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Artykuły w czasopismach na temat "Inhibitory Network Model"
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Skinner, F. K., J. Y. J. Chung, I. Ncube, P. A. Murray i S. A. Campbell. "Using Heterogeneity to Predict Inhibitory Network Model Characteristics". Journal of Neurophysiology 93, nr 4 (kwiecień 2005): 1898–907. http://dx.doi.org/10.1152/jn.00619.2004.
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From modeling studies it has been known for >10 years that purely inhibitory networks can produce synchronous output given appropriate balances of intrinsic and synaptic parameters. Several experimental studies indicate that synchronous activity produced by inhibitory networks is critical to the production of population rhythms associated with various behavioral states. Heterogeneity of inputs to inhibitory networks strongly affect their ability to synchronize. In this paper, we explore how the amount of input heterogeneity to two-cell inhibitory networks affects their dynamics. Using numerical simulations and bifurcation analyses, we find that the ability of inhibitory networks to synchronize in the face of heterogeneity depends nonmonotonically on each of the synaptic time constant, synaptic conductance and external drive parameters. Because of this, an optimal set of parameters for a given cellular model with various biophysical characteristics can be determined. We suggest that this could be a helpful approach to use in determining the importance of different, underlying biophysical details. We further find that two-cell coherence properties are maintained in larger 10-cell networks. As such, we think that a strategy of “embedding” small network dynamics in larger networks is a useful way to understand the contribution of biophysically derived parameters to population dynamics in large networks.
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Vassiliev, P. M., A. A. Spasov, A. N. Kochetkov, M. A. Perfilev i A. R. Koroleva. "Consensus ensemble neural network multitarget model of RAGE inhibitory activity of chemical compounds". Biomeditsinskaya Khimiya 67, nr 3 (2021): 268–77. http://dx.doi.org/10.18097/pbmc20216703268.
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RAGE signal transduction via the RAGE-NF-κB signaling pathway is one of the mechanisms of inflammatory reactions that cause severe complications in diabetes mellitus. RAGE inhibitors are promising pharmacological compounds that require the development of new predictive models. Based on the methodology of artificial neural networks, consensus ensemble neural network multitarget model has been constructed. This model describes the dependence of the level of the RAGE inhibitory activity on the affinity of compounds for 34 target proteins of the RAGE-NF-κB signal pathway. For this purpose an expanded database of valid three-dimensional models of target proteins of the RAGE-NF-κB signal chain was created on the basis of a previously created database of three-dimensional models of relevant biotargets. Ensemble molecular docking of known RAGE inhibitors from a verified database into the sites of added models of target proteins was performed, and the minimum docking energies for each compound in relation to each target were determined. An extended training set for neural network modeling was formed. Using seven variants of sampling by the method of artificial multilayer perceptron neural networks, three ensembles of classification decision rules were constructed to predict three level of the RAGE-inhibitory activity based on the calculated affinity of compounds for significant target proteins of the RAGE-NF-κB signaling pathway. Using a simple consensus of the second level, the predictive ability of the created model was assessed and its high accuracy and statistical significance were shown. The resultant consensus ensemble neural network multitarget model has been used for virtual screening of new derivatives of different chemical classes. The most promising substances have been synthesized and sent for experimental studies.
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Bryson, Alexander, Samuel F. Berkovic, Steven Petrou i David B. Grayden. "State transitions through inhibitory interneurons in a cortical network model". PLOS Computational Biology 17, nr 10 (15.10.2021): e1009521. http://dx.doi.org/10.1371/journal.pcbi.1009521.
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Inhibitory interneurons shape the spiking characteristics and computational properties of cortical networks. Interneuron subtypes can precisely regulate cortical function but the roles of interneuron subtypes for promoting different regimes of cortical activity remains unclear. Therefore, we investigated the impact of fast spiking and non-fast spiking interneuron subtypes on cortical activity using a network model with connectivity and synaptic properties constrained by experimental data. We found that network properties were more sensitive to modulation of the fast spiking population, with reductions of fast spiking excitability generating strong spike correlations and network oscillations. Paradoxically, reduced fast spiking excitability produced a reduction of global excitation-inhibition balance and features of an inhibition stabilised network, in which firing rates were driven by the activity of excitatory neurons within the network. Further analysis revealed that the synaptic interactions and biophysical features associated with fast spiking interneurons, in particular their rapid intrinsic response properties and short synaptic latency, enabled this state transition by enhancing gain within the excitatory population. Therefore, fast spiking interneurons may be uniquely positioned to control the strength of recurrent excitatory connectivity and the transition to an inhibition stabilised regime. Overall, our results suggest that interneuron subtypes can exert selective control over excitatory gain allowing for differential modulation of global network state.
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Chou, Kenny F., i Kamal Sen. "AIM: A network model of attention in auditory cortex". PLOS Computational Biology 17, nr 8 (27.08.2021): e1009356. http://dx.doi.org/10.1371/journal.pcbi.1009356.
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Attentional modulation of cortical networks is critical for the cognitive flexibility required to process complex scenes. Current theoretical frameworks for attention are based almost exclusively on studies in visual cortex, where attentional effects are typically modest and excitatory. In contrast, attentional effects in auditory cortex can be large and suppressive. A theoretical framework for explaining attentional effects in auditory cortex is lacking, preventing a broader understanding of cortical mechanisms underlying attention. Here, we present a cortical network model of attention in primary auditory cortex (A1). A key mechanism in our network is attentional inhibitory modulation (AIM) of cortical inhibitory neurons. In this mechanism, top-down inhibitory neurons disinhibit bottom-up cortical circuits, a prominent circuit motif observed in sensory cortex. Our results reveal that the same underlying mechanisms in the AIM network can explain diverse attentional effects on both spatial and frequency tuning in A1. We find that a dominant effect of disinhibition on cortical tuning is suppressive, consistent with experimental observations. Functionally, the AIM network may play a key role in solving the cocktail party problem. We demonstrate how attention can guide the AIM network to monitor an acoustic scene, select a specific target, or switch to a different target, providing flexible outputs for solving the cocktail party problem.
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Rich, Scott, Michal Zochowski i Victoria Booth. "Effects of Neuromodulation on Excitatory–Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure". Journal of Nonlinear Science 30, nr 5 (4.01.2018): 2171–94. http://dx.doi.org/10.1007/s00332-017-9438-6.
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Abstract Acetylcholine (ACh), one of the brain’s most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E–I networks), which are ubiquitous in the brain. Utilizing biophysical models of E–I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E–I synapses and I–E synapses), and the strengths of intra-connections among excitatory cells (E–E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network’s intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.
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Cao, Ying, Xiaoyan He, Yuqing Hao i Qingyun Wang. "Transition Dynamics of Epileptic Seizures in the Coupled Thalamocortical Network Model". International Journal of Bifurcation and Chaos 28, nr 08 (lipiec 2018): 1850104. http://dx.doi.org/10.1142/s0218127418501043.
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In this paper, based on the two-compartment unidirectionally coupled thalamocortical model network, we investigated the transition dynamics of epileptic seizures, by considering the inhibitory coupling strength from cortical inhibitory interneuronal (IN) population to excitatory pyramidal (PY) neuronal population as the key bifurcation parameter. The results show that in the single compartment thalamocortical model, inner-compartment inhibitory functions of IN can make the system transit from the absence seizures to the tonic oscillations. In the case of two-compartment coupled thalamocortical model network, the inter-compartment inhibitory coupling functions from the first compartment can drive the second compartment to more easily initiate the absence and tonic seizures at the lower inhibitory coupling strengths, respectively. Also, the driven functions can make the amplitudes of these seizures vary irregularly. Detailed investigations reveal that along with the various state transitions, the system consecutively undergoes Hopf bifurcations, fold of cycles bifurcations and torus bifurcations, respectively. In particular, the reinforcing inter-compartment inhibitory coupling function can induce the chaotic dynamics. We highlight the unidirectional coupling functions between two compartments which might give new insights into the propagation and evolution dynamics of epileptic seizures.
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Tiesinga, Paul H. E. "Stimulus Competition by Inhibitory Interference". Neural Computation 17, nr 11 (1.11.2005): 2421–53. http://dx.doi.org/10.1162/0899766054796905.
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When two stimuli are present in the receptive field of a V4 neuron, the firing rate response is between the weakest and strongest response elicited by each of the stimuli when presented alone (Reynolds, Chelazzi, & Desimone, 1999). When attention is directed toward the stimulus eliciting the strongest response (the preferred stimulus), the response to the pair is increased, whereas the response decreases when attention is directed to the other stimulus (the poor stimulus). When attention is directed to either of the two stimuli presented alone, the firing rate remains the same or increases slightly, but the coherence between the neuron's spike train and the local field potential can increase (Fries, Reynolds, Rorie, & Desimone, 2001). These experimental results were reproduced in a model of a V4 neuron under the assumption that attention modulates the activity of local interneuron networks. The V4 model neuron received stimulus-specific excitation from V2 and synchronous inhibitory inputs from two local interneuron networks in V4. Each interneuron network was driven by stimulus-specific excitatory inputs from V2 and was modulated by the activity of the frontal eye fields. Stimulus competition was present because of a delay in arrival time of synchronous volleys from each interneuron network. For small delays, the firing rate was close to the rate elicited by the preferred stimulus alone, whereas for larger delays, it approached the firing rate of the poor stimulus. When either stimulus was presented alone, the neuron's response was not altered by the change in delay, but could change due to modulation of the degree of synchrony of the corresponding interneuron network. The model suggests that top-down attention biases the competition between V2 columns for control of V4 neurons primarily by changing the relative timing of inhibition, whereas changes in the degree of synchrony of interneuron networks modulate the response to a single stimulus. The new mechanism proposed here for attentional modulation of firing rate, gain modulation by inhibitory interference, is likely to have more general applicability to cortical information processing.
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YAMAZAKI, TADASHI, i SHIGERU TANAKA. "A NEURAL NETWORK MODEL FOR TRACE CONDITIONING". International Journal of Neural Systems 15, nr 01n02 (luty 2005): 23–30. http://dx.doi.org/10.1142/s0129065705000037.
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We studied the dynamics of a neural network that has both recurrent excitatory and random inhibitory connections. Neurons started to become active when a relatively weak transient excitatory signal was presented and the activity was sustained due to the recurrent excitatory connections. The sustained activity stopped when a strong transient signal was presented or when neurons were disinhibited. The random inhibitory connections modulated the activity patterns of neurons so that the patterns evolved without recurrence with time. Hence, a time passage between the onsets of the two transient signals was represented by the sequence of activity patterns. We then applied this model to represent the trace eyeblink conditioning, which is mediated by the hippocampus. We assumed this model as CA3 of the hippocampus and considered an output neuron corresponding to a neuron in CA1. The activity pattern of the output neuron was similar to that of CA1 neurons during trace eyeblink conditioning, which was experimentally observed.
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Andreev, Andrey, i Vladimir Maksimenko. "Synchronization in coupled neural network with inhibitory coupling". Cybernetics and Physics, Volume 8, 2019, Number 4 (30.12.2019): 199–204. http://dx.doi.org/10.35470/2226-4116-2019-8-4-199-204.
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A theoretical model of a network of neuron-like elements was constructed. The network included several subnetworks. The first subnetwork was used to translate a constant-amplitude signal into a spike sequence (conversion of amplitude to frequency). A similar process occurs in the brain when perceiving visual information. With an increase in the flow of information, the generation frequency of the neural ensemble participating in the processing increases. Further, the first subnetwork transmitted excitation to two large interconnected subnetworks. These subnetworks simulated the dynamics of the cortical neuronal populations. It was shown that in the presence of inhibitory coupling, the neuronal ensembles demonstrate antiphase dynamics. Various connectivity topologies and various types of neuron-like oscillators were investigated. We compare the results obtained in a discrete neuron model (Rulkov model) and a continuous-time model (Hodgkin-Huxley). It is shown that in the case of a discrete neuron model, the periodic dynamics is manifested in the alternate excitation of various neural ensembles. In the case of the continuous-time model, periodic modulation of the synchronization index of neural ensembles is observed.
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Blazis, Diana E. J., Thomas M. Fischer i Thomas J. Carew. "A Neural Network Model of Inhibitory Information Processing in Aplysia". Neural Computation 5, nr 2 (marzec 1993): 213–27. http://dx.doi.org/10.1162/neco.1993.5.2.213.
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Recent cellular studies have revealed a novel form of inhibitory information processing in the siphon withdrawal reflex of the marine mollusc Aplysia: Motorneuronal output is significantly reduced by activity-dependent potentiation of recurrent inhibition within the siphon withdrawal network (Fischer and Carew 1991, 1993). This inhibitory modulation is mediated by two types of identified interneurons, L29s and L30s. In an effort to describe and analyze this and other forms of inhibitory information processing in Aplysia, and to compare it with similar processing in other nervous systems, we have constructed a neural network model that incorporates many empirically observed features of these interneurons. The model generates important aspects of the interactions of cells L29 and L30, and with no further modification, exhibits many network level phenomena that were not explicitly incorporated into the model.
Rozprawy doktorskie na temat "Inhibitory Network Model"
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Ahn, Sungwoo. "Transient and Attractor Dynamics in Models for Odor Discrimination". The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1280342970.
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Denecker, Thomas. "Bioinformatique et analyse de données multiomiques : principes et applications chez les levures pathogènes Candida glabrata et Candida albicans Functional networks of co-expressed genes to explore iron homeostasis processes in the pathogenic yeast Candida glabrata Efficient, quick and easy-to-use DNA replication timing analysis with START-R suite FAIR_Bioinfo: a turnkey training course and protocol for reproducible computational biology Label-free quantitative proteomics in Candida yeast species: technical and biological replicates to assess data reproducibility Rendre ses projets R plus accessibles grâce à Shiny Pixel: a content management platform for quantitative omics data Empowering the detection of ChIP-seq "basic peaks" (bPeaks) in small eukaryotic genomes with a web user-interactive interface A hypothesis-driven approach identifies CDK4 and CDK6 inhibitors as candidate drugs for treatments of adrenocortical carcinomas Characterization of the replication timing program of 6 human model cell lines". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL010.
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Plusieurs évolutions sont constatées dans la recherche en biologie. Tout d’abord, les études menées reposent souvent sur des approches expérimentales quantitatives. L’analyse et l’interprétation des résultats requièrent l’utilisation de l’informatique et des statistiques. Également, en complément des études centrées sur des objets biologiques isolés, les technologies expérimentales haut débit permettent l’étude des systèmes (caractérisation des composants du système ainsi que des interactions entre ces composants). De très grandes quantités de données sont disponibles dans les bases de données publiques, librement réutilisables pour de nouvelles problématiques. Enfin, les données utiles pour les recherches en biologie sont très hétérogènes (données numériques, de textes, images, séquences biologiques, etc.) et conservées sur des supports d’information également très hétérogènes (papiers ou numériques). Ainsi « l’analyse de données » s’est petit à petit imposée comme une problématique de recherche à part entière et en seulement une dizaine d’années, le domaine de la « Bioinformatique » s’est en conséquence totalement réinventé. Disposer d’une grande quantité de données pour répondre à un questionnement biologique n’est souvent pas le défi principal. La vraie difficulté est la capacité des chercheurs à convertir les données en information, puis en connaissance. Dans ce contexte, plusieurs problématiques de recherche en biologie ont été abordées lors de cette thèse. La première concerne l’étude de l’homéostasie du fer chez la levure pathogène Candida glabrata. La seconde concerne l’étude systématique des modifications post-traductionnelles des protéines chez la levure pathogène Candida albicans. Pour ces deux projets, des données « omiques » ont été exploitées : transcriptomiques et protéomiques. Des outils bioinformatiques et des outils d’analyses ont été implémentés en parallèle conduisant à l’émergence de nouvelles hypothèses de recherche en biologie. Une attention particulière et constante a aussi été portée sur les problématiques de reproductibilité et de partage des résultats avec la communauté scientifiqueBiological research is changing. First, studies are often based on quantitative experimental approaches. The analysis and the interpretation of the obtained results thus need computer science and statistics. Also, together with studies focused on isolated biological objects, high throughput experimental technologies allow to capture the functioning of biological systems (identification of components as well as the interactions between them). Very large amounts of data are also available in public databases, freely reusable to solve new open questions. Finally, the data in biological research are heterogeneous (digital data, texts, images, biological sequences, etc.) and stored on multiple supports (paper or digital). Thus, "data analysis" has gradually emerged as a key research issue, and in only ten years, the field of "Bioinformatics" has been significantly changed. Having a large amount of data to answer a biological question is often not the main challenge. The real challenge is the ability of researchers to convert the data into information and then into knowledge. In this context, several biological research projects were addressed in this thesis. The first concerns the study of iron homeostasis in the pathogenic yeast Candida glabrata. The second concerns the systematic investigation of post-translational modifications of proteins in the pathogenic yeast Candida albicans. In these two projects, omics data were used: transcriptomics and proteomics. Appropriate bioinformatics and analysis tools were developed, leading to the emergence of new research hypotheses. Particular and constant attention has also been paid to the question of data reproducibility and sharing of results with the scientific community
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Dasgupta, Dabanjan. "Plasticity of Intrinsic Excitability in Fast Spiking Interneurons of the Dentate Gyrus & Its Implications for Neuronal Network Dynamics". Thesis, 2015. https://etd.iisc.ac.in/handle/2005/4079.
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Inhibitory GABAergic neurons, although forming a minor proportion of the neuronal population in the central nervous system, have been reported to be crucial for different physiological states of the brain. Among the vast diversity of this neuronal subpopulation, the fast spiking interneurons (FSINs) have been studied in great detail owing to their morphological and physiological attributes and functional correlates. Due to their perisomatic targeting and rapid spiking nature, they have been strongly associated with spike time and gain control of their target neurons in neuronal microcircuits across different regions of the brain. Plastic alterations of neuronal synaptic and intrinsic properties have been associated with learning and memory. However, a vast majority of the studies performed so far pertains to excitatory neurons. Although some recent studies have looked into plasticity of inhibition, little is known about plastic changes in the inhibitory neurons. Owing to the morpho-physiological properties of the FSINs and their massive connectivity, plastic alterations in them can cascade to their connected neuronal microcircuit.
The dentate gyrus (DG) forms an important gateway of information for the hippocampus and has been associated with pattern separation. The granule cells which are predominantly known to target interneurons discharge in the gamma frequency range. Hilar interneurons including the FSINs are known to show membrane potential oscillations phase-locked with the extracellularly recorded oscillations. However, the consequent response of a FSIN to repetitive excitatory gamma synaptic bursts presented either in isolation or in association with membrane potential
modulations has not received attention. We show that the FSINs of the DG sub field express a robust long lasting decrease in intrinsic excitability after experiencing bursts of synaptic stimulation of the mossy fiber pathway at gamma frequency (30 Hz), repeated at delta (2 Hz) or theta frequency (4 Hz). Interestingly, the GCs did not express any plasticity of intrinsic excitability upon experiencing similar gamma bursts repeated at delta frequency. The change in intrinsic excitability in the FSINs was observed to be strongly dependent on the somatic current supplement that altered the membrane potential in phase with the synaptic gamma bursts. The plasticity was found to be dependent on the post synaptic calcium flux through the calcium-permeable AMPA receptors (CP-AMPARs) and also on post synaptic HCN channel conductance. Further, decreased excitability in the FSINs exhibited decreased inhibition in the post-synaptic putative granule cells. Additionally, we have used network simulations to predict that the spiking rate of an excitatory neuron is strongly dependent on the intrinsic excitability of a perisomatic targeting interneuron; both integrated in a feedback microcircuit.
Given the importance of FSINs in network synchronization, understanding how intrinsic excitability and its plasticity in the FSINs can affect the network attributes is of seminal interest in the field of neuronal circuit dynamics and plasticity. We used computational simulation of physiologically scaled down neuronal networks consisting of experimentally constrained models of neurons to address this question. Intrinsic excitability in FSINs has been experimentally observed to be altered due to changes in their input resistance and changes in their action potential threshold. To alter the input resistance of the FSINs, we changed the specific membrane resistance (Rm), while to change the action potential threshold we altered the peak delayed potassium conductance (gKDbar) In Wang-Buzsaki type FSIN-FSIN interconnected network
models (II network) we observed an increase in the network frequency with increase in FSIN Rm while the network coherence did not change due to the altered FSIN Rm. However, in the same network there was a drastic decrease in both network coherence and network frequency with increase in gKDbar. Next, we built an EI network using 250 model excitatory neurons (ENs) and 50 model FSINs. The ENs were reciprocally connected to the FSINs. Moreover, the FSINs were also interconnected among themselves while the ENs were not. In these EI networks we observed that decreased FSIN Rm, which decreased their excitability, caused a monotonic increase in the excitatory network coherence. However, increased FSIN gKDbar which also decreased their excitability caused a decrease in the excitatory network coherence. The excitatory network frequency was decreased with decreased FSIN Rm or with increased FSIN gKDbar. However, EI networks having decreased FSIN input resistance (~ 50 MΩ) could partially rescue the excitatory network coherence from the desynchronizing effect of increased FSIN gKDbar. In EI networks having higher FSIN input resistance (~ 110 MΩ); even a small increase in FSIN gKDbar caused a drastic decrease in the excitatory network coherence. The phenomenon of altered EI network activity due to altered FSIN Rm or FSIN gKDbar was observed to be significantly independent of the proportion of the FSIN population undergoing the alterations. The observation that even a small proportion of the entire FSIN population (10% and 40%; for FSIN Rm dependence and FSIN gKDbar dependence respectively) can cause a massive shift in the EI network activity indicated the strong influence of FSIN intrinsic excitability on network dynamics. We also observed that the dependence of FSIN Rm on EI network activity was quite robust in the physiological range of the network synaptic parameters.
Overall from these studies we observed that DG FSINs express activity dependent plasticity of intrinsic excitability after experiencing near physiological synaptic excitation. Further, altered intrinsic excitability of FSINs can cause robust changes in the connected network. The study suggests possible intrinsic strategies in FSINs which might be functional in neuronal microcircuits during different physiological and pathological conditions.
Części książek na temat "Inhibitory Network Model"
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Dagnew, Tewodros M., Claudio Silvestri, Debora Slanzi i Irene Poli. "A Neural Network Model for Lead Optimization of MMP12 Inhibitors". W Pattern Recognition. ICPR International Workshops and Challenges, 323–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68799-1_23.
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Aida, Masaki, i Ayako Hashizume. "Activator-Inhibitor Model for Describing Interactions Between Fake News and Their Corrections". W Complex Networks & Their Applications X, 54–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93413-2_5.
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Bem, Tiaza, i John Hallam. "Characterisation of Multiple Patterns of Activity in Networks of Relaxation Oscillators with Inhibitory and Electrical Coupling". W Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy, 164–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02264-7_18.
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Kharybina, Zoia. "A Model of Neurodynamics of Hippocampal Formation Neurons Performing Spatial Processing Based on Even Cyclic Inhibitory Networks". W Advances in Intelligent Systems and Computing, 79–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32554-5_11.
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Palop, Jorge J., Lennart Mucke i Erik D. Roberson. "Quantifying Biomarkers of Cognitive Dysfunction and Neuronal Network Hyperexcitability in Mouse Models of Alzheimer’s Disease: Depletion of Calcium-Dependent Proteins and Inhibitory Hippocampal Remodeling". W Methods in Molecular Biology, 245–62. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-744-0_17.
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De Pauw, Ines, Carolien Boeckx i An Wouters. "Mechanisms of Cetuximab Resistance and How to Overcome It". W Critical Issues in Head and Neck Oncology, 21–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63234-2_3.
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AbstractDeregulated or increased signalling of the epidermal growth factor receptor (EGFR) plays an integral role in the development of various cancer types, including head and neck squamous cell carcinoma (HNSCC), making it a compelling drug target. However, after initially promising results of EGFR-targeted therapies, such as the monoclonal antibody cetuximab, it became clear that both intrinsic and acquired therapeutic resistance are major roadblocks in the field of personalised cancer treatments.In order to unravel and overcome resistance to cetuximab, at least two strategies can be adopted.Firstly, therapeutic resistance to anti-EGFR therapy may arise from mechanisms that can compensate for reduced EGFR signalling and/or mechanisms that can modulate EGFR-dependent signalling. In this chapter, we discuss which mechanisms of cetuximab resistance are already known and which ones deserve further investigation. This enhanced knowledge will guide us to rationally design and test novel combination therapies that overcome resistance to EGFR-targeting agents in cancer treatment.Secondly, an urgent need remains to develop novel targeted treatments for single-agent or combined therapy use. In this view, due to the particular mode of activation of the EGFR receptor, involving ligand-induced homo- and heterodimerization of the four HER receptors, an increased inhibition scope of HER receptors most likely results in a more potent blockade of the HER network, preventing premature emergence of resistance and leading to a more pronounced therapeutic benefit. We discuss two multitargeted compounds, being MEHD7945A (duligotuzumab) and afatinib, in this chapter.Despite the huge efforts to unravel the molecular landscape of HNSCC, the main clinically validated target remains EGFR. However, immune checkpoints, like programmed cell death protein 1 (PD-1), are gaining clinical approvals as well. We underscore the importance of adopting rational drug combinations to enhance the therapeutic effect of the EGFR-inhibitor cetuximab and highlight the ongoing search for predictive biomarkers, with the ultimate goal of delivering individualized cancer therapy to HNSCC patients.
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Ghosh, Joydeep, Hung-Jen Chang i Kadir Liano. "A Macroscopic Model of Oscillation in Ensembles of Inhibitory and Excitatory Neurons". W Neural Networks and Pattern Recognition, 143–69. Elsevier, 1998. http://dx.doi.org/10.1016/b978-012526420-4/50006-9.
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Tsarouchas, Nick. "Clinical Neurophysiology of Epileptogenic Networks". W Neurophysiology - Networks, Plasticity, Pathophysiology, and Behavior [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.104952.
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Current theories and models of brain rhythm generation are based on (1) the excitability of individual neurons and whole networks, (2) the structural and functional connectivity of neuronal ensembles, (3) the dynamic interaction of excitatory and inhibitory network components, and (4) the importance of transient local and global states. From the interplay of the above, systemic network properties arise which account for activity overdrive or suppression, and critical-level synchronization. Under certain conditions or states, small-to-large scale neuronal networks can be entrained into excessive and/or hypersynchronous electrical brain activity (epileptogenesis). In this chapter we demonstrate with artificial neuronal network simulations how physiological brain oscillations (delta, theta, alpha, beta and gamma range, and transients thereof, including sleep spindles and larger sleep waves) are generated and how epileptiform phenomena can potentially emerge, as observed at a macroscopic scale on scalp and intracranial EEG recordings or manifested with focal and generalized, aware and unaware, motor and nonmotor or absence seizures in man. Fast oscillations, ripples and sharp waves, spike and slow wave discharges, sharp and rhythmical slow waves, paroxysmal depolarization and DC shifts or attenuation and electrodecremental responses seem to underlie key mechanisms of epileptogenesis across different scales of neural organization and bear clinical implications for the pharmacological and surgical treatment of the various types of epilepsy.
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Bulashevska, Svetlana. "Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model". W Handbook of Research on Computational Methodologies in Gene Regulatory Networks, 108–38. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-685-3.ch005.
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This chapter describes the model of genetic regulatory interactions. The model has a Boolean logic semantics representing the cooperative influence of regulators (activators and inhibitors) on the expression of a gene. The model is a probabilistic one, hence allowing for the statistical learning to infer the genetic interactions from microarray gene expression data. Bayesian approach to model inference is employed enabling flexible definitions of a priori probability distributions of the model parameters. Markov Chain Monte Carlo (MCMC) simulation technique Gibbs sampling is used to facilitate Bayesian inference. The problem of identifying actual regulators of a gene from a high number of potential regulators is considered as a Bayesian variable selection task. Strategies for the definition of parameters reducing the parameter space and efficient MCMC sampling methods are the matter of the current research.
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Al-Hajri, Salim, i Arthur Tatnall. "A Socio-Technical Study of the Adoption of Internet Technology in Banking, Re-Interpreted as an Innovation Using Innovation Translation". W Social and Professional Applications of Actor-Network Theory for Technology Development, 207–20. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2166-4.ch016.
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This article presents a re-interpretation of research done in the mid-2000s on uptake of Internet technologies in the banking industry in Oman, compared with that in Australia. It addresses the question: What are the enablers and the inhibitors of Internet technology adoption in the Omani banking industry compared with those in the Australian banking industry? The research did not attempt a direct comparison of the banking industries in these two very different countries, but rather considered Internet technology adoption in Oman, informed by the more mature Australian experience. The original study considered Internet banking as an innovation and used an approach to theorising this innovation that was based on Diffusion of Innovations and the Technology Acceptance Model (TAM). Given the socio-technical nature of this investigation, however, another approach to adoption of innovations was worth investigating, and this article reports a re-interpretation of the original study using innovation translation from actor-network theory (ANT).
Streszczenia konferencji na temat "Inhibitory Network Model"
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Hedayati, B. Keshavarz, R. Parra-Hernandez, E. M. Laxdal, N. J. Dimopoulos, P. Alexiou i V. J. Demopoulos. "An improved neural network ensemble model of Aldose Reductase inhibitory activity". W 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252798.
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Hui, Qing, Wassim M. Haddad, James M. Bailey i Tomohisa Hayakawa. "A stochastic mean field model for an excitatory and inhibitory synaptic drive cortical neuronal network". W 2012 IEEE 51st Annual Conference on Decision and Control (CDC). IEEE, 2012. http://dx.doi.org/10.1109/cdc.2012.6426144.
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Cannon, Mark W. "A model for spatial interactions among contrast sensitive mechanisms". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thp4.
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Recent research demonstrating that the perceived contrast of a small central grating patch can be strongly influenced by the presence of another grating in an annular surround implied the presence of two types of lateral interaction networks, one excitatory and one inhibitory. The present paper describes the development of a model for these networks, under the conditions where both center and surround contain gratings of the same spatial frequency and orientation. Two different interconnection networks were studied. In the feed-forward system, the gain of each member of a 2-D array of contrast sensitive mechanisms is adjusted by the weighted sum of the stimulus inputs to adjacent mechanisms. In the feedback system, the gain of each member of the array of contrast sensitive mechanisms is adjusted by a weighted sum of the outputs of adjacent mechanisms. Simulations indicate that psychophysical data can be accounted for only by the feed-back type network and that individual differences in suppression and enhancement effects can be accounted for by minor differences in the shapes of the spatial weighting functions.
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Yang, Xiang Y., Taiwei Lu i Francis T. S. Yu. "Generalized interpattern association neural network". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mn6.
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The interpattem-association (IPA) neuralnetwork model emphasizes the effect of the special features,1 which makes it particularly suitable for application to reference patterns that are similar to one another. To achieve robustness of the neural network, some interconnection redundancy must be introduced. The IPA algorithm proposed in our previous publication is based on logic rules that determine the excitory and inhibitory interconnection. However, the interconnections were excessively redundant, and common features were not effectively suppressed.
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MARNELLOS, G., G. A. DEBLANDRE, E. MJOLSNESS i C. KINTNER. "DELTA-NOTCH LATERAL INHIBITORY PATTERNING IN THE EMERGENCE OF CILIATED CELLS IN XENOPUS: EXPERIMENTAL OBSERVATIONS AND A GENE NETWORK MODEL". W Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 1999. http://dx.doi.org/10.1142/9789814447331_0031.
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Weible, K. J., N. Collings, W. Xue, G. Pedrini i R. Dändliker. "Experimental comparison of different associative memory techniques implemented optically by the same system architecture". W Optical Computing. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/optcomp.1991.me9.
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In recent years, much work has been going on in the optical implementation of artificial neural network systems. The parallel and crosstalk free interconnection characteristics of optical systems are well suited to exploit fully the desired parallel characteristics of artificial neural networks. The application of these systems as associative memories has been explored in many cases.1-4 To facilitate optical implementation of these neural systems various modifications to the original Hopfield model have been proposed.5 It has been shown both theoretically and experimentally, that the storage and recall capacity of neural systems based on the Hopfield model are not significantly reduced when only the inhibitory (negative) interconnections are used.6,7
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Yu, Francis T. S., Taiwei Lu i Xiang Y. Yang. "Optical heteroassociative memory for character translation". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mj3.
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Character translation can be accomplished by a heteroassociative memory in an artificial neural network. Because of the similarity among the characters, the special features of the patterns are important in pattern recognition. In this paper, a neural-network model based on the interpattem association (IPA) concept is presented.1 Generalized logical rules are developed to construct the excitatory and inhibitory interconnections in the heteroassociative memory. An adaptive optical neural network using high-resolution liquid-crystal televisions2 is used to translate between English letters and Chinese characters. Experimental and computer-simulated results have revealed that the IPA model is more effective in recognizing input among similar characters and has a larger storage capacity than the Hopfield model. Furthermore, the IPA model has shown two major advantages: fewer interconnections and fewer gray levels.
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Blake, Randolph, i Mark Nawrot. "Stereopsis and kinetic depth: two sides of the same coin?" W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.fd2.
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Several lines of evidence indicate that stereoscopic information and motion information strongly interact to specify object shape and layout in depth. For example, adaptation to an unambiguous stereoscopic object temporarily stabilizes perception of an otherwise ambiguous kinetic (KD) display. In fact, stereoscopic adaptation can even create the perception of structure and depth in a display consisting entirely of dynamic random noise. Conversely, perceived depth in a KD display can bias depth segregation in a stereoscopic display. These and related findings have inspired a simple neural model that incorporates KD and disparity processing within a single network. The model comprises two levels: a layer of monocular directionally selective motion detectors that provide input to a second layer of disparity selective and direction selective binocular mechanisms. The network of facilitatory and inhibitory connections between binocular mechanisms promotes segregation of activity into separate neural pools specifying global motion in different depth planes. In the absence of disparity information, activity fluctuates in a manner mimicking the fluctuations in perception experienced when viewing a KD animation sequence. Unresolved questions include the role of head movements in modifying activity within the network.
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Zhang, Tielin, Yi Zeng, Dongcheng Zhao i Bo Xu. "Brain-inspired Balanced Tuning for Spiking Neural Networks". W Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/229.
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Due to the nature of Spiking Neural Networks (SNNs), it is challenging to be trained by biologically plausible learning principles. The multi-layered SNNs are with non-differential neurons, temporary-centric synapses, which make them nearly impossible to be directly tuned by back propagation. Here we propose an alternative biological inspired balanced tuning approach to train SNNs. The approach contains three main inspirations from the brain: Firstly, the biological network will usually be trained towards the state where the temporal update of variables are equilibrium (e.g. membrane potential); Secondly, specific proportions of excitatory and inhibitory neurons usually contribute to stable representations; Thirdly, the short-term plasticity (STP) is a general principle to keep the input and output of synapses balanced towards a better learning convergence. With these inspirations, we train SNNs with three steps: Firstly, the SNN model is trained with three brain-inspired principles; then weakly supervised learning is used to tune the membrane potential in the final layer for network classification; finally the learned information is consolidated from membrane potential into the weights of synapses by Spike-Timing Dependent Plasticity (STDP). The proposed approach is verified on the MNIST hand-written digit recognition dataset and the performance (the accuracy of 98.64%) indicates that the ideas of balancing state could indeed improve the learning ability of SNNs, which shows the power of proposed brain-inspired approach on the tuning of biological plausible SNNs.
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Spooner, Victoria E., Robert Stalker, Rob Wright i Gordon M. Graham. "Improving Scale Inhibitor Squeeze Design for Naturally Fractured Reservoirs". W SPE International Oilfield Scale Conference and Exhibition. SPE, 2014. http://dx.doi.org/10.2118/spe-169755-ms.
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Abstract Scale Inhibitor (SI) squeeze treatments continue to be an important method for delivering chemical to the production system. However, while SI squeeze treatments in unfractured reservoirs can generally be readily simulated in matrix flow models, designing such treatments for application in fractured reservoirs is less routine, and resulting field treatment lifetimes can be disappointing. One reason for this is that the flow process and transport mechanisms by which the inhibitors are retained in fractured formations differs considerably from simple matrix flow. In this paper we expand upon previously published work examining the impact of squeeze treatment design on the outcome of a SI squeeze treatment for a fractured well using a novel fractured well squeeze model. In previous papers, we highlighted the importance of diffusion-controlled transport of SI in low permeability tight matrix fractured reservoirs where little matrix flow is possible. In this paper, we report continuing developments of the fracture squeeze model and demonstrate how differences between advection and diffusion-controlled inhibitor transport can significantly alter the predicted squeeze treatment lifetimes, and suggest appropriate treatment design modifications to improve SI squeeze treatments in such fractured reservoirs. This paper will demonstrate that such differences in transport mechanisms directly impacts the distribution of scale inhibitor within the near-wellbore region during the treatment phase. In fractured systems, this SI distribution is affected both by the extent of propagation of injection fluid through the fracture network and rate of diffusion into the surrounding matrix rock. This work examines the influence of factors such as injection rate, soak time, inhibitor diffusivity and retention/release properties on the matrix material. By adjusting injection parameters such as injection rate and soak time in the treatment design, a more desirable distribution of scale inhibitor can be obtained, resulting in improved predicted treatment lifetimes. Thus, using the fracture squeeze model to provide a fuller simulation of the inhibitor transport, retention and release mechanism active in a fractured reservoir, we highlight potential placement issues for such reservoirs and demonstrate methods to improve squeeze design for fractured wells.
Raporty organizacyjne na temat "Inhibitory Network Model"
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Tian, Cong, Jianlong Shu, Wenhui Shao, Zhengxin Zhou, Huayang Guo i Jingang Wang. The efficacy and safety of IL Inhibitors, TNF-α Inhibitors, and JAK Inhibitor on ankylosing spondylitis: A Bayesian network meta-analysis of a “randomized, double-blind, placebo-controlled” trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, wrzesień 2022. http://dx.doi.org/10.37766/inplasy2022.9.0117.
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Review question / Objective: In this study, we conducted a Bayesian network meta-analysis to evaluate the efficacy and safety of interleukin (IL) inhibitors, tumor necrosis factor-alpha (TNF-α) inhibitors, and Janus kinase (JAK) inhibitors on ankylosing spondylitis (AS).The purpose of this study is to compare the effectiveness and safety of different interventions for treating AS to provide insights into the decision-making in clinicalpractice. Condition being studied: Ankylosing spondylitis. Based on the Bayesian hierarchical model, we conducted a network meta-analysis using the gemtc package in R software (version 4.1.3) and Stata software (version 15.1). Cong Tian and Jianlong Shu contributed to the conception and design of the study and supervised the tweet classification. All authors drafted the manuscript. Wenhui Shao, Zhengxin Zhou, Huayang Guo and Jingang Wang contributed to data management and tweet classification. Cong Tian, Jianlong Shu and Zhengxin Zhou performed the statistical analysis. Cong Tian, Jianlong Shu, Wenhui Shao and Zhengxin Zhou reviewed the manuscript.