral clustering method with conductance and edge weights. In this method, the most similar nodes are agglomerated based on eigenvector space and edge weights. In addition, the conductance is used to identify densely connected clusters while agglomerating. The proposed method shows improved performance in related works and proves to be efficient for real life complex networks from experiments.
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Liao, Zhifang, Lite Gu, Xiaoping Fan, Yan Zhang, and Chuanqi Tang. "Detecting the Structural Hole for Social Communities Based on Conductance–Degree." Applied Sciences 10, no. 13 (June 29, 2020): 4525. http://dx.doi.org/10.3390/app10134525.
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It has been shown that identifying the structural holes in social networks may help people analyze complex networks, which is crucial in community detection, diffusion control, viral marketing, and academic activities. Structural holes bridge different communities and gain access to multiple sources of information flow. In this paper, we devised a structural hole detection algorithm, known as the Conductance–Degree structural hole detection algorithm (CD-SHA), which computes the conductance and degree score of a vertex to identify the structural hole spanners in social networks. Next, we proposed an improved label propagation algorithm based on conductance (C-LPA) to filter the jamming nodes, which have a high conductance and degree score but are not structural holes. Finally, we evaluated the performance of the algorithm on different real-world networks, and we calculated several metrics for both structural holes and communities. The experimental results show that the algorithm can detect the structural holes and communities accurately and efficiently.
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Li, Xujun, Yezheng Liu, Yuanchun Jiang, and Xiao Liu. "Identifying social influence in complex networks: A novel conductance eigenvector centrality model." Neurocomputing 210 (October 2016): 141–54. http://dx.doi.org/10.1016/j.neucom.2015.11.123.
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Case, Daniel J., Jean-Régis Angilella, and Adilson E. Motter. "Spontaneous oscillations and negative-conductance transitions in microfluidic networks." Science Advances 6, no. 20 (May 2020): eaay6761. http://dx.doi.org/10.1126/sciadv.aay6761.
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The tendency for flows in microfluidic systems to behave linearly poses challenges for designing integrated flow control schemes to carry out complex fluid processing tasks. This hindrance precipitated the use of numerous external control devices to manipulate flows, thereby thwarting the potential scalability and portability of lab-on-a-chip technology. Here, we devise a microfluidic network exhibiting nonlinear flow dynamics that enable new mechanisms for on-chip flow control. This network is shown to exhibit oscillatory output patterns, bistable flow states, hysteresis, signal amplification, and negative-conductance transitions, all without reliance on dedicated external control hardware, movable parts, flexible components, or oscillatory inputs. These dynamics arise from nonlinear fluid inertia effects in laminar flows that we amplify and harness through the design of the network geometry. These results, which are supported by theory and simulations, have the potential to inspire development of new built-in control capabilities, such as on-chip timing and synchronized flow patterns.
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CARTLING, BO. "A LOW-DIMENSIONAL, TIME-RESOLVED AND ADAPTING MODEL NEURON." International Journal of Neural Systems 07, no. 03 (July 1996): 237–46. http://dx.doi.org/10.1142/s012906579600021x.
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A low-dimensional, time-resolved and adapting model neuron is formulated and evaluated. The model is an extension of the integrate-and-fire type of model with respect to adaptation and of a recent adapting firing-rate model with respect to time-resolution. It is obtained from detailed conductance-based models by a separation of fast and slow ionic processes of action potential generation. The model explicitly includes firing-rate regulation via the slow afterhyperpolarization phase of action potentials, which is controlled by calcium-sensitive potassium channels. It is demonstrated that the model closely reproduces the firing pattern and excitability behaviour of a detailed multicompartment conductance-based model of a neocortical pyramidal cell. The inclusion of adaptation in a model neuron is important for its capability to generate complex dynamics of networks of interconnected neurons. The time-resolution is required for studies of systems in which the temporal aspects of neural coding are important. The simplicity of the model facilitates analytical studies, insight into neurocomputational mechanisms and simulations of large-scale systems. The capability to generate complex network computations may also make the model useful in practical applications of artificial neural networks.
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di Volo, Matteo, Alberto Romagnoni, Cristiano Capone, and Alain Destexhe. "Biologically Realistic Mean-Field Models of Conductance-Based Networks of Spiking Neurons with Adaptation." Neural Computation 31, no. 4 (April 2019): 653–80. http://dx.doi.org/10.1162/neco_a_01173.
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Accurate population models are needed to build very large-scale neural models, but their derivation is difficult for realistic networks of neurons, in particular when nonlinear properties are involved, such as conductance-based interactions and spike-frequency adaptation. Here, we consider such models based on networks of adaptive exponential integrate-and-fire excitatory and inhibitory neurons. Using a master equation formalism, we derive a mean-field model of such networks and compare it to the full network dynamics. The mean-field model is capable of correctly predicting the average spontaneous activity levels in asynchronous irregular regimes similar to in vivo activity. It also captures the transient temporal response of the network to complex external inputs. Finally, the mean-field model is also able to quantitatively describe regimes where high- and low-activity states alternate (up-down state dynamics), leading to slow oscillations. We conclude that such mean-field models are biologically realistic in the sense that they can capture both spontaneous and evoked activity, and they naturally appear as candidates to build very large-scale models involving multiple brain areas.
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Rote, Günter. "Characterization of the Response Maps of Alternating-Current Networks." Electronic Journal of Linear Algebra 36, no. 36 (October 14, 2020): 698–703. http://dx.doi.org/10.13001/ela.2020.4981.
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In an alternating-current network, each edge has a complex conductance with positive real part. The response map is the linear map from the vector of voltages at a subset of boundary nodes to the vector of currents flowing into the network through these nodes. In this paper, it is proved that the known necessary conditions for a linear map to be a response map are sufficient, and we show how to construct an appropriate network for a given response map.
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Morell, Antoni, Elvis Díaz Machado, Enrique Miranda, Guillem Boquet, and Jose Lopez Vicario. "Ternary Neural Networks Based on on/off Memristors: Set-Up and Training." Electronics 11, no. 10 (May 10, 2022): 1526. http://dx.doi.org/10.3390/electronics11101526.
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Neuromorphic systems based on hardware neural networks (HNNs) are expected to be an energy and time-efficient computing architecture for solving complex tasks. In this paper, we consider the implementation of deep neural networks (DNNs) using crossbar arrays of memristors. More specifically, we considered the case where such devices can be configured in just two states: the low-resistance state (LRS) and the high-resistance state (HRS). HNNs suffer from several non-idealities that need to be solved when mapping our software-based models. A clear example in memristor-based neural networks is conductance variability, which is inherent to resistive switching devices, so achieving good performance in an HNN largely depends on the development of reliable weight storage or, alternatively, mitigation techniques against weight uncertainty. In this manuscript, we provide guidelines for a system-level designer where we take into account several issues related to the set-up of the HNN, such as what the appropriate conductance value in the LRS is or the adaptive conversion of current outputs at one stage to input voltages for the next stage. A second contribution is the training of the system, which is performed via offline learning, and considering the hardware imperfections, which in this case are conductance fluctuations. Finally, the resulting inference system is tested in two well-known databases from MNIST, showing that is competitive in terms of classification performance against the software-based counterpart. Additional advice and insights on system tuning and expected performance are given throughout the paper.
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Chizhov, Anton V., and Lyle J. Graham. "A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex." PLOS Computational Biology 17, no. 8 (August 16, 2021): e1009007. http://dx.doi.org/10.1371/journal.pcbi.1009007.
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A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general operating principles. We present a strategy that successively maps a relatively detailed biophysical population model, comprising conductance-based Hodgkin-Huxley type neuron models with connectivity rules derived from anatomical data, to various representations with fewer parameters, finishing with a firing rate network model that permits analysis. We apply this methodology to primary visual cortex of higher mammals, focusing on the functional property of stimulus orientation selectivity of receptive fields of individual neurons. The mapping produces compact expressions for the parameters of the abstract model that clearly identify the impact of specific electrophysiological and anatomical parameters on the analytical results, in particular as manifested by specific functional signatures of visual cortex, including input-output sharpening, conductance invariance, virtual rotation and the tilt after effect. Importantly, qualitative differences between model behaviours point out consequences of various simplifications. The strategy may be applied to other neuronal systems with appropriate modifications.
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Merle, L., A. Delpoux, A. Mlayah, and J. Grisolia. "Multiscale modeling of the dynamical conductivity of self-assembled nanoparticle networks: Numerical simulations vs analytical models." Journal of Applied Physics 132, no. 1 (July 7, 2022): 015107. http://dx.doi.org/10.1063/5.0097997.
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Impedance spectroscopy experiments are able to reveal the fundamental charge transport properties of a wide variety of complex disordered and nano-structured materials provided that appropriate modeling tools are used. In this paper, we present a numerical simulation-based approach to model the dynamical conductivity of networks formed by self-assembled metal nanoparticles. Inter-particle nano-resistance and nano-capacitance are implemented at the nano-scale assuming inter-particle charge transfer and accumulation mechanisms that can be adapted depending on the nature of the nano-particles and the surrounding medium. The actual positions and spatial arrangements of the nanoparticles within the network are taken into consideration, allowing the attributes of percolating conducting routes to be extracted, classified, and compared in terms of path conductance and statistical distribution of path lengths. Our findings are contrasted to those obtained using analytic models, which are commonly used, but rely on strong assumptions about the electric properties of the conducting paths. We address these assumptions and show that in the case of weakly disordered systems, there is a general agreement between numerical simulations and analytic modeling-based approaches. In the case of disordered networks where the nano-particle size and position fluctuations are included, we show that the path length distribution is frequency-dependent and can differ significantly from the lognormal distribution usually assumed in the analytic models. The impedance of individual pathways may be extracted from the numerical simulations; we discovered that the conductance and susceptance of a specific path are frequency-dependent and inversely proportional to the path length only in ordered networks. Strong scattering of conductance values is caused by disorder effects. The developed numerical approach is generic and applies to most nano-devices where charge transport relies on percolation; it allows to bridge the gap between the nano-scale and micro-scale electric characteristics and, thus, permits a deeper understanding of the charge transport properties of nano-structured materials.
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Yang, Xin, Guangjun Zhang, Xueren Li, and Dong Wang. "The Synchronization Behaviors of Coupled Fractional-Order Neuronal Networks under Electromagnetic Radiation." Symmetry 13, no. 11 (November 18, 2021): 2204. http://dx.doi.org/10.3390/sym13112204.
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Previous studies on the synchronization behaviors of neuronal networks were constructed by integer-order neuronal models. In contrast, this paper proposes that the above topics of symmetrical neuronal networks are constructed by fractional-order Hindmarsh–Rose (HR) models under electromagnetic radiation. They are then investigated numerically. From the research results, several novel phenomena and conclusions can be drawn. First, for the two symmetrical coupled neuronal models, the synchronization degree is influenced by the fractional-order q and the feedback gain parameter k1. In addition, the fractional-order or the parameter k1 can induce the synchronization transitions of bursting synchronization, perfect synchronization and phase synchronization. For perfect synchronization, the synchronization transitions of chaotic synchronization and periodic synchronization induced by q or parameter k1 are also observed. In particular, when the fractional-order is small, such as 0.6, the synchronization transitions are more complex. Then, for a symmetrical ring neuronal network under electromagnetic radiation, with the change in the memory-conductance parameter β of the electromagnetic radiation, k1 and q, compared with the fractional-order HR model’s ring neuronal network without electromagnetic radiation, the synchronization behaviors are more complex. According to the simulation results, the influence of k1 and q can be summarized into three cases: β>0.02, −0.06<β<0.02 and β<−0.06. The influence rules and some interesting phenomena are investigated.
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Guerrero, Manuel, Consolación Gil, Francisco G. Montoya, Alfredo Alcayde, and Raúl Baños. "Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks." Mathematics 8, no. 11 (November 17, 2020): 2048. http://dx.doi.org/10.3390/math8112048.
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Real-world complex systems are often modeled by networks such that the elements are represented by vertices and their interactions are represented by edges. An important characteristic of these networks is that they contain clusters of vertices densely linked amongst themselves and more sparsely connected to nodes outside the cluster. Community detection in networks has become an emerging area of investigation in recent years, but most papers aim to solve single-objective formulations, often focused on optimizing structural metrics, including the modularity measure. However, several studies have highlighted that considering modularityas a unique objective often involves resolution limit and imbalance inconveniences. This paper opens a new avenue of research in the study of multi-objective variants of the classical community detection problem by applying multi-objective evolutionary algorithms that simultaneously optimize different objectives. In particular, they analyzed two multi-objective variants involving not only modularity but also the conductance metric and the imbalance in the number of nodes of the communities. With this aim, a new Pareto-based multi-objective evolutionary algorithm is presented that includes advanced initialization strategies and search operators. The results obtained when solving large-scale networks representing real-life power systems show the good performance of these methods and demonstrate that it is possible to obtain a balanced number of nodes in the clusters formed while also having high modularity and conductance values.
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Mall, Raghvendra, Ehsan Ullah, Khalid Kunji, Michele Ceccarelli, and Halima Bensmail. "An unsupervised disease module identification technique in biological networks using novel quality metric based on connectivity, conductance and modularity." F1000Research 7 (March 26, 2018): 378. http://dx.doi.org/10.12688/f1000research.14258.1.
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Disease processes are usually driven by several genes interacting in molecular modules or pathways leading to the disease. The identification of such modules in gene or protein networks is the core of computational methods in biomedical research. With this pretext, the Disease Module Identification (DMI) DREAM Challenge was initiated as an effort to systematically assess module identification methods on a panel of 6 diverse genomic networks. In this paper, we propose a generic refinement method based on ideas of merging and splitting the hierarchical tree obtained from any community detection technique for constrained DMI in biological networks. The only constraint was that size of community is in the range [3, 100]. We propose a novel model evaluation metric, called F-score, computed from several unsupervised quality metrics like modularity, conductance and connectivity to determine the quality of a graph partition at given level of hierarchy. We also propose a quality measure, namely Inverse Confidence, which ranks and prune insignificant modules to obtain a curated list of candidate disease modules (DM) for biological network. The predicted modules are evaluated on the basis of the total number of unique candidate modules that are associated with complex traits and diseases from over 200 genome-wide association study (GWAS) datasets. During the competition, we identified 42 modules, ranking 15th at the official false detection rate (FDR) cut-off of 0.05 for identifying statistically significant DM in the 6 benchmark networks. However, for stringent FDR cut-offs 0.025 and 0.01, the proposed method identified 31 (rank 9) and 16 DMIs (rank 10) respectively. From additional analysis, our proposed approach detected a total of 44 DM in the networks in comparison to 60 for the winner of DREAM Challenge. Interestingly, for several individual benchmark networks, our performance was better or competitive with the winner.
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Gomes, Tristan da Câmara Santa Clara, Nicolas Marchal, Flavio Abreu Araujo, and Luc Piraux. "Flexible Active Peltier Coolers Based on Interconnected Magnetic Nanowire Networks." Nanomaterials 13, no. 11 (May 25, 2023): 1735. http://dx.doi.org/10.3390/nano13111735.
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Thermoelectric energy conversion based on flexible materials has great potential for applications in the fields of low-power heat harvesting and solid-state cooling. Here, we show that three-dimensional networks of interconnected ferromagnetic metal nanowires embedded in a polymer film are effective flexible materials as active Peltier coolers. Thermocouples based on Co-Fe nanowires exhibit much higher power factors and thermal conductivities near room temperature than other existing flexible thermoelectric systems, with a power factor for Co-Fe nanowire-based thermocouples of about 4.7 mW/K2m at room temperature. The effective thermal conductance of our device can be strongly and rapidly increased by active Peltier-induced heat flow, especially for small temperature differences. Our investigation represents a significant advance in the fabrication of lightweight flexible thermoelectric devices, and it offers great potential for the dynamic thermal management of hot spots on complex surfaces.
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Zhou, Dawei, Si Zhang, Mehmet Yigit Yildirim, Scott Alcorn, Hanghang Tong, Hasan Davulcu, and Jingrui He. "High-Order Structure Exploration on Massive Graphs." ACM Transactions on Knowledge Discovery from Data 15, no. 2 (April 2021): 1–26. http://dx.doi.org/10.1145/3425637.
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Modeling and exploring high-order connectivity patterns, also called network motifs, are essential for understanding the fundamental structures that control and mediate the behavior of many complex systems. For example, in social networks, triangles have been proven to play the fundamental role in understanding social network communities; in online transaction networks, detecting directed looped transactions helps identify money laundering activities; in personally identifiable information networks, the star-shaped structures may correspond to a set of synthetic identities. Despite the ubiquity of such high-order structures, many existing graph clustering methods are either not designed for the high-order connectivity patterns, or suffer from the prohibitive computational cost when modeling high-order structures in the large-scale networks. This article generalizes the challenges in multiple dimensions. First ( Model ), we introduce the notion of high-order conductance, and define the high-order diffusion core, which is based on a high-order random walk induced by the user-specified high-order network structure. Second ( Algorithm ), we propose a novel high-order structure-preserving graph clustering framework named HOSGRAP , which partitions the graph into structure-rich clusters in polylogarithmic time with respect to the number of edges in the graph. Third ( Generalization ), we generalize our proposed algorithm to solve the real-world problems on various types of graphs, such as signed graphs, bipartite graphs, and multi-partite graphs. Experimental results on both synthetic and real graphs demonstrate the effectiveness and efficiency of the proposed algorithms.
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Hagiwara, Naruki, Shoma Sekizaki, Yuji Kuwahara, Tetsuya Asai, and Megumi Akai-Kasaya. "Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses." Polymers 13, no. 2 (January 19, 2021): 312. http://dx.doi.org/10.3390/polym13020312.
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Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.
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Yang, Fen, Hossein Moayedi, and Amir Mosavi. "Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks." Sustainability 13, no. 17 (September 3, 2021): 9898. http://dx.doi.org/10.3390/su13179898.
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Predicting the level of dissolved oxygen (DO) is an important issue ensuring the sustainability of the inhabitants of a river. A prediction model can predict the DO level using a historical dataset with regard to water temperature, pH, and specific conductance for a given river. The model can be built using sophisticated computational procedures such as multi-layer perceptron-based artificial neural networks. Different types of networks can be constructed for this purpose. In this study, the authors constructed three networks, namely, multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE). The networks were trained using the datasets collected from the Klamath River Station, Oregon, USA, for the period 2015–2018. We found that the trained networks could predict the DO level of 2019. We also found that both BHA- and SCE-based networks could predict the level of DO using a relatively simple configuration compared to that of MVO. From the viewpoints of absolute errors and Pearson’s correlation coefficient, MVO- and SCE-based networks performed better than BHA-based networks. In synopsis, the authors recommend MVO- and MLP-based artificial neural networks for predicting the DO level of a river.
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Zhang, Jie, Lingkai Tang, Bo Liao, Xiaoshu Zhu, and Fang-Xiang Wu. "Finding Community Modules of Brain Networks Based on PSO with Uniform Design." BioMed Research International 2019 (November 17, 2019): 1–14. http://dx.doi.org/10.1155/2019/4979582.
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The brain has the most complex structures and functions in living organisms, and brain networks can provide us an effective way for brain function analysis and brain disease detection. In brain networks, there exist some important neural unit modules, which contain many meaningful biological insights. It is appealing to find the neural unit modules and obtain their affiliations. In this study, we present a novel method by integrating the uniform design into the particle swarm optimization to find community modules of brain networks, abbreviated as UPSO. The difference between UPSO and the existing ones lies in that UPSO is presented first for detecting community modules. Several brain networks generated from functional MRI for studying autism are used to verify the proposed algorithm. Experimental results obtained on these brain networks demonstrate that UPSO can find community modules efficiently and outperforms the other competing methods in terms of modularity and conductance. Additionally, the comparison of UPSO and PSO also shows that the uniform design plays an important role in improving the performance of UPSO.
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Hong, Mei. "Key Technology of Electronic Nose Gas Recognizer Based on Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 14, no. 10 (October 26, 2018): 68. http://dx.doi.org/10.3991/ijoe.v14i10.9304.
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<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">Electronic nose gas recognizer is a kind of instrument simulating biological olfactory function for gas detection, which is widely applied in underground construction, aerospace, medical treatment and other fields. The sensing mechanism of the wireless sensor is complex. The wireless sensor array can realize the cross-response of the mixed gas, as well as data acquisition, processing and transmission by wireless transmission. This study applies the wireless sensor array to the electronic nose gas recognition technology, and conducts detection and recognition of three kinds of volatile gas, as well as analyzes the transient response of four wireless sensors and the transient response of wireless sensor array. It is found that the transient response curves are related to the characteristics and sample properties of wireless sensors, but not directly related to sample components. The whole transient response process includes four processes, namely steady state, rising process, maximum response and falling process. The response curve change of wireless sensor array to engine oil volatile gas is similar to that of diesel oil, but the conductance value is smaller than that of diesel oil gas response curve.</span>
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Lourenço, J., Q. R. Al-Taai, A. Al-Khalidi, E. Wasige, and J. Figueiredo. "Resonant Tunnelling Diode – Photodetectors for spiking neural networks." Journal of Physics: Conference Series 2407, no. 1 (December 1, 2022): 012047. http://dx.doi.org/10.1088/1742-6596/2407/1/012047.
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Abstract Spike-based neuromorphic devices promise to alleviate the energy greed of the artificial intelligence hardware by using spiking neural networks (SNNs), which employ neuron like units to process information through the timing of the spikes. These neuron-like devices only consume energy when active. Recent works have shown that resonant tunnelling diodes (RTDs) incorporating optoelectronic functionalities such as photodetection and light emission can play a major role on photonic SNNs. RTDs are devices that display an N-shaped current-voltage characteristics capable of providing negative differential conductance (NDC) over a range of the operating voltages. Specifically, RTD photodetectors (RTD-PDs) show promise due to their unique mixture of the structural simplicity while simultaneously providing highly complex non-linear behavior. The goal of this work is to present a systematic study of the how the thickness of the RTD-PD light absorption layers (100, 250, 500 nm) and the device size impacts on the performance of InGaAs RTD-PDs, namely on its responsivity and time response when operating in the third (1550 nm) optical transmission window. Our focus is on the overall characterization of the device optoelectronic response including the impact of the light absorption on the device static current-voltage characteristic, the responsivity and the photodetection time response. For the static characterization, the devices I-V curves were measured under dark conditions and under illumination, giving insights on the light induced I-V tunability effect. The RTD-PD responsivity was compared to the response of a commercial photodetector. The characterization of the temporal response included its capacity to generate optical induced neuronal-like electrical spike, that is, when working as an opto-to-electrical spike converter. The experimental data obtained at each characterization phase is being used for the evaluation and refinement of a behavioral model for RTD-PD devices under construction.
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Haider, Bilal, Alvaro Duque, Andrea R. Hasenstaub, Yuguo Yu, and David A. McCormick. "Enhancement of Visual Responsiveness by Spontaneous Local Network Activity In Vivo." Journal of Neurophysiology 97, no. 6 (June 2007): 4186–202. http://dx.doi.org/10.1152/jn.01114.2006.
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Spontaneous activity within local circuits affects the integrative properties of neurons and networks. We have previously shown that neocortical network activity exhibits a balance between excitatory and inhibitory synaptic potentials, and such activity has significant effects on synaptic transmission, action potential generation, and spike timing. However, whether such activity facilitates or reduces sensory responses has yet to be clearly determined. We examined this hypothesis in the primary visual cortex in vivo during slow oscillations in ketamine-xylazine anesthetized cats. We measured network activity (Up states) with extracellular recording, while simultaneously recording postsynaptic potentials (PSPs) and action potentials in nearby cells. Stimulating the receptive field revealed that spiking responses of both simple and complex cells were significantly enhanced (>2-fold) during network activity, as were spiking responses to intracellular injection of varying amplitude artificial conductance stimuli. Visually evoked PSPs were not significantly different in amplitude during network activity or quiescence; instead, spontaneous depolarization caused by network activity brought these evoked PSPs closer to firing threshold. Further examination revealed that visual responsiveness was gradually enhanced by progressive membrane potential depolarization. These spontaneous depolarizations enhanced responsiveness to stimuli of varying contrasts, resulting in an upward (multiplicative) scaling of the contrast response function. Our results suggest that small increases in ongoing balanced network activity that result in depolarization may provide a rapid and generalized mechanism to control the responsiveness (gain) of cortical neurons, such as occurs during shifts in spatial attention.
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Yactayo-Chang, Jessica P., and Anna K. Block. "The impact of climate change on maize chemical defenses." Biochemical Journal 480, no. 16 (August 25, 2023): 1285–98. http://dx.doi.org/10.1042/bcj20220444.
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Climate change is increasingly affecting agriculture, both at the levels of crops themselves, and by altering the distribution and damage caused by insect or microbial pests. As global food security depends on the reliable production of major crops such as maize (Zea mays), it is vital that appropriate steps are taken to mitigate these negative impacts. To do this a clear understanding of what the impacts are and how they occur is needed. This review focuses on the impact of climate change on the production and effectiveness of maize chemical defenses, including volatile organic compounds, terpenoid phytoalexins, benzoxazinoids, phenolics, and flavonoids. Drought, flooding, heat stress, and elevated concentrations of atmospheric carbon dioxide, all impact the production of maize chemical defenses, in a compound and tissue-specific manner. Furthermore, changes in stomatal conductance and altered soil conditions caused by climate change can impact environmental dispersal and effectiveness certain chemicals. This can alter both defensive barrier formation and multitrophic interactions. The production of defense chemicals is controlled by stress signaling networks. The use of similar networks to co-ordinate the response to abiotic and biotic stress can lead to complex integration of these networks in response to the combinatorial stresses that are likely to occur in a changing climate. The impact of multiple stressors on maize chemical defenses can therefore be different from the sum of the responses to individual stressors and challenging to predict. Much work remains to effectively leverage these protective chemicals in climate-resilient maize.
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Chiossi, Francesco, Thomas Kosch, Luca Menghini, Steeven Villa, and Sven Mayer. "SensCon: Embedding Physiological Sensing into Virtual Reality Controllers." Proceedings of the ACM on Human-Computer Interaction 7, MHCI (September 11, 2023): 1–32. http://dx.doi.org/10.1145/3604270.
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Virtual reality experiences increasingly use physiological data for virtual environment adaptations to evaluate user experience and immersion. Previous research required complex medical-grade equipment to collect physiological data, limiting real-world applicability. To overcome this, we present SensCon for skin conductance and heart rate data acquisition. To identify the optimal sensor location in the controller, we conducted a first study investigating users' controller grasp behavior. In a second study, we evaluated the performance of SensCon against medical-grade devices in six scenarios regarding user experience and signal quality. Users subjectively preferred SensCon in terms of usability and user experience. Moreover, the signal quality evaluation showed satisfactory accuracy across static, dynamic, and cognitive scenarios. Therefore, SensCon reduces the complexity of capturing and adapting the environment via real-time physiological data. By open-sourcing SensCon, we enable researchers and practitioners to adapt their virtual reality environment effortlessly. Finally, we discuss possible use cases for virtual reality-embedded physiological sensing.
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Valverde, P., T. Kawai, and M. A. Taubman. "Potassium Channel-blockers as Therapeutic Agents to Interfere with Bone Resorption of Periodontal Disease." Journal of Dental Research 84, no. 6 (June 2005): 488–99. http://dx.doi.org/10.1177/154405910508400603.
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Inflammatory lesions of periodontal disease contain all the cellular components, including abundant activated/memory T- and B-cells, necessary to control immunological interactive networks and to accelerate bone resorption by RANKL-dependent and -independent mechanisms. Blockade of RANKL function has been shown to ameliorate periodontal bone resorption and other osteopenic disorders without affecting inflammation. Development of therapies aimed at decreasing the expression of RANKL and pro-inflammatory cytokines by T-cells constitutes a promising strategy to ameliorate not only bone resorption, but also inflammation. Several reports have demonstrated that the potassium channels Kv1.3 and IKCa1, through the use of selective blockers, play important roles in T-cell-mediated events, including T-cell proliferation and the production of pro-inflammatory cytokines. More recently, a potassium channel-blocker for Kv1.3 has been shown to down-regulate bone resorption by decreasing the ratio of RANKL-to-OPG expression by memory-activated T-cells. In this article, we first summarize the mechanisms by which chronically activated/memory T-cells, in concert with B-cells and macrophages, trigger inflammatory bone resorption. Then, we describe the main structural and functional characteristics of potassium channels Kv1.3 and IKCa1 in some of the cells implicated in periodontal disease progression. Finally, this review elucidates some recent advances in the use of potassium channel-blockers of Kv1.3 and IKCa1 to ameliorate the clinical signs or side-effects of several immunological disorders and to decrease inflammatory bone resorption in periodontal disease. ABBREVIATIONS: AICD, activation-induced cell death; APC, antigen-presenting cells; B(K), large conductance; CRAC, calcium release-activated calcium channels; DC, dendritic cell; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; IFN-γ, interferon-γ; IP3, inositol (1,4,5)-triphosphate; (K)ir, inward rectifier; JNK, c-Jun N-terminal kinase; I(K), intermediate conductance; LPS, lipopolysaccharide; L, ligand; MCSF, macrophage colony-stimulating factor; MHC, major histocompatibility complex; NFAT, nuclear factor of activated T-cells; RANK, receptor activator of nuclear factor-κB; TCM, central memory T-cells; TEM, effector memory T-cells; TNF, tumor necrosis factor; TRAIL, TNF-related apoptosis-inducing ligand; OPG, osteoprotegerin; Omp29, 29-kDa outer membrane protein; PKC, protein kinase C; PLC, phospholipase C; RT-PCR, reverse-transcriptase polymerase chain-reaction; S(K), small conductance; TCR, T-cell receptor; and (K)v, voltage-gated.
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Jhangiani-Jashanmal, Iman T., Ryo Yamamoto, Nur Zeynep Gungor, and Denis Paré. "Electroresponsive properties of rat central medial thalamic neurons." Journal of Neurophysiology 115, no. 3 (March 1, 2016): 1533–41. http://dx.doi.org/10.1152/jn.00982.2015.
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The central medial thalamic (CMT) nucleus is a poorly known component of the middle thalamic complex that relays nociceptive inputs to the basolateral amygdala and cingulate cortex and plays a critical role in the control of awareness. The present study was undertaken to characterize the electroresponsive properties of CMT neurons. Similar to relay neurons found throughout the dorsal thalamus, CMT cells assumed tonic or burst-firing modes, depending on their membrane potentials (Vm). However, they showed little evidence of the hyperpolarization-activated mixed cationic conductance (IH)-mediated inward rectification usually displayed by dorsal thalamic relay cells at hyperpolarized Vm. Two subtypes of CMT neurons were identified when comparing their responses with depolarization applied from negative potentials. Some cells generated a low-threshold spike burst followed by tonic firing, whereas others remained silent after the initial burst, irrespective of the amount of depolarizing current injected. Equal proportions of the two cell types were found among neurons retrogradely labeled from the basolateral amygdala. Their morphological properties were heterogeneous but distinct from the classical bushy relay cell type that prevails in most of the dorsal thalamus. We propose that the marginal influence of IH in CMT relative to other dorsal thalamic nuclei has significant network-level consequences. Because IH promotes the genesis of highly coherent delta oscillations in thalamocortical networks during sleep, these oscillations may be weaker or less coherent in CMT. Consequently, delta oscillations would be more easily disrupted by peripheral inputs, providing a potential mechanism for the reported role of CMT in eliciting arousal from sleep or anesthesia.
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Bouhadjar, Younes, Sebastian Siegel, Tom Tetzlaff, Markus Diesmann, Rainer Waser, and Dirk J. Wouters. "Sequence learning in a spiking neuronal network with memristive synapses." Neuromorphic Computing and Engineering 3, no. 3 (September 1, 2023): 034014. http://dx.doi.org/10.1088/2634-4386/acf1c4.
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Abstract Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They endow systems with self learning capabilities, efficient energy usage, and high storage capacity. A core concept that lies at the heart of brain computation is sequence learning and prediction. This form of computation is essential for almost all our daily tasks such as movement generation, perception, and language. Understanding how the brain performs such a computation is not only important to advance neuroscience, but also to pave the way to new technological brain-inspired applications. A previously developed spiking neural network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. An emerging type of hardware that may efficiently run this type of algorithm is neuromorphic hardware. It emulates the way the brain processes information and maps neurons and synapses directly into a physical substrate. Memristive devices have been identified as potential synaptic elements in neuromorphic hardware. In particular, redox-induced resistive random access memories (ReRAM) devices stand out at many aspects. They permit scalability, are energy efficient and fast, and can implement biological plasticity rules. In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model. We implement and simulate the model including the ReRAM plasticity using the neural network simulator NEST. We investigate two types of ReRAM memristive devices: (i) a gradual, analog switching device, and (ii) an abrupt, binary switching device. We study the effect of different device properties on the performance characteristics of the sequence learning model, and demonstrate that, in contrast to many other artificial neural networks, this architecture is resilient with respect to changes in the on-off ratio and the conductance resolution, device variability, and device failure.
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Ren, Huiying, Erol Cromwell, Ben Kravitz, and Xingyuan Chen. "Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks." Hydrology and Earth System Sciences 26, no. 7 (April 5, 2022): 1727–43. http://dx.doi.org/10.5194/hess-26-1727-2022.
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Abstract. Quantifying the spatiotemporal dynamics in subsurface hydrological flows over a long time window usually employs a network of monitoring wells. However, such observations are often spatially sparse with potential temporal gaps due to poor quality or instrument failure. In this study, we explore the ability of recurrent neural networks to fill gaps in a spatially distributed time-series dataset. We use a well network that monitors the dynamic and heterogeneous hydrologic exchanges between the Columbia River and its adjacent groundwater aquifer at the U.S. Department of Energy's Hanford site. This 10-year-long dataset contains hourly temperature, specific conductance, and groundwater table elevation measurements from 42 wells with gaps of various lengths. We employ a long short-term memory (LSTM) model to capture the temporal variations in the observed system behaviors needed for gap filling. The performance of the LSTM-based gap-filling method was evaluated against a traditional autoregressive integrated moving average (ARIMA) method in terms of error statistics and accuracy in capturing the temporal patterns of river corridor wells with various dynamics signatures. Our study demonstrates that the ARIMA models yield better average error statistics, although they tend to have larger errors during time windows with abrupt changes or high-frequency (daily and subdaily) variations. The LSTM-based models excel in capturing both high-frequency and low-frequency (monthly and seasonal) dynamics. However, the inclusion of high-frequency fluctuations may also lead to overly dynamic predictions in time windows that lack such fluctuations. The LSTM can take advantage of the spatial information from neighboring wells to improve the gap-filling accuracy, especially for long gaps in system states that vary at subdaily scales. While LSTM models require substantial training data and have limited extrapolation power beyond the conditions represented in the training data, they afford great flexibility to account for the spatial correlations, temporal correlations, and nonlinearity in data without a priori assumptions. Thus, LSTMs provide effective alternatives to fill in data gaps in spatially distributed time-series observations characterized by multiple dominant frequencies of variability, which are essential for advancing our understanding of dynamic complex systems.
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CARTLING, BO. "CONTROL OF THE COMPLEXITY OF ASSOCIATIVE MEMORY DYNAMICS BY NEURONAL ADAPTATION." International Journal of Neural Systems 04, no. 02 (June 1993): 129–41. http://dx.doi.org/10.1142/s0129065793000122.
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An abstract neural network model of the Hopfield type is extended to incorporate neuronal adaptation by defining the state of a neuron in terms of two variables, activity and excitability. The model is formulated to represent the regulation of the firing rate of action potentials in a biological system via the neuron cell membrane afterhyperpolarization by the effect of intracellular calcium ion concentration on the conductance of calcium sensitive potassium channels. It is shown that the complexity, and thus the exploratory degree, of associative memory dynamics are controlled by neuronal adaptability. At low adaptability, the dynamics have fixed point attractors corresponding to direct memory retrieval. In a subsequent region of adaptability values, a simple limit cycle persists with frequency increasing with adaptability. The range of frequencies agrees with that observed for theta rhythms of activity in the brain. A higher degree of freedom of the associative process corresponding to more complex dynamics, either limit cycles of varying complexity and period or chaotic behaviour, results at higher adaptability. In the brain, the neuronal adaptability is regulated by neuromodulators which suppress adaptation and increase absolute firing rates of action potentials. An associative process can be started at low concentration of neuromodulators as an exploratory search of state space during which firing rates are low. As the concentration of neuromodulators increases, the state space search becomes simpler cyclic and more restricted, and firing rates increase. Eventually, a particular stored state is retrieved and its activity is high. This correspondence between the complexity of associative memory dynamics and the concentration of neuromodulators is consistent with the observation for Alzheimer's disease of selective degeneracy of neurons releasing the neuromodulator acetylcholine. In an artificial neural network, inclusion of adaptation among neuronal properties allows control of the degree of freedom of associative processes and thus extends the range of possible applications.
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Rudolph, Michael, Zuzanna Piwkowska, Mathilde Badoual, Thierry Bal, and Alain Destexhe. "A Method to Estimate Synaptic Conductances From Membrane Potential Fluctuations." Journal of Neurophysiology 91, no. 6 (June 2004): 2884–96. http://dx.doi.org/10.1152/jn.01223.2003.
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In neocortical neurons, network activity can activate a large number of synaptic inputs, resulting in highly irregular subthreshold membrane potential ( Vm) fluctuations, commonly called “synaptic noise.” This activity contains information about the underlying network dynamics, but it is not easy to extract network properties from such complex and irregular activity. Here, we propose a method to estimate properties of network activity from intracellular recordings and test this method using theoretical and experimental approaches. The method is based on the analytic expression of the subthreshold Vm distribution at steady state in conductance-based models. Fitting this analytic expression to Vm distributions obtained from intracellular recordings provides estimates of the mean and variance of excitatory and inhibitory conductances. We test the accuracy of these estimates against computational models of increasing complexity. We also test the method using dynamic-clamp recordings of neocortical neurons in vitro. By using an on-line analysis procedure, we show that the measured conductances from spontaneous network activity can be used to re-create artificial states equivalent to real network activity. This approach should be applicable to intracellular recordings during different network states in vivo, providing a characterization of the global properties of synaptic conductances and possible insight into the underlying network mechanisms.
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Kvaková, Karolína, and Daniel Kvak. "FROM IMAGE TO INSIGHT: A REVIEW OF DEEP LEARNING APPROACHES FOR CYSTIC FIBROSIS DETECTION IN COMPUTED TOMOGRAPHY." Medsoft 35, no. 1 (December 10, 2023): 1–7. http://dx.doi.org/10.35191/medsoft_2023_1_35_kvak.
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Cystic fibrosis (CF) is a genetic disease caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene. This disorder causes a wide range of clinical complications, primarily affecting the respiratory and digestive systems and extending its impact to other physiological areas. Early detection and careful monitoring are paramount to mitigate disease progression and improve the quality of life of individuals with CF. Computed tomography (CT), particularly high-resolution CT (HRCT), has become a key diagnostic method for detecting pulmonary manifestations of CF. However, manual analysis of CT images requires a high level of expertise and is time consuming. The combination of artificial intelligence (AI) and deep learning with CT imaging predicts significant advances in CF detection. Deep learning, a subset of AI, uses neural networks to analyse complex morphological patterns indicative of disease from large datasets. This review traces the journey from the earliest attempts to use artificial intelligence in CF detection to recent advances made using deep learning algorithms. By exploring various deep learning architectures and their integration into clinical practice, this review illuminates the potential of these new technologies to revolutionize CF detection using CT imaging. Automated and accurate analysis enabled by deep learning aims to reduce the diagnostic burden on radiologists, speed up the diagnostic process and pave the way for timely and personalized therapeutic interventions, which is in line with the ultimate goal of improving patient care.
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Zirkle, Thomas A., Matthew J. Filmer, Jonathan Chisum, Alexei O. Orlov, Eva Dupont-Ferrier, Joffrey Rivard, Matthew Huebner, Marc Sanquer, Xavier Jehl, and Gregory L. Snider. "Radio Frequency Reflectometry of Single-Electron Box Arrays for Nanoscale Voltage Sensing Applications." Applied Sciences 10, no. 24 (December 9, 2020): 8797. http://dx.doi.org/10.3390/app10248797.
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Single-electron tunneling transistors (SETs) and boxes (SEBs) exploit the phenomenon of Coulomb blockade to achieve unprecedented charge sensitivities. Single-electron boxes, however, despite their simplicity compared to SETs, have rarely been used for practical applications. The main reason for that is that unlike a SET where the gate voltage controls conductance between the source and the drain, an SEB is a two terminal device that requires either an integrated SET amplifier or high-frequency probing of its complex admittance by means of radio frequency reflectometry (RFR). The signal to noise ratio (SNR) for a SEB is small, due to its much lower admittance compared to a SET and thus matching networks are required for efficient coupling ofSEBs to an RFR setup. To boost the signal strength by a factor of N (due to a random offset charge) SEBs can be connected in parallel to form arrays sharing common gates and sources. The smaller the size of the SEB, the larger the charging energy of a SEB enabling higher operation temperature, and using devices with a small footprint (<0.01 µm2), a large number of devices (>1000) can be assembled into an array occupying just a few square microns. We show that it is possible to design SEB arrays that may compete with an SET in terms of sensitivity. In this, we tested SETs using RF reflectometry in a configuration with no DC through path (“DC-decoupled SET” or DCD SET) along with SEBs connected to the same matching network. The experiment shows that the lack of a path for a DC current makes SEBs and DCD SETs highly electrostatic discharge (ESD) tolerant, a very desirable feature for applications. We perform a detailed analysis of experimental data on SEB arrays of various sizes and compare it with simulations to devise several ways for practical applications of SEB arrays and DCD SETs.
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Xiang, Zixiu, and David A. Prince. "Heterogeneous Actions of Serotonin on Interneurons in Rat Visual Cortex." Journal of Neurophysiology 89, no. 3 (March 1, 2003): 1278–87. http://dx.doi.org/10.1152/jn.00533.2002.
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The effects of serotonin (5-HT) on excitability of two cortical interneuronal subtypes, fast-spiking (FS) and low threshold spike (LTS) cells, and on spontaneous inhibitory postsynaptic currents (sIPSCs) in layer V pyramidal cells were studied in rat visual cortical slices using whole-cell recording techniques. Twenty-two of 28 FS and 26 of 35 LTS interneurons responded to local application of 5-HT. In the group of responsive neurons, 5-HT elicited an inward current in 50% of FS cells and 15% of LTS cells, an outward current was evoked in 41% of FS cells and 81% of LTS cells, and an inward current followed by an outward current in 9% of FS cells and 4% LTS cells. The inward and outward currents were blocked by a 5-HT3 receptor antagonist, tropisetron, and a 5-HT1A receptor antagonist, NAN-190, respectively. The 5-HT–induced inward and outward currents were both associated with an increase in membrane conductance. The estimated reversal potential was more positive than −40 mV for the inward current and close to the calculated K+equilibrium potential for the outward current. The 5-HT application caused an increase, a decrease, or an increase followed by a decrease in the frequency of sIPSCs in pyramidal cells. The 5-HT3 receptor agonist 1-( m-chlorophenyl) biguanide increased the frequency of larger and fast-rising sIPSCs, whereas the 5-HT1Areceptor agonist (±)8-hydroxydipropylaminotetralin hydrobromide elicited opposite effects and decreased the frequency of large events. These data indicate that serotonergic activation imposes complex actions on cortical inhibitory networks, which may lead to changes in cortical information processing.
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Tateno, T., and H. P. C. Robinson. "Integration of Broadband Conductance Input in Rat Somatosensory Cortical Inhibitory Interneurons: An Inhibition-Controlled Switch Between Intrinsic and Input-Driven Spiking in Fast-Spiking Cells." Journal of Neurophysiology 101, no. 2 (February 2009): 1056–72. http://dx.doi.org/10.1152/jn.91057.2008.
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Quantitative understanding of the dynamics of particular cell types when responding to complex, natural inputs is an important prerequisite for understanding the operation of the cortical network. Different types of inhibitory neurons are connected by electrical synapses to nearby neurons of the same type, enabling the formation of synchronized assemblies of neurons with distinct dynamical behaviors. Under what conditions is spike timing in such cells determined by their intrinsic dynamics and when is it driven by the timing of external input? In this study, we have addressed this question using a systematic approach to characterizing the input–output relationships of three types of cortical interneurons (fast spiking [FS], low-threshold spiking [LTS], and nonpyramidal regular-spiking [NPRS] cells) in the rat somatosensory cortex, during fluctuating conductance input designed to mimic natural complex activity. We measured the shape of average conductance input trajectories preceding spikes and fitted a two-component linear model of neuronal responses, which included an autoregressive term from its own output, to gain insight into the input–output relationships of neurons. This clearly separated the contributions of stimulus and discharge history, in a cell-type dependent manner. Unlike LTS and NPRS cells, FS cells showed a remarkable switch in dynamics, from intrinsically driven spike timing to input-fluctuation–controlled spike timing, with the addition of even a small amount of inhibitory conductance. Such a switch could play a pivotal role in the function of FS cells in organizing coherent gamma oscillations in the local cortical network. Using both pharmacological perturbations and modeling, we show how this property is a consequence of the particular complement of voltage-dependent conductances in these cells.
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DESTEXHE, A., and T. J. SEJNOWSKI. "Interactions Between Membrane Conductances Underlying Thalamocortical Slow-Wave Oscillations." Physiological Reviews 83, no. 4 (October 2003): 1401–53. http://dx.doi.org/10.1152/physrev.00012.2003.
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Destexhe, A., and T. J. Sejnowski. Interactions Between Membrane Conductances Underlying Thalamocortical Slow-Wave Oscillations. Physiol Rev 83: 1401-1453, 2003; 10.1152/physrev.00012.2003.—Neurons of the central nervous system display a broad spectrum of intrinsic electrophysiological properties that are absent in the traditional “integrate-and-fire” model. A network of neurons with these properties interacting through synaptic receptors with many time scales can produce complex patterns of activity that cannot be intuitively predicted. Computational methods, tightly linked to experimental data, provide insights into the dynamics of neural networks. We review this approach for the case of bursting neurons of the thalamus, with a focus on thalamic and thalamocortical slow-wave oscillations. At the single-cell level, intrinsic bursting or oscillations can be explained by interactions between calcium- and voltage-dependent channels. At the network level, the genesis of oscillations, their initiation, propagation, termination, and large-scale synchrony can be explained by interactions between neurons with a variety of intrinsic cellular properties through different types of synaptic receptors. These interactions can be altered by neuromodulators, which can dramatically shift the large-scale behavior of the network, and can also be disrupted in many ways, resulting in pathological patterns of activity, such as seizures. We suggest a coherent framework that accounts for a large body of experimental data at the ion-channel, single-cell, and network levels. This framework suggests physiological roles for the highly synchronized oscillations of slow-wave sleep.
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Abbott, L. F. "Decoding neuronal firing and modelling neural networks." Quarterly Reviews of Biophysics 27, no. 3 (August 1994): 291–331. http://dx.doi.org/10.1017/s0033583500003024.
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Biological neural networks are large systems of complex elements interacting through a complex array of connexions. Individual neurons express a large number of active conductances (Connors et al. 1982; Adams & Gavin, 1986; Llinás, 1988; McCormick, 1990; Hille, 1992) and exhibit a wide variety of dynamic behaviours on time scales ranging from milliseconds to many minutes (Llinás, 1988; Harris-Warrick & Marder, 1991; Churchland & Sejnowski, 1992; Turrigiano et al. 1994).
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Rudolph-Lilith, Michelle, Mathieu Dubois, and Alain Destexhe. "Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics and Realistic Postsynaptic Potential Time Course for Event-Driven Simulation Strategies." Neural Computation 24, no. 6 (June 2012): 1426–61. http://dx.doi.org/10.1162/neco_a_00278.
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In a previous paper (Rudolph & Destexhe, 2006 ), we proposed various models, the gIF neuron models, of analytical integrate-and-fire (IF) neurons with conductance-based (COBA) dynamics for use in event-driven simulations. These models are based on an analytical approximation of the differential equation describing the IF neuron with exponential synaptic conductances and were successfully tested with respect to their response to random and oscillating inputs. Because they are analytical and mathematically simple, the gIF models are best suited for fast event-driven simulation strategies. However, the drawback of such models is they rely on a nonrealistic postsynaptic potential (PSP) time course, consisting of a discontinuous jump followed by a decay governed by the membrane time constant. Here, we address this limitation by conceiving an analytical approximation of the COBA IF neuron model with the full PSP time course. The subthreshold and suprathreshold response of this gIF4 model reproduces remarkably well the postsynaptic responses of the numerically solved passive membrane equation subject to conductance noise, while gaining at least two orders of magnitude in computational performance. Although the analytical structure of the gIF4 model is more complex than that of its predecessors due to the necessity of calculating future spike times, a simple and fast algorithmic implementation for use in large-scale neural network simulations is proposed.
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Del Negro, Christopher A., Naohiro Koshiya, Robert J. Butera, and Jeffrey C. Smith. "Persistent Sodium Current, Membrane Properties and Bursting Behavior of Pre-Bötzinger Complex Inspiratory Neurons In Vitro." Journal of Neurophysiology 88, no. 5 (November 1, 2002): 2242–50. http://dx.doi.org/10.1152/jn.00081.2002.
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We measured persistent Na+current and membrane properties of bursting-pacemaker and nonbursting inspiratory neurons of the neonatal rat pre-Bötzinger complex (pre-BötC) in brain stem slice preparations with a rhythmically active respiratory network in vitro. In whole-cell recordings, slow voltage ramps (≤100 mV/s) inactivated the fast, spike-generating Na+ current and yielded N-shaped current-voltage relationships with nonmonotonic, negative-slope regions between −60 and −35 mV when the voltage-sensitive component was isolated. The underlying current was a TTX-sensitive persistent Na+ current ( I NaP) since the inward current was present at slow voltage ramp speeds (3.3–100 mV/s) and the current was blocked by 1 μM TTX. We measured the biophysical properties of I NaP after subtracting the voltage-insensitive “leak” current ( I Leak) in the presence of Cd2+ and in some cases tetraethylammonium (TEA). Peak I NaP ranged from −50 to −200 pA at a membrane potential of −30 mV. Decreasing the speed of the voltage ramp caused time-dependent I NaPinactivation, but this current was present at ramp speeds as low as 3.3 mV/s. I NaP activated at −60 mV and obtained half-maximal activation near −40 mV. The subthreshold voltage dependence and slow inactivation kinetics of I NaP, which closely resemble those of I NaP mathematically modeled as a burst-generation mechanism in pacemaker neurons of the pre-BötC, suggest that I NaP predominantly influences bursting dynamics of pre-BötC inspiratory pacemaker neurons in vitro. We also found that the ratio of persistent Na+conductance to leak conductance ( g NaP/ g Leak) can distinguish the phenotypic subpopulations of bursting pacemaker and nonbursting inspiratory neurons: pacemaker neurons showed g NaP/ g Leak> g NaP/ g Leakin nonpacemaker cells ( P < 0.0002). We conclude that I NaP is ubiquitously expressed by pre-BötC inspiratory neurons and that bursting pacemaker behavior within the heterogeneous population of inspiratory neurons is achieved with specific ratios of these two conductances, g NaP and g Leak.
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Hechenleitner, E. Martín, Gerald Grellet-Tinner, Matthew Foley, Lucas E. Fiorelli, and Michael B. Thompson. "Micro-CT scan reveals an unexpected high-volume and interconnected pore network in a Cretaceous Sanagasta dinosaur eggshell." Journal of The Royal Society Interface 13, no. 116 (March 2016): 20160008. http://dx.doi.org/10.1098/rsif.2016.0008.
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The Cretaceous Sanagasta neosauropod nesting site (La Rioja, Argentina) was the first confirmed instance of extinct dinosaurs using geothermal-generated heat to incubate their eggs. The nesting strategy and hydrothermal activities at this site led to the conclusion that the surprisingly 7 mm thick-shelled eggs were adapted to harsh hydrothermal microenvironments. We used micro-CT scans in this study to obtain the first three-dimensional microcharacterization of these eggshells. Micro-CT-based analyses provide a robust assessment of gas conductance in fossil dinosaur eggshells with complex pore canal systems, allowing calculation, for the first time, of the shell conductance through its thickness. This novel approach suggests that the shell conductance could have risen during incubation to seven times more than previously estimated as the eggshell erodes. In addition, micro-CT observations reveal that the constant widening and branching of pore canals form a complex funnel-like pore canal system. Furthermore, the high density of pore canals and the presence of a lateral canal network in the shell reduce the risks of pore obstruction during the extended incubation of these eggs in a relatively highly humid and muddy nesting environment.
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Rubin, Daniel B., and Thomas A. Cleland. "Dynamical Mechanisms of Odor Processing in Olfactory Bulb Mitral Cells." Journal of Neurophysiology 96, no. 2 (August 2006): 555–68. http://dx.doi.org/10.1152/jn.00264.2006.
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In the olfactory system, the contribution of dynamical properties such as neuronal oscillations and spike synchronization to the representation of odor stimuli is a matter of substantial debate. While relatively simple computational models have sufficed to guide current research in large-scale network dynamics, less attention has been paid to modeling the membrane dynamics in bulbar neurons that may be equally essential to sensory processing. We here present a reduced, conductance-based compartmental model of olfactory bulb mitral cells that exhibits the complex dynamical properties observed in these neurons. Specifically, model neurons exhibit intrinsic subthreshold oscillations with voltage-dependent frequencies that shape the timing of stimulus-evoked action potentials. These oscillations rely on a persistent sodium conductance, an inactivating potassium conductance, and a calcium-dependent potassium conductance and are reset via inhibitory input such as that delivered by periglomerular cell shunt inhibition. Mitral cells fire bursts, or clusters, of spikes when continuously stimulated. Burst properties depend critically on multiple currents, but a progressive deinactivation of IA over the course of a burst is an important regulator of burst termination. Each of these complex properties exhibits appropriate dynamics and pharmacology as determined by electrophysiological studies. Additionally, we propose that a second, inconsistently observed form of infrathreshold bistability in mitral cells may derive from the activation of ATP-activated potassium currents responding to hypoxic conditions. We discuss the integration of these cellular properties in the larger context of olfactory bulb network operations.
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Ludowicz, Wojciech, and Rafał M. Wojciechowski. "Analysis of the Distributions of Displacement and Eddy Currents in the Ferrite Core of an Electromagnetic Transducer Using the 2D Approach of the Edge Element Method and the Harmonic Balance Method." Energies 14, no. 13 (July 2, 2021): 3980. http://dx.doi.org/10.3390/en14133980.
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The negative impact of the displacement currents on the operation of electromagnetic converters results in additional losses and faster insulation degradation, as well as the self-resonance phenomenon. Effective measurement of the dielectric displacement currents in converters is quite complex; thus, advanced simulation programs should be used. However, currently, they do not enable the analysis of the systems in terms of the displacement currents distribution. In order to elaborate an effective tool for analyzing the distribution of the displacement currents by means of the Finite Element Method, we have decided to supplement the well-known reluctance-conductance network model with an additional capacitance model. In the paper, equations for the linked reluctance-conductance-capacitance network model have been presented and discussed in detail. Moreover, we introduce in the algorithm the Harmonic Balance Finite Element Method (HBFEM) and the Fixed-Point Method. This approach enables us to create a field model of electromagnetic converters, which includes the electromagnetic core’s saturation effect. The application of these methods for the reluctance-conductance-capacitance model of the finite element has allowed us to develop a practical tool ensuring complex analysis of the magnetic flux, eddy, and the displacement currents’ distribution in electromagnetic converters with an axial symmetrical structure.
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McClure, Michelle L., Stephen Barnes, Jeffrey L. Brodsky, and Eric J. Sorscher. "Trafficking and function of the cystic fibrosis transmembrane conductance regulator: a complex network of posttranslational modifications." American Journal of Physiology-Lung Cellular and Molecular Physiology 311, no. 4 (October 1, 2016): L719—L733. http://dx.doi.org/10.1152/ajplung.00431.2015.
Full textAbstract:
Posttranslational modifications add diversity to protein function. Throughout its life cycle, the cystic fibrosis transmembrane conductance regulator (CFTR) undergoes numerous covalent posttranslational modifications (PTMs), including glycosylation, ubiquitination, sumoylation, phosphorylation, and palmitoylation. These modifications regulate key steps during protein biogenesis, such as protein folding, trafficking, stability, function, and association with protein partners and therefore may serve as targets for therapeutic manipulation. More generally, an improved understanding of molecular mechanisms that underlie CFTR PTMs may suggest novel treatment strategies for CF and perhaps other protein conformational diseases. This review provides a comprehensive summary of co- and posttranslational CFTR modifications and their significance with regard to protein biogenesis.
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Timofeev, Igor, François Grenier, and Mircea Steriade. "Spike-Wave Complexes and Fast Components of Cortically Generated Seizures. IV. Paroxysmal Fast Runs in Cortical and Thalamic Neurons." Journal of Neurophysiology 80, no. 3 (September 1, 1998): 1495–513. http://dx.doi.org/10.1152/jn.1998.80.3.1495.
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Timofeev, Igor, François Grenier, and Mircea Steriade. Spike-wave complexes and fast components of cortically generated seizures. IV. Paroxysmal fast runs in cortical and thalamic neurons. J. Neurophysiol. 80: 1495–1513, 1998. In the preceding papers of this series, we have analyzed the cellular patterns and synchronization of neocortical seizures occurring spontaneously or induced by electrical stimulation or cortical infusion of bicuculline under a variety of experimental conditions, including natural states of vigilance in behaving animals and acute preparations under different anesthetics. The seizures consisted of two distinct components: spike-wave (SW) or polyspike-wave (PSW) at 2–3 Hz and fast runs at 10–15 Hz. Because the thalamus is an input source and target of cortical neurons, we investigated here the seizure behavior of thalamic reticular (RE) and thalamocortical (TC) neurons, two major cellular classes that have often been implicated in the generation of paroxysmal episodes. We performed single and dual simultaneous intracellular recordings, in conjunction with multisite field potential and extracellular unit recordings, from neocortical areas and RE and/or dorsal thalamic nuclei under ketamine-xylazine and barbiturate anesthesia. Both components of seizures were analyzed, but emphasis was placed on the fast runs because of their recent investigation at the cellular level. 1) The fast runs occurred at slightly different frequencies and, therefore, were asynchronous in various cortical neuronal pools. Consequently, dorsal thalamic nuclei, although receiving convergent inputs from different neocortical areas involved in seizure, did not express strongly synchronized fast runs. 2) Both RE and TC cells were hyperpolarized during seizure episodes with SW/PSW complexes and relatively depolarized during the fast runs. As known, hyperpolarization of thalamic neurons deinactivates a low-threshold conductance that generates high-frequency spike bursts. Accordingly, RE neurons discharged prolonged high-frequency spike bursts in close time relation with the spiky component of cortical SW/PSW complexes, whereas they fired single action potentials, spike doublets, or triplets during the fast runs. In TC cells, the cortical fast runs were reflected as excitatory postsynaptic potentials appearing after short latencies that were compatible with monosynaptic activation through corticothalamic pathways. 3) The above data suggested the cortical origin of these seizures. To further test this hypothesis, we performed experiments on completely isolated cortical slabs from suprasylvian areas 5 or 7 and demonstrated that electrical stimulation within the slab induces seizures with fast runs and SW/PSW complexes, virtually identical to those elicited in intact-brain animals. The conclusion of all papers in this series is that complex seizure patterns, resembling those described at the electroencephalogram level in different forms of clinical seizures with SW/PSW complexes and, particularly, in the Lennox-Gastaut syndrome of humans, are generated in neocortex. Thalamic neurons reflect cortical events as a function of membrane potential in RE/TC cells and degree of synchronization in cortical neuronal networks.
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Ros, Eduardo, Richard Carrillo, Eva M. Ortigosa, Boris Barbour, and Rodrigo Agís. "Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics." Neural Computation 18, no. 12 (December 2006): 2959–93. http://dx.doi.org/10.1162/neco.2006.18.12.2959.
Full textAbstract:
Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.
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Loritz, Ralf, Maoya Bassiouni, Anke Hildebrandt, Sibylle K. Hassler, and Erwin Zehe. "Leveraging sap flow data in a catchment-scale hybrid model to improve soil moisture and transpiration estimates." Hydrology and Earth System Sciences 26, no. 18 (September 28, 2022): 4757–71. http://dx.doi.org/10.5194/hess-26-4757-2022.
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Abstract. Sap flow encodes information about how plants regulate the opening and closing of stomata in response to varying soil water supply and atmospheric water demand. This study leverages this valuable information with model–data integration and deep learning to estimate canopy conductance in a hybrid catchment-scale model for more accurate hydrological simulations. Using data from three consecutive growing seasons, we first highlight that integrating canopy conductance inferred from sap flow data in a hydrological model leads to more realistic soil moisture estimates than using the conventional Jarvis–Stewart equation, particularly during drought conditions. The applicability of this first approach is, however, limited to the period where sap flow data are available. To overcome this limitation, we subsequently train a recurrent neural network (RNN) to predict catchment-averaged sap velocities based on standard hourly meteorological data. These simulated velocities are then used to estimate canopy conductance, allowing simulations for periods without sap flow data. We show that the hybrid model, which uses the canopy conductance from the machine learning (ML) approach, matches soil moisture and transpiration equally as well as model runs using observed sap flow data and has good potential for extrapolation beyond the study site. We conclude that such hybrid approaches open promising avenues for parametrizations of complex water–plant dynamics by improving our ability to incorporate novel or untypical data sets into hydrological models.
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KATORI, YUICHI, ERIC J. LANG, MIHO ONIZUKA, MITSUO KAWATO, and KAZUYUKI AIHARA. "QUANTITATIVE MODELING OF SPATIO-TEMPORAL DYNAMICS OF INFERIOR OLIVE NEURONS WITH A SIMPLE CONDUCTANCE-BASED MODEL." International Journal of Bifurcation and Chaos 20, no. 03 (March 2010): 583–603. http://dx.doi.org/10.1142/s0218127410025909.
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Inferior olive (IO) neurons project to the cerebellum and contribute to motor control. They can show intriguing spatio-temporal dynamics with rhythmic and synchronized spiking. IO neurons are connected to their neighbors via gap junctions to form an electrically coupled network, and so it is considered that this coupling contributes to the characteristic dynamics of this nucleus. Here, we demonstrate that a gap junction-coupled network composed of simple conductance-based model neurons (a simplified version of a Hodgkin–Huxley type neuron) reproduce important aspects of IO activity. The simplified phenomenological model neuron facilitated the analysis of the single cell and network properties of the IO while still quantitatively reproducing the spiking patterns of complex spike activity observed by simultaneous recording in anesthetized rats. The results imply that both intrinsic bistability of each neuron and gap junction coupling among neurons play key roles in the generation of the spatio-temporal dynamics of IO neurons.
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Fox, S. E. "Location of membrane conductance changes by analysis of the input impedance of neurons. I. Theory." Journal of Neurophysiology 54, no. 6 (December 1, 1985): 1578–93. http://dx.doi.org/10.1152/jn.1985.54.6.1578.
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If the membrane conductance of a neuron changes, its response to injected current changes. If the change in membrane conductance is restricted to a given subregion of the neuron, that region can be located by analysis of the form of the change in the response of the neuron to current injection. The theoretical basis of this method is rigorously developed in this paper. Location of the membrane conductance change is possible because the higher-frequency components of the injected currents are progressively attenuated by the axial resistance and membrane capacitance of the neuron as they pass from the injection site to electrotonically more distant regions. For the lower-frequency components, this attenuation is less pronounced. Therefore, when a conductance change occurs relatively far from the recording/current-passing electrode, only the lower frequency components of the response are altered, because the higher-frequency components of the current do not even reach that site. When such a conductance change occurs relatively near the electrode, both the lower and the higher frequency components of the response are altered. Treating the neuron as a passive network, the input impedance at a given frequency is simply the voltage response of the neuron at that frequency divided by the current injected at that frequency. This is a complex value, having both magnitude and phase components. The change in the magnitude of the input impedance due to a conductance change occurring distally drops off more rapidly with increasing frequency than that due to a proximal conductance change. In addition, for distal conductance increases the magnitude of the input impedance can increase in the higher range of frequencies. This paradoxical effect is treated in APPENDIX I. For many neurons an estimate of the electrotonic location of a conductance change can be made knowing only the change in input resistance, the change in the magnitude of the input impedance at the characteristic frequency (omega 0 = 1/tau 0), and a reasonable estimate of the total electrotonic length of the neuron (L). The sensitivity of the method depends on the electrotonic length of the neuron. The method is most useful in neurons with dendritic trees longer than approximately 0.5 length constants. The dendritic-to-somatic conductance ratio of the neuron does not appreciably affect the forms of the responses. The time constant merely shifts the frequency range of interest.(ABSTRACT TRUNCATED AT 400 WORDS)
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Visnovcova, Zuzana, Lucia Bona Olexova, Nikola Sekaninova, Igor Ondrejka, Igor Hrtanek, Dana Cesnekova, Simona Kelcikova, Ivan Farsky, and Ingrid Tonhajzerova. "Spectral and Nonlinear Analysis of Electrodermal Activity in Adolescent Anorexia Nervosa." Applied Sciences 10, no. 13 (June 29, 2020): 4514. http://dx.doi.org/10.3390/app10134514.
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Anorexia nervosa (AN) is an eating disorder with increasing prevalence in childhood and adolescence. Sympathetic dysregulation is supposed to be the underlying mechanism of increased cardiovascular risk in AN. Thus, we assess the electrodermal activity (EDA) as a non-invasive index of sympathetic cholinergic activity using linear and nonlinear analysis in adolescent AN with the aim of detecting potential biomarkers for AN-linked cardiovascular risk. We examined 25 adolescent girls with AN and 25 age-matched controls. EDA was continuously recorded during a 5-min resting phase. Evaluated parameters were: time-domain (skin conductance level, non-specific skin conductance responses), frequency-domain (EDA in very low, low, sympathetic, high and very high frequency bands) and nonlinear (approximate, sample, symbolic information entropies, detrended fluctuation analysis (DFA)) parameters of EDA and peripheral skin temperature. Our findings revealed lower EDA values indicating a decrease in the sympathetic nervous activity in female adolescents with the acute phase of AN. Further, we found higher nonlinear index DFA in AN vs. controls. We assumed that nonlinear index DFA could provide novel and independent information on the complex sympathetic regulatory network. We conclude that the parameters of complex EDA analysis could be used as sensitive biomarkers for the assessment of sympathetic cholinergic dysregulation as a risk factor for AN-linked cardiovascular morbidity.