Academic literature on the topic 'Layer 5 pyramidal neuron'
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Journal articles on the topic "Layer 5 pyramidal neuron"
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Nicoll, Andrew, and Colin Blakemore. "Patterns of Local Connectivity in the Neocortex." Neural Computation 5, no. 5 (September 1993): 665–80. http://dx.doi.org/10.1162/neco.1993.5.5.665.
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Dual intracellular recording of nearby pairs of pyramidal cells in slices of rat visual cortex has shown that there are significant differences in functional connectivity between the superficial and deep layers (Mason et al. 1991; Nicoll and Blakemore 1993). For pairs of cells no farther than 300 μm apart, synaptic connections between layer 2/3 pyramidal neurons were individually weaker (median peak amplitude, A, of single-fiber excitatory postsynaptic potentials, EPSPs, = 0.4 mV) but more frequent (connection probability, p = 0.087) than those between layer 5 pyramidal neurons (mean A = 0.8 mV, p < 0.015). Taken in combination with plausible estimates of the density of pyramidal cells, the total numbers of synapses on them and the number of synapses formed on their intracortical axons, the present analysis of the above data suggests that roughly 70% of the excitatory synapses on any layer 2/3 pyramid, but fewer than 1% of those on a layer 5 pyramidal neuron, are derived from neighboring pyramidal neurons in its near vicinity. Even assuming very extreme values for some parameters, chosen to erode this difference, the calculated proportion of "local synapses" for layer 5 pyramids was always markedly lower than for layer 2/3 pyramidal neurons. These results imply that local excitatory connections are much more likely to provide significant "intracortical amplification" of afferent signals in layer 2/3 than in layer 5 of rat visual cortex.
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Urrego, Diana, Julieta Troncoso, and Alejandro Múnera. "Layer 5 Pyramidal Neurons’ Dendritic Remodeling and Increased Microglial Density in Primary Motor Cortex in a Murine Model of Facial Paralysis." BioMed Research International 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/482023.
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This work was aimed at characterizing structural changes in primary motor cortex layer 5 pyramidal neurons and their relationship with microglial density induced by facial nerve lesion using a murine facial paralysis model. Adult transgenic mice, expressing green fluorescent protein in microglia and yellow fluorescent protein in projecting neurons, were submitted to either unilateral section of the facial nerve or sham surgery. Injured animals were sacrificed either 1 or 3weeks after surgery. Two-photon excitation microscopy was then used for evaluating both layer 5 pyramidal neurons and microglia in vibrissal primary motor cortex (vM1). It was found that facial nerve lesion induced long-lasting changes in the dendritic morphology of vM1 layer 5 pyramidal neurons and in their surrounding microglia. Dendritic arborization of the pyramidal cells underwent overall shrinkage. Apical dendrites suffered transient shortening while basal dendrites displayed sustained shortening. Moreover, dendrites suffered transient spine pruning. Significantly higher microglial cell density was found surrounding vM1 layer 5 pyramidal neurons after facial nerve lesion with morphological bias towards the activated phenotype. These results suggest that facial nerve lesions elicit active dendrite remodeling due to pyramidal neuron and microglia interaction, which could be the pathophysiological underpinning of some neuropathic motor sequelae in humans.
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Callaway, Edward M., and Anne K. Wiser. "Contributions of individual layer 2–5 spiny neurons to local circuits in macaque primary visual cortex." Visual Neuroscience 13, no. 5 (September 1996): 907–22. http://dx.doi.org/10.1017/s0952523800009159.
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AbstractWe studied excitatory local circuits in the macaque primary visual cortex (V1) to investigate their relationships to the magnocellular (M) and parvocellular (P) streams. Sixty-two intracellularly labeled spiny neurons in layers 2–5 were analyzed. We made detailed observations of the laminar and columnar specificity of axonal arbors and noted correlations with dendritic arbors. We find evidence for considerable mixing of M and P streams by the local circuitry in V1. Such mixing is provided by neurons in the primary geniculate recipient layer 4C, as well as by neurons in both the supragranular and infragranular layers. We were also interested in possible differences in the axonal projections of neurons with different dendritic morphologies. We found that layer 4B spiny stellate and pyramidal neurons have similar axonal arbors. However, we identified two types of layer 5 pyramidal neuron. The majority have a conventional pyramidal dendritic morphology, a dense axonal arbor in layers 2–4B, and do not project to the white matter. Layer 5 projection neurons have an unusual “backbranching” dendritic morphology (apical dendritic branches arc downward rather than upward) and weak or no axonal arborization in layers 2–4B, but have long horizontal axonal projections in layer 5B. We find no strong projection from layer 5 pyramidal neurons to layer 6. In macaque V1 there appears to be no single source of strong local input to layer 6; only a minority of cells in layers 2–5 have axonal branches in layer 6 and these are sparse. Our results suggest that local circuits in V1 mediate interactions between M and P input that are complex and not easily incorporated into a simple framework.
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Seo, Hyeon, Natalie Schaworonkow, Sung Chan Jun, and Jochen Triesch. "A multi-scale computational model of the effects of TMS on motor cortex." F1000Research 5 (February 17, 2017): 1945. http://dx.doi.org/10.12688/f1000research.9277.2.
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The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different pyramidal cell types in motor cortex due to TMS. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to calculate activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also shows differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of cortical pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical pyramidal cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.
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Seo, Hyeon, Natalie Schaworonkow, Sung Chan Jun, and Jochen Triesch. "A multi-scale computational model of the effects of TMS on motor cortex." F1000Research 5 (May 12, 2017): 1945. http://dx.doi.org/10.12688/f1000research.9277.3.
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The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different pyramidal cell types in motor cortex due to TMS. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to calculate activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also shows differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of cortical pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical pyramidal cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.
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Seo, Hyeon, Natalie Schaworonkow, Sung Chan Jun, and Jochen Triesch. "A multi-scale computational model of the effects of TMS on motor cortex." F1000Research 5 (August 10, 2016): 1945. http://dx.doi.org/10.12688/f1000research.9277.1.
Full textAbstract:
The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different cell types in motor cortex due to transcranial magnetic stimulation. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict detailed neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to predict activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also predicts differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of corctial pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.
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Zhou, F. M., and J. J. Hablitz. "Layer I neurons of rat neocortex. I. Action potential and repetitive firing properties." Journal of Neurophysiology 76, no. 2 (August 1, 1996): 651–67. http://dx.doi.org/10.1152/jn.1996.76.2.651.
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1. Whole cell patch-clamp techniques, combined with direct visualization of neurons, were used to study action potential (AP) and repetitive firing properties of layer I neurons in slices of rat neocortex. 2. Layer I neurons had resting membrane potentials (RMP) of -59.8 +/- 4.7 mV (mean +/- SD) and input resistances (RN) of 592 +/- 284 M Omega. Layer II/III pyramidal neurons had RMPs and RNs of -61.5 +/- 5.6 mV and 320 +/- 113 M omega, respectively. A double exponential function was needed to describe the charging curves of both neuron types. In layer I neurons, tau(0) was 45 +/- 22 ms and tau(1) was 5 +/- 3.3 ms whereas in layer II/III pyramidal neurons, tau(0) was 41 +/- 11 ms and tau(1) was 3 +/- 2.6 ms. Estimates of specific membrane resistance (Rm) for layer I and layer II/III cells were 45 +/- 22 and 41 +/- 11 k omega cm2, respectively (Cm was assumed to be 1 microF/cm2). 3. AP threshold was -41 +/- 2 mV in layer I neurons. Spike amplitudes, measured from threshold to peak, were 90.6 +/- 7.7 mV. AP durations, measured both at the base and half maximal amplitude, were 2.5 +/- 0.4 and 1.1 +/- 0.2 ms, respectively. AP 10-90% rise and repolarization times were 0.6 +/- 0.1 and 1.1 +/- 0.2 ms, respectively. In layer II/III pyramidal neurons, AP threshold was -41 +/- 2.5 mV and spike amplitude was 97 +/- 9.7 mV. AP duration at base and half maximal amplitude was 5.4 +/- 1.1 ms and 1.8 +/- 0.2 ms, respectively. AP 10-90% rise and decay times were 0.6 +/- 0.1 ms and 2.8 +/- 0.6 ms, respectively. 4. Layer I neurons were fast spiking cells that showed little frequency adaptation, a large fast afterhyperpolarization (fAHP), and no slow afterhyperpolarization (sAHP). Some cells had a medium afterhyperpolarization (mAHP) and a slow afterdepolarization (sADP). All pyramidal cells in layer II/III and "atypical" pyramidal neurons in upper layer II showed regular spiking behavior, prominent frequency adaptation, and marked sAHPs. 5. In both layer I neurons and layer II/III pyramidal neurons, changes in membrane potential did not greatly alter AP properties. The duration of APs evoked from -50 to -60 mV was only slightly longer, from -80 to -90 mV. The latency to first spike also was not solely dependent on membrane potential. 6. During repetitive firing, APs broadened in both layer I neurons and layer II/III pyramidal neurons. This was most prominent in pyramidal cells. Broadening was dependent on spike frequency and appeared to result from partial inactivation of both outward potassium and inward sodium currents. 7. In layer I neurons, removing Ca2+ from the bathing solution slightly prolonged spike duration and modestly increased AP firing frequency. These results indicate minimal involvement of Ca2+-dependent K+ currents in AP repolarization. fAHPs were reduced whereas sADPs were abolished. In layer II/III pyramidal neurons, removing Ca2+ reduced or blocked mAHPs and sAHPs and decreased or abolished frequency adaptation. 8. Low concentrations (50 microM) of 4-aminopyridine (4-AP) prolonged APs and induced burst-like firing in layer I neurons. In the presence of 4-AP, the spiking behavior of layer I neurons resembled that of regular spiking layer II/III pyramidal cells. At high concentrations (4 mM), 4-AP could induce a delayed depolarization (DD) after each spike in layer I neurons and in a minority of pyramidal neurons. 9. All layer I neurons had a prominent fAHP that was absent or very small in layer II/III pyramidal neurons. fAHP amplitude was inversely related to AP duration. The reduction of fAHPs by 4-AP or during repetitive firing was accompanied by AP prolongation, suggesting that the current underlying fAHP played an essential role in AP repolarization. The fAHP of layer I neurons could be effectively blocked by 4-AP but only slightly reduced by removing Ca2+ from bathing solution, indicating that the fAHP was mediated primarily by a voltage-dependent transient outward current.(ABSTRACT TRUNCATED)
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Rheims, Sylvain, Marat Minlebaev, Anton Ivanov, Alfonso Represa, Rustem Khazipov, Gregory L. Holmes, Yehezkel Ben-Ari, and Yuri Zilberter. "Excitatory GABA in Rodent Developing Neocortex In Vitro." Journal of Neurophysiology 100, no. 2 (August 2008): 609–19. http://dx.doi.org/10.1152/jn.90402.2008.
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GABA depolarizes immature cortical neurons. However, whether GABA excites immature neocortical neurons and drives network oscillations as in other brain structures remains controversial. Excitatory actions of GABA depend on three fundamental parameters: the resting membrane potential ( Em), reversal potential of GABA ( EGABA), and threshold of action potential generation ( Vthr). We have shown recently that conventional invasive recording techniques provide an erroneous estimation of these parameters in immature neurons. In this study, we used noninvasive single N-methyl-d-aspartate and GABA channel recordings in rodent brain slices to measure both Em and EGABA in the same neuron. We show that GABA strongly depolarizes pyramidal neurons and interneurons in both deep and superficial layers of the immature neocortex (P2–P10). However, GABA generates action potentials in layer 5/6 (L5/6) but not L2/3 pyramidal cells, since L5/6 pyramidal cells have more depolarized resting potentials and more hyperpolarized Vthr. The excitatory GABA transiently drives oscillations generated by L5/6 pyramidal cells and interneurons during development (P5–P12). The NKCC1 co-transporter antagonist bumetanide strongly reduces [Cl−]i, GABA-induced depolarization, and network oscillations, confirming the importance of GABA signaling. Thus a strong GABA excitatory drive coupled with high intrinsic excitability of L5/6 pyramidal neurons and interneurons provide a powerful mechanism of synapse-driven oscillatory activity in the rodent neocortex in vitro. In the companion paper, we show that the excitatory GABA drives layer-specific seizures in the immature neocortex.
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Markram, H. "A network of tufted layer 5 pyramidal neurons." Cerebral Cortex 7, no. 6 (September 1, 1997): 523–33. http://dx.doi.org/10.1093/cercor/7.6.523.
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Rhodes, Paul A., and Charles M. Gray. "Simulations of Intrinsically Bursting Neocortical Pyramidal Neurons." Neural Computation 6, no. 6 (November 1994): 1086–110. http://dx.doi.org/10.1162/neco.1994.6.6.1086.
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Neocortical layer 5 intrinsically bursting (IB) pyramidal neurons were simulated using compartment model methods. Morphological data as well as target neurophysiological responses were taken from a series of published studies on the same set of rat visual cortex pyramidal neurons (Mason, A. and Larkman, A. J., 1990. J. Neurosci. 9,1440-1447; Larkman, A. J. 1991. J. Comp. Neurol. 306, 307-319). A dendritic distribution of ion channels was found that reproduced the range of in vitro responses of layer 5 IB pyramidal neurons, including the transition from repetitive bursting to the burst/tonic spiking mode seen in these neurons as input magnitude increases. In light of available data, the simulation results suggest that in these neurons bursts are driven by an inward flow of current during a high threshold Ca2+ spike extending throughout both the basal and apical dendritic branches.
Dissertations / Theses on the topic "Layer 5 pyramidal neuron"
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Kaufmann, Timothy J. "The electrophysiological impact of oligomeric alpha-Synuclein on thick-tufted layer 5 pyramidal neurons in the neocortex of mice." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/77758/.
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Parkinson’s disease (PD) is one of the most prevalent movement disorders in the world. A clinical hallmark of PD is the appearance of proteinaceous Lewy Bodies throughout the brain that are predominantly formed from aggregation of the presynaptic protein alpha-Synuclein (αSyn). Increasing evidence, however, suggests that the soluble annular αSyn oligomers, formed during early stages of aggregation, are more toxic and pathologically relevant than the larger fibrils which form at later stages of aggregation. The underlying mechanism(s) through which αSyn oligomers exert their toxicity is still largely unknown. This thesis investigates how the toxic nature of αSyn oligomers may affect the electrophysiological properties of neurons. A population of soluble oligomers, termed mOligomers, were isolated from the early stages of in vitro aggregation. In addition, a separate oligomeric species was recovered from the fragmentation of large fibrils; termed fOligomers. Structural characterisation of these two species revealed them to be similar in size and ring-like in shape but showed subtle differences in their secondary structure. Purified, oligomeric αSyn was injected directly into the somata of thick-tufted layer 5 pyramidal neurons in mouse neocortical brain slices during whole-cell patch clamp recording and compared to the effects of equivalent concentrations of αSyn monomer. Using a combined experimental and modelling approach, a wide range of neuronal parameters were extracted and demonstrated oligomer-specific changes in neuronal electrophysiology that were time dependent. Perfusion with αSyn oligomers markedly reduced input resistance, enhanced the current required to trigger an action potential and reduced the firing rate illustrating a reduction in excitability that has the potential to impact both neural circuitry and cognitive output.
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Kerr, Michael I. "The role of adenosine in the modulation of synaptic transmission and action potential firing of thick-tufted layer 5 pyramidal neurons." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/57669/.
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The actions of many neuromodulators induce changes in synaptic transmission and membrane excitability, and many of these effects are well documented in neurons across the CNS. Adenosine acts as a powerful modulator across the CNS and while its actions have been characterised in some neurons in the neocortex, its effects on excitatory transmission in layer 5 remain unstudied. Adenosine has been implicated in the modulation of spontaneous activity generated in the layer 5 excitatory network, thus understanding its actions in this area are of substantial importance. This study used a combined approach of paired intracellular recordings and quantitative modelling to investigate the actions of adenosine on thick-tufted layer 5 pyramidal neurons in the rat somatosensory cortex. Adenosine was found to powerfully suppress synaptic transmission between these neurons and the changes in synaptic dynamics could be precisely captured as a change only in probability of release in a simple phenomenological model. Recordings conducted at three post-natal ages provide evidence that an increased tone of endogenous adenosine is responsible for the previously described developmental shift in short-term dynamics and reliability of this synapse. The data illustrates both that this endogenous activation of A1 receptors is highly heterogeneous, with variation between neighbouring synapses, and that it plays a significant role in EPSP parameters observed at mature connections. An investigation into adenosine's post-synaptic actions using an approach that measures the neurons' I-V response to naturalistic current inputs demonstrates how adenosine's actions on membrane excitability translate to a strong suppression of spiking. Simultaneous dendritic and somatic recordings demonstrate that this effect is enhanced when current is injected from the dendrite and that back-propagating bursts of action potentials are selectively suppressed by adenosine. As a whole the work illustrates that the effects of adenosine can be well captured by mathematically tractable quantitative models.
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Benhassine, Narimane. "Biophysical properties, distribution and functional importance of large-conductance Calcium-dependent Potassium channels in layer 5 pyramidal neurons of the rat somatosensory cortex /." [S.l.] : [s.n.], 2005. http://www.zb.unibe.ch/download/eldiss/05benhassine_n.pdf.
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Sharifullina, Elvira [Verfasser], Arthur [Akademischer Betreuer] Konnerth, Thomas [Akademischer Betreuer] Misgeld, and Jana Eveline [Akademischer Betreuer] Hartmann. "Structure and function studies in layer 5 pyramidal neurons of the mouse vibrissal cortex / Elvira Sharifullina. Gutachter: Thomas Misgeld ; Arthur Konnerth ; Jana Eveline Hartmann. Betreuer: Arthur Konnerth." München : Universitätsbibliothek der TU München, 2011. http://d-nb.info/1056936584/34.
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Shin, Jiyun. "Perirhinal feedback input controls neocortical memory formation via layer 1." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22312.
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Das deklarative Gedächtnis beruht auf Wechselwirkungen zwischen dem medialen Temporallappens (MTL) und Neokortex. Aufgrund der verteilten Natur neokortikaler Netzwerke bleiben zelluläre Ziele und Mechanismen der Gedächtnisbildung im Neokortex jedoch schwer fassbar. Im sechsschichtigen Säugetier-Neokortex konvergieren die Top-Down-Inputs auf Schicht 1 (L1). Wir untersuchten, wie Top-Down-Inputs von MTL die neokortikale Aktivität während der Gedächtnisbildung modulieren. Wir haben zunächst ein Kortex- und Hippocampus-abhängiges Lernparadigma angepasst, in dem Tiere gelernt haben, direkte kortikale Mikrostimulation und Belohnung zu assoziieren. Neuronen in den tiefen Schichten des perirhinalen Kortex lieferten monosynaptische Eingaben in L1 des primären somatosensorischen Kortex (S1), wo die Mikrostimulation vorgestellt wurde. Die chemogenetische Unterdrückung der perirhinalen Inputs in L1 von S1 störte die Gedächtnisbildung, hatte jedoch keinen Einfluss auf die Leistung der Tiere nach abgeschlossenem Lernen. Dem Lernen folgte das Auftreten einer klaren Subpopulation von Pyramidenneuronen der Schicht 5 (L5), die durch hochfrequentes Burst-Feuern gekennzeichnet war und durch Blockieren der perirhinalen Inputs zu L1 reduziert werden konnte. Interessanterweise zeigte ein ähnlicher Anteil an apikalen Dendriten von L5-Pyramidenneuronen ebenfalls eine signifikant erhöhte Ca2+-Aktivität während des Gedächtnisabrufs bei Expertentieren. Wichtig ist, dass die Störung der dendritischen Ca2+-Aktivität das Lernen beeinträchtigte, was darauf hindeutet, dass apikale Dendriten von L5-Pyramidenneuronen eine entscheidende Rolle bei der Bildung des neokortikalen Gedächtnisses spielen. Wir schließen daraus, dass MTL-Eingaben das Lernen über einen perirhinalen vermittelten Gating-Prozess in L1 steuern, der sich in einer erhöhten dendritischen Ca2+-Aktivität und einem Burst-Firing in pyramidalen L5-Neuronen manifestiert.Declarative memory relies on interactions between the medial temporal lobe (MTL) and neocortex. However, due the distributed nature of neocortical networks, cellular targets and mechanisms of memory formation in the neocortex remain elusive. In the six-layered mammalian neocortex, top-down inputs converge on its outermost layer, layer 1 (L1). We examined how layer-specific top-down inputs from MTL modulate neocortical activity during memory formation. We first adapted a cortical- and hippocampal-dependent learning paradigm, in which animals learned to associate direct cortical microstimulation and reward, and characterized the learning behavior of rats and mice. We next showed that neurons in the deep layers of the perirhinal cortex not only provide monosynaptic inputs to L1 of the primary somatosensory cortex (S1), where microstimulation was presented, but also actively reflect the behavioral outcome. Chemogenetic suppression of perirhinal inputs to L1 of S1 disrupted early memory formation but did not affect animals’ performance after learning. The learning was followed by an emergence of a distinct subpopulation of layer 5 (L5) pyramidal neurons characterized by high-frequency burst firing, which could be reduced by blocking perirhinal inputs to L1. Interestingly, a similar proportion of apical dendrites (~10%) of L5 pyramidal neurons also displayed significantly enhanced calcium (Ca2+) activity during memory retrieval in expert animals. Importantly, disrupting dendritic Ca2+ activity impaired learning, suggesting that apical dendrites of L5 pyramidal neurons have a critical role in neocortical memory formation. Taken together, these results suggest that MTL inputs control learning via a perirhinal-mediated gating process in L1, manifested by elevated dendritic Ca2+ activity and burst firing in L5 pyramidal neurons. The present study provides insights into cellular mechanisms of learning and memory representations in the neocortex.
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Voelker, Courtney Christine Joan. "Differential gene expression of cortical layer V pyramidal neuron subpopulations during development." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436930.
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Frackowiak, Stephanie. "Dendritic propagation of excitatory post-synaptic potentials in rat layer 5 pyramidal neurones." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393445.
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Farinella, M. "Synaptic integration in layer 5 cortical pyramidal cells and the role of background synaptic input explored with compartmental modeling." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1397661/.
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Pyramidal cells are the principal excitatory neurons in the cerebral cortex and those in layer 5 (L5) form its primary output. Tufted L5 pyramidal cells present a complex morphology, with non-uniform distributions of active membrane conductances. Their dendritic tree receives thousands of synaptic inputs from local circuits as well as long range inputs from other cortical regions and thalamic nuclei. Hence, the timing of their synaptic inputs are likely to span a wide range of temporal scales, raising the question of how an individual L5 pyramidal cell combines and transforms such temporally and spatially diverse signals. The integrative properties of pyramidal neurons have been extensively studied in vitro and several models have been suggested for the computations performed by these cells. However, cortical pyramidal cells in vivo are constantly bombarded by asynchronous synaptic input, re ecting the activity of the network in which they are embedded. Little is known about how the resulting background activity interacts with nonlinear dendritic properties. I have used experimentally-constrained models of L5 pyramidal neurons to explore synaptic integration under a range of di erent conditions including those measured in vivo. The major result from this study is that background synaptic activity can profoundly alter the integrative properties of pyramidal cells, by activating a distributed NMDA receptor conductance. This distributed nonlinear conductance lowers the threshold for dendritic spikes generation, extends the spikes duration and increases the probability of additional regenerative events occurring in neighbouring branches. Simulations with mixed excitatory/inhibitory background also suggest that dendritic inhibition may be speci cally tuned to regulate this powerful re-generative mechanism. My results suggest a new role for NMDA receptors. During the network activity experienced by pyramidal neurons in vivo, the distributed NMDA conductance may enable pyramidal cells to integrate synaptic input over extended spatio-temporal scales.
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Holland, Carl Seiler. "Electrophysiological properties of layer 5 pyramidal neurons in a mouse model of autism spectrum disorder." Thesis, 2016. https://hdl.handle.net/2144/17017.
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Both neuroinflammation, and an increase in microglial cells, have been associated with Autism Spectrum Disorder (ASD) through observation in human subjects as well as in mouse models. A mother having an infection early in pregnancy increases the chances for autism in her child. (Atladottir et al., 2012). This process is known as Maternal Immune Activation (MIA), and the proposed mechanism is that inflammatory signals cross from the mother to child; then in response to increased pro-inflammatory cytokines, microglia within the brain are activated to combat the infection. Microglia are essential to healthy synaptogenesis and neuronal growth, and a change in their signaling early in development has been shown to alter behavior in mouse models that replicate MIA. We use microglial depletion as a therapy to counteract the potentially harmful pro-inflammatory response in the developing mouse brain. Four experimental groups - control, MIA, microglial depleted, and a therapy group (MIA plus microglial depletion)- were run through a comprehensive series of behavioral and electrophysiological assessments. Layer 5 pyramidal cells (L5PNs) were targeted for recording in medial frontal cortex – a mouse cortical area important for cognition and social behavior. L5PNs are a heterogeneous population with cortical and subcortical targeting. Subcortical targeting neurons are thick tufted morphologically, and have an intrinsically bursting spike pattern. Analysis of the intrinsically bursting neurons revealed significant differences between the maternal inflammation and the microglial depletion groups across multiple physiological properties. Therefore, the therapy group had electrophysiological characteristics more consistent with the microglial depleted model than the autism model.
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Chang, Ting-Hsuan Daniel, and 張珽瑄. "Comparison of Transmission at Synapses of Layers 2/3 Input onto Layer 5 Pyramidal and GABAergic Neurons in Rostral Agranular Insular Cortex." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/93585570272002491439.
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碩士國立臺灣大學
生命科學系
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It has been shown that inhibition or lesion of the rostral agranular insular cortex (RAIC) results in analgesia, it suggests that RAIC tonically produces hyperalgesia signal. RAIC is a cortical area where nociceptive output originates, and it has been reported to activate in chronic pain perception. It’s believed that chronic pain is associated with the long-term change in synaptic plasticity. Moreover, the imbalance of excitatory and inhibitory (E/I) synaptic signaling in neural circuits is responsible to modulate synaptic plasticity in certain behavior disorders. In our lab, previous study had reported that the induction of chronic pain induced differential activation in pyramidal cells and GABAergic neurons in RAIC. We propose here that E/I imbalance in RAIC may contribute to the increased cortical output of nociceptive signal in chronic pain. To test this possibility, we compared synaptic transmission of layers 2/3 (L2/3) inputs onto layer 5 (L5) pyramidal cells (PC), which are the descending projection neurons, and onto local GABAergic interneurons (IntN) in RAIC. We performed dual-patch recording from a paired IntN-PC in layer 5, and elicited EPSC by putting an electrode in layer 2/3. We found functional connectivity in 34.2% of all recorded IntN-PC pairs. There was no significant difference in data sampled from IntN-PC pairs with and without functional connectivity, and all data were pooled. Our data showed no significant difference in paring-pulse ratio between transmission at L2/3-PC synapses and at L2/3-IntN synapses. L2/3-IntN seemed to have higher releasing probability than L2/3-PC synapse in quantum study. The ratio of NMDA and non-NMDA EPSCs component was larger at L2/3-PC synapses than at L2/3-IntN synapses. Furthermore, the rising and decay of EPSCs were much faster at L2/3-IntN synapse than at L2/3-PC synapse. We further examined the modulation of pERK on IntN-PC pairs by applying PKC activator Phorbol 12,13- diacetate (PDA). PDA enhanced the postsynaptic currents at L2/3-PC synapses and L2/3- IntN synapses. The further issue of chronic pain model is under studying.
Book chapters on the topic "Layer 5 pyramidal neuron"
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Li, Xiumin, Kenji Morita, Hugh P. C. Robinson, and Michael Small. "Gamma-Frequency Synaptic Input Enhances Gain Modulation of the Layer V Pyramidal Neuron Model." In Advances in Cognitive Neurodynamics (II), 183–87. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9695-1_28.
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Koch, Christof. "Synaptic Input to a Passive Tree." In Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.003.0024.
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Now that we have quantified the behavior of the cell in response to current pulses and current steps as delivered by the physiologist's microelectrode, let us study the behavior of the cell responding to a more physiological input. For instance, a visual stimulus in the environment will activate cells in the retina and its target, neurons in the lateral geniculate nucleus. These, in turn, make on the order of 50 excitatory synapses onto the apical tree of a layer 5 pyramidal cell in primary visual cortex such as the one we use throughout the book, and about 100-150 synapses onto a layer 4 spiny stellate cell (Peters and Payne, 1993; Ahmed et al., 1994, 1996; Peters, Payne, and Rudd, 1994). All of these synapses will be triggered within a fraction of a millisecond (Alonso, Usrey, and Reid, 1996). Thus, any sensory input to a neuron is likely to activate on the order of 102 synapses, rather than one or two very specific synapses as envisioned in Chap. 5 in the discussion of synaptic AND-NOT logic. This chapter will reexamine the effect of synaptic input to a realistic dendritic tree. We will commence by considering a single synaptic input as a sort of baseline condition. This represents a rather artificial condition; but because the excitatory postsynaptic potential and current at the soma are frequently experimentally recorded and provide important insights into the situation prevailing in the presence of massive synaptic input, we will discuss them in detail. Next we will treat the case of many temporally dispersed synaptic inputs to a leaky integrate-and-fire model and to the passive dendritic tree of the pyramidal cell. In particular, we are interested in uncovering the exact relationship between the temporal input jitter and the output jitter. The bulk of this chapter deals with the effect of massive synaptic input onto the firing behavior of the cell, by making use of the convenient fiction that the detailed temporal arrangement of action potentials is irrelevant for neuronal information processing. This allows us to derive an analytical expression relating the synaptic input to the somatic current and ultimately to the output frequency of the cell.
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Koch, Christof. "Passive Dendritic Trees." In Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.003.0009.
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The previous chapter dealt with the solution of the cable equation in response to current pulses and steps within a single unbranched cable. However, real nerve cells possess highly branched and extended dendritic trees with quite distinct morphologies. Figure 3.1 illustrates the fantastic variety of dendritic trees found throughout the animal kingdom, ranging from neurons in the locust to human brain cells and cells from many different parts of the nervous system. Some of these cells are spatially compact, such as retinal amacrine cells, which are barely one-fifth of a millimeter across, while some cells have immense dendritic trees, such as α motoneurones in the spinal cord extending across several millimeters. Yet, in all cases, neurons are very tightly packed: in vertebrates, peak density appears to be reached in the granule cell layer of the human cerebellum with around 5 million cells per cubic millimeter (Braitenberg and Atwood, 1958) while the packing density of the cells filling the 0.25 mm3 nervous system of the housefly Musca domestica is around 1.2 million cells per cubic millimeter (Strausfeld, 1976). The dendritic arbor of some cell types encompasses a spherical volume, such as for thalamic relay cells, while other cells, such as the cerebellar Purkinje cell, fill a thin slablike volume with a width less than one-tenth of their extent. Different cell types do not appear at random in the brain but are unique to specific parts of the brain. By far the majority of excitatory cells in the cortex are the pyramidal cells. Yet even within this class, considerable diversity exists. But why this diversity of shapes? To what extent do these quite distinct dendritic architectures reflect differences in their roles in information processing and computation? What influence does the dendritic morphology have on the electrical properties of the cell, or, in other words, what is the relationship between the morphological structure of a cell and its electrical function? One of the few cases where a quantitative relationship between form and some aspect of neuronal function has been established is the retinal neurons.
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CAULLER, LARRY J., and BARRY W. CONNORS. "Functions of Very Distal Dendrites: Experimental and Computational Studies of Layer I Synapses on Neocortical Pyramidal Cells." In Single Neuron Computation, 199–229. Elsevier, 1992. http://dx.doi.org/10.1016/b978-0-12-484815-3.50014-1.
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Markov, Krassimir, Koen Vanhoof, Iliya Mitov, Benoit Depaire, Krassimira Ivanova, Vitalii Velychko, and Victor Gladun. "Intelligent Data Processing Based on Multi-Dimensional Numbered Memory Structures." In Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems, 156–84. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-1900-5.ch007.
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The Multi-layer Pyramidal Growing Networks (MPGN) are memory structures based on multidimensional numbered information spaces (Markov, 2004), which permit us to create association links (bonds), hierarchically systematizing, and classification the information simultaneously with the input of it into memory. This approach is a successor of the main ideas of Growing Pyramidal Networks (Gladun, 2003), such as hierarchical structuring of memory that allows reflecting the structure of composing instances and gender-species bonds naturally, convenient for performing different operations of associative search. The recognition is based on reduced search in the multi-dimensional information space hierarchies. In this chapter, the authors show the advantages of using the growing numbered memory structuring via MPGN in the field of class association rule mining. The proposed approach was implemented in realization of association rules classifiers and has shown reliable results.
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Galiautdinov, Rinat, and Vardan Mkrttchian. "Brain Machine Interface for Avatar Control and Estimation for Educational Purposes Based on Neural AI Plugs." In Avatar-Based Control, Estimation, Communications, and Development of Neuron Multi-Functional Technology Platforms, 294–316. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1581-5.ch016.
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In the future education process where Avatar will be used, it is critically important to have a layer which is responsible for transferring the knowledge from a Student's Avatar to Student. In this research, authors show the method and high-level architecture of how it could be done. And although the suggested approach works, the current level of technology does not allow creating a mobile set which could implement this approach. The general high-level schema of the Avatar methodology used in the education process is enclosed in the following. There are 4 layers which interact with each other. On the first layer there is a professor, who is an expert in some domain. The professor transfers their knowledge to the second layer which is a computer program, performing the role of the Professor's Avatar. The knowledge gets transferred to a Student's Avatar, which is the 3rd layer, and eventually the Student's Avatar transfers the newly received knowledge to the Student, the 4th layer in this schema. So, the most difficult part here is to transfer the knowledge from machine to human.
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Christofi, Gerry, and Guy Leschziner. "Neurology." In The Pocketbook for PACES. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199574186.003.0014.
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The neurology section of the PACES examination is often the major cause of (unnecessary!) anxiety for MRCP candidates. The key is to approach the patient in a logical fashion. Some neurology cases are simply an exercise in pattern recognition – noticing the frontal balding and ptosis of myotonic dystrophy, the distal wasting and pes cavus of Charcot–Marie–Tooth disease, for example. However, in those cases without obvious clues to the underlying diagnosis, a clear systematic approach will usually pay dividends. When faced with a neurological problem, the first question that should be posed is the site of the lesion. During the course of the examination, identify signs that might help in localization: • Cortex: signs of dysfunction of higher cognitive function. • Subcortical: upper motor neuron (UMN) signs (hypertonia, pyramidal pattern of weakness, hyper-reflexia, extensor plantars), slowness of thought. • Basal ganglia: cogwheel rigidity, resting tremor, bradykinesia, postural instability, dyskinesias, dystonias. • Brainstem: cranial nerve abnormalities with contralateral UMN signs. • Cerebellum: gait ataxia, nystagmus, finger-nose ataxia, past-pointing. • Spinal cord: bilateral UMN signs, presence of a sensory level. • Nerve root: lower motor neuron (LMN) signs (wasting, weakness, hyporeflexia, sensory loss) in a myotomal or dermatomal distribution. • Single or multiple nerve/plexus: LMN signs that are focal, and are not consistent with a nerve root lesion. • Polyneuropathy: LMN signs, more pronounced distally, affecting the legs more than the hands, diminished reflexes, sensory signs. • Neuromuscular junction: weakness without sensory involvement or significant wasting, usually but not invariably proximal, which fluctuates (either with time of day or during the course of the examination). • Muscle: wasting and weakness with normal reflexes and sensation. Once the lesion has been localized, consider the disease processes that commonly affect that site. Clues may be obtained from the history, if you are permitted to ask questions. The most helpful aspect of the history is usually the speed of onset: • Seconds: electrical disturbance (i.e. epilepsy), trauma. • <5 minutes: infarction. • > 5 minutes: migraine, haemorrhage. • Minutes–hours: infection, inflammation, drugs. • Hours–days: infection, inflammation, nutritional, drugs.
Conference papers on the topic "Layer 5 pyramidal neuron"
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Li, Guoshi, Harvey C. Cline, Pierre Blier, and Satish Nair. "Computational Studies of Gain Modification by Serotonin in Pyramidal Neurons of Prefrontal Coxtex." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15080.
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Serotonin (5-HT) is widely implicated in brain functions and diseases, but the cellular mechanisms underlying 5-HT functions in the brain are not well understood (Zhang and Arsenault, 2005). Recent experiments (Zhang and Arsenault, 2005) have shown that 5-HT substantially increased the slope (gain) of the firing rate current (F-I) curve in layer 5 pyramidal neurons of the rat prefrontal cortex and this effect was limited to the range of firing rate (0-10 Hz) that is known to behaviourally relevant. Furthermore, it was found that 5-HT mediated gain increase was due to a reduction of the afterhyperpolarization (AHP) and an induction of the slow afterdepolarization (ADP), regardless of changes in the membrane potential, the input resistance or the properties of action potentials. To investigate this frequency-dependent gain modulation of 5-HT on the prefrontal cortex neurons, conductance-based Hodgkin-Huxley type models of the regular spiking (RS) cells in the prefrontal cortex are developed using a step by step approach. We first show that a model with an A current displays a square-root form F-I curve with higher slope at low frequency. However, for the same range of current injection steps used in experiment, the frequency range goes beyond 20 Hz, suggesting the presence of other hyperpolarizing currents in the model. As suggested by the experiment (Zhang and Arsenault, 2005), AHP currents (fast AHP, medium AHP and slow AHP) are included in the model to simulate 5-HT effect. Simulations show that AHP currents effectively linearize the F-I curve and decrease the slope of F-I curve in general, thus reducing the neuronal excitability. Since the slow AHP current is a target of 5-HT, the strength of this current is reduced gradually and the F-I curves are plotted together for comparison. The results indicate that with decreasing slow AHP strength, the current thresholds for repetitive spiking decreases and the slopes of the F-I curves increase in general. A square-root form F-I curve is not evident until the slow AHP current is blocked completely. This suggests that the medium AHP current also play a role in linearizing the F-I curve besides the slow AHP current. Based on current findings, a full model with both A current and AHP currents is being constructed to match the experimental data more closely so the mechanism of 5-HT on gain modulation of prefrontal cortical neurons can be better understood.
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Zaqout, Sami, Lena-Luise Becker, Ayman Mustafa, Nadine Krame, Ulf Strauss, and Angela M. Kaindl. "Role of Cdk5rap2 in neocortical inhibition and excitation balance." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0117.
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Autosomal recessive primary microcephaly type 3 (MCPH3) is characterized by congenital microcephaly and intellectual disability. Further features include hyperactivity and seizures. The disease is caused by biallelic mutations in the Cyclin-dependent kinase 5 regulatory subunit-associated protein 2 gene CDK5RAP2. In the mouse, Cdk5rap2 mutations similarly result in reduced brain size and a strikingly thin neocortex already at early stages of neurogenesis that persists through adulthood. The microcephaly phenotype in MCPH arises from a neural stem cell proliferation defect. Here, we report a novel role for Cdk5rap2 in the regulation of dendritic development and synaptogenesis of neocortical layer 2/3 pyramidal neurons using a combined morphological and electrophysiological approach
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Han, Lang, Jianqiang Shan, and Bin Zhang. "Application of ANNs in Tube CHF Prediction: Effect of Neuron Number in Hidden Layer." In 12th International Conference on Nuclear Engineering. ASMEDC, 2004. http://dx.doi.org/10.1115/icone12-49112.
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Prediction of the Critical Heat Flux (CHF) for upward flow of water in uniformly heated vertical round tube is studied with Artificial Neuron Networks (ANNs) method utilizing different neuron number in hidden layers. This study is based on thermal equilibrium conditions. The neuron number in hidden layers is chosen to vary from 5 to 30 with the step of 5. The effect due to the variety of the neuron number in hidden layers is analyzed. The analysis shows that the neuron number in hidden layers should be appropriate, too less will affect the prediction accuracy and too much may result in abnormal parametric trends. It is concluded that the appropriate neuron number in two hidden layers should be [15 15] in the article.
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Talapatra, Siddharth, and Joseph Katz. "Three Dimensional Volumetric Velocity Measurements in the Inner Part of a Turbulent Boundary Layer Over a Rough Wall Using Digital HPIV." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-30813.
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Microscopic digital Holographic PIV is used to measure the 3D velocity distributions in the roughness sublayer of a turbulent boundary layer over a rough wall. The sample volume extends from the surface, including the space between the tightly packed, 0.45 mm high, pyramidal roughness elements, up to about 5 roughness heights away from the wall. To facilitate observations though a rough surface, experiments are performed in a facility containing fluid that has the same optical refractive index as the acrylic rough walls. Magnified in line holograms are recorded on a 4864×3248 pixel camera at a resolution of 0.67μm/pixel. The flow field is seeded with 2μm silver coated glass particles, which are injected upstream of the same volume. A multiple-step particle tracking procedure is used for matching the particle pairs. In recently obtained data, we have typically matched ∼5000 particle images per hologram pair. The resulting unstructured 3D vectors are projected onto a uniform grid with spacing of 60 μm in all three directions in a 3.2×1.8×1.8 mm sample volume. The paper provides sample data showing that the flow in the roughness sublayer is dominated by slightly inclined, quasi-streamwise vortices whose coherence is particularly evident close to the top of the roughness elements.