Academic literature on the topic 'Granular neuron'
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Journal articles on the topic "Granular neuron"
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Alloway, K. D., M. J. Johnson, and M. B. Wallace. "Thalamocortical interactions in the somatosensory system: interpretations of latency and cross-correlation analyses." Journal of Neurophysiology 70, no. 3 (September 1, 1993): 892–908. http://dx.doi.org/10.1152/jn.1993.70.3.892.
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1. Isolated extracellular neuronal responses to cutaneous stimulation were simultaneously recorded from corresponding peripheral representations in the ventrobasal nucleus and primary somatosensory cortex of intact, halothane-anesthetized rats. Thalamic and cortical neurons representing hairy skin on the forelimb were activated by hair movements produced by a series of 50 or 100 discrete air jets. A corresponding set of neurons representing the glabrous pads of the hind paw were activated by a similar number of punctate mechanical displacements. 2. Cortical electrode penetrations were histologically reconstructed, and 118 neurons in the glabrous skin representation exhibited cutaneous responses that were categorized into supragranular, granular, or infragranular groups according to their laminar position. Minimum latencies of cortical neurons responding to glabrous skin displacement were analyzed, and significant differences were found in the distribution of minimum latencies for the different cortical layers. Mean values for minimum latencies in the infragranular and granular layers were 15.8 and 16.3 ms, respectively, whereas supragranular neurons were characterized by minimum latencies having a mean of 20 ms. The differences between these groups suggests that stimulus-induced afferent activity reaches infragranular and granular layers before contacting supragranular neurons. Average latencies were also calculated on responses occurring during the 1st 20 trials, but the cortical distributions of these values overlapped considerably, and differences between the laminar groups were not statistically significant. 3. In several recording sites, two cortical neurons were recorded simultaneously, and the response latencies of these matched pairs were often substantially different despite the similarity in laminar position. This result indicates that laminar location is not the only determinant of response latency and that serially organized circuits are distributed within, as well as between, cortical layers. 4. From a sample of 302 neurons exhibiting cutaneous responses within histologically identified regions of thalamus or cortex, a set of 143 pairs of neurons recorded simultaneously from both regions was available for cross-correlation analysis. Significant thalamocortical interactions were found in 38 neurons pairs. Analysis of these significant interactions revealed that thalamocortical connection strength, as measured by neuronal efficacy, was two to four times larger for neuron pairs having the cortical cell in granular layer IV than for neuron pairs having an extragranular layer cortical neuron. There was no difference in thalamocortical connection strength between neuron pairs containing supra- or infragranular cortical neurons. 5. Summed peristimulus time histograms revealed stimulus-locked inhibition of spontaneous activity in 4% (8/195) or cortical and 18% (20/107) of thalamic neurons.(ABSTRACT TRUNCATED AT 400 WORDS)
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Sajjad, Hassan, Nadir Durrani, and Fahim Dalvi. "Neuron-level Interpretation of Deep NLP Models: A Survey." Transactions of the Association for Computational Linguistics 10 (2022): 1285–303. http://dx.doi.org/10.1162/tacl_a_00519.
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Abstract The proliferation of Deep Neural Networks in various domains has seen an increased need for interpretability of these models. Preliminary work done along this line, and papers that surveyed such, are focused on high-level representation analysis. However, a recent branch of work has concentrated on interpretability at a more granular level of analyzing neurons within these models. In this paper, we survey the work done on neuron analysis including: i) methods to discover and understand neurons in a network; ii) evaluation methods; iii) major findings including cross architectural comparisons that neuron analysis has unraveled; iv) applications of neuron probing such as: controlling the model, domain adaptation, and so forth; and v) a discussion on open issues and future research directions.
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Collins, Christine E., Emily C. Turner, Eva Kille Sawyer, Jamie L. Reed, Nicole A. Young, David K. Flaherty, and Jon H. Kaas. "Cortical cell and neuron density estimates in one chimpanzee hemisphere." Proceedings of the National Academy of Sciences 113, no. 3 (January 4, 2016): 740–45. http://dx.doi.org/10.1073/pnas.1524208113.
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The density of cells and neurons in the neocortex of many mammals varies across cortical areas and regions. This variability is, perhaps, most pronounced in primates. Nonuniformity in the composition of cortex suggests regions of the cortex have different specializations. Specifically, regions with densely packed neurons contain smaller neurons that are activated by relatively few inputs, thereby preserving information, whereas regions that are less densely packed have larger neurons that have more integrative functions. Here we present the numbers of cells and neurons for 742 discrete locations across the neocortex in a chimpanzee. Using isotropic fractionation and flow fractionation methods for cell and neuron counts, we estimate that neocortex of one hemisphere contains 9.5 billion cells and 3.7 billion neurons. Primary visual cortex occupies 35 cm2 of surface, 10% of the total, and contains 737 million densely packed neurons, 20% of the total neurons contained within the hemisphere. Other areas of high neuron packing include secondary visual areas, somatosensory cortex, and prefrontal granular cortex. Areas of low levels of neuron packing density include motor and premotor cortex. These values reflect those obtained from more limited samples of cortex in humans and other primates.
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Medini, Chaitanya, Bipin Nair, Egidio D'Angelo, Giovanni Naldi, and Shyam Diwakar. "Modeling Spike-Train Processing in the Cerebellum Granular Layer and Changes in Plasticity Reveal Single Neuron Effects in Neural Ensembles." Computational Intelligence and Neuroscience 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/359529.
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The cerebellum input stage has been known to perform combinatorial operations on input signals. In this paper, two types of mathematical models were used to reproduce the role of feed-forward inhibition and computation in the granular layer microcircuitry to investigate spike train processing. A simple spiking model and a biophysically-detailed model of the network were used to study signal recoding in the granular layer and to test observations like center-surround organization and time-window hypothesis in addition to effects of induced plasticity. Simulations suggest that simple neuron models may be used to abstract timing phenomenon in large networks, however detailed models were needed to reconstruct population coding via evoked local field potentials (LFP) and for simulating changes in synaptic plasticity. Our results also indicated that spatio-temporal code of the granular network is mainly controlled by the feed-forward inhibition from the Golgi cell synapses. Spike amplitude and total number of spikes were modulated by LTP and LTD. Reconstructing granular layer evoked-LFP suggests that granular layer propagates the nonlinearities of individual neurons. Simulations indicate that granular layer network operates a robust population code for a wide range of intervals, controlled by the Golgi cell inhibition and is regulated by the post-synaptic excitability.
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Zhang, Mengliang, and Kevin D. Alloway. "Stimulus-Induced Intercolumnar Synchronization of Neuronal Activity in Rat Barrel Cortex: A Laminar Analysis." Journal of Neurophysiology 92, no. 3 (September 2004): 1464–78. http://dx.doi.org/10.1152/jn.01272.2003.
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We used cross-correlation analysis to characterize the coordination of stimulus-induced neuronal activity in the primary somatosensory barrel cortex of isoflurane-anesthetized rats. On each trial, multiple whiskers were simultaneously deflected at frequencies that corresponded to 2, 5, 8, or 11 Hz. Among 476 neuron pairs that we examined, 342 (71.8%) displayed significant peaks of synchronized activity that exceeded the 99.9% confidence limits. The incidence and strength of these functional associations varied across different cortical layers. Only 52.9% of neuron pairs in layer IV displayed synchronized responses, whereas 84.1% of the infragranular neuron pairs were synchronized during whisker stimulation. Neuronal synchronization was strongest in the infragranular layers, weakest in layer IV, and varied according to the columnar configuration of the neuron pairs. Thus correlation coefficients were largest for neuron pairs in the same whisker barrel row but were smallest for neurons in different rows and arcs. Spontaneous activity in the infragranular layers was also synchronized to a greater degree than in the other layers. Although infragranular neuron pairs displayed similar amounts of synchronization in response to each stimulus frequency, granular and supragranular neurons were synchronized mainly during stimulation at 2 or 5 Hz. These results are consistent with previous studies indicating that infragranular neurons have intrinsic properties that facilitate synchronized activity, and they suggest that neuronal synchronization plays an important role in transmitting sensory information to other cortical or subcortical brain regions.
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Conrad, Rachel, and Mia C. N. Perez. "Congenital Granular Cell Epulis." Archives of Pathology & Laboratory Medicine 138, no. 1 (January 1, 2014): 128–31. http://dx.doi.org/10.5858/arpa.2012-0306-rs.
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Congenital granular cell epulis is a rarely reported lesion of unknown histogenesis with a strong predilection for the maxillary alveolar ridge of newborn girls. Microscopically, it demonstrates nests of polygonal cells with granular cytoplasm, a prominent capillary network, and attenuated overlying squamous epithelium. The lesion lacks immunoreactivity for S-100, laminin, chromogranin, and most other markers except neuron-specific enolase and vimentin. Through careful observation of its unique clinical, histopathologic, and immunohistochemical features, this lesion can be distinguished from the more common adult granular cell tumor as well as other differential diagnoses.
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Cavaliere, Antonio, Angelo Sidoni, Ivana Ferri, and Brunangelo Falini. "Granular Cell Tumor: An Immunohistochemical Study." Tumori Journal 80, no. 3 (June 1994): 224–28. http://dx.doi.org/10.1177/030089169408000312.
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Aims and background Granular cell tumor, usually a benign neoplasm, has been the object of many studies because of its uncertain histogenesis and based on many immunohistochemical and ultrastructural studies it has been suggested that it originates from the Schwann cell. Our recent observation that granular cell tumor is positive with PG-M1, a new anti-macrophage monoclonal antibody, led us to further investigate the immunophenotypic profile of the tumor. Study design We studied 11 granular cell tumors using a panel of 20 antibodies, 13 monoclonal and 7 polyclonal. Results The immunohistochemical study showed in all cases a constant diffuse positivity for S-100 protein, neuron-specific enolase, vimentin, KP1 and PG-M1, as well as occasional and focal positivity for alpha-1-antitrypsin, alpha-1-antichymotrypsin and lysozyme. Conclusions The immunophenotypic profile constantly observed could be the expression, on one hand, of the neuroectodermic nature of the neoplasm, proven by positivity for S-100 protein, neuron specific enolase and vimentin, and on the other could be the expression of the phagocytic activity of the tumor cell, proven by positivity for KP1 and PG-M1 antibodies and also by the presence of numerous phagolysosomes.
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Howarth, Clare, Claire M. Peppiatt-Wildman, and David Attwell. "The Energy Use Associated with Neural Computation in the Cerebellum." Journal of Cerebral Blood Flow & Metabolism 30, no. 2 (November 4, 2009): 403–14. http://dx.doi.org/10.1038/jcbfm.2009.231.
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The brain's energy supply determines its information processing power, and generates functional imaging signals, which are often assumed to reflect principal neuron spiking. Using measured cellular properties, we analysed how energy expenditure relates to neural computation in the cerebellar cortex. Most energy is used on information processing by non-principal neurons: Purkinje cells use only 18% of the signalling energy. Excitatory neurons use 73% and inhibitory neurons 27% of the energy. Despite markedly different computational architectures, the granular and molecular layers consume approximately the same energy. The blood vessel area supplying glucose and O2 is spatially matched to energy consumption. The energy cost of storing motor information in the cerebellum was also estimated.
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Rosso, Renato, Mario Scelsi, and Luciano Carnevali. "Granular Cell Traumatic Neuroma." Archives of Pathology & Laboratory Medicine 124, no. 5 (May 1, 2000): 709–11. http://dx.doi.org/10.5858/2000-124-0709-gctn.
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Abstract Background.—Granular cell changes can be observed in a variety of benign and malignant tumors, and are seen more commonly in granular cell tumors, which in about 5% of cases develop in the breast. Granular cells also have been observed in sites of previous trauma, such as surgery, and are found to be inflammatory reactions of histiocytic origin. Methods and Results.—We investigated, morphologically and immunohistochemically, 2 granular cell lesions occurring in mastectomy scars after surgery for carcinoma. Both lesions were composed of strands and nests of large granular cells, haphazardly set in a background of fibrous tissue, with sparse inflammatory infiltrates. Several tortuous hypertrophic nerve bundles were also embedded in the fibrous tissue. A few of these nerve bundles showed degenerative changes and contained granular cells. Immunohistochemically, granular cells were positive for S100 protein, neuron-specific enolase, vimentin, and CD68 antigen. Conclusions.—We consider these proliferative lesions of peripheral nerves to have the features of both granular cell tumor and traumatic neuroma. These cases indicate that traumatic neuroma can undergo extensive granular cell changes and constitute a previously unrecognized entity, which we provisionally label granular cell traumatic neuroma. Granular cell traumatic neuroma has to be taken into consideration when evaluating lesions occurring at mastectomy scars and should be differentiated from malignant tumors with granular cells, such as apocrine carcinoma and alveolar soft part sarcoma.
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Artuković, Branka, Andrea Gudan Kurilj, Ivana Mihoković Buhin, Lidija Medven Zagradišnik, Ivan-Conrado Šoštarić-Zuckermann, and Marko Hohšteter. "Granular cell tumor in the central nervous system of a ferret (Mustela putorius furo) - a case report." Veterinarski arhiv 92, no. 2 (April 29, 2022): 205–12. http://dx.doi.org/10.24099/vet.arhiv.1705.
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A granular cell tumor (GCT) in the central nervous system (CNS) of a ferret is a rare finding. In this case a cerebral GCT is described in a 5-year-old castrated female ferret. The animal developed lameness in right hind leg which progreseed to total ataxia. The animal died and a necropsy revealed the mass in the medial to caudal part of the left frontal lobe of the brain. Based on histological and imunohistochemical findings, tumor was diagnosed as granular cell tumor. Immunohistochemically, granular cells were diffusely positive for vimentin and neuron-specific enolase (NSE) and weakly focal reactivity for S-100 protein was seen. Neoplastic cells did not express cytokeratins and glial fibrillary acidic protein (GFAP). Although immunohistochemistry was performed, histogenesis of this tumor remains unsolved and controversial.
Dissertations / Theses on the topic "Granular neuron"
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Mortessagne, Pierre. "Characterization of the different populations of granular neurons in the dentate gyrus of the hippocampus : from morphology to function." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0402.
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Dans le gyrus denté (GD) de la formation hippocampique, la génération des neurones granulaires (NGs) commence vers la fin de l'embryogenèse, atteint un pic autour de la naissance, puis se poursuit à un faible niveau à l'âge adulte. Cette neurogénèse continue fait du GD une structure cérébrale unique, composée de NGs d'origines temporelles distinctes qui forment des sous-populations potentiellement dotées de caractéristiques anatomiques et fonctionnelles spécifiques dans l'hippocampe. Étonnamment, cette hypothèse a reçu peu d'attention. Dans ce contexte, ce travail de thèse a visé à élucider les caractéristiques morphologiques, électrophysiologiques et comportementales des sous-populations de NGs en fonction de leur origine temporelle. S’appuyant sur des découvertes antérieures de notre équipe qui avaient mis en évidence des différences dendritiques entre ces populations, nous avons centré nos investigations sur leurs axones, appelés fibres moussues. En utilisant des stratégies de marquage épars — l'électroporation pour cibler les NGs nés durant la période embryonnaire(E14.5) et néonatale (P0), ainsi que des injections rétrovirales pour les NGs nés à l’adolescence(P21) et à l’âge adulte (P84) — nous avons mis en évidence que les NGs générés à des périodes plus tardives développent des boutons plus larges avec davantage de filopodes et présentent un segment initial axonal plus court. De plus, en utilisant les lignées de souris Osteocalcin-Cre etAscl1CreERT2 pour marquer de larges cohortes de NGs générés durant la période embryonnaire et à l’âge adulte, respectivement, nous avons observé que les neurones nés précocement projettent davantage vers le CA2 comparativement aux neurones générés plus tardivement au cours de la vie. Suite à ces découvertes, nous avons étudié les caractéristiques fonctionnelles des NGs d’origines temporelles différentes, au niveau électrophysiologique et comportemental.Les études électrophysiologiques ont révélé que les NGs nés en période néonatale partagent des propriétés intrinsèques similaires à celles des NGs nés en période adulte, mais possèdent une transmission basale plus élevée, reflétant potentiellement un nombre plus important de sites actifs. Enfin, nous avons examiné le rôle des NGs nés en période embryonnaire dans le comportement de type social et montré qu’une inhibition aiguë de ces neurones retardait l’expression de la préférence sociale. Cependant, ces données fonctionnelles restent préliminaires et nécessitent des investigations supplémentaires.En conclusion, ce travail de thèse met en évidence l'impact significatif de l'origine temporelle des NGs sur leurs caractéristiques anatomiques et potentiellement fonctionnelles, soulignant l’importance de prendre en compte l’origine temporelle des NGs dans toute étude s’intéressant à l’aspect structurel ou fonctionnel du DGIn the dentate gyrus (DG) of the hippocampus, the generation of dentate granule neurons(DGNs) starts during late embryogenesis, peaks around birth and continues at low levels during adulthood. This continuous neurogenesis makes the DG a unique structure, composed of DGNs from distinct temporal origins, which form subpopulations potentially bearing unique anatomical characteristics and functional roles in hippocampal physiology. Surprisingly, this hypothesis has received limited attention. In this context, our research aimed to elucidate the morphological, electrophysiological, and behavioral characteristics of DGNs subpopulations based on their temporal origin. Building on prior findings from our team that high lighted dendritic differences between these populations, we focused on examining the features of their axons, called mossy fibers (MFs). Using sparse labeling strategies — electroporation to targetembryonically-born (E14.5) and neonatally-born (P0) DGNs, and retroviral injections foradolescent-born (P21) and adult-born (P84) DGNs — we uncovered that DGNs generated laterin life develop larger MF boutons with more filopodia, and exhibit a shorter axon initialsegment. Additionally, using the Osteocalcin-Cre and Ascl1CreERT2 mouse lines to selectivelylabel large cohorts of embryonically-born and adult-born DGNs, respectively, we found thatearlier-born neurons project further onto the CA2 compared to later-born neurons. Following these morphological findings, we further investigated the functional characteristics of temporally distinct DGNs at both the electrophysiological and behavioral levels. The electrophysiological studies revealed similar intrinsic properties between neonatally- and adult born DGNs, and higher basal transmission in neonatally-born DGNs, potentially reflecting alarger number of active sites. Finally, we examined the role of embryonic-born DGNs in socialbehavior, and showed that acute inhibition of these neurons delayed the expression of social preference. However, these functional data remain preliminary and need further investigation.Altogether, this PhD work highlights the significant impact of the birthdate of DGNs on their anatomical and potentially functional characteristics, and emphasizes the importance of considering their precise temporal origin in any structural or functional analysis of the DG
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Čurilla, Matej. "Neuronové sítě a hrubé množiny." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-264945.
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Rough sets and neural networks both offer good theoretical background for data processing and analysis. However, both of them suffer from few issues. This thesis will investigate methods by which these two concepts are merged, and few such solutions will be implemented and compared with conventional algorithm to study the benefits of this approach.
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Leite, Daniel Furtado. "Evolving granular systems = Sistemas granulares evolutivos." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260761.
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Orientador: Fernando Antonio Campos GomideTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-21T02:35:33Z (GMT). No. of bitstreams: 1 Leite_DanielFurtado_D.pdf: 12632242 bytes, checksum: f537e4a24d7322d87dc0aca9ba758b48 (MD5) Previous issue date: 2012
Resumo: Recentemente tem-se observado um crescente interesse em abordagens de modelagem computacional para lidar com fluxos de dados do mundo real. Métodos e algoritmos têm sido propostos para obtenção de conhecimento a partir de conjuntos de dados muito grandes e, a princípio, sem valor aparente. Este trabalho apresenta uma plataforma computacional para modelagem granular evolutiva de fluxos de dados incertos. Sistemas granulares evolutivos abrangem uma variedade de abordagens para modelagem on-line inspiradas na forma com que os humanos lidam com a complexidade. Esses sistemas exploram o fluxo de informação em ambiente dinâmico e extrai disso modelos que podem ser linguisticamente entendidos. Particularmente, a granulação da informação é uma técnica natural para dispensar atenção a detalhes desnecessários e enfatizar transparência, interpretabilidade e escalabilidade de sistemas de informação. Dados incertos (granulares) surgem a partir de percepções ou descrições imprecisas do valor de uma variável. De maneira geral, vários fatores podem afetar a escolha da representação dos dados tal que o objeto representativo reflita o significado do conceito que ele está sendo usado para representar. Neste trabalho são considerados dados numéricos, intervalares e fuzzy; e modelos intervalares, fuzzy e neuro-fuzzy. A aprendizagem de sistemas granulares é baseada em algoritmos incrementais que constroem a estrutura do modelo sem conhecimento anterior sobre o processo e adapta os parâmetros do modelo sempre que necessário. Este paradigma de aprendizagem é particularmente importante uma vez que ele evita a reconstrução e o retreinamento do modelo quando o ambiente muda. Exemplos de aplicação em classificação, aproximação de função, predição de séries temporais e controle usando dados sintéticos e reais ilustram a utilidade das abordagens de modelagem granular propostas. O comportamento de fluxos de dados não-estacionários com mudanças graduais e abruptas de regime é também analisado dentro do paradigma de computação granular evolutiva. Realçamos o papel da computação intervalar, fuzzy e neuro-fuzzy em processar dados incertos e prover soluções aproximadas de alta qualidade e sumário de regras de conjuntos de dados de entrada e saída. As abordagens e o paradigma introduzidos constituem uma extensão natural de sistemas inteligentes evolutivos para processamento de dados numéricos a sistemas granulares evolutivos para processamento de dados granulares
Abstract: In recent years there has been increasing interest in computational modeling approaches to deal with real-world data streams. Methods and algorithms have been proposed to uncover meaningful knowledge from very large (often unbounded) data sets in principle with no apparent value. This thesis introduces a framework for evolving granular modeling of uncertain data streams. Evolving granular systems comprise an array of online modeling approaches inspired by the way in which humans deal with complexity. These systems explore the information flow in dynamic environments and derive from it models that can be linguistically understood. Particularly, information granulation is a natural technique to dispense unnecessary details and emphasize transparency, interpretability and scalability of information systems. Uncertain (granular) data arise from imprecise perception or description of the value of a variable. Broadly stated, various factors can affect one's choice of data representation such that the representing object conveys the meaning of the concept it is being used to represent. Of particular concern to this work are numerical, interval, and fuzzy types of granular data; and interval, fuzzy, and neurofuzzy modeling frameworks. Learning in evolving granular systems is based on incremental algorithms that build model structure from scratch on a per-sample basis and adapt model parameters whenever necessary. This learning paradigm is meaningful once it avoids redesigning and retraining models all along if the system changes. Application examples in classification, function approximation, time-series prediction and control using real and synthetic data illustrate the usefulness of the granular approaches and framework proposed. The behavior of nonstationary data streams with gradual and abrupt regime shifts is also analyzed in the realm of evolving granular computing. We shed light upon the role of interval, fuzzy, and neurofuzzy computing in processing uncertain data and providing high-quality approximate solutions and rule summary of input-output data sets. The approaches and framework introduced constitute a natural extension of evolving intelligent systems over numeric data streams to evolving granular systems over granular data streams
Doutorado
Automação
Doutor em Engenharia Elétrica
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Mahoney, Sally-Ann. "A role for tissue transglutaminase in neuron / glial interaction and development of cerebellar granule neurons." Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361111.
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Pushpalatha, Kavya Vinayan. "Remodelage des condensats RNP neuronaux au cours du vieillissement chez la drosophile." Electronic Thesis or Diss., Université Côte d'Azur, 2021. http://theses.univ-cotedazur.fr/2021COAZ6007.
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Dans la cellule, les molécules d’ARN s’assemblent avec des protéines de liaison aux ARNs pour former des assemblages macromoléculaires très dynamiques appelés granules ribonucléoprotéiques (RNP). Ces assemblages régulent l’expression génique en contrôlant le transport, la stabilité et/ou la traduction des ARNs associés. Des travaux réalisés in vitro ont montré que la formation et la composition des granules RNP reposent sur l’établissement de réseaux denses d’interactions établis entre protéines et ARN, ainsi que sur leur stoechiométrie. Comment les propriétés des granules RNP sont régulées en contexte physiologique, et en particulier lors du vieillissement, est cependant actuellement peu connu. Mon projet de thèse visait à répondre à cette question par une étude in vivo des granules RNP présents dans les cellules neuronales du cerveau de drosophile.A cette fin, j’ai analysé dans des cerveaux d’âge croissant des granules RNP caractérisés par la présence de la protéine de liaison aux ARNs Imp/ZBP1 et de la DEAD-box hélicase Me31B/DDX6. Mes travaux ont révélé une augmentation progressive de la condensation de Imp et Me31B en larges granules au cours du vieillissement. Ces granules sont dynamiques et ne co-localisent pas avec des marqueurs d’agrégation, suggérant qu’ils ne correspondent pas à des agrégats protéiques statiques. Remarquablement, la condensation de Imp et Me31B est associée à la perte des granules Me31B+ Imp- observées dans les cerveaux jeunes, et à la coalescence de Me31B et Imp pour former des granules uniques Me31B+ Imp+. De plus, ce processus est accompagné d’une inhibition spécifique de la traduction des ARNms associés aux granules, parmi lesquels profilin. Par une analyse fonctionnelle, j’ai mis en évidence qu’une modification de la concentration en Me31B est responsable de la condensation de Me31B dans les cerveaux âgés. Alors qu’une augmentation de la quantité de Me31B est observée au cours du vieillissement, enlever une copie de me31B supprime la condensation age-dépendante de ce composant. Étant donné que la condensation de Imp n’est que partiellement affectée dans ce contexte, j’ai réalisé un crible génétique afin d’identifier des régulateurs de ce processus. Ceci m’a permis de montrer que l’activité de la kinase PKA est essentielle d’une part à la condensation de Imp chez les drosophiles âgées, et d’autre part à la répression traductionnelle des ARNms associés aux granules.En conclusion, mon travail a montré pour la première fois que les propriétés des granules RNP neuronaux sont modifiées au cours du vieillissement, un phénomène qui ne reflète pas une altération générale de l’homéostasie des ARNs, mais plutôt une modulation spécifique de la concentration en composants RNP combinée à l’activité de kinase conservée. Ces résultats démontrent comment les systèmes biologiques peuvent moduler des paramètres clés initialement identifiés dans des contextes in vitro, et ouvrent de nouvelles perspectives dans le domaine de la régulation de l’expression génique au cours du vieillissementNascent mRNAs complex with RNA binding proteins (RBPs) to form highly dynamic, phase-separated organelles termed ribonucleoprotein (RNP) granules. These macromolecular assemblies can regulate gene expression by controlling the transport, decay and/or translation of associated RNA molecules. As mostly shown in vitro, RNP granule assembly and function rely on the interaction networks established by individual components and on their stoichiometry. To date, how the properties of constitutive RNP granules are regulated in different physiological context is unclear. In particular, the impact of physiological aging is unclear. My PhD project aimed at addressing this question by analyzing in vivo in long-lived neuronal cells the properties of RNP granules. To this end, I have analysed in flies of increasing age RNP granules characterized by the presence of the conserved RBP Imp/ZBP1 and DEAD-box RNA helicase Me31B/DDX6. Strikingly, a progressive increase in the condensation of Imp and Me31B into granules was observed upon aging. The large granules observed in aged flies were dynamic, contained profilin mRNA, and did not colocalize with Ubiquitin or aggregation markers, suggesting that they do not correspond to static protein aggregates. Increased condensation also associated with the loss of Me31B+ Imp- granules observed in young brains and the collapse of RNP component into a unique class of Me31B+ Imp+ granule. Furthermore, it was accompanied by a specific inhibition of the translation of granule-associated mRNAs, among which the Imp RNA target profilin. Through functional analysis, I uncovered that changes in Me31B stoichiometry trigger Me31B condensation in aged flies. While an increase in Me31B protein levels was observed upon aging, decreasing the dosage of Me31B suppressed its age-dependent condensation. As Imp condensation was only partially suppressed in this context, I performed a selective screen to identify regulators of this process. This revealed that downregulating PKA activity by different genetic means both drastically reduced Imp recruitment and prevented the age-dependent translational repression of granule-associated mRNAs. Taken together, my work thus showed for the first time in vivo that the properties of neuronal RNP granules change upon aging, a phenomenon that does not reflect general alterations in RNA homeostasis but rather specific modulation of RNP component stoichiometry and kinase activity. These results demonstrate how biological systems can modulate key parameters initially defined based on in vitro framework, and also open new perspectives in the field of age-dependent regulation of gene expression
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Kerloch, Thomas. "Etude du développement des neurones granulaires du gyrus denté : morphogénèse et régulation par Rnd2." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0254.
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Dans la plupart des régions cérébrales, les neurones sont générés pendant l’embryogénèse. A l’inverse, dans le gyrus denté (DG) de l'hippocampe, la majorité des neurones granulaires (NGs) est générée en période postnatale et cette production neuronale se poursuit tout au long de l'âge adulte. Cette découverte selon laquelle de nouveaux neurones sont générés dans le cerveau des mammifères adultes a ouvert de nouvelles perspectives pour réparer le cerveau et a conduit de nombreuses recherches, au cours des 20 dernières années, à caractériser comment les nouveaux neurones se différencient et s'intègrent aux circuits neuronaux adultes. Cependant, d'autres études sont nécessaires pour mieux comprendre les mécanismes et les cascades de signalisation impliqués dans ce processus. Dans ce contexte, nous nous sommes concentrés sur Rnd2, une RhoGTPase particulièrement enrichie dans le DG adulte et décrite comme une actrice clé dans la régulation de la neurogenèse corticale embryonnaire. Nous avons montré, in vivo, que la suppression de Rnd2 spécifiquement dans les néo-neurones hippocampiques diminue la survie de ces cellules, et dans les cellules survivantes, conduit à une hypertrophie du soma, augmente l'arborisation dendritique et induit un mauvais positionnement. De façon intéressante cette suppression augmente également le comportement anxiogène des souris, identifiant ainsi Rnd2 comme un régulateur critique de la neurogénèse adulte hippocampique. De plus, nos données montrent que Rnd2 ne joue pas les mêmes fonctions dans les NGs nés à P0, mettant en évidence une régulation différentielle de la neurogenèse développementale et adulte dans la DG. Dans le même ordre d'idées, nous démontrons également que les NGs nés en période périnatale, en particulier les neurones embryonnaires, sont morphologiquement distincts par rapport aux NGs nés plus tard. L'ensemble de ces travaux de thèse apporte donc de nouvelles connaissances sur le développement des différentes populations de NGs dans la DG, soulignant davantage la particularité de cette structure cérébraleIn most areas of the brain, neurons are born during embryogenesis. In contrast, the majority of granule neurons in the dentate gyrus (DG) of the hippocampus are born postnatally and their generation continues throughout adulthood. This finding that new neurons are generated in the adult mammalian brain has opened novel avenues for brain repair and has initiated, in the last 20 years, tremendous efforts to characterize how new neurons differentiate and integrate into adult neural circuitries. However, further studies are needed to better understand the mechanisms and signaling cascades involved in this process. In this context, we focused on Rnd2, a RhoGTPase particularly enriched in the adult neurogenic DG and described as a key player in the regulation of embryonic cortical neurogenesis. We found, in vivo, that the deletion of Rnd2 specifically in adult-born hippocampal neurons decreases the survival of these cells, and in the surviving ones, leads to soma hypertrophy, increases dendritic arborization and induces mispositioning. Importantly, this deletion also increases anxiety-like behavior in mice, thus identifying Rnd2 as a critical regulator of adult newborn neuron development and function. In addition, our data show that Rnd2 does not play the same functions in granule neurons born at P0, highlighting a differential regulation of developmental and adult neurogenesis in the DG. In the same vein, we also demonstrate that perinatally-born granule neurons, especially the embryonic ones, are morphologically distinct compared with later-born neurons. Altogether, this PhD work provides new insights into the development of the different populations of granule neurons in the DG, further emphasizing the peculiarity of this brain structure
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Landeira, Bruna Soares. "Elimina??o de neur?nios infragranulares afeta a especifica??o de neur?nios granulares e supragranulares do c?rtex cerebral em desenvolvimento." PROGRAMA DE P?S-GRADUA??O EM NEUROCI?NCIAS, 2017. https://repositorio.ufrn.br/jspui/handle/123456789/23364.
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O c?rtex cerebral de mam?feros ? histologicamente organizado em diferentes camadas de neur?nios excitat?rios que possuem diversos padr?es de conex?o com alvos corticais e subcorticais. Durante o desenvolvimento, essas camadas corticais se estabelecem sequencialmente atrav?s de uma intrincada combina??o de especifica??o neuronal e migra??o em um padr?o radial conhecida como ?de dentro para fora?: neur?nios infragranulares s?o gerados primeiro do que os neur?nios granulares e supragranulares. Nas ?ltimas d?cadas, diversos genes codificando fatores de transcri??o envolvidos na especifica??o de neur?nios destinados a diferentes camadas corticais foram identificados. Todavia, a influ?ncia dos neur?nios infragranulares sobre a especifica??o das coortes neuronais subsequentes permanece pouco entendida. Para investigar os poss?veis efeitos da abla??o de neur?nios infragranulares sobre a especifica??o de neur?nios supragranulares, n?s induzimos a morte seletiva de neur?nios corticais das camadas V e VI antes da gera??o dos neur?nios destinados ?s camadas II-IV. Nossos dados revelam que um dia ap?s a abla??o, progenitores continuaram a gerar neur?nios destinados a camada VI que expressam o fator de transcri??o TBR1, enquanto praticamente nenhum neur?nio expressando TBR1 foi gerado na mesma etapa do desenvolvimento em controles com a mesma idade. Curiosamente, alguns neur?nios TBR1-positivos gerados ap?s a abla??o de neur?nios infragranulares se estabeleceram em camadas corticais superficiais, como esperado para neur?nios supragranulares gerados neste est?gio, sugerindo que a migra??o de neur?nios corticais pode ser controlada independentemente da sua especifica??o molecular. Al?m disso, n?s observamos um aumento em neur?nios de camada V que expressam CTIP2 e neur?nios calosos que expressam SATB2 ? custa da diminui??o neur?nios de camada IV em animais P0. Quando estes animais se tornam adultos jovens (P30) o aumento de neur?nios SATB2 e CTIP2 n?o existe mais, todavia encontramos esses neur?nios distribu?dos de forma diferente na ?rea somatossensorial dos animais que sofreram abla??o. Experimentos in vitro revelaram que a organiza??o citoarquitet?nica laminar do c?rtex ? necess?ria para gerar novamente os neur?nios TBR1+ que foram eliminados anteriormente. Al?m disso, experimentos in vitro indicam que em condi??o de baixa densidade celular os neur?nios tem seu fen?tipo alterado, expressando v?rios fatores de transcri??o ao mesmo tempo. Em conjunto, nossos dados indicam a exist?ncia de um mecanismo regulat?rio entre neur?nios infragranulares e progenitores envolvidos na gera??o de neur?nios supragranulares e/ou entre neur?nios infragranulares e neur?nios p?s-mit?ticos gerados em seguida. Este mecanismo poderia ajudar a controlar o n?mero de neur?nios em diferentes camadas e contribuir para o estabelecimento de diferentes ?reas corticais.
The cerebral cortex of mammals is histologically organized into in different layers of excitatory neurons that have distinct patterns of connections with cortical or subcortical targets. During development, these cortical layers are sequentially established through an intricate combination of neuronal specification and migration in a radial pattern known as "inside-out": deep-layer neurons are generated prior to upper-layer neurons. In the last few decades, several genes encoding transcription factors involved in the specification of neurons destined to different cortical layers have been identified. However, the influence of early-generated neurons in to the specification of subsequent neuronal cohorts remains unclear. To investigate the possible effects early born neurons ablation on the specification of late born neurons, we induced the selective death of cortical neurons from layers V and VI neurons before the generation of neurons destined to layers II, III and IV. Our data shows that oneday after ablation, progenitors resumed generation of layer VI neurons expressing the transcription factor TBR1, whereas virtually no TBR1-expressing neuron was generated at the same developmental stage in age-matched controls. Interestingly, many TBR1-positive neurons generated after deep-layer ablation settled within superficial cortical layers, as expected for upper-layer neurons generated at that stage, suggesting that migration post-mitotic neurons is independent of fate-specification. Furthermore, we observed an increase in layer V neurons expressing CTIP2 and cortico-cortical neurons expressing SATB2 at the expense of layer IV neurons in P0 animals. When these animals became young adults (P30) the increase os SATB2 and CTIP2 neurons is no longer observed, however these neurons are distributed in a different way in somatosensory areas from ablated animals. In vitro experiments show that the laminar cytoarchitectural organization of the cortex is necessary to regenerate the previously deleted TBR1 + neurons. In addition, in vitro experiments indicate that in a condition of low cell density the neurons phnotype is altered, they express several transcription factors at the same time. Together, our data indicate the existence of feedback mechanism either from early-generated neurons to progenitors involved in the generation of upper-layer neurons or from deep-layer neurons to postmitotic neurons generated subsequently. This mechanism could help to control the number of neurons in different layers and contribute to the establishment of different cortical areas.
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Al-Shammaa, Mohammed. "Granular computing approach for intelligent classifier design." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13686.
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Granular computing facilitates dealing with information by providing a theoretical framework to deal with information as granules at different levels of granularity (different levels of specificity/abstraction). It aims to provide an abstract explainable description of the data by forming granules that represent the features or the underlying structure of corresponding subsets of the data. In this thesis, a granular computing approach to the design of intelligent classification systems is proposed. The proposed approach is employed for different classification systems to investigate its efficiency. Fuzzy inference systems, neural networks, neuro-fuzzy systems and classifier ensembles are considered to evaluate the efficiency of the proposed approach. Each of the considered systems is designed using the proposed approach and classification performance is evaluated and compared to that of the standard system. The proposed approach is based on constructing information granules from data at multiple levels of granularity. The granulation process is performed using a modified fuzzy c-means algorithm that takes classification problem into account. Clustering is followed by a coarsening process that involves merging small clusters into large ones to form a lower granularity level. The resulted granules are used to build each of the considered binary classifiers in different settings and approaches. Granules produced by the proposed granulation method are used to build a fuzzy classifier for each granulation level or set of levels. The performance of the classifiers is evaluated using real life data sets and measured by two classification performance measures: accuracy and area under receiver operating characteristic curve. Experimental results show that fuzzy systems constructed using the proposed method achieved better classification performance. In addition, the proposed approach is used for the design of neural network classifiers. Resulted granules from one or more granulation levels are used to train the classifiers at different levels of specificity/abstraction. Using this approach, the classification problem is broken down into the modelling of classification rules represented by the information granules resulting in more interpretable system. Experimental results show that neural network classifiers trained using the proposed approach have better classification performance for most of the data sets. In a similar manner, the proposed approach is used for the training of neuro-fuzzy systems resulting in similar improvement in classification performance. Lastly, neural networks built using the proposed approach are used to construct a classifier ensemble. Information granules are used to generate and train the base classifiers. The final ensemble output is produced by a weighted sum combiner. Based on the experimental results, the proposed approach has improved the classification performance of the base classifiers for most of the data sets. Furthermore, a genetic algorithm is used to determine the combiner weights automatically.
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Pekanovic, Ana. "Aging of cerebellar granule neurons in vitro." [S.l. : s.n.], 2006.
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Lee, Bongjoon. "Analysis of the Kinetics of Filler Segregation in Granular Block copolymer Microstructure." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/705.
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Block copolymers have attracted interests for potential application ranging from dynamic photonic sensors to solid-state ion conductors. However, due to nucleation and growth mechanism, block copolymer inherently forms granular microstructure with defects such as grain boundaries. Understanding the microstructure of block copolymer is thus crucial in many applications because the microstructure determines the transport property of functional fillers such as ions in block copolymer template. Previous research has shown that athermal filler segregated to grain boundary of lamellae block copolymer and retards the grain coarsening. However, the kinetics of this grain boundary segregation during thermal annealing has not been revealed. Polystyrene-b-polyisoprene blended with deuterated polystyrene is used for neutron scattering study on studying the kinetics of grain boundary segregation. Deuterated polystyrene will segregate to grain boundaries, therefore, decorate grain boundary. The filler segregation behavior will be studied by comparing neutron scattering of polystyrene-b-polyisoprene/deuterated polystyrene with different annealing times (at T=130 deg C, duration of 0hr, 3hr, 1day, 3day and 7day, respectively). Invariant (Q) analysis along with grain mapping is conducted to quantitatively analyze the kinetics of grain boundary segregation. This kinetic was in good agreement with the McLean’s kinetic model for grain boundary segregation in metals. By applying Langmuir-Mclean’s segregation isotherm equation, we have predicted the equilibrium concentration of filler in grain boundary by calculating the strain energy stored in grain boundary.
Books on the topic "Granular neuron"
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Pal, Sankar K., Shubhra S. Ray, and Avatharam Ganivada. Granular Neural Networks, Pattern Recognition and Bioinformatics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57115-7.
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Sanchez, Daniela, and Patricia Melin. Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28862-8.
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Pal, Sankar K., Shubhra S. Ray, and Avatharam Ganivada. Granular Neural Networks, Pattern Recognition and Bioinformatics. Springer, 2018.
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Pal, Sankar K., Shubhra S. Ray, and Avatharam Ganivada. Granular Neural Networks, Pattern Recognition and Bioinformatics. Springer, 2017.
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Melin, Patricia, and Daniela Sanchez. Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation. Springer London, Limited, 2016.
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Melin, Patricia, and Daniela Sanchez. Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation. Springer, 2016.
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Gugel, Karl S. Partitioning artificial neural networks onto coarse granular parallel systems. 1993.
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Barasch, Jonathan Matthew. Secretory granules and neural characteristics of a serotonergic endocrine cell. 1987.
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Luca, Ariane Myriam de. Modulation of cell death in cultured cerebellar granule neurons by cytokines and growth factors. 1996.
Find full textBook chapters on the topic "Granular neuron"
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Apolloni, B., D. Iannizzi, D. Malchiodi, and W. Pedrycz. "Granular Regression." In Neural Nets, 147–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731177_22.
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Avatharam, G., and Sankar K. Pal. "Robust Granular Neural Networks, Fuzzy Granules and Classification." In Lecture Notes in Computer Science, 220–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16248-0_34.
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Akers, Lex A. "Neural and Constrained Interconnect Automata." In Granular Nanoelectronics, 441–61. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-3689-9_28.
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Dick, Scott, and Abraham Kandel. "Granular Computing in Neural Networks." In Granular Computing, 275–305. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1823-9_12.
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Zhang, Yan-Qing. "Granular Neural Networks." In Encyclopedia of Complexity and Systems Science, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-642-27737-5_261-2.
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Zhang, Yan-Qing. "Granular Neural Network." In Computational Complexity, 1455–63. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_93.
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Zhang, Yan-Qing. "Granular Neural Networks." In Encyclopedia of Complexity and Systems Science Series, 265–77. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-2628-3_261.
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Zhang, Yan-Qing. "Granular Neural Network." In Encyclopedia of Complexity and Systems Science, 4402–11. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_261.
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Maravall, Darío, and Javier de Lope. "ANLAGIS: Adaptive Neuron-Like Network Based on Learning Automata Theory and Granular Inference Systems with Applications to Pattern Recognition and Machine Learning." In Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy, 97–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02264-7_11.
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Pedrycz, Witold. "Knowledge-Based Networking in Granular Worlds." In Rough-Neural Computing, 109–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18859-6_5.
Full textConference papers on the topic "Granular neuron"
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Bai, Xiujun, Ying Miao, Lin Wu, and Yuhao Long. "The News Delivery Channel Recommendation Based on Granular Neural Network." In 2024 International Conference on Culture-Oriented Science & Technology (CoST), 71–76. IEEE, 2024. http://dx.doi.org/10.1109/cost64302.2024.00023.
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Jiali Feng. "Qualitative Mapping, Criterion Trasformation and Artificial Neuron." In 2005 IEEE International Conference on Granular Computing. IEEE, 2005. http://dx.doi.org/10.1109/grc.2005.1547330.
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Qun Liu, Lanfen Wang, and Yu Wu. "Bifurcating periodic solutions for a single delayed neuron model under periodic excitation." In 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664652.
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Liu, Qun, Xiaofeng Liao, Degang Yang, and Songtao Guo. "The Research for Hopf Bifurcation in a Single Inertial Neuron Model with External Forcing." In 2007 IEEE International Conference on Granular Computing (GRC 2007). IEEE, 2007. http://dx.doi.org/10.1109/grc.2007.4403155.
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Liu, Qun, Xiaofeng Liao, Degang Yang, and Songtao Guo. "The Research for Hopf Bifurcation in a Single Inertial Neuron Model with External Forcing." In 2007 IEEE International Conference on Granular Computing (GRC 2007). IEEE, 2007. http://dx.doi.org/10.1109/grc.2007.85.
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Nair, Manjusha, Nidheesh Melethadathil, Bipin Nair, and Shyam Diwakar. "Information processing via post-synaptic EPSP-spike complex and model-based predictions of induced changes during plasticity in cerebellar granular neuron." In the 1st Amrita ACM-W Celebration. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1858378.1858383.
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Fortunato, Danielle, Márcio Santana, Jader Gomes, and Daniel Leite. "Modelagem Granular Neuro-Fuzzy Evolutiva para Classificação de Distúrbios em Sistemas de Distribuição de Potência." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1666.
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Distúrbios de qualidade de energia elétrica ocorrem em várias partes de um sistema de potência e podem causar prejuízos financeiros a todos que estão a ele conectado. Portanto, é de fundamental importância a classificação automática destes distúrbios, com alto nível de acurácia e baixo custo computacional. São consideradas as redes neuro-fuzzy granulares evolutivas as quais são capazes de adaptar continuamente sua estrutura e atualizar seus parâmetros de acordo com um fluxo de dados. Devido ao seu processo de aprendizagem recursivo, as redes neuro-fuzzy evolutivas podem adaptar-se às não-estacionariedades que ocorrem em um sistema, evoluindo continuamente ao longo da vida. A rede neuro-fuzzy proposta é a eGNN (evolving Granular Neural Network). Na etapa de pré-processamento dos dados para extração de atributos é considerado o valor eficaz das tensões de fase e o filtro de Hodrick-Prescott. Este separa o sinal de entrada em componente de tendência e componente cíclica –suprimindo o ruído presente no sinal de tendência. A classificação de quatro distúrbios e da operação normal do sistema (problema de cinco classes) foi alcançada com acurácia média de 98%.
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Leite, Daniel F., Pyramo Costa, and Fernando Gomide. "Evolving granular classification neural networks." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178895.
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Dick, S., A. Tappenden, Curtis Badke, and O. Olarewaju. "A Novel Granular Neural Network Architecture." In NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2007. http://dx.doi.org/10.1109/nafips.2007.383808.
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Ding, Zejin, and Yan-Qing Zhang. "Granular Neural Networks with Decision Fusion." In 2010 IEEE International Conference on Granular Computing (GrC-2010). IEEE, 2010. http://dx.doi.org/10.1109/grc.2010.56.
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