Relevant bibliographies by topics / Thalamic neuron

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Academic literature on the topic 'Thalamic neuron'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Thalamic neuron"

1

Alloway, K. D., M. B. Wallace, and M. J. Johnson. "Cross-correlation analysis of cuneothalamic interactions in the rat somatosensory system: influence of receptive field topography and comparisons with thalamocortical interactions." Journal of Neurophysiology 72, no. 4 (October 1, 1994): 1949–72. http://dx.doi.org/10.1152/jn.1994.72.4.1949.

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1. We simultaneously recorded neuronal responses to cutaneous stimulation from matched somatotopic representations in the nucleus cuneatus and ventrobasal complex of intact, halothane-anesthetized rats. A total of 95 cuneate and 86 thalamic neurons representing hairy skin on the forelimb were activated by hair movements produced by air jets at multiple skin sites. Mean responsiveness was higher among neurons in nucleus cuneatus (34.4 spikes per stimulus) than in thalamus (23.7 spikes per stimulus), a result that was consistent with the greater proportion of “sustained” responses recorded in nucleus cuneatus (80%) than in the thalamus (62%). 2. Cross-correlation analysis of 166 pairs of cuneate and thalamic neurons showed that 56 neuron pairs displayed time-locked correlations in activity that were characterized primarily by excitatory interactions (44 pairs) or a combination of excitatory and inhibitory interactions (10 pairs). Unilateral interactions in the cuneothalamic direction (31 pairs) and reverse direction (11 pairs) were observed, as well as multiphasic interactions in both directions (14 pairs). Most excitatory interactions involved intervals of 1–7 ms between successive cuneate and thalamic discharges, whereas most inhibitory influences involved intervals > 7 ms. Connection strength, defined by the ratio of time-linked interactions to the number of cuneate discharges, varied widely among neuron pairs but was largest for interactions involving interspike intervals of < or = 15 ms. 3. The relationship between connection strength and receptive field topography was analyzed in 103 cuneate-thalamic neuron pairs. The region of skin shared by both neurons varied substantially among neuron pairs and the probability of detecting interactions increased proportionately with larger amounts of receptive field overlap. Neuron pairs with moderate (25–50%) amounts of receptive field overlap had connection strengths 3–4 times greater than neuron pairs with minimal (0–25%) overlap. Connection strength was essentially identical, however, for neuron pairs with moderate or large (> 50%) amounts of overlap. 4. Cuneate-thalamic neuron pairs displaying functional connections were usually tested at multiple peripheral sites, but only 37% (18 of 49) of these neuron pairs displayed interactions at more than one stimulation site. Stimulation at different sites altered the timing of interactions in seven neuron pairs, including three that showed timing shifts across time zero in the cross-correlation histogram. In neuron pairs displaying interactions at multiple sites, connection strengths for 67% of the cases were strongest when stimulation was delivered within the region of receptive field overlap.(ABSTRACT TRUNCATED AT 400 WORDS)
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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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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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Lytton, William W., Diego Contreras, Alain Destexhe, and Mircea Steriade. "Dynamic Interactions Determine Partial Thalamic Quiescence in a Computer Network Model of Spike-and-Wave Seizures." Journal of Neurophysiology 77, no. 4 (April 1, 1997): 1679–96. http://dx.doi.org/10.1152/jn.1997.77.4.1679.

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Lytton, William W., Diego Contreras, Alain Destexhe, and Mircea Steriade. Dynamic interactions determine partial thalamic quiescence in a computer network model of spike-and-wave seizures. J. Neurophysiol. 77: 1679–1696, 1997. In vivo intracellular recording from cat thalamus and cortex was performed during spontaneous spike-wave seizures characterized by synchronously firing cortical neurons correlated with the electroencephalogram. During these seizures, thalamic reticular (RE) neurons discharged with long spike bursts riding on a depolarization, whereas thalamocortical (TC) neurons were either entrained into the seizures (40%) or were quiescent (60%). During quiescence, TC neurons showed phasic inhibitory postsynaptic potentials (IPSPs) that coincided with paroxysmal depolarizing shifts in the simultaneously recorded cortical neuron. Computer simulations of a reciprocally connected TC-RE pair showed two major modes of TC-RE interaction. In one mode, a mutual oscillation involved direct TC neuron excitation of the RE neuron leading to a burst that fed back an IPSP into the TC neuron, producing a low-threshold spike. In the other, quiescent mode, the TC neuron was subject to stronger coalescing IPSPs. Simulated cortical stimulation could trigger a transition between the two modes. This transition could go in either direction and was dependent on the precise timing of the input. The transition did not always follow the stimulation immediately. A larger, multicolumnar simulation was set up to assess the role of the TC-RE pair in the context of extensive divergence and convergence. The amount of TC neuron spiking generally correlated with the strength of total inhibitory input, but large variations in the amount of spiking could be seen. Evidence for mutual oscillation could be demonstrated by comparing TC neuron firing with that in reciprocally connected RE neurons. An additional mechanism for TC neuron quiescence was assessed with the use of a cooperative model of γ-aminobutyric acid-B (GABAB)-mediated responses. With this model, RE neurons receiving repeated strong excitatory input produced TC neuron quiescence due to burst-duration-associated augmentation of GABAB current. We predict the existence of spatial inhomogeneity in apparently generalized spike-wave seizures, involving a center-surround pattern. In the center, intense cortical and RE neuron activity would be associated with TC neuron quiescence. In the surround, less intense hyperpolarization of TC neurons would allow low-threshold spikes to occur. This surround, an “epileptic penumbra,” would be the forefront of the expanding epileptic wave during the process of initial seizure generalization. Therapeutically, we would then predict that agents that reduce TC neuron activity would have a greater effect on seizure onset than on ongoing spike-wave seizures or other thalamic oscillations.
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Kasten, Michael R., and Matthew P. Anderson. "Self-regulation of adult thalamocortical neurons." Journal of Neurophysiology 114, no. 1 (July 2015): 323–31. http://dx.doi.org/10.1152/jn.00800.2014.

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The thalamus acts as a conduit for sensory and other information traveling to the cortex. In response to continuous sensory stimulation in vivo, the firing rate of thalamocortical neurons initially increases, but then within a minute firing rate decreases and T-type Ca2+ channel-dependent action potential burst firing emerges. While neuromodulatory systems could play a role in this inhibitory response, we instead report a novel and cell-autonomous inhibitory mechanism intrinsic to the thalamic relay neuron. Direct intracellular stimulation of thalamocortical neuron firing initially triggered a continuous and high rate of action potential discharge, but within a minute membrane potential ( Vm) was hyperpolarized and firing rate to the same stimulus was decreased. This self-inhibition was observed across a wide variety of thalamic nuclei, and in a subset firing mode switched from tonic to bursting. The self-inhibition resisted blockers of intracellular Ca2+ signaling, Na+-K+-ATPases, and G protein-regulated inward rectifier (GIRK) channels as implicated in other neuron subtypes, but instead was in part inhibited by an ATP-sensitive K+ channel blocker. The results identify a new homeostatic mechanism within the thalamus capable of gating excitatory signals at the single-cell level.
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Khatri, Vivek, Randy M. Bruno, and Daniel J. Simons. "Stimulus-Specific and Stimulus-Nonspecific Firing Synchrony and Its Modulation by Sensory Adaptation in the Whisker-to-Barrel Pathway." Journal of Neurophysiology 101, no. 5 (May 2009): 2328–38. http://dx.doi.org/10.1152/jn.91151.2008.

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The stimulus-evoked response of a cortical neuron depends on both details of the afferent signal and the momentary state of the larger network in which it is embedded. Consequently, identical sensory stimuli evoke highly variable responses. Using simultaneous recordings of thalamic barreloid and/or cortical barrel neurons in the rat whisker-to-barrel pathway, we determined the extent to which the responses of pairs of cells covary on a trial-by-trial basis. In the thalamus and cortical layer IV, a substantial component of trial-to-trial variability is independent of the specific parameters of the stimulus, probed here using deflection angle. These stimulus-nonspecific effects resulted in greater-than-chance similarities in trial-averaged angular tuning among simultaneously recorded pairs of barrel neurons. Such effects were not observed among simultaneously recorded thalamic and cortical barrel neurons, suggesting strong intracortical mechanisms of synchronization. Sensory adaptation produced by prior whisker deflections reduced response magnitudes and enhanced the joint angular tuning of simultaneously recorded neurons. Adaptation also decorrelated stimulus-evoked responses, rendering trial-by-trial responses of neuron pairs less similar to each other. Adaptation-induced decorrelation coupled with sharpened joint tuning could enhance the saliency of cells within thalamus or cortex that continue to fire synchronously during ongoing tactile stimulation associated with active touch.
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Azimirad, Vahid, and Mohammad Fattahi Sani. "Experimental Study of Reinforcement Learning in Mobile Robots Through Spiking Architecture of Thalamo-Cortico-Thalamic Circuitry of Mammalian Brain." Robotica 38, no. 9 (November 18, 2019): 1558–75. http://dx.doi.org/10.1017/s0263574719001632.

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SUMMARYIn this paper, the behavioral learning of robots through spiking neural networks is studied in which the architecture of the network is based on the thalamo-cortico-thalamic circuitry of the mammalian brain. According to a variety of neurons, the Izhikevich model of single neuron is used for the representation of neuronal behaviors. One thousand and ninety spiking neurons are considered in the network. The spiking model of the proposed architecture is derived and prepared for the learning problem of robots. The reinforcement learning algorithm is based on spike-timing-dependent plasticity and dopamine release as a reward. It results in strengthening the synaptic weights of the neurons that are involved in the robot’s proper performance. Sensory and motor neurons are placed in the thalamus and cortical module, respectively. The inputs of thalamo-cortico-thalamic circuitry are the signals related to distance of the target from robot, and the outputs are the velocities of actuators. The target attraction task is used as an example to validate the proposed method in which dopamine is released when the robot catches the target. Some simulation studies, as well as experimental implementation, are done on a mobile robot named Tabrizbot. Experimental studies illustrate that after successful learning, the meantime of catching target is decreased by about 36%. These prove that through the proposed method, thalamo-cortical structure could be trained successfully to learn to perform various robotic tasks.
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Viaene, Angela N., Iraklis Petrof, and S. Murray Sherman. "Synaptic Properties of Thalamic Input to Layers 2/3 and 4 of Primary Somatosensory and Auditory Cortices." Journal of Neurophysiology 105, no. 1 (January 2011): 279–92. http://dx.doi.org/10.1152/jn.00747.2010.

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We studied the synaptic profile of thalamic inputs to cells in layers 2/3 and 4 of primary somatosensory (S1) and auditory (A1) cortices using thalamocortical slices from mice age postnatal days 10–18. Stimulation of the ventral posterior medial nucleus (VPM) or ventral division of the medial geniculate body (MGBv) resulted in two distinct classes of responses. The response of all layer 4 cells and a minority of layers 2/3 cells to thalamic stimulation was Class 1, including paired-pulse depression, all-or-none responses, and the absence of a metabotropic component. On the other hand, the majority of neurons in layers 2/3 showed a markedly different, Class 2 response to thalamic stimulation: paired-pulse facilitation, graded responses, and a metabotropic component. The Class 1 and Class 2 response characteristics have been previously seen in inputs to thalamus and have been described as drivers and modulators, respectively. Driver input constitutes a main information bearing pathway and determines the receptive field properties of the postsynaptic neuron, whereas modulator input influences the response properties of the postsynaptic neuron but is not a primary information bearing input. Because these thalamocortical projections have comparable properties to the drivers and modulators in thalamus, we suggest that a driver/modulator distinction may also apply to thalamocortical projections. In addition, our data suggest that thalamus is likely to be more than just a simple relay of information and may be directly modulating cortex.
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Thomas, Elizabeth, and Thierry Grisar. "Increased Synchrony with Increase of a Low-Threshold Calcium Conductance in a Model Thalamic Network: A Phase-Shift Mechanism." Neural Computation 12, no. 7 (July 1, 2000): 1553–71. http://dx.doi.org/10.1162/089976600300015268.

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A computer model of a thalamic network was used in order to examine the effects of an isolated augmentation in a low-threshold calcium current. Such an isolated augmentation has been observed in the reticular thalamic (RE) nucleus of the genetic absence epilepsy rat from the Strasbourg (GAERS) model of absence epilepsy. An augmentation of the low-threshold calcium conductance in the RE neurons (gTs) of the model thalamic network was found to lead to an increase in the synchronized firing of the network. This supports the hypothesis that the isolated increase in gTs may be responsible for epileptic activity in the GAERS rat. The increase of gTs in the RE neurons led to a slight increase in the period of the isolated RE neuron firing. In contrast, the low-threshold spike of the RE neuron remained relatively unchanged by the increase of gTs. This suggests that the enhanced synchrony in the network was primarily due to a phase shift in the firing of the RE neurons with respect to the thalamocortical neurons. The ability of this phase-shift mechanism to lead to changes in synchrony was further examined using the model thalamic network. A similar increase in the period of RE neuron oscillations was obtained through an increase in the conductance of the calcium-mediated potassium channel. This change was once again found to increase synchronous firing in the network.
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Johnson, M. J., and K. D. Alloway. "Cross-correlation analysis reveals laminar differences in thalamocortical interactions in the somatosensory system." Journal of Neurophysiology 75, no. 4 (April 1, 1996): 1444–57. http://dx.doi.org/10.1152/jn.1996.75.4.1444.

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1. Spontaneous and stimulus-induced activity were recorded from corresponding somatotopic representations in the ventroposterolateral nucleus (VPL) of the thalamus and primary somatosensory (SI) cortex of intact, halothane-anesthetized cats. Thalamic and cortical neurons with overlapping receptive fields on the hairy skin of the forelimb were excited by a series of interleaved air jets aimed at multiple skin sites. 2. The laminar locations of 68% (240 of 355) of the neurons recorded in SI cortex were histologically reconstructed and responses of these 240 SI neurons were analyzed with respect to responses recorded from 118 thalamic neurons. Maximum responsiveness during the initial onset (1st 100 ms) of air jet stimulation was similar for neurons distributed throughout all layers of SI cortex (2-4 spikes per stimulus) and did not differ significantly from VPL responses. During the subsequent plateau phase of the stimulus, VPL neurons discharged at a mean rate of 19.0 spikes/ s and neurons in cortical layers II, IIIa, IIIb, and IV discharged at similar rates. Mean responsiveness during the plateau phase of the stimulus was significantly reduced among neurons in cortical layers V and VI and only averaged 7.1 and 3.9 spikes/s, respectively. 3. Responses recorded simultaneously from pairs of thalamic and cortical neurons were analyzed with cross-correlation analysis to determine differences in the incidence and strength of neuronal interactions as a function of cortical layer. Among 421 thalamocortical neuron pairs displaying stimulus-induced responses, 68 neuron pairs exhibited significant interactions during air jet stimulation. A laminar analysis revealed that 28% (45 of 163) of the neurons in the middle cortical layers displayed significant interactions with thalamic neurons, whereas only 14% (13 of 92) of superficial layer neurons and 6% (10 of 166) of deep layer neurons were synchronized with thalamic activity during air jet stimulation. When thalamocortical efficacy for different layers of cortex was plotted as a cumulative frequency distribution, the strongest interactions in the middle cortical layers were twice as strong as interactions involving the superficial or deep cortical layers. 4. More than 70% of stimulus-induced interactions involved thalamic discharges followed by subsequent cortical discharges and the majority of these interactions involved interspike intervals of < or = 3 ms. Nearly 75% (27 of 37) of interactions in the thalamocortical direction that involved cortical neurons in layers IIIb and IV transpired within a 3-ms interspike interval. For interactions with superficial or deep cortical layers, the proportion of thalamocortical interactions transpiring within 3 ms was only 58% (7 of 12) and 33% (2 of 6), respectively. 5. Cross-correlation analysis of spontaneous activity indicated that 124 pairs of thalamic and cortical neurons displayed synchronous activity in the absence of sensory stimulation. A laminar analysis indicated that similar proportions of cortical neurons in each layer were synchronized with thalamic activity in the absence of cutaneous stimulation. Thus 27% (44 of 163) of middle layer neurons, 30% (28 of 92) of superficial layer neurons, and 31% (51 of 166) of deep layer neurons displayed spontaneous interactions with thalamic neurons. The temporal pattern of spontaneous activity was examined with autocorrelation analysis to determine whether neuronal oscillations were essential for coordinating thalamic and cortical activity in the absence of peripheral stimulation. Only 18.5% (23 of 124) of spontaneous interactions between thalamic and cortical neurons were associated with periodic activity, which suggests that thalamocortical synchronization occurs before the constituent neurons begin to oscillate. 6. The influence of sensory stimulation on spontaneous interactions was examined in 31 pairs of thalamic and cortical neurons that exhibited interactions during prestimulus and stimulus in
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Goldberg, Jesse H., Michael A. Farries, and Michale S. Fee. "Integration of cortical and pallidal inputs in the basal ganglia-recipient thalamus of singing birds." Journal of Neurophysiology 108, no. 5 (September 1, 2012): 1403–29. http://dx.doi.org/10.1152/jn.00056.2012.

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The basal ganglia-recipient thalamus receives inhibitory inputs from the pallidum and excitatory inputs from cortex, but it is unclear how these inputs interact during behavior. We recorded simultaneously from thalamic neurons and their putative synaptically connected pallidal inputs in singing zebra finches. We find, first, that each pallidal spike produces an extremely brief (∼5 ms) pulse of inhibition that completely suppresses thalamic spiking. As a result, thalamic spikes are entrained to pallidal spikes with submillisecond precision. Second, we find that the number of thalamic spikes that discharge within a single pallidal interspike interval (ISI) depends linearly on the duration of that interval but does not depend on pallidal activity prior to the interval. In a detailed biophysical model, our results were not easily explained by the postinhibitory “rebound” mechanism previously observed in anesthetized birds and in brain slices, nor could most of our data be characterized as “gating” of excitatory transmission by inhibitory pallidal input. Instead, we propose a novel “entrainment” mechanism of pallidothalamic transmission that highlights the importance of an excitatory conductance that drives spiking, interacting with brief pulses of pallidal inhibition. Building on our recent finding that cortical inputs can drive syllable-locked rate modulations in thalamic neurons during singing, we report here that excitatory inputs affect thalamic spiking in two ways: by shortening the latency of a thalamic spike after a pallidal spike and by increasing thalamic firing rates within individual pallidal ISIs. We present a unifying biophysical model that can reproduce all known modes of pallidothalamic transmission—rebound, gating, and entrainment—depending on the amount of excitation the thalamic neuron receives.
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Dissertations / Theses on the topic "Thalamic neuron"

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Kuramoto, Eriko. "Two types of thalamocortical projections from the motor thalamic nuclei of the rat: a single neuron tracing study using viral vectors." Kyoto University, 2009. http://hdl.handle.net/2433/124305.

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Nakamura, Hisashi. "Different cortical projections from three subdivisions of the rat lateral posterior thalamic nucleus: a single neuron tracing study with viral vectors." Kyoto University, 2016. http://hdl.handle.net/2433/216156.

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Final publication is available at http://dx.doi.org/10.1111/ejn.12882
Kyoto University (京都大学)
0048
新制・論文博士
博士(医学)
乙第13040号
論医博第2115号
新制||医||1017(附属図書館)
33032
京都大学大学院医学研究科医学専攻
(主査)教授 渡邉 大, 教授 影山 龍一郎, 教授 髙橋 良輔
学位規則第4条第2項該当
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SERRA, LINDA. "Role of the Sox2 and COUP-TF1 transcription factors in the development of the visual system by conditional knock-out in mouse." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/261939.

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Il fattore di trascrizione Sox2 è espresso nel sistema nervoso dall’inizio del suo sviluppo dove è richiesto per il mantenimento delle cellule staminali. Nell'uomo, le mutazioni eterozigoti di Sox2 sono collegate a vari difetti del sistema nervoso centrale, inclusi i difetti visivi. Il sistema visivo è composto dall'occhio, dal nucleo talamico genicolato dorsolaterale (dLGN) e dalla corteccia visiva, che sono altamente interconnessi. L'occhio, infatti, invia le afferenze retiniche ad uno specifico nucleo talamico dorsale, il dLGN, i cui neuroni a loro volta proiettano verso l'area corticale visiva. La corteccia visiva elabora input visivi e proietta al dLGN in un circuito complesso. Numerosi geni sono importanti per il corretto sviluppo del sistema visivo e Sox2 è uno di questi. Sox2 è espresso in tutti e tre i componenti del sistema visivo nel topo; mentre il suo ruolo nello sviluppo della retina è ben descritto si sa poco riguardo al suo ruolo nel talamo. Per studiare l’importanza di Sox2 nel talamo per il corretto sviluppo dell'asse visivo, abbiamo generato un knockout condizionale talamico di Sox2 nei neuroni post-mitotici. Abbiamo osservato che la perdita di Sox2 nel dLGN porta a una forte riduzione delle dimensioni del dLGN, all’alterazione delle proiezioni neuronali retino-talamiche, talamo-corticali e cortico-talamiche e, di conseguenza, a una difettiva definizione dell'area visiva corticale. Abbiamo scoperto che nei mutanti talamici di Sox2 il gene Efna5, importante nel guidare gli assoni retinici verso il dLGN, e i geni SERT e vMAT2 che codificano per trasportatori di serotonina, importanti per la corretta formazione di proiezioni talamo-corticali, sono fortemente sottoregolati nel dLGN mutante. Per identificare tutti i potenziali geni che potrebbero mediare la funzione di Sox2 nel talamo, abbiamo eseguito il sequenziamento dell'RNA (RNA-seq) su dLGN di controlli e mutanti di Sox2. Abbiamo scoperto che i geni deregolati sono arricchiti in geni che codificano per molecole importanti per la guida degli assoni e per molecole coinvolte nella neurotrasmissione e nelle sinapsi. È interessante notare che l'ablazione talamica di un altro fattore di trascrizione, COUP-TF1, porta a difetti del sistema visivo simili a quelli descritti per Sox2. Inoltre, le mutazioni eterozigoti nel gene COUP-TF1 nell'uomo portano all'atrofia ottica e a disabilità intellettive. Abbiamo scoperto che Sox2 e COUP-TF1 sono co-espressi negli stessi neuroni post-mitotici del dLGN. Sorprendentemente, l'espressione di COUP-TF1 non varia nei mutanti talamici di Sox2, facendo nascere la possibilità che Sox2 e COUP-TF abbiano target comuni nel talamo. Pertanto, abbiamo esaminato l'espressione, nei mutanti COUP-TF1, di geni sottoregolati nei mutanti talamici di Sox2 e sorprendentemente abbiamo scoperto che sembrano sovraregolati, suggerendo che i due fattori di trascrizione potrebbero agire sugli stessi geni ma in modo opposto. Per capire meglio se i due fattori di trascrizione regolano geni comuni, stiamo eseguendo l'analisi dell'espressione genica mediante RNA-seq anche sui mutanti talamici COUP-TF1. Inoltre, stiamo generando topi doppi mutanti per Sox2 e COUP-TF1 per scoprire come questi geni regolano espressione genica; è plausibile che regolino geni comuni per bilanciare la loro espressione nei neuroni talamici.
The transcription factor Sox2 is expressed in the nervous system from the beginning of its development where it is required for stem cells maintenance. In humans, Sox2 heterozygous mutations are linked to various central nervous system defects, including visual defects. The visual system is composed of the eye, the dorsolateral geniculate thalamic nucleus (dLGN) and the visual cortex, which are highly interconnected. The eye, in fact, sends retinal afferent to a specific dorsal thalamic nucleus, the dLGN, whose neurons in turn project to the visual cortical area. The visual cortex elaborates visual inputs and projects back to the dLGN in a complex circuit. Several genes are important for the correct development of the visual system and Sox2 is one of them. Sox2 is expressed in all the three components of the visual system in mouse; while its role in the development of the retina is well characterized little is known about its role in the thalamus. To investigate Sox2 requirement in the thalamus for the correct establishment of the visual axis, we generated a thalamic Sox2 conditional knock-out in post-mitotic neurons. We observed that Sox2 loss in the dLGN leads to a strong reduction in size of the dLGN, aberrant retino-geniculate, thalamo-cortical and cortico-thalamic neural projections and, consequently, to a defective patterning of the cortical visual area. We found that in Sox2 thalamic mutants the Efna5 gene, important in guiding retinal axons towards the dLGN, and the serotonin transporters encoding genes SERT and vMAT2, involved in the establishment of thalamo-cortical projections, are strongly downregulated in the mutant dLGN. To identify all the potential genes that could mediate Sox2 function in the thalamus, we performed RNA sequencing (RNA-seq) on control and Sox2 mutant dLGNs. We noticed that misregulated genes are enriched in genes encoding axon guidance molecules and molecules involved in neurotransmission and synapses. Interestingly, thalamic ablation of another transcription factor, COUP-TF1, leads to defects of the visual system similar to the ones described for Sox2. In addition, heterozygous mutations in the COUP-TF1 gene in human lead to optic atrophy and intellectual disabilities. Interestingly, we found that Sox2 and COUP-TF1 are co-expressed in the same post-mitotic neurons of the dLGN. Surprisingly, COUP-TF1 expression does not vary in Sox2 thalamic mutants, arising the possibility that Sox2 and COUP-TF have common target in the thalamus. Therefore, we looked at the expression, in COUP-TF1 mutants, of genes downregulated in Sox2 thalamic mutants and we surprisingly found that they appear upregulated, suggesting that the two transcription factors could act on the same genes but in an opposite way. To better understand if the two transcription factors regulate common genes, we are performing gene expression analyses by RNA-seq also on COUP-TF1 thalamic mutants, with the aim to identify an overlap with Sox2 regulated genes. Moreover, we are generating Sox2 and COUP-TF1 double mutant mice to unveil how these genes regulate gene expression; it is plausible that they regulate common genes to balance their expression in thalamic neurons.
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Dacre, Joshua Rupert Heaton. "Thalamic control of motor behaviour." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29530.

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The primary motor cortex (M1) is a key brain area for the generation and control of motor behaviour. Output from M1 can be driven in part by long-range inputs from a collection of thalamic nuclei termed the motor thalamus (MTh), but how MTh input shapes activity in M1 and forelimb motor behaviour remains largely unresolved. To address this issue, we first defined the 3D anatomical coordinates of mouse forelimb motor thalamus (MThFL) by employing conventional retrograde and virus-based tracing methods targeted to the forelimb region of M1 (M1FL). These complimentary approaches defined MThFL as a ~0.8 mm wide cluster of neurons with anatomical coordinates 1.1 mm caudal, 0.9 mm lateral to bregma and 3.2 mm below the pial surface. Thus, MThFL incorporates defined areas of the ventrolateral, ventral anterior and anteromedial thalamic nuclei. To investigate the importance of M1FL and MThFL during skilled motor behaviour, we developed and optimised a quantitative behavioural paradigm in which head-restrained mice execute forelimb lever pushes in response to an auditory cue to receive a water reward. Forelimb movement trajectories were mapped using high-speed digital imaging and multi-point kinematic analysis. We inactivated both M1FL and MThFL of mice performing this motor behaviour using a pharmacological strategy, which in both cases resulted in a significant reduction in task performance. Inactivating M1FL significantly affected forelimb coordination and dexterity, resulting in erratic motion and posture. In contrast, mice with MThFL inactivated displayed a reduction in total motor output, although correct posture was maintained. We performed extracellular recordings in MThFL of expert-level mice, demonstrating that motor thalamic output during execution of task was dominated by a robust response to the onset of the auditory cue. Cue-evoked responses were also observed in motor thalamic neurons of naive mice. We have developed a novel solution to the stability problem encountered when performing whole-cell patch-clamp recordings from the motor cortex of head-restrained mice performing forelimb motor behaviour, and present preliminary recordings maintained through the execution of forelimb behaviour.
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Wu, Huiying. "Modeling thalamic activity and neural bursting." Thesis, The University of Sydney, 2009. https://hdl.handle.net/2123/28236.

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The human brain is the most complicated organ in the central nervous system. Although an enormous number of studies have been conducted to attempt to understand the brain and its activity, most of the functions of the brain and the mechanisms of its large scale activities are still not clear. In this thesis, a mean— field approach is used to study the thalamus, which is the sensory gateway of the brain and a key component of the thalamocortical system, and also neural bursting, which is an important type of neural activity. Chapter 1 provides an overview of the thesis, as well as brief a background on the thalamus and neural bursting, including their neurophysiology and re— lated modeling studies. In Chapter 2, a physiologically based mean-field model is used to study the role of the thalamus and its substructures in genesis of electroencephalograms (EEGS), particularly spindle oscillations. An existing corticothalamic mean-field model is modified to represent an isolated thala— mus. In addition to the populations of neurons previously considered in the context of this model, an additional population of thalamic neurons, the 10— cal interneurons (Lls), is added. Interconnections between thalamic nuclei are then studied and substructures of the thalamus are analyzed. It is found that the isolated reticular (RE) nucleus cannot generate spindle oscillations on its own, but is nonetheless essential to their genesis. This finding is consistent with experiments in vitro. The MS can generate spindle oscillations in con— junction with the other relay cells and are shown to have similar effects to the RE nucleus, except that they are purely inhibitory, whereas the latter nucleus has both direct inhibitory and indirect excitatory effects on the relay cells. Chapters 3 and 4 study neural bursting based on a method of obtaining a neural rate equation from a conductance—based model, which is in Appendix A. In Chapter 3, to study neural bursting, or bursting neurons, a conductance-based model is incorporated into a mean—field model to obtain a prototype mean—field bursting model. Properties of the model are explored via study of its frequency responses under various inputs, which include sinusoidal signals and white noise perturbations. The main finding of this study is that neurons with various initial states are capable of phase-locked or intermittent firing, depending on their baseline voltage. Moreover, depending on this voltage, the bursting frequency of the neurons either slaves to the original unperturbed bursting frequency or approaches a steady value as the external driving fre— quency increases. White noise peturbations produce a bursting frequency similar to the one seen in the unperturbed case, which indicates that the dy— namics of the system are robust and a more general external stimulus only alters the firing pattern slightly. Chapter 4 contains a study of a simplified version of the thalamocortical— corticothalamic (TC-CT) loop structure, whose activity is modeled by the mean—field bursting model developed in Chapter 3. To explore the effects of the feedback loop, the frequency response of the system is studied as a function of the loop coupling strength and time delay. At a fixed time delay, variation of coupling strength causes the dominant response frequency to switch from the unperturbed bursting frequency and its harmonics to the response frequency of the loop and its harmonics. Furthermore, there are many fine structures in spectral density plots that are shown to be affected by dendritic parameters. However, the full mechanism behind these fine structures is not clear. Depending on the magnitude of the coupling strength, frequency response patterns are different as the time delay is varied. Although the response frequency al— ternates between the frequencies induced by the bursting neurons and those induced by the loop structure for both small and large coupling strengths, the patterns of alternation are different. None of these frequency responses, except the bursting dynamics at very weak coupling strengths, are seen in Chapter 3. This indicates that the loop structure produces rich and complicated dynamics. Chapter 5 briefly reviews the main outcomes of this thesis and outlining possible future investigations. Overall, the present work provides improved understanding of brain rhythm genesis in the thalamus and a prototype mean— field bursting model that can be used in future studies of the thalamocortical system.
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Lee, Stephanie G. "Medial lemniscal evoked responses in thalamic ethmoid neurons." Thesis, University of British Columbia, 2006. http://hdl.handle.net/2429/31658.

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This thesis describes electrophysiological and pharmacological properties of neurons in the ethmoid nucleus of the rat thalamus. According to the atlas by Paxinos and Watson, the ethmoid nucleus is located dorsal to the medial lemniscus, the major somatosensory input to the thalamus. The ethmoid also lies ventral to the parafascicular nucleus, caudal to the ventrobasal thalamus and rostral to the scaphoid nucleus. The ethmoid is considered a higher order nucleus, which implies that it serves as a link in corticothalamo- cortical pathways that process sensory information. The literature on this nucleus is scarce and this thesis represents the first known attempt to study these neurons. Hence, a major objective of this thesis was to determine the passive and active properties of ethmoid neurons. Recent evidence from this laboratory has shown that stimulation of the medial lemniscus produced glycinergic and GABAergic inhibition in ventrobasal neurons. This inhibition was not sensitive to ionotropic glutamate receptor antagonism by kynurenate. A prediction from these studies was that the ethmoid was an intermediate nucleus in a circuit between the medial lemniscus and neurons of the ventrobasal thalamus. Thus, a key aim of this thesis was to examine the possible involvement of ethmoid neurons in this novel circuit. This thesis provides evidence that ethmoid neurons have passive and active properties similar to other neurons of the dorsal thalamus. Ethmoid neurons have a mean resting membrane potential of ~ -53 mV, a mean input resistance of ~670 MΩ and a mean membrane time constant of ~64 ms. Ethmoid neurons also have the ability to generate spikes in the tonic and burst firing modes. The active properties were sensitive to blockade by internal application of QX-314, a quaternary blocker of Na⁺ channels and by extracellular application of Ni2 ⁺ , a Ca2 ⁺ channel antagonist. These observations are consistent with Na⁺ dependent action potentials when a neuron is in the tonic firing mode and low threshold Ca2 ⁺ spikes when in the burst firing mode. We showed that stimulation of the medial lemniscus evoked depolarizations in all recorded ethmoid neurons. We categorized the depolarization responses into two groups. Group I were monophasic depolarizations and group II were biphasic depolarizations. The medial lemniscal evoked depolarization of ethmoid neurons persisted in the presence of kynurenate. These observations are consistent with the participation of ethmoid neurons in a circuit that results in glycinergic inhibition in ventrobasal neurons. This thesis marks the first known attempt to study neurons of the ethmoid nucleus. The observations provide evidence for functional similarities of ethmoid neurons to other thalamic neurons, as well as evidence for novel inputs activated by stimulation of the medial lemniscus.
Medicine, Faculty of
Anesthesiology, Pharmacology and Therapeutics, Department of
Graduate
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7

Pudenz, Christiane [Verfasser]. "Thalamo-cortical circuits for the processing of tactile information : thalamic inputs onto excitatory neurons in layer IV of the mouse barrel cortex." Freiburg : Universität, 2010. http://d-nb.info/1115490478/34.

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Shiraishi, Atsushi. "Generation of thalamic neurons from mouse embryonic stem cells." Kyoto University, 2018. http://hdl.handle.net/2433/230993.

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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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Ruffo, Mark. "The role of the corticothalamic projection in the primate motor thalamus /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/10626.

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Books on the topic "Thalamic neuron"

1

1939-, Jones Edward G., and Llinás R. 1934-, eds. Thalamic oscillations and signaling. New York: Wiley, 1989.

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Steriade, Mircea. Thalamic oscillations and signaling. New York: Wiley, 1990.

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Diego, Minciacchi, ed. Thalamic networks for relay and modulation. Oxford [England]: Pergamon Press, 1993.

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MacMillan, Meeka. Responses of human thalamic and subthalamic nucleus neurons during sequential movements. Ottawa: National Library of Canada, 2002.

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Patra, Sanjay. Response properties of human thalamic neurons to high frequency micro-stimulation. Ottawa: National Library of Canada, 2001.

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Marina, Bentivoglio, and Spreafico Roberto, eds. Cellular thalamic mechanisms: Based on contributions to the symposium held in Verona, Italy, 22-25 August 1987. Amsterdam: Excerpta Medica, 1988.

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Zoltán, Molnár. Development of thalamocortical connections. Berlin: Springer, 1998.

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

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Saalmann, Yuri B., and Sabine Kastner. Neural Mechanisms of Spatial Attention in the Visual Thalamus. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.013.

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Neural mechanisms of selective attention route behaviourally relevant information through brain networks for detailed processing. These attention mechanisms are classically viewed as being solely implemented in the cortex, relegating the thalamus to a passive relay of sensory information. However, this passive view of the thalamus is being revised in light of recent studies supporting an important role for the thalamus in selective attention. Evidence suggests that the first-order thalamic nucleus, the lateral geniculate nucleus, regulates the visual information transmitted from the retina to visual cortex, while the higher-order thalamic nucleus, the pulvinar, regulates information transmission between visual cortical areas, according to attentional demands. This chapter discusses how modulation of thalamic responses, switching the response mode of thalamic neurons, and changes in neural synchrony across thalamo-cortical networks contribute to selective attention.
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Montgomery, Erwin B. Discrete Neural Oscillators. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190259600.003.0017.

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The therapeutic mechanisms of action of DBS likely involve neural and neuronal oscillators. “Neuronal oscillators” describes periodic fluctuations of electrical potentials across the neuronal membrane, particularly in the soma, which is reflected in an action-potential-initiating segment. “Neural oscillators” describes closed loop (feedback) multi-neuronal polysynaptic circuits, on account of the propagations of action potentials through the circuit. Neural oscillators are the focus of this chapter. The features, properties and dyanmics introduced in Chapter 16 – Basic Oscillators are extended from continuous harmonic oscillators to discrete neural oscillators. While discrete oscillators received scant attention to date, systems of discrete oscillators have much richer set of dynamics that could provide better understanding of the pathophysiology and physiology of neural systems, such as the basal ganglia-thalamic-cortical system as well as greater insights into the therapeutic mechanisms of action underlying DBS.
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Book chapters on the topic "Thalamic neuron"

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Davis, Karen D., and Jonathan O. Dostrovsky. "Human Thalamic Nociceptive Neurons." In Encyclopedia of Pain, 1503–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28753-4_1791.

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Harrison, David W. "Thalamic and Hypothalamic Syndromes." In Brain Asymmetry and Neural Systems, 169–80. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13069-9_10.

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Destexhe, A., and A. Babloyantz. "Cortical Coherent Activity Induced by Thalamic Oscillations." In Neural Network Dynamics, 234–49. London: Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_17.

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Cisternas, Jaime E., Thomas M. Wasylenko, and Ioannis G. Kevrekidis. "Lurching waves in thalamic neuronal networks." In Localized States in Physics: Solitons and Patterns, 265–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16549-8_13.

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Guo, Daqing, Mingming Chen, Yang Xia, and Dezhong Yao. "Self-connection of Thalamic Reticular Nucleus Modulating Absence Seizures." In Neural Information Processing, 613–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70093-9_65.

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Kataoka, Kazuo, Kazuo Yamada, Tatsuya Tokuno, Sumio Kondo, Toshiharu Asai, Siko Chichibu, Mamoru Taneda, Toru Hayakawa, Ryotaro Kuroda, and Masahiko Ioku. "Neurofunctional Changes in Thalamic Neurons After Cortical Ablation in Adult Rats: Effect of Basic Fibroblast Growth Factor upon Thalamic Neurons." In Recent Advances in Neurotraumatology, 227–31. Tokyo: Springer Japan, 1993. http://dx.doi.org/10.1007/978-4-431-68231-8_51.

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Wang, Xiao-Jing, John Rinzel, and Michael A. Rogawski. "Low Threshold Spikes and Rhythmic Oscillations in Thalamic Neurons." In Analysis and Modeling of Neural Systems, 85–91. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-4010-6_8.

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Ströhmann, B. "Signaltransmission in auditorischen Neuronen im Thalamus." In Sitzungsbericht, 121. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85188-9_100.

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Miller, Michael W., and Richard T. Robertson. "Development of Cingulate Cortex: Proteins, Neurons, and afferents." In Neurobiology of Cingulate Cortex and Limbic Thalamus, 151–80. Boston, MA: Birkhäuser Boston, 1993. http://dx.doi.org/10.1007/978-1-4899-6704-6_5.

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Ohye, Chihiro, Sumito Sato, and Tohru Shibazaki. "Activity of Thalamic Ventralis Oralis Neurons in Rigid-Type Parkinson’s Disease." In Advances in Behavioral Biology, 563–71. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0340-2_43.

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Conference papers on the topic "Thalamic neuron"

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Su, Fei, Min Chen, Hong Wang, and Linlu Zu. "FPGA Implementation of the Single Thalamic Neuron Model." In 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2019. http://dx.doi.org/10.1109/cisp-bmei48845.2019.8965996.

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Yin, Huibing, Charles L. Cox, Prashant G. Mehta, and Uday V. Shanbhag. "Bifurcation analysis of a thalamic relay neuron model." In 2009 American Control Conference. IEEE, 2009. http://dx.doi.org/10.1109/acc.2009.5160443.

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Kazemi, Amirhosein, Arash Ahmadi, and Shaghayegh Gomar. "A digital synthesis of hindmarsh-rose neuron: A thalamic neuron model of the brain." In 2014 22nd Iranian Conference on Electrical Engineering (ICEE). IEEE, 2014. http://dx.doi.org/10.1109/iraniancee.2014.6999539.

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Panetsos, Fivos, Elena Diaz-de Cerio, Abel Sanchez-Jimenez, and Celia Herrera-Rincon. "Thalamic visual neuroprostheses: Comparison of visual percepts generated by natural stimulation of the eye and electrical stimulation of the thalamus." In 2009 4th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2009. http://dx.doi.org/10.1109/ner.2009.5109233.

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Jandel, Magnus. "Thalamic bursts mediate pattern recognition." In 2009 4th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2009. http://dx.doi.org/10.1109/ner.2009.5109358.

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Rufino, Adonai Alencar, Beatriz Girão Portela, Alan Alves de Lima Cidrão, Deborah Moreira Rangel, and Vitor Araújo Marinho. "Unraveling the mysteries of the midbrain – A case report." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.623.

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Context: The rostral midbrain and thalamomesencephalic junction are the supranuclear premotor control of vertical eye movements, and is supplied by the posterior thalamo-subthalamic paramedian artery originated from P1 segment of posterior cerebral artery. Case report: A 51-year-old man presented with sudden speech difficulties, dizziness and dyplopia, associated with moderate intensity headache. Neuroophthalmological examination revealed incomplete ptosis of the right eye, with mydriatic pupil, poorly reactive to light. No eye movements were present on attempted upward gaze. On attempted downward gaze, depression of the left eye was observed but with absent saccades. Lateral gaze to the right was intact, while attempted gaze deviation to the left revealed adduction deficit of the right eye with incomplete abduction of the left eye without nystagmus. Convergence was absent. He exhibited left hemiataxia with left hypoestesia. MRI showed acute right paramedian thalamic and mesencephalic stroke. Conclusions: About the vertical one and a half syndrome, it was suggested damage in the pathway to contralateral downgaze neurons before its decussation with the unilateral interstitial nucleus of Cajal. As for the contralateral lateral rectus palsy we infer that this patient’s abduction deficit was due to pseudo-abducens palsy, with several mechanisms that could explain abduction deficits associated with upgaze palsy. Claude’s syndrome is usually explained by a lesion of oculomotor nerve fascicle and the superior cerebellar peduncle, affecting cerebellothalamic connections.
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Krebs, Hermano I., Neville Hogan, Bruce Volpe, Mindy Aisen, Lisa Edelstein, and Christa Diels. "Robot-Aided Neuro-Rehabilitation in Stroke: Neuro-Recovery for Thalamic Lesion." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0081.

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Abstract We are applying robotics and information technology to assist, enhance, and quantify neuro-rehabilitation. Our goal is a new class of interactive, user-affectionate clinical devices designed not only for evaluating patients, but also for delivering meaningful therapy via engaging “video games.” Notably, the novel robot MIT-MANUS has been designed and programmed for clinical neurological applications, and has undergone extensive clinical trials for more than four years at Burke Rehabilitation Hospital – White Plains, NY. This paper will review the main result of the pilot clinical trial of the 20 patients focusing on the consequences of thalamic lesions.
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Pendyam, Sandeep, Dongbeom Kim, Gregory J. Quirk, and Satish S. Nair. "Acquisition of Fear and Extinction in Lateral Amygdala: A Modeling Study." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4218.

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The lateral nucleus of amygdala (LA) is known to be a critical storage site for conditioned fear memory. Synaptic plasticity at auditory inputs to the dorsal LA (LAd) is critical for the formation and storage of auditory fear memories. Recent evidence suggests that two different cell populations (transient- and long-term plastic cells) are present in LAd and are responsible for fear learning. However, the mechanisms involved in the formation and storage of fear are not well understood. As an extension of previous work, a biologically realistic computational model of the LAd circuitry is developed to investigate these mechanisms. The network model consists of 52 LA pyramidal neurons and 13 interneurons. Auditory and somatosensory information reaches LA from both thalamic and cortical inputs. The model replicated the tone responses observed in the two LAd cell populations during conditioning and extinction. The model provides insights into the role of thalamic and cortical inputs in fear memory formation and storage.
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Cruttenden, Corey, Mahdi Ahmadi, Xiao-Hong Zhu, Wei Chen, and Rajesh Rajamani. "An MRI Compatible Brain Probe for Signal Recording and Deep Brain Stimulation." In 2018 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dmd2018-6951.

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Electrical stimulation of neural tissue is a promising therapy for a variety of neurological diseases. For example, electrical stimulation of deep thalamic nuclei has been used extensively to treat symptoms of Parkinson’s disease, and there is growing interest in treating other conditions including epilepsy and depression with similar techniques. However, the mechanisms of electrical brain stimulation for disease therapy are not fully understood [1].
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Millard, Daniel C., Qi Wang, and Garrett B. Stanley. "Nonlinear system identification of the thalamocortical circuit in response to thalamic microstimulation." In 5th International IEEE/EMBS Conference on Neural Engineering (NER 2011). IEEE, 2011. http://dx.doi.org/10.1109/ner.2011.5910475.

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Reports on the topic "Thalamic neuron"

1

Morrow, Thomas J. Modulation of Thalamic Somatosensory Neurons by Arousal and Attention. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada200073.

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