Готові списки джерел за темами / Inhibitory Neurons

Добірка наукової літератури з теми "Inhibitory Neurons"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Inhibitory Neurons"

1

Pesavento, Michael J., Cynthia D. Rittenhouse, and David J. Pinto. "Response Sensitivity of Barrel Neuron Subpopulations to Simulated Thalamic Input." Journal of Neurophysiology 103, no. 6 (June 2010): 3001–16. http://dx.doi.org/10.1152/jn.01053.2009.

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Анотація:
Our goal is to examine the relationship between neuron- and network-level processing in the context of a well-studied cortical function, the processing of thalamic input by whisker-barrel circuits in rodent neocortex. Here we focus on neuron-level processing and investigate the responses of excitatory and inhibitory barrel neurons to simulated thalamic inputs applied using the dynamic clamp method in brain slices. Simulated inputs are modeled after real thalamic inputs recorded in vivo in response to brief whisker deflections. Our results suggest that inhibitory neurons require more input to reach firing threshold, but then fire earlier, with less variability, and respond to a broader range of inputs than do excitatory neurons. Differences in the responses of barrel neuron subtypes depend on their intrinsic membrane properties. Neurons with a low input resistance require more input to reach threshold but then fire earlier than neurons with a higher input resistance, regardless of the neuron's classification. Our results also suggest that the response properties of excitatory versus inhibitory barrel neurons are consistent with the response sensitivities of the ensemble barrel network. The short response latency of inhibitory neurons may serve to suppress ensemble barrel responses to asynchronous thalamic input. Correspondingly, whereas neurons acting as part of the barrel circuit in vivo are highly selective for temporally correlated thalamic input, excitatory barrel neurons acting alone in vitro are less so. These data suggest that network-level processing of thalamic input in barrel cortex depends on neuron-level processing of the same input by excitatory and inhibitory barrel neurons.
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2

Weissenberger, Felix, Marcelo Matheus Gauy, Xun Zou, and Angelika Steger. "Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic Interaction." Neural Computation 31, no. 11 (November 2019): 2252–65. http://dx.doi.org/10.1162/neco_a_01230.

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Анотація:
In computational neural network models, neurons are usually allowed to excite some and inhibit other neurons, depending on the weight of their synaptic connections. The traditional way to transform such networks into networks that obey Dale's law (i.e., a neuron can either excite or inhibit) is to accompany each excitatory neuron with an inhibitory one through which inhibitory signals are mediated. However, this requires an equal number of excitatory and inhibitory neurons, whereas a realistic number of inhibitory neurons is much smaller. In this letter, we propose a model of nonlinear interaction of inhibitory synapses on dendritic compartments of excitatory neurons that allows the excitatory neurons to mediate inhibitory signals through a subset of the inhibitory population. With this construction, the number of required inhibitory neurons can be reduced tremendously.
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3

Nykamp, Duane Q., and Daniel Tranchina. "A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Extension to Slow Inhibitory Synapses." Neural Computation 13, no. 3 (March 1, 2001): 511–46. http://dx.doi.org/10.1162/089976601300014448.

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Анотація:
A previously developed method for efficiently simulating complex networks of integrate-and-fire neurons was specialized to the case in which the neurons have fast unitary postsynaptic conductances. However, inhibitory synaptic conductances are often slower than excitatory ones for cortical neurons, and this difference can have a profound effect on network dynamics that cannot be captured with neurons that have only fast synapses. We thus extend the model to include slow inhibitory synapses. In this model, neurons are grouped into large populations of similar neurons. For each population, we calculate the evolution of a probability density function (PDF), which describes the distribution of neurons over state-space. The population firing rate is given by the flux of probability across the threshold voltage for firing an action potential. In the case of fast synaptic conductances, the PDF was one-dimensional, as the state of a neuron was completely determined by its transmembrane voltage. An exact extension to slow inhibitory synapses increases the dimension of the PDF to two or three, as the state of a neuron now includes the state of its inhibitory synaptic conductance. However, by assuming that the expected value of a neuron's inhibitory conductance is independent of its voltage, we derive a reduction to a one-dimensional PDF and avoid increasing the computational complexity of the problem. We demonstrate that although this assumption is not strictly valid, the results of the reduced model are surprisingly accurate.
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4

Hu, Xiaolin, and Zhigang Zeng. "Bridging the Functional and Wiring Properties of V1 Neurons Through Sparse Coding." Neural Computation 34, no. 1 (January 1, 2022): 104–37. http://dx.doi.org/10.1162/neco_a_01453.

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Анотація:
Abstract The functional properties of neurons in the primary visual cortex (V1) are thought to be closely related to the structural properties of this network, but the specific relationships remain unclear. Previous theoretical studies have suggested that sparse coding, an energy-efficient coding method, might underlie the orientation selectivity of V1 neurons. We thus aimed to delineate how the neurons are wired to produce this feature. We constructed a model and endowed it with a simple Hebbian learning rule to encode images of natural scenes. The excitatory neurons fired sparsely in response to images and developed strong orientation selectivity. After learning, the connectivity between excitatory neuron pairs, inhibitory neuron pairs, and excitatory-inhibitory neuron pairs depended on firing pattern and receptive field similarity between the neurons. The receptive fields (RFs) of excitatory neurons and inhibitory neurons were well predicted by the RFs of presynaptic excitatory neurons and inhibitory neurons, respectively. The excitatory neurons formed a small-world network, in which certain local connection patterns were significantly overrepresented. Bidirectionally manipulating the firing rates of inhibitory neurons caused linear transformations of the firing rates of excitatory neurons, and vice versa. These wiring properties and modulatory effects were congruent with a wide variety of data measured in V1, suggesting that the sparse coding principle might underlie both the functional and wiring properties of V1 neurons.
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5

Liu, Ming-Zhe, Xiao-Jun Chen, Tong-Yu Liang, Qing Li, Meng Wang, Xin-Yan Zhang, Yu-Zhuo Li, Qiang Sun, and Yan-Gang Sun. "Synaptic control of spinal GRPR+neurons by local and long-range inhibitory inputs." Proceedings of the National Academy of Sciences 116, no. 52 (December 5, 2019): 27011–17. http://dx.doi.org/10.1073/pnas.1905658116.

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Анотація:
Spinal gastrin-releasing peptide receptor-expressing (GRPR+) neurons play an essential role in itch signal processing. However, the circuit mechanisms underlying the modulation of spinal GRPR+neurons by direct local and long-range inhibitory inputs remain elusive. Using viral tracing and electrophysiological approaches, we dissected the neural circuits underlying the inhibitory control of spinal GRPR+neurons. We found that spinal galanin+GABAergic neurons form inhibitory synapses with GRPR+neurons in the spinal cord and play an important role in gating the GRPR+neuron-dependent itch signaling pathway. Spinal GRPR+neurons also receive inhibitory inputs from local neurons expressing neuronal nitric oxide synthase (nNOS). Moreover, spinal GRPR+neurons are gated by strong inhibitory inputs from the rostral ventromedial medulla. Thus, both local and long-range inhibitory inputs could play important roles in gating itch processing in the spinal cord by directly modulating the activity of spinal GRPR+neurons.
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6

Tamura, Hiroshi, Hidekazu Kaneko, Keisuke Kawasaki, and Ichiro Fujita. "Presumed Inhibitory Neurons in the Macaque Inferior Temporal Cortex: Visual Response Properties and Functional Interactions With Adjacent Neurons." Journal of Neurophysiology 91, no. 6 (June 2004): 2782–96. http://dx.doi.org/10.1152/jn.01267.2003.

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Анотація:
Neurons in area TE of the monkey inferior temporal cortex respond selectively to images of particular objects or their characteristic visual features. The mechanism of generation of the stimulus selectivity, however, is largely unknown. This study addresses the role of inhibitory TE neurons in this process by examining their visual response properties and interactions with adjacent target neurons. We applied cross-correlation analysis to spike trains simultaneously recorded from pairs of adjacent neurons in anesthetized macaques. Neurons whose activity preceded a decrease in activity from their partner were presumed to be inhibitory neurons. Excitatory neurons were also identified as the source neuron of excitatory linkage as evidenced by a sharp peak displaced from the 0-ms bin in cross-correlograms. Most inhibitory neurons responded to a variety of visual stimuli in our stimulus set, which consisted of several dozen geometrical figures and photographs of objects, with a clear stimulus preference. On average, 10% of the stimuli increased firing rates of the inhibitory neurons. Both excitatory and inhibitory neurons exhibited a similar degree of stimulus selectivity. Although inhibitory neurons occasionally shared the most preferred stimuli with their target neurons, overall stimulus preferences were less similar between adjacent neurons with inhibitory linkages than adjacent neurons with common inputs and/or excitatory linkages. These results suggest that inhibitory neurons in area TE are activated selectively and exert stimulus-specific inhibition on adjacent neurons, contributing to shaping of stimulus selectivity of TE neurons.
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7

Shosaku, A. "Cross-correlation analysis of a recurrent inhibitory circuit in the rat thalamus." Journal of Neurophysiology 55, no. 5 (May 1, 1986): 1030–43. http://dx.doi.org/10.1152/jn.1986.55.5.1030.

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Анотація:
Spontaneous activities of vibrissa-responding neurons in the rat ventrobasal complex (VB) and somatosensory part of the thalamic reticular nucleus (S-TR) were simultaneously recorded and subjected to cross-correlation analysis to investigate the functional organization of recurrent inhibitory action of the S-TR on VB neurons. Excitatory and/or inhibitory interactions were found between approximately 75% (25/34) of the pairs of S-TR and VB neurons with receptive fields (RFs) on the same vibrissa. In contrast, there was no significant interaction between 54 pairs of neurons having RFs on different vibrissae. Among the pairs of neurons with RFs on the same vibrissa, there were four types of correlations, which indicate the following connections: monosynaptic excitation from a VB to an S-TR neuron (7 pairs), monosynaptic inhibition from an S-TR to a VB neuron (10 pairs), reciprocal connection combining the above two types (7 pairs), and common excitation in addition to inhibition from an S-TR to a VB neuron (1 pair). Examples of divergence and convergence of connections between S-TR and VB neurons were demonstrated by testing one S-TR (VB) neuron with more than one VB (S-TR) neuron. Vibrissa-suppressed VB cells, which had exclusively inhibitory RFs, were included in eight pairs of the above samples. These VB cells were more likely to receive inhibitory inputs from S-TR neurons than other VB neurons. Cells with RFs on multiple vibrissae were included in the other 10 pairs. These multiple-vibrissa cells had no interaction with single-vibrissa cells but did with multiple-vibrissa cells. From the incidence of four types of correlation between S-TR and VB neurons with RFs on the same vibrissa, the following connection pattern is suggested: One S-TR neuron receives excitatory inputs from approximately 40% of the VB neurons with RFs on the same vibrissa and sends inhibitory outputs to approximately 55%. Since these two groups of VB neurons were overlapping, the S-TR neuron has reciprocal connections with approximately 20% of the VB neurons with RFs on the same vibrissa. The same estimate was applied to connectivity of one VB neuron. These results indicate that both inputs and outputs of S-TR neurons are precisely and topographically organized, although there is convergence to and divergence from a substantial number of VB neurons with RFs on the same vibrissa. It is proposed that the recurrent inhibitory circuit through the S-TR plays a role in improving discrimination of sensory information transmitted through the VB.
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Lu, Yun-Fei, Yykio Hattori, Akiyoshi Moriwaki, Yasushi Hayashi, and Yasuo Hori. "Inhibition of neurons in the rat medial amygdaloid nucleus in vitro by somatostatin." Canadian Journal of Physiology and Pharmacology 73, no. 5 (May 1, 1995): 670–74. http://dx.doi.org/10.1139/y95-086.

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Анотація:
Effects of somatostatin (SRIF) on neurons in the medial amygdaloid nucleus were investigated in rat brain slice preparations, using extracellular recordings. Following bath application of SRIF at 10−7–10−6 M, 63 of 81 (78%) medial amygdala neurons showed an inhibitory response. The inhibitory effect of SRIF was dose dependent, and the threshold concentration was approximately 10−9 M. The inhibitory response to SRIF persisted during synaptic blockade in two-thirds of neurons tested. The inhibitory effect of SRIF was reduced by picrotoxin, a GABAA receptor antagonist, in one-third of neurons. These results suggest that SRIF exerts an inhibitory effect on medial amygdala neurons through either a direct action on SRIF receptors or a GABAergic synaptic involvement.Key words: somatostatin, amygdala, brain slice, neuron activity, picrotoxin.
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9

Unda, Brianna K., Vickie Kwan, and Karun K. Singh. "Neuregulin-1 Regulates Cortical Inhibitory Neuron Dendrite and Synapse Growth through DISC1." Neural Plasticity 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7694385.

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Анотація:
Cortical inhibitory neurons play crucial roles in regulating excitatory synaptic networks and cognitive function and aberrant development of these cells have been linked to neurodevelopmental disorders. The secreted neurotrophic factor Neuregulin-1 (NRG1) and its receptor ErbB4 are established regulators of inhibitory neuron connectivity, but the developmental signalling mechanisms regulating this process remain poorly understood. Here, we provide evidence that NRG1-ErbB4 signalling functions through the multifunctional scaffold protein, Disrupted in Schizophrenia 1 (DISC1), to regulate the development of cortical inhibitory interneuron dendrite and synaptic growth. We found that NRG1 increases inhibitory neuron dendrite complexity and glutamatergic synapse formation onto inhibitory neurons and that this effect is blocked by expression of a dominant negative DISC1 mutant, or DISC1 knockdown. We also discovered that NRG1 treatment increases DISC1 expression and its localization to glutamatergic synapses being made onto cortical inhibitory neurons. Mechanistically, we determined that DISC1 binds ErbB4 within cortical inhibitory neurons. Collectively, these data suggest that a NRG1-ErbB4-DISC1 signalling pathway regulates the development of cortical inhibitory neuron dendrite and synaptic growth. Given that NRG1, ErbB4, and DISC1 are schizophrenia-linked genes, these findings shed light on how independent risk factors may signal in a common developmental pathway that contributes to neural connectivity defects and disease pathogenesis.
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Christensen, Thomas A., and John G. Hildebrand. "Coincident Stimulation With Pheromone Components Improves Temporal Pattern Resolution in Central Olfactory Neurons." Journal of Neurophysiology 77, no. 2 (February 1, 1997): 775–81. http://dx.doi.org/10.1152/jn.1997.77.2.775.

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Christensen, Thomas A. and John G. Hildebrand. Coincident stimulation with pheromone components improves temporal pattern resolution in central olfactory neurons. J. Neurophysiol. 77: 775–781, 1997. Male moths must detect and resolve temporal discontinuities in the sex pheromonal odor signal emitted by a conspecific female moth to orient to and locate the odor source. We asked how sensory information about two key components of the pheromone influences the ability of certain sexually dimorphic projection (output) neurons in the primary olfactory center of the male moth's brain to encode the frequency and duration of discrete pulses of pheromone blends. Most of the male-specific projection neurons examined gave mixed postsynaptic responses, consisting of an early suppressive phase followed by activation of firing, to stimulation of the ipsilateral antenna with a blend of the two behaviorally essential pheromone components. Of 39 neurons tested, 33 were excited by the principal (most abundant) pheromone component but inhibited by another, less abundant but nevertheless essential component of the blend. We tested the ability of each neuron to encode intermittent pheromonal stimuli by delivering trains of 50-ms pulses of the two-component blend at progressively higher rates from 1 to 10 per second. There was a strong correlation between 1) the amplitude of the early inhibitory postsynaptic potential evoked by the second pheromone component and 2) the maximal rate of odor pulses that neuron could resolve ( r = 0.92). Projection neurons receiving stronger inhibitory input encoded the temporal pattern of the stimulus with higher fidelity. With the principal, excitatory component of the pheromone alone as the stimulus, the dynamic range for encoding stimulus intermittency was reduced in nearly 60% of the neurons tested. The greatest reductions were observed in those neurons that could be shown to receive the strongest inhibitory input from the second behaviorally essential component of the blend. We also tested the ability of these neurons to encode stimulus duration. Again there was a strong correlation between the strength of the inhibitory input to a neuron mediated by the second pheromone component and that neuron's ability to encode stimulus duration. Neurons that were strongly inhibited by the second component could accurately encode pulses of the blend from 50 to 500 ms in duration ( r = 0.94), but that ability was reduced in neurons receiving little or no inhibitory input ( r = 0.23). This study confirms that certain olfactory projection neurons respond optimally to a particular odor blend rather than to the individual components of the blend. The key components activate opposing synaptic inputs that enable this subset of central neurons to copy the duration and frequency of intermittent odor pulses that are a fundamental feature of airborne olfactory stimuli.
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Більше джерел

Дисертації з теми "Inhibitory Neurons"

1

Husson, Zoé. "Glycinergic neurons and inhibitory transmission in the cerebellar nuclei." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066279/document.

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Анотація:
Le cervelet, composé d'un cortex et de noyaux, est responsable du contrôle moteur fin des mouvements et de la posture. En combinant une approche génétique (basée sur l'utilisation de lignées de souris transgéniques) avec des traçages anatomiques, des marquages immunohistochimiques et des expériences d'électrophysiologie et d'optogénétique, nous établissons les caractères distinctifs des neurones inhibiteurs des noyaux cérébelleux et en détaillons la connectivité ainsi que les fonctions dans le circuit cérébelleux. Les neurones inhibiteurs glycinergiques des noyaux profonds constituent une population de neurones distincts des autres types cellulaires identifiables par leur phénotype inhibiteur mixte GABAergique/glycinergique. Ces neurones se distinguent également par leur plexus axonal qui comporte une arborisation locale dans les noyaux cérébelleux où ils contactent les neurones principaux et une projection vers le cortex cérébelleux où ils contactent les cellules de Golgi. Ces neurones inhibiteurs reçoivent également des afférences inhibitrices des cellules de Purkinje et pourraient être contactés par les fibres moussues ou les fibres grimpantes.Nous apportons ainsi la première étude d'une transmission mixte fonctionnelle par les neurones inhibiteurs des noyaux cérébelleux, projetant à la fois dans les noyaux et le cortex cérébelleux. L'ensemble de nos données établissent les neurones inhibiteurs mixtes des noyaux cérébelleux comme la troisième composante cellulaire des noyaux profonds. Leur importance dans l'organisation modulaire du cervelet, ainsi que leur impact sur l'intégration sensori-motrice, devront être confirmés par des études optogénétiques in vivo
The cerebellum is composed of a three-layered cortex and of nuclei and is responsible for the learned fine control of posture and movements. I combined a genetic approach (based on the use of transgenic mouse lines) with anatomical tracings, immunohistochemical stainings, electrophysiological recordings and optogenetic stimulations to establish the distinctive characteristics of the inhibitory neurons of the cerebellar nuclei and to detail their connectivity and their role in the cerebellar circuitry.We showed that the glycinergic inhibitory neurons of the cerebellar nuclei constitute a distinct neuronal population and are characterized by their mixed inhibitory GABAergic/glycinergic phenotype. Those inhibitory neurons are also distinguished by their axonal plexus which includes a local arborization with the cerebellar nuclei where they contact principal output neurons and a projection to the granular layer of the cerebellar cortex where they end onto Golgi cells dendrites. Finally, the inhibitory neurons of the cerebellar nuclei receive inhibitory afferents from Purkinje cells and may be contacted by mossy fibers or climbing fibers.We provided the first evidence of functional mixed transmission in the cerebellar nuclei and the first demonstration of a mixed inhibitory nucleo-cortical projection. Overall, our data establish the inhibitory neurons as the third cellular component of the cerebellar nuclei. Their importance in the modular organization of the cerebellum and their impact on sensory-motor integration need to be confirmed by optogenetic experiments in vivo
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2

Li, Yan. "Inhibitory synpatic transmission in striatal neurons after transient cerebral ischemia." Connect to resource online, 2009. http://hdl.handle.net/1805/2021.

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Анотація:
Thesis (Ph.D.)--Indiana University, 2009.
Title from screen (viewed on December 1, 2009). Department of Anatomy and Cell Biology, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Zao C. Xu, Feng C. Zhou, Charles R. Yang, Theodore R. Cummins. Includes vitae. Includes bibliographical references (leaves 115-135).
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3

Bampasakis, Dimitris. "Inhibitory synaptic plasticity and gain modulation in cerebellar nucleus neurons." Thesis, University of Hertfordshire, 2016. http://hdl.handle.net/2299/17179.

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Анотація:
Neurons can encode information using the rate of their action potentials, making the relation between input rate and output rate a prominent feature of neuronal information processing. This relation, known as I{O function, can rapidly change in response to various factors or neuronal processes. Most noticeably, a neuron can undergo a multiplicative operation, resulting in a change of the slope of its I{O curve, also know as gain change. Gain changes represent multiplicative operations, and they are wide- spread. They have been found to play an important role in the encoding of spatial location and coordinate transformation, to signal amplification, and other neuronal functions. One of the factors found to introduce and control neuronal gain is synaptic Short Term Depression (STD). We use both integrate-and- re and conductance based neuron models to identify the effect of STD in excitatory and inhibitory modulatory input. More specifically, we are interested in the effect of STD at the inhibitory synapse from Purkinje cells to cerebellar nucleus neurons. Using a previously published, biologically realistic model, we find that the presence of STD results in a gain change. Most importantly we identify STD at the inhibitory synapse to enable excitation-mediated gain control. To isolate the mechanism that allows excitation to control gain, even though STD is applied at a different synapse, we first show that the overall effect is mediated by average conductance. Having done this, we find that the effect of STD is based on the non-linearity introduced in the relation between input rate and average conductance. We find this effect to vary, depending on the position of the I{O function on the input rate axis. Modulatory input shifts the I{O curve along the input rate axis, consequently shifting it to a position where STD has a different effect. The gain differences in the STD effects between the two positions enable excitation to perform gain control.
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4

Wang, Hui. "Structural and functional studies of the neuronal growth inhibitory factor, human metallothionein-3." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/HKUTO/record/B39559014.

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5

Wang, Hui, and 王暉. "Structural and functional studies of the neuronal growth inhibitory factor, human metallothionein-3." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B39559014.

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6

Mardinly, Alan Robert. "Regulation of Synapse Development by Activity Dependent Transcription in Inhibitory Neurons." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10739.

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Анотація:
Neuronal activity and subsequent calcium influx activates a signaling cascade that causes transcription factors in the nucleus to rapidly induce an early-response program of gene expression. This early-response program is composed of transcriptional regulators that in turn induce transcription of late-response genes, which are enriched for regulators of synaptic development and plasticity that act locally at the synapse.
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7

Chik, Tai-wai David. "Global coherent activities in inhibitory neural systems Chik Tai Wai David." Click to view the E-thesis via HKUTO, 2004. http://sunzi.lib.hku.hk/hkuto/record/B31040408.

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8

Lofredi, Roxanne [Verfasser]. "Characterization of inhibitory and projection specific neurons of the presubiculum / Roxanne Lofredi." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2017. http://d-nb.info/1126505005/34.

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9

Pangalos, Maria. "Analysis of hippocampal inhibitory and excitatory neurons during sharp wave-associated ripple." Doctoral thesis, Humboldt-Universität zu Berlin, Lebenswissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17590.

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Анотація:
Im Hippokampus gibt es verschiedene Netzwerkoszillationen mit unterschiedlichen Frequenzen. Ein Typ dieser Oszillationen sind die ”Ripple” mit einer Frequenz von etwa 200 Hz, welche in Komplexen mit einer Aktivitätswelle, der ”Sharp wave” auftreten. Sharp wave-ripple Komplexe (SWR) werden mit der Konsolidierung von Gedächtnis in Zusammenhang gebracht. Das Netzwerk, das den SWR unterliegt, hat bestimmte Mechanismen, von denen einige in der vorliegenden Arbeit näher untersucht werden. Im ersten Teil wird untersucht, wie ein hemmendes Interneuron in der hippokampalen Region CA1, das ”oriens-lacunosum moleculare” (O-LM) Interneuron, während der SWR in das Netzwerk eingebunden ist. Wir konnten zeigen, dass O-LM Zellen während der SWR starke synaptische Exzitation erhalten. Die Exzitation tritt spät während des Ripples im lokalen Feldpotential (LFP) auf und zeigt eine Phasenankopplung an die Ripple. In etwa der Hälfte der O-LM Zellen konnten wir Aktionspotentiale während der SWR zeigen, die an die Ripple-Phase im LFP gebunden sind und nach dem Ripple-Maximum auftreten. Der zweite Teil der Arbeit bezieht sich auf die hippokampale Region CA1 und vergleicht während SWR den synaptischen Eingang in zwei Untertypen von Pyramidenzellen, die tiefen und die oberflächlichen Pyramidenzellen. Beide Untertypen bekommen synaptische Eingänge während der SWR. Diese Eingänge sind eine Mischung aus exzitatorischen und inhibitorischen Eingängen, die in den Untertypen in ihrer Stärke vergleichbar sind. Im dritten Teil untersuchen wir die SWR in der Region CA2 des Hippokampus und zeigen, dass Pyramidenzellen in CA2 in das Netzwerk während SWR eingebunden sind. Wir können sowohl exzitatorische als auch inhibitorische synaptische Eingänge in den Pyramidenzellen darstellen und konnten eine Phasenkopplung der synaptischen Eingänge an die SWR im LFP zeigen. Aufgrund der Phasenverschiebung bei verschiedenen Haltepotentialen vermuten wir einen Oszillator für die Exzitation und einen für die Hemmung.
In the hippocampus there are different patterns of activity also known as network oscillations. These oscillations express different frequencies, and one oscillation is the ripple oscillation at around 200 Hz. It is associated with an activity wave called sharp wave and form a so-called sharp wave-ripple complex (SWR). SWRs are implicated in memory consolidation. In this thesis we investigate mechanisms underlying sharp wave-ripple complexes. In the first part of this thesis I examine one type of inhibitory neurons in the region CA1 of the hippocampus during SWR. Oriens-lacunosum moleculare (O-LM) interneurons receive strong excitatory synaptic input during ripples. This input arrives after the ripple maximum and is phase locked with the ripple cycles. Around half of the probed O-LM cells fire during the SWR and thereby show an active participation during SWR. The magnitude of excitation in O-LM cells and the ratio between excitation and inhibition determine if an O-LM cell is active during the SWR. Action potentials in these cells occur late during the SWR and are phase locked. In the second part the synaptic input onto excitatory pyramidal cells were investigated during ripple oscillations. Previous work has identified two different types of pyramidal cells in area CA1. We recorded from deep and superficial pyramidal cells. For both types of pyramidal cells the inhibitory and excitatory synaptic inputs temporally associated with ripples express comparable strength. In the last and third part, I recorded SWR in the CA2 region of the hippocampus and showed incidence, frequency and amplitude of ripples and SWR. Pyramidal cells in the CA2 region are integrated into the network during SWR. They receive SWR associated synaptic input during SWR. The excitatory and inhibitory synaptic inputs in CA2 pyramidal cells were investigated in detail. Phase analysis show phase locking of local field potential ripples and synaptic inputs to the ascending phase of the ripple cycle.
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Chik, Tai-wai David, and 戚大衛. "Global coherent activities in inhibitory neural systems: Chik Tai Wai David." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B31040408.

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Книги з теми "Inhibitory Neurons"

1

Developmental plasticity of inhibitory circuitry. New York: Springer, 2010.

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2

Takao, Kumazawa, Kruger Lawrence, and Mizumura Kazue, eds. The polymodal receptor: A gateway to pathological pain. Amsterdam: Elsevier, 1996.

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Pallas, Sarah L. Developmental Plasticity of Inhibitory Circuitry. Springer, 2014.

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4

Saraga, Fernanda. Use of compartmental models to predict physiological properties of hippocampal inhibitory neurons. 2006.

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5

GABA(A) receptors that mediate a tonic inhibitory current in hippocampal neurons: Modulation by antagonists and anti-convulsants. Ottawa: National Library of Canada, 2002.

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6

Dickenson, Tony. A new theory of pain. Edited by Paul Farquhar-Smith, Pierre Beaulieu, and Sian Jagger. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198834359.003.0007.

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Of all the seminal papers on pain, the one described in this chapter must be one of the most influential. It has been cited over 11,000 times. This paper proposed the theory that the transmission of pain from peripheral fibres through the spinal cord to the brain was not a passive fixed process but was subject to modulation and alteration. It also suggested that there was interplay between different afferent fibres, spinal excitatory neurons, and inhibitory spinal neuron and that the brain could exert influence on the spinal cord. Most of modern pain science and clinical management is based on this theory, which is now clearly backed up by facts.
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Levine, Michael S., Elizabeth A. Wang, Jane Y. Chen, Carlos Cepeda, and Véronique M. André. Altered Neuronal Circuitry. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199929146.003.0010.

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In mouse models of Huntington’s disease (HD), synaptic alterations in the cerebral cortex and striatum are present before overt behavioral symptoms and cell death. Similarly, in HD patients, it is now widely accepted that early deficits can occur in the absence of neural atrophy or overt motor symptoms. In addition, hyperkinetic movements seen in early stages are followed by hypokinesis in the late stages, indicating that different processes may be affected. In mouse models, such behavioral alterations parallel complex biphasic changes in glutamate-mediated excitatory, γ‎-aminobutyric acid (GABA)-mediated inhibitory synaptic transmission and dopamine modulation in medium spiny neurons of the striatum as well as in cortical pyramidal neurons. The progressive electrophysiologic changes in synaptic communication that occur with disease stage in the cortical and basal ganglia circuits of HD mouse models strongly indicate that therapeutic interventions and strategies in human HD must be targeted to different mechanisms in each stage and to specific subclasses of neurons.
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8

Mather, George. Two-Stroke Apparent Motion. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0073.

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“Two-stroke” apparent motion is a powerful illusion of directional motion generated by alternating just two animation frames, which occurs when a brief blank interframe interval is inserted at alternate frame transitions. This chapter discusses this illusion, which can be explained in terms of the receptive field properties of motion-sensing neurons in the human visual system. The temporal response of these neurons contains both an excitatory phase and an inhibitory phase; when the timing of the interframe interval just matches the switch in response sign, the illusion occurs. Concepts covered in this chapter include four-stroke as well as two-stroke apparent motion, motion aftereffect, and motion detection.
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9

Schaible, Hans-Georg, and Rainer H. Straub. Pain neurophysiology. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0059.

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Physiological pain is evoked by intense (noxious) stimuli acting on healthy tissue functioning as a warning signal to avoid damage of the tissue. In contrast, pathophysiological pain is present in the course of disease, and it is often elicited by low-intensity stimulation or occurs even as resting pain. Causes of pathophysiological pain are either inflammation or injury causing pathophysiological nociceptive pain or damage to nerve cells evoking neuropathic pain. The major peripheral neuronal mechanism of pathophysiological nociceptive pain is the sensitization of peripheral nociceptors for mechanical, thermal and chemical stimuli; the major peripheral mechanism of neuropathic pain is the generation of ectopic discharges in injured nerve fibres. These phenomena are created by changes of ion channels in the neurons, e.g. by the influence of inflammatory mediators or growth factors. Both peripheral sensitization and ectopic discharges can evoke the development of hyperexcitability of central nociceptive pathways, called central sensitization, which amplifies the nociceptive processing. Central sensitization is caused by changes of the synaptic processing, in which glial cell activation also plays an important role. Endogenous inhibitory neuronal systems may reduce pain but some types of pain are characterized by the loss of inhibitory neural function. In addition to their role in pain generation, nociceptive afferents and the spinal cord can further enhance the inflammatory process by the release of neuropeptides into the innervated tissue and by activation of sympathetic efferent fibres. However, in inflamed tissue the innervation is remodelled by repellent factors, in particular with a loss of sympathetic nerve fibres.
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Stafstrom, Carl E. Disorders Caused by Botulinum Toxin and Tetanus Toxin. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0156.

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Anaerobic organisms of the genus Clostridia (C) can cause significant human disease. Exotoxins secreted by C botulinum and C tetani cause botulism and tetanus, respectively (summarized in Table 156.1). Botulinum neurotoxin causes neuromuscular blockade by interfering with vesicular acetylcholine release, leading to cholinergic blockade at the neuromuscular junctions of skeletal muscle, and consequently, symmetric flaccid paralysis. Tetanus toxin prevents release of inhibitory neurotransmitters at central synapses, leading to overactivity of motor neurons and muscle rigidity and spasms. This chapter reviews clinical features of botulism and tetanus and discusses their pathophysiological basis.
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Частини книг з теми "Inhibitory Neurons"

1

Kawaguchi, Yasuo. "Local Circuit Neurons in the Frontal Cortico-Striatal System." In Excitatory-Inhibitory Balance, 125–48. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0039-1_9.

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2

Ito, Masao. "Historical Overview: The Search for inhibitory neurons and their function." In Excitatory-Inhibitory Balance, 1–10. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4615-0039-1_1.

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3

Trussell, Laurence O. "Inhibitory Neurons in the Auditory Brainstem." In Synaptic Mechanisms in the Auditory System, 165–85. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9517-9_7.

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4

Theile, Jonathan W., Rueben A. Gonzales, and Richard A. Morrisett. "Ethanol Modulation of GABAergic Inhibition in Midbrain Dopamine Neurons: Implications for the Development of Alcohol-Seeking Behaviors." In Inhibitory Synaptic Plasticity, 75–88. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6978-1_6.

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5

Golomb, David, and John Rinzel. "Synchronization among heterogeneous inhibitory RTN neurons globally coupled." In Computation in Neurons and Neural Systems, 27–32. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2714-5_5.

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6

Lopez-Gutierrez, Javier, and B. Mario Cervantes. "Achalasia." In Mastering Endo-Laparoscopic and Thoracoscopic Surgery, 201–6. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3755-2_31.

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AbstractAchalasia is the result of a progressive degeneration process of the ganglion cells of the myenteric plexus, located in the esophageal wall. The disorder motility that characterizes achalasia appears to result primarily from the loss of inhibitory neurons within the wall of the esophagus itself. This loss of the inhibitory innervation in the LOS causes the basal sphincter pressure to rise and renders the sphincter muscle incapable of normal relaxation. The loss of inhibitory neurons from the smooth muscle portion of the esophageal body results in aperistalais [1]. The manifestations of the disease depend on the degree and location of ganglion cell loss [2]. Loss of peristalsis in the distal esophagus and LOS failure to relax with swallowing, both impair esophageal emptying. Most of the signs and symptoms of achalasia are due to the defect in LES relaxation. Esophagogastric junction (OGJ) outflow obstruction. The risk of developing esophageal cancer increases up to 3.3% after a mean symptom duration of 13 years [3].
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7

Lambert, Nevin A., and Neil L. Harrison. "GABAB Receptors on Inhibitory Neurons in the Hippocampus." In Presynaptic Receptors in the Mammalian Brain, 143–60. Boston, MA: Birkhäuser Boston, 1993. http://dx.doi.org/10.1007/978-1-4684-6825-0_9.

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8

Bacigalupo, Juan, Bernardo Morales, Pedro Labarca, Gonzalo Ugarte, and Rodolfo Madrid. "Inhibitory Responses to Odorants in Vertebrate Olfactory Neurons." In From Ion Channels to Cell-to-Cell Conversations, 269–84. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-1795-9_16.

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9

Nicolaus, Jill M., and Philip S. Ulinski. "Inward Rectifying Conductances in Inhibitory Neurons of Turtle Visual Cortex." In Computation in Neurons and Neural Systems, 91–96. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2714-5_15.

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10

Rathmayer, Werner. "Inhibition Through Neurons of the Common Inhibitory Type (CI-Neurons) in Crab Muscles." In Frontiers in Crustacean Neurobiology, 271–78. Basel: Birkhäuser Basel, 1990. http://dx.doi.org/10.1007/978-3-0348-5689-8_31.

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Тези доповідей конференцій з теми "Inhibitory Neurons"

1

Ziari, Mehrdad, William H. Steier, and Robert L. S. Devine. "Nonlinear Neurons Using the Fieldshielding Effect in Photorefractive CdTe." In Photorefractive Materials, Effects, and Devices II. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/pmed.1990.h3.

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A key element in the optical implementation of any neural network is the nonlinear neuron. The most common required feature of this device is a saturating response to an activation input. The activation input is the weighted sum of all the incident beams which are routed through an interconnection network to the neuron. We report on nonlinear neurons using the fieldshielding effect in photorefractive crystals which perform an incoherent addition of the incident intensities1,2. The device responds to CW or synchronously pulsed inputs in a variety of saturated, thresholded or bidirectional manners. Using photorefractive CdTe:In, we demonstrate a response to both inhibitory and excitatory inputs with a high sensitivity to incident light in the 1.0-1.4 μm range.
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2

Rakymzhan, Adiya, and Alberto Vazquez. "The Contribution of Cortical Neuronal Populations to Resting-State Cerebrovascular Regulation Revealed by Two-Photon Microscopy Imaging." In Optics and the Brain. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/brain.2023.btu1b.2.

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We recorded calcium activity in excitatory neurons and inhibitory Parvalbumin neurons, while concurrently measuring vascular changes with two-photon imaging. We demonstrate that spontaneous vascular fluctuations are largely modulated by ongoing activity of Parvalbumin neurons.
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3

Zhu, Guibo, Zhaoxiang Zhang, Xu-Yao Zhang, and Cheng-Lin Liu. "Diverse Neuron Type Selection for Convolutional Neural Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/498.

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The activation function for neurons is a prominent element in the deep learning architecture for obtaining high performance. Inspired by neuroscience findings, we introduce and define two types of neurons with different activation functions for artificial neural networks: excitatory and inhibitory neurons, which can be adaptively selected by self-learning. Based on the definition of neurons, in the paper we not only unify the mainstream activation functions, but also discuss the complementariness among these types of neurons. In addition, through the cooperation of excitatory and inhibitory neurons, we present a compositional activation function that leads to new state-of-the-art performance comparing to rectifier linear units. Finally, we hope that our framework not only gives a basic unified framework of the existing activation neurons to provide guidance for future design, but also contributes neurobiological explanations which can be treated as a window to bridge the gap between biology and computer science.
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4

Liu, Zhiheng, and Xia Shi. "Modeling of Synchronous Behaviors of Excitatory and Inhibitory Neurons in Complex Neuronal Networks." In 2018 IEEE 4th International Conference on Computer and Communications (ICCC). IEEE, 2018. http://dx.doi.org/10.1109/compcomm.2018.8780741.

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5

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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6

Ullah, Ihsan, Sean Reilly, and Michael G. Madden. "Enhancing Semantic Segmentation of Aerial Images with Inhibitory Neurons." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9413021.

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7

Wang, Xiaodan, Annie R. Bice, and Adam Q. Bauer. "Mapping Local and Global Interactions between Parvalbumin Inhibitory Neurons and Excitatory Neurons over the Cortex in Awake Mice." In Optics and the Brain. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/brain.2023.btu1b.4.

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We created a novel method for mapping the interactions between parvalbumin inhibitory interneurons (PV-INs) and excitatory neurons over the cortex in mice. Local and distant influences of PV-INs are region-specific and can span hemispheres.
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Grot, Annette, Steven Lin, and Demetri Psaltis. "Optoelectronic neurons using MSM detectors in GaAs." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.mk4.

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We present a new GaAs integrated optoelectronic neuron for use in neural networks. We have previously demonstrated that the integration of photodetectors, thresholding transistors, and a light source on a single substrate allows one to have high neuron density with acceptable power dissipation. In this paper we report a circuit in which we used a double heterostructure LED (light emitting diode) as the light source. We use an LED rather than laser diodes because LEDs can be operated with small currents, due to the lack of a threshold current. To minimize the total mesa height and maintain high LED quantum efficiency, MSM (metal–semiconductor–metal) photodetectors were used. The thresholding transistor was a MESFET (metal–semiconductor field-effect transistor). The total mesa height is less than 3 μm. Since no two devices share an epitaxial layer, each device is individually optimized. Our circuit uses two photodetectors, one to set the threshold voltage and the other to detect the signal. With this circuit, we can also build both excitatory and inhibitory neurons. Results from our experimental studies are presented.
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Tsuji, Shigeki, Tetsushi Ueta, Hiroshi Kawakami, and Kazuyuki Aihara. "Synchronization and Bifurcation Phenomena in Inhibitory Neurons with Gap-junction." In 2006 IEEE/NLM Life Science Systems and Applications Workshop. IEEE, 2006. http://dx.doi.org/10.1109/lssa.2006.250420.

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10

Andreev, Andrey. "Oscillations of synchronization in inhibitory coupled Hodgkin-Huxley neurons network." In 2020 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR). IEEE, 2020. http://dx.doi.org/10.1109/dcnair50402.2020.9216937.

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Звіти організацій з теми "Inhibitory Neurons"

1

Johnson, Don H. Simulation of Excitatory/Inhibitory Interactions in Single Auditory Neurons. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada253614.

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2

Enderle, John D., and Edward J. Engelken. Simulation of Oculomotor Post-Inhibitory Rebound Burst Firing using a Hodgkin-Huxley Model of a Neuron. Fort Belvoir, VA: Defense Technical Information Center, February 1995. http://dx.doi.org/10.21236/ada293821.

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3

Polt, Robin. Enzyme Inhibitors of Cell-Surface Carbohydrates: Insects as Model Systems for Neuronal Development and Repair Mechanisms. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada397723.

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4

Polt, Robin. Enzyme Inhibitors of Cell-Surface Carbohydrates: Insects as Model Systems for Neuronal Development and Repair Mechanisms. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada382533.

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