Relevant bibliographies by topics / In silico neurons and synapses

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'In silico neurons and synapses'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "In silico neurons and synapses"

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Kanazawa, Yusuke, Tetsuya Asai, and Yoshihito Amemiya. "Basic Circuit Design of a Neural Processor: Analog CMOS Implementation of Spiking Neurons and Dynamic Synapses." Journal of Robotics and Mechatronics 15, no. 2 (April 20, 2003): 208–18. http://dx.doi.org/10.20965/jrm.2003.p0208.

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We discuss the integration architecture of spiking neurons, predicted to be next-generation basic circuits of neural processor and dynamic synapse circuits. A key to development of a brain-like processor is to learn from the brain. Learning from the brain, we try to develop circuits implementing neuron and synapse functions while enabling large-scale integration, so large-scale integrated circuits (LSIs) realize functional behavior of neural networks. With such VLSI, we try to construct a large-scale neural network on a single semiconductor chip. With circuit integration now reaching micron levels, however, problems have arisen in dispersion of device performance in analog IC and in the influence of electromagnetic noise. A genuine brain computer should solve such problems on the network level rather than the element level. To achieve such a target, we must develop an architecture that learns brain functions sufficiently and works correctly even in a noisy environment. As the first step, we propose an analog circuit architecture of spiking neurons and dynamic synapses representing the model of artificial neurons and synapses in a form closer to that of the brain. With the proposed circuit, the model of neurons and synapses can be integrated on a silicon chip with metal-oxide-semiconductor (MOS) devices. In the sections that follow, we discuss the dynamic performance of the proposed circuit by using a circuit simulator, HSPICE. As examples of networks using these circuits, we introduce a competitive neural network and an active pattern recognition network by extracting firing frequency information from input information. We also show simulation results of the operation of networks constructed with the proposed circuits.
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Hjorth, J. J. Johannes, Alexander Kozlov, Ilaria Carannante, Johanna Frost Nylén, Robert Lindroos, Yvonne Johansson, Anna Tokarska, et al. "The microcircuits of striatum in silico." Proceedings of the National Academy of Sciences 117, no. 17 (April 22, 2020): 9554–65. http://dx.doi.org/10.1073/pnas.2000671117.

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The basal ganglia play an important role in decision making and selection of action primarily based on input from cortex, thalamus, and the dopamine system. Their main input structure, striatum, is central to this process. It consists of two types of projection neurons, together representing 95% of the neurons, and 5% of interneurons, among which are the cholinergic, fast-spiking, and low threshold-spiking subtypes. The membrane properties, soma–dendritic shape, and intrastriatal and extrastriatal synaptic interactions of these neurons are quite well described in the mouse, and therefore they can be simulated in sufficient detail to capture their intrinsic properties, as well as the connectivity. We focus on simulation at the striatal cellular/microcircuit level, in which the molecular/subcellular and systems levels meet. We present a nearly full-scale model of the mouse striatum using available data on synaptic connectivity, cellular morphology, and electrophysiological properties to create a microcircuit mimicking the real network. A striatal volume is populated with reconstructed neuronal morphologies with appropriate cell densities, and then we connect neurons together based on appositions between neurites as possible synapses and constrain them further with available connectivity data. Moreover, we simulate a subset of the striatum involving 10,000 neurons, with input from cortex, thalamus, and the dopamine system, as a proof of principle. Simulation at this biological scale should serve as an invaluable tool to understand the mode of operation of this complex structure. This platform will be updated with new data and expanded to simulate the entire striatum.
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Vogelstein, R. Jacob, Udayan Mallik, Eugenio Culurciello, Gert Cauwenberghs, and Ralph Etienne-Cummings. "A Multichip Neuromorphic System for Spike-Based Visual Information Processing." Neural Computation 19, no. 9 (September 2007): 2281–300. http://dx.doi.org/10.1162/neco.2007.19.9.2281.

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We present a multichip, mixed-signal VLSI system for spike-based vision processing. The system consists of an 80 × 60 pixel neuromorphic retina and a 4800 neuron silicon cortex with 4,194,304 synapses. Its functionality is illustrated with experimental data on multiple components of an attention-based hierarchical model of cortical object recognition, including feature coding, salience detection, and foveation. This model exploits arbitrary and reconfigurable connectivity between cells in the multichip architecture, achieved by asynchronously routing neural spike events within and between chips according to a memory-based look-up table. Synaptic parameters, including conductance and reversal potential, are also stored in memory and are used to dynamically configure synapse circuits within the silicon neurons.
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Boegerhausen, Malte, Pascal Suter, and Shih-Chii Liu. "Modeling Short-Term Synaptic Depression in Silicon." Neural Computation 15, no. 2 (February 1, 2003): 331–48. http://dx.doi.org/10.1162/089976603762552942.

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We describe a model of short-term synaptic depression that is derived from a circuit implementation. The dynamics of this circuit model is similar to the dynamics of some theoretical models of short-term depression except that the recovery dynamics of the variable describing the depression is nonlinear and it also depends on the presynaptic frequency. The equations describing the steady-state and transient responses of this synaptic model are compared to the experimental results obtained from a fabricated silicon network consisting of leaky integrate-and-fire neurons and different types of short-term dynamic synapses. We also show experimental data demonstrating the possible computational roles of depression. One possible role of a depressing synapse is that the input can quickly bring the neuron up to threshold when the membrane potential is close to the resting potential.
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Bofill-i-Petit, A., and A. F. Murray. "Synchrony Detection and Amplification by Silicon Neurons With STDP Synapses." IEEE Transactions on Neural Networks 15, no. 5 (September 2004): 1296–304. http://dx.doi.org/10.1109/tnn.2004.832842.

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Vogelstein, R. Jacob, Udayan Mallik, Joshua T. Vogelstein, and Gert Cauwenberghs. "Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses." IEEE Transactions on Neural Networks 18, no. 1 (January 2007): 253–65. http://dx.doi.org/10.1109/tnn.2006.883007.

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Covi, E., R. George, J. Frascaroli, S. Brivio, C. Mayr, H. Mostafa, G. Indiveri, and S. Spiga. "Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons." Journal of Physics D: Applied Physics 51, no. 34 (July 30, 2018): 344003. http://dx.doi.org/10.1088/1361-6463/aad361.

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Sueviriyapan, Natthapong, Chak Foon Tso, Erik D. Herzog, and Michael A. Henson. "Astrocytic Modulation of Neuronal Activity in the Suprachiasmatic Nucleus: Insights from Mathematical Modeling." Journal of Biological Rhythms 35, no. 3 (April 14, 2020): 287–301. http://dx.doi.org/10.1177/0748730420913672.

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The suprachiasmatic nucleus (SCN) of the hypothalamus consists of a highly heterogeneous neuronal population networked together to allow precise and robust circadian timekeeping in mammals. While the critical importance of SCN neurons in regulating circadian rhythms has been extensively studied, the roles of SCN astrocytes in circadian system function are not well understood. Recent experiments have demonstrated that SCN astrocytes are circadian oscillators with the same functional clock genes as SCN neurons. Astrocytes generate rhythmic outputs that are thought to modulate neuronal activity through pre- and postsynaptic interactions. In this study, we developed an in silico multicellular model of the SCN clock to investigate the impact of astrocytes in modulating neuronal activity and affecting key clock properties such as circadian rhythmicity, period, and synchronization. The model predicted that astrocytes could alter the rhythmic activity of neurons via bidirectional interactions at tripartite synapses. Specifically, astrocyte-regulated extracellular glutamate was predicted to increase neuropeptide signaling from neurons. Consistent with experimental results, we found that astrocytes could increase the circadian period and enhance neural synchronization according to their endogenous circadian period. The impact of astrocytic modulation of circadian rhythm amplitude, period, and synchronization was predicted to be strongest when astrocytes had periods between 0 and 2 h longer than neurons. Increasing the number of neurons coupled to the astrocyte also increased its impact on period modulation and synchrony. These computational results suggest that signals that modulate astrocytic rhythms or signaling (e.g., as a function of season, age, or treatment) could cause disruptions in circadian rhythm or serve as putative therapeutic targets.
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Jenkner, Martin, Bernt Müller, and Peter Fromherz. "Interfacing a silicon chip to pairs of snail neurons connected by electrical synapses." Biological Cybernetics 84, no. 4 (March 23, 2001): 239–49. http://dx.doi.org/10.1007/s004220000218.

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Rasch, Malte J., Klaus Schuch, Nikos K. Logothetis, and Wolfgang Maass. "Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1." Journal of Neurophysiology 105, no. 2 (February 2011): 757–78. http://dx.doi.org/10.1152/jn.00845.2009.

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A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.
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Dissertations / Theses on the topic "In silico neurons and synapses"

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Vissani, Matteo. "Multisensory features of peripersonal space representation: an analysis via neural network modelling." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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The peripersonal space (PPS) is the space immediately surrounding the body. It is coded in the brain in a multisensory, body part-centered (e.g. hand-centered, trunk-centered), modular fashion. This is supported by the existence of multisensory neurons (in fronto-parietal areas) with tactile receptive field on a specific body part (hand, arm, trunk, etc.) and visual/auditory receptive field surrounding the same body part. Recent behavioural results (Serino et al. Sci Rep 2015), obtained by using an audio-tactile paradigm, have further supported the existence of distinct PPS representations, each specific of a single body part (hand, trunk, face) and characterized by specific properties. That study has also evidenced that the PPS representations– although distinct – are not independent. In particular, the hand-PPS loses its properties and assumes those of the trunk-PPS when the hand is close to the trunk, as the hand-PPS was encapsulated within the trunk-PPS. Similarly, the face-PPS appears to be englobed into the trunk-PPS. It remains unclear how this interaction, which manifests behaviourally, can be implemented at a neural level by the modular organization of PPS representations. The aim of this Thesis is to propose a neural network model to help the comprehension of the underlying neurocomputational mechanisms. The model includes three subnetworks devoted to the single PPS representations around the hand, face and the trunk. Furthermore, interaction mechanisms– controlled by proprioceptive neurons – have been postulated among the subnetworks. The network is able to reproduce the behavioural data, explaining them in terms of neural properties and response. Moreover, the network provides some novel predictions, that can be tested in vivo. One of this prediction has been tested in this work, by performing an ad-hoc behavioural experiment at the Laboratory of Cognitive Neuroscience (Campus Biotech, Geneva) under the supervision of the neuropsychologist Dr Serino.
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Boussa, Sofiane. "Réseaux hybrides in silico/in vitro par connexion dynamique entre cellule excitable et modèles numériques." Le Havre, 2009. http://www.theses.fr/2009LEHA0002.

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Au sein du système nerveux, les neurones, massivement interconnectés, planifient les commandes motrices, régulent les sécrétions hormonales, assurent l'homéostasie du milieu intérieur, maintiennent les fonctions végétatives et supportent les mécanismes cognitifs supérieurs. Les fonctions neuronales reposent à la fois sur la topologie des projections nerveuses et sur des processus intégratifs et adaptatifs propres à chaque cellule comme la plasticité synaptique ou l'apprentissage par coïncidence pré/post. Dans le domaine de l’automatique les réseaux neuronaux artificiels, sont des systèmes adaptatifs et doués d’apprentissage. Ils trouvent des applications diverses dans la reconnaissance de formes, la fouille de données (data mining), le traitement du signal, le diagnostic. Ils sont définis principalement par une topologie, une fonction de seuillage et un algorithme d’apprentissage qui agit en faisant varier les poids synaptiques. L'objectif de cette thèse est d’étudier et de réaliser des connexions neuronales hybrides, afin d’en extraire des informations utiles aux recherches sur l’apprentissage et la plasticité synaptique. Ce travail se veut aussi une contribution à la mise en œuvre de la technique du dynamic-clamp. Cette technique récente reste peu utilisée car d’emploi ardu. Plusieurs expérimentations ont été menées au cours de cette thèse. Notamment, la greffe d’une synapse artificielle récurrente sur une cellule spontanément active à l'aide d'un prototype construit autour d’un DSP (Digital Signal processing). Nos travaux ont également conduit à faire interagir une cellule excitable et un perceptron doté d’un algorithme d’apprentissage artificiel. Enfin, ce travail ouvre des perspectives pour étendre cette étude sur une population de neurones en utilisant à cet effet une matrice de microélectrodes (MEA, Micro-Electrode Array)
In nervous system, neurons massively interconnected regulate hormonal secretions, control muscle contractions, ensure the homeostatic regulation of the internal environment and lead cognitive functions. In order to ensure the correct coordination of the entire organism, neurons have to communicate and interact with their neighbours. This communication is based on topology and morphology of dendritic field and several processes of integration and adaptation such as synaptic plasticity and learning by spike-time-dependant plasticity. In automatic field, the artificial neural networks (ANN) are adaptive systems; they try to simulate functional aspects of biological neural networks. They are characterized by a specific topology, threshold function and learning algorithm which change the structure of the network by updating the synaptic weights. Artificial neural networks are used in several applications, such as, pattern recognition, data mining, signal processing, control and diagnostic. The aim of this thesis is to study and analyze hybrid neuronal connexions, in order to learn more about neuronal learning processes and synaptic plasticity. This work is also a contribution to the dynamic-clamp technique. This electrophysiological technique remains confined to some laboratories, because of its hard use. In this work, we have developed a complete and dedicated hardware dynamic-clamp solution, based on a DSP-board (Digital Signal processing) programmed with C-coded routines. This setup has been validated especially by creating a virtual recurrent synapse (autaptic) connection in frog melanotrope cell. In addition, an interaction between an excitable cell and a Perceptron learning algorithm has been carried out. The obtained results open a window to extend this study with neurons population by using a matrix of micro-electrodes (MEA, Micro-Electrode Array)
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Merz, David C. (David Christian). "Synapse formation between identified leech neurons." Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=28848.

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The formation of patterns of functionally appropriate chemical synapses is one of the key aspects of nervous system development. I have investigated the cellular interactions that culminate in the formation of an inhibitory synapse between the R and P neurons of the leech. These neurons may be isolated and maintained in culture, where they reform synaptic connections under easily manipulable conditions. An early event in the formation of this synapse is the loss from sites of contact in the postsynaptic P cell of an excitatory response to the transmitter serotonin. The loss of this response was triggered specifically by contact with the presynaptic R neuron, and not by contact with other leech neurons, including other serotonergic neurons. Furthermore, contact with the R neurons of the reproductive ganglia, which do not innervate P cells, was also ineffective in causing the loss of response. This highly specific cellular interaction was prevented by treatment of the R cell surface with the proteolytic enzyme trypsin or with the lectin wheat germ agglutinin (WGA), suggesting involvement of an R cell surface glycoprotein. WGA blocked not only the loss of the excitatory response, but also prevented the formation of the R-P synapse. An antibody library against the R cell generated using a novel phage-display system produced antibodies which bound to subsets of leech neurons, including the R neurons, but none of these was specific for the R cells. I conclude that an early event in the formation of the R-P synapse is the recognition by the P cell of its correct synaptic partner through an R cell-specific surface molecule.
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Ching, Shim. "Synaptogenesis between identified neurons." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=55449.

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Serotonergic Retzius (R) neurons of the leech Hirudo medicinalis in culture reform inhibitory synapses with pressure sensitive (P) neurons while selectively reducing an extrasynaptic, depolarizing response to serotonin (5-HT) in the P neuron. We have examined if the selection of 5-HT responses is restricted to sites of contact between processes and growth cones of these cells. As measured by intracellular recording at the soma, focal application of 5-HT depolarized uncontacted P cell bodies, neurites and growth cones but not processes contacted by R cells. In patch clamp recordings of the depolarizing channels, application of 5-HT modulated channel activity in uncontacted but not in contacted growth cones. The selection of transmitter responses during synaptogenesis is therefore localized to discrete sites of contact specifically between synaptic partners.
Prior experiments have shown that tyrosine kinases play a crucial part in the selection of responses to 5-HT that occurs in the P cell (Catarsi and Drapeau, 1993). To further examine the mechanism responsible for this change in transmitter responses, we have utilized a monoclonal antibody against phosphotyrosine to determine if tyrosine phosphorylation could be detected in P and R cell pairs placed in contact. Our results revealed bright, punctate cytoplasmic staining in P cells paired with R cells.
Embryonic leeches were used to examine how R to P synaptogenesis proceeds in vivo. By filling the R and P neurons with different fluorescent dyes (Lucifer Yellow and Rhodamine-Dextran), confocal microscopy established that putative contact between neuropilar processes were made as early as 11 days of development. Spontaneous, chloride-dependent synaptic potentials in embryonic P cells similar to those seen in adult P cells were observed as early as day 10 of development.
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Reese, David R. "Neuropilar synaptogenesis between identified central neurons in vivo." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0007/MQ44256.pdf.

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Meuth, Patrick [Verfasser], and Martin [Akademischer Betreuer] Burger. "Thalamic neurons in silico / Patrick Meuth. Betreuer: Martin Burger." Münster : Universitäts- und Landesbibliothek der Westfälischen Wilhelms-Universität, 2011. http://d-nb.info/1027017827/34.

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Javalet, Charlotte. "Rôle des exosomes comme nouvelle voie de communication entre les neurones." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAV028/document.

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Les exosomes sont des vésicules d’origine endosomale sécrétées par les cellules dans leur environnement après fusion à la membrane plasmique des endosomes multivésiculés. Les exosomes représentent un nouveau mode de communication entre les cellules en permettant un transfert direct de protéines, de lipides et d’ARN. L’objectif de ma thèse était d’étudier le rôle des exosomes dans la communication entre les neurones. Précédemment, le laboratoire a montré que les neurones sécrètent des exosomes de manière régulée par l’activité synaptique. Nous avons observé que les exosomes neuronaux ne sont endocytés que par les neurones. Après avoir montré qu’ils ne contiennent que des ARN courts, nous avons réalisé un séquençage complet de leurs microARN et observé que ces microARN étaient sélectivement exportés dans les exosomes. Nos observations suggèrent que les microARN contenus dans les exosomes peuvent modifier la physiologie des neurones receveurs. Nos résultats renforcent l’hypothèse du rôle des exosomes dans la communication entre les neurones via le transfert de microARN
Exosomes are vesicles of endocytic origin released by cells into their environment following fusion of multivesicular endosomes with the plasma membrane. Exosomes represent a novel mechanism of cell communication allowing direct transfer of proteins, lipids and RNA. The goal of my PhD thesis was to study that exosomes represent a novel way of interneuronal communication. Our team has previously reported that neurons release exosomes in a way tightly regulated by synaptic activity. We observed that exosomes released by neurons are only endocytosed by neurons. We found that exosomes contain only small RNA and did a deep sequencing of all their microRNA. MicroRNA are selectively exported into exosomes. It seems that exosomal microRNA can modify the physiology of receiving neurons. Our results strengthen the hypothesis of the role of exosomes in the interneuronal communication by the way of microARN transfert
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Pallotto, Marta. "GABAergic signaling and synaptic integration of adult-generated neurons." Paris 6, 2012. http://www.theses.fr/2012PA066677.

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La neurogenèse adulte représente une forme unique de plasticité neuronale, qui, chez les mammifères, est limitée à l’hippocampe et au système olfactif. Dans le bulbe olfactif (BO) les neurones s’intègrent sans cesse aux réseaux préexistants et se différentient. La majorité de ces cellules forment une population d’interneurones GABAergique : Les cellules granulaires (GCs). Dans ce travail, j’ai étudié l’intégration synaptique des GCs nouvellement générées dans le BO de souris adultes. Afin de visualiser ces cellules, j’ai injecté un vecteur viral codant pour l’eGFP dans le courant de migration menant les cellules vers le BO. J’ai ainsi pu suivre leur développement en les observant à différents jours post-injection (jpi). J’ai pu constater que les jeunes GCs commencent à recevoir des contacts synaptiques dés qu’elles atteignent leur destination finale dans le BO. En effet les premiers contacts synaptiques sur des cellules GFP+ sont détectés dans la couche des cellules granulaires à 3 jpi. J’ai aussi constaté que, durant une première phase, les synapses GABAergiques sont plus nombreuses que les synapses glutamatergique. Ceci m’a conduit à émettre l’hypothèse suivante : Puisque la transmission GABAergique est prédominante dans les premières phases du développement des GCs nouveau-nées, elle pourrait jouer un rôle important dans leur intégration synaptique, leur maturation et leur survie. Afin de vérifier cette hypothèse, j’ai utilisé la délétion conditionnelle (CRE-LoxP) du gène Gabra2 codant pour la sous-unité α2 du récepteur GABA (GABAAR) qui est la principale sous-unité exprimée par les GCs. Utilisant deux modèles de souris transgéniques permettant la délétion spécifique dans une population de cellules produites chez l’adulte, j'ai vérifié que l'ablation de la sous-unité α2 est accompagnée par une réduction dramatique de fréquence et d’amplitude des courants synaptiques GABAergiques reçues par les GCs. Cette activité GABAergique réduite n’affecte pas la survie des GCs, mais retarde dramatiquement leur maturation. Dans les cellules mutantes, la complexité de l’arborisation dendritique et la densité des épines sont réduites. Ces altérations sont accompagnées par un déplacement des synapses excitatrices, des épines vers les troncs dendritiques. De plus, la délétion de la sous-unité α2 réduit la plasticité structurale des cellules normalement induite par la manipulation de l’environnement sensoriel olfactif et donc de l’activité perçue par les cellules. Ces résultats montrent qu'une signalisation GABAergique adéquate est requise pour le développement morphologique et l'intégration synaptique des GCs produites chez l’adulte, et révèle un rôle inattendu des synapses GABAergiques dans la formation de l’arborisation dendritique et dans la synaptogenèse glutamatergique
Adult neurogenesis represents a unique form of brain plasticity. In mammals the genesis of new neurons is mainly restricted to the dentate gyrus of the hippocampus and the olfactory bulbs. In the olfactory bulb (OB), neurons are continuously added to pre-existing networks and differentiate mainly into GABAergic local interneurons: granule cells (GCs) and periglomerular cells (PGCs). These interneurons mature and integrate in the OB network acquiring an adult phenotype. In the present work, I investigated the synaptic integration of adult-generated GCs in the mouse OB. I took advantage of local injections of eGFP encoding lentiviral vector to visualize through GFP fluorscent labelling new-born GCs in the adult OB at different times after their genesis. I found that adult-generated GCs start to receive synaptic contacts as soon as they reach their final destination in the OB. In fact, the first synaptic inputs onto GFP-positive cells were detected in the granule cell layer at 3 days post-injection (dpi). Interestingly, I found that at early stages GABAergic synapses were more abundant than glutamatergic contacts, suggesting that GABA may play an important role in the synaptic integration, maturation and survival of newborn GCs. To verify this hypothesis, I used Cre-mediated conditional deletion of the Gabra2 gene encoding for the 2 subunit of the GABAA receptor (GABAAR) to functionally disrupt afferent GABAergic transmission in migrating GC precursors. Using two different transgenic mouse models, I found that ablation of the 2-subunit was accompanied by a dramatic reduction in the frequency and amplitude of spontaneous or evoked GABAergic IPSCs. Remarkably, this reduced GABAergic activity did not affect GC survival but delayed dramatically their maturation. In mutant cells, dendritic branching and spine density were reduced, and spine loss was accompanied by a mislocation of excitatory synapses from spine heads to dendritic shafts. Moreover, deletion of the 2 subunit occluded structural plasticity of spines inducible by odor-enrichment and odor-deprivation protocols. These results show that proper GABAergic signaling is required for the morphological development and synaptic integration of adult-born GCs, and reveal an unexpected function of early GABAergic inputs in controlling spine formation and glutamatergic synaptogenesis
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Banks, Glen B. "The role of synapse formation on motoneuron survival during embryonic development /." [St. Lucia, Qld.], 2003. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17453.pdf.

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Hetsch, Florian Jan Alexander [Verfasser]. "Induction of Synapses by Agrin in Cultured Cortical Neurons / Florian Jan Alexander Hetsch." Berlin : Freie Universität Berlin, 2015. http://d-nb.info/1072622262/34.

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Books on the topic "In silico neurons and synapses"

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Haycock, R. J. Hardware neurons and synapses for pulse stream neural networks. Manchester: UMIST, 1997.

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Masashi, Inoue, ed. Nyūron no seibutsu butsuri. 2nd ed. Tōkyō-to Chiyoda-ku: Maruzen Shuppan, 2013.

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Proteins, transmitters, and synapses. Oxford: Blackwell Scientific Publications, 1994.

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Parvin, Manoucher. Out of the gray: A concerto for neurons and synapses, a novel. Bethesda, MD: Ibex Publishers, 2011.

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1933-, Shepherd Gordon M., Black Ira B, and Killackey Herbert P, eds. Synapses, circuits, and the beginnings of memory. Cambridge, Mass: MIT Press, 1986.

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Shepherd, Gordon M. The Synaptic Organization of the Brain. New York: Oxford University Press, 1990.

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1949-, Schüz A., ed. Anatomy of the cortex: Statistics and geometry. Berlin: Springer-Verlag, 1991.

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van, Pelt J., ed. Development, dynamics, and pathology of neuronal networks: From molecules to functional circuits : proceedings of the 23rd International Summer School of Brain Research, held at the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands, from 25-29 August 2003. Amsterdam: Elsevier, 2005.

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Braitenberg, Valentino. Cortex: Statistics and geometry of neuronal connectivity. 2nd ed. Berlin: Springer, 1998.

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Christof, Koch, and Segev Idan, eds. Methods in neuronal modeling: From synapses to networks. Cambridge, Mass: MIT Press, 1992.

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Book chapters on the topic "In silico neurons and synapses"

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Lazzaro, John, and John Wawrzynek. "Low-Power Silicon Neurons, Axons and Synapses." In Silicon Implementation of Pulse Coded Neural Networks, 153–64. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2680-3_8.

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Watts, Lloyd. "Designing Networks of Spiking Silicon Neurons and Synapses." In Computation and Neural Systems, 127–31. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3254-5_20.

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Lytton, William W., and Cliff C. Kerr. "Computational Neuroscience of Synapses and Neurons." In Neuroscience in the 21st Century, 2275–99. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-1997-6_86.

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Lytton, William W., and Cliff C. Kerr. "Computational Neuroscience of Synapses and Neurons." In Neuroscience in the 21st Century, 3011–35. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3474-4_86.

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Lee, Bang W., and Bing J. Sheu. "Programmable Synapses and Gain-Adjustable Neurons." In Hardware Annealing in Analog VLSI Neurocomputing, 89–115. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3984-1_4.

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Giacobini, Ezio. "Development of Peripheral Parasympathetic Neurons and Synapses." In Developmental Neurobiology of the Autonomic Nervous System, 29–67. Totowa, NJ: Humana Press, 1986. http://dx.doi.org/10.1007/978-1-59259-459-7_2.

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Ramacher, Ulrich, and Christoph von der Malsburg. "Elementary Circuits for Neurons, Synapses, and Photosensors." In On the Construction of Artificial Brains, 195–231. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-00189-5_13.

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Bolam, J. P. "Synapses of Identified Neurons in the Neostriatum." In Ciba Foundation Symposium 107 - Functions of the Basal Ganglia, 30–47. Chichester, UK: John Wiley & Sons, Ltd., 2008. http://dx.doi.org/10.1002/9780470720882.ch3.

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Peters, Alan. "Number of Neurons and Synapses in Primary Visual Cortex." In Cerebral Cortex, 267–94. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4615-6616-8_7.

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Braitenberg, Valentino, and Almut Schüz. "Comparison Between the Densities of Neurons, Synapses and Axons." In Cortex: Statistics and Geometry of Neuronal Connectivity, 43–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03733-1_8.

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Conference papers on the topic "In silico neurons and synapses"

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Najem, Joseph S., Graham J. Taylor, Charles P. Collier, and Stephen A. Sarles. "Synapse-Inspired Variable Conductance in Biomembranes: A Preliminary Study." In ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/smasis2017-3820.

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Abstract:
Memristors are solid-state devices that exhibit voltage-controlled conductance. This tunable functionality enables the implementation of biologically-inspired synaptic functions in solid-state neuromorphic computing systems. However, while memristors are meant to emulate an intricate signal transduction process performed by soft biomolecular structures, they are commonly constructed from silicon- or polymer-based materials. As a result, the volatility, intricate design, and high-energy resistance switching in memristive devices, usually, leads to energy consumption in memristors that is several orders of magnitude higher than in natural synapses. Additionally, solid-state memristors fail to achieve the coupled dynamics and selectivity of synaptic ion exchange that are believed to be necessary for initiating both short- and long-term potentiation (STP and LTP) in neural synapses, as well as paired-pulse facilitation (PPF) in the presynaptic terminal. LTP is a phenomenon mostly responsible for driving synaptic learning and memory, features that enable signal transduction between neurons to be history-dependent and adaptable. In contrast, current memristive devices rely on engineered external programming parameters to imitate LTP. Because of these fundamental differences, we believe a biomolecular approach offers untapped potential for constructing synapse-like systems. Here, we report on a synthetic biomembrane system with biomolecule-regulated (alamethicin) variable ion conductance that emulates vital operational principals of biological synapse. The proposed system consists of a synthetic droplet interface bilayer (DIB) assembled at the conjoining interface of two monolayer-encased aqueous droplets in oil. The droplets contain voltage-activated alamethicin (Alm) peptides, capable of creating conductive pathways for ion transport through the impermeable lipid membrane. The insertion of the peptides and formation of transmembrane ion channels is achieved at externally applied potentials higher than ∼70 m V. Just like in biological synapses, where the incorporation of additional receptors is responsible for changing the synaptic weight (i.e. conductance), we demonstrate that the weight of our synaptic mimic may be changed by controlling the number of alamethicin ion channels created in a synthetic lipid membrane. More alamethicin peptides are incorporated by increasing the post-threshold external potential, thus leading to higher conductance levels for ion transport. The current-voltage responses of the alamethicin-based synapse also exhibit significant “pinched” hysteresis — a characteristic of memristors that is fundamental to mimicking synapse plasticity. We demonstrate the system’s capability of exhibiting STP/PPF behaviors in response to high-frequency 50 ms, 150 mV voltage pulses. We also present and discuss an analytical model for an alamethicin-based memristor, classifying that later as a “generic memristor”.
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Ziegler, Martin, Mirko Hansen, Marina Ignatov, and Hermann Kohlstedt. "Building memristive neurons and synapses." In 2014 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2014. http://dx.doi.org/10.1109/iscas.2014.6865323.

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Xu, Peng, Timothy K. Horiuchi, Anshu Sarje, and Pamela Abshire. "Stochastic Synapse with Short-Term Depression for Silicon Neurons." In 2007 IEEE Biomedical Circuits and Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/biocas.2007.4463318.

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Zheng, Le, Sangho Shin, and Sung-Mo Steve Kang. "Memristor-based synapses and neurons for neuromorphic computing." In 2015 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2015. http://dx.doi.org/10.1109/iscas.2015.7168842.

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Ioka, Eri, Yasuyuki Matusya, and Hiroyuki Kitajima. "Bifurcation in mutually coupled three neurons with inhibitory synapses." In 2011 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2011. http://dx.doi.org/10.1109/ecctd.2011.6043617.

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Lee, B. W., J. C. Lee, and B. J. Sheu. "VLSI image processor using analog programmable synapses and neurons." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137630.

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Corinto, Fernando, Valentina Lanza, Alon Ascoli, and Marco Gilli. "Synchronization in networks of FitzHugh-Nagumo neurons with memristor synapses." In 2011 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2011. http://dx.doi.org/10.1109/ecctd.2011.6043616.

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Natarajan, Aishwarya, and Jennifer Hasler. "Implementation of Synapses with Hodgkin Huxley Neurons on the FPAA." In 2019 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2019. http://dx.doi.org/10.1109/iscas.2019.8702489.

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Mejías, Jorge F., and Joaquín J. Torres. "Signal detection in networks of spiking neurons with dynamical synapses." In COOPERATIVE BEHAVIOR IN NEURAL SYSTEMS: Ninth Granada Lectures. AIP, 2007. http://dx.doi.org/10.1063/1.2709589.

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Valentian, A., F. Rummens, E. Vianello, T. Mesquida, C. Lecat-Mathieu de Boissac, O. Bichler, and C. Reita. "Fully Integrated Spiking Neural Network with Analog Neurons and RRAM Synapses." In 2019 IEEE International Electron Devices Meeting (IEDM). IEEE, 2019. http://dx.doi.org/10.1109/iedm19573.2019.8993431.

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Reports on the topic "In silico neurons and synapses"

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Brown, Thomas H. Self-Organization of Hebbian Synapses on Hippocampal Neurons. Fort Belvoir, VA: Defense Technical Information Center, January 1996. http://dx.doi.org/10.21236/ada309810.

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Brown, Thomas H. Self-Organization of Hebbian Synapses on Hippocampal Neurons. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada299559.

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