Academic literature on the topic 'Neurons'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Neurons.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Neurons"
1
Holmstrom, Lars, Patrick D. Roberts, and Christine V. Portfors. "Responses to Social Vocalizations in the Inferior Colliculus of the Mustached Bat Are Influenced by Secondary Tuning Curves." Journal of Neurophysiology 98, no. 6 (December 2007): 3461–72. http://dx.doi.org/10.1152/jn.00638.2007.
Full textAbstract:
Neurons in the inferior colliculus (IC) of the mustached bat integrate input from multiple frequency bands in a complex fashion. These neurons are important for encoding the bat's echolocation and social vocalizations. The purpose of this study was to quantify the contribution of complex frequency interactions on the responses of IC neurons to social vocalizations. Neural responses to single tones, two-tone pairs, and social vocalizations were recorded in the IC of the mustached bat. Three types of data driven stimulus-response models were designed for each neuron from single tone and tone pair stimuli to predict the responses of individual neurons to social vocalizations. The first model was generated only using the neuron's primary frequency tuning curve, whereas the second model incorporated the entire hearing range of the animal. The extended model often predicted responses to many social vocalizations more accurately for multiply tuned neurons. One class of multiply tuned neuron that likely encodes echolocation information also responded to many of the social vocalizations, suggesting that some neurons in the mustached bat IC have dual functions. The third model included two-tone frequency tunings of the neurons. The responses to vocalizations were better predicted by the two-tone models when the neuron had inhibitory frequency tuning curves that were not near the neuron's primary tuning curve. Our results suggest that complex frequency interactions in the IC determine neural responses to social vocalizations and some neurons in IC have dual functions that encode both echolocation and social vocalization signals.
2
Kirch, Christoph, and Leonardo L. Gollo. "Spatially resolved dendritic integration: towards a functional classification of neurons." PeerJ 8 (November 24, 2020): e10250. http://dx.doi.org/10.7717/peerj.10250.
Full textAbstract:
The vast tree-like dendritic structure of neurons allows them to receive and integrate input from many neurons. A wide variety of neuronal morphologies exist, however, their role in dendritic integration, and how it shapes the response of the neuron, is not yet fully understood. Here, we study the evolution and interactions of dendritic spikes in excitable neurons with complex real branch structures. We focus on dozens of digitally reconstructed illustrative neurons from the online repository NeuroMorpho.org, which contains over 130,000 neurons. Yet, our methods can be promptly extended to any other neuron. This approach allows us to estimate and map specific and heterogeneous patterns of activity observed across extensive dendritic trees with thousands of compartments. We propose a classification of neurons based on the location of the soma (centrality) and the number of branches connected to the soma. These are key topological factors in determining the neuron’s energy consumption, firing rate, and the dynamic range, which quantifies the range in synaptic input rate that can be reliably encoded by the neuron’s firing rate. Moreover, we find that bifurcations, the structural building blocks of complex dendrites, play a major role in increasing the dynamic range of neurons. Our results provide a better understanding of the effects of neuronal morphology in the diversity of neuronal dynamics and function.
3
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.
Full textAbstract:
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.
4
Cardi, P., and F. Nagy. "A rhythmic modulatory gating system in the stomatogastric nervous system of Homarus gammarus. III. Rhythmic control of the pyloric CPG." Journal of Neurophysiology 71, no. 6 (June 1, 1994): 2503–16. http://dx.doi.org/10.1152/jn.1994.71.6.2503.
Full textAbstract:
1. Two modulatory neurons, P and commissural pyloric (CP), known to be involved in the long-term maintenance of pyloric central pattern generator operation in the rock lobster Homarus gammarus, are members of the commissural pyloric oscillator (CPO), a higher-order oscillator influencing the pyloric network. 2. The CP neuron was endogenously oscillating in approximately 30% of the preparations in which its cell body was impaled. Rhythmic inhibitory feedback from the pyloric pacemaker anterior burster (AB) neuron stabilized the CP neuron's endogenous rhythm. 3. The organization of the CPO is described. Follower commissural neurons, the F cells, and the CP neuron receive a common excitatory postsynaptic potential from another commissural neuron, the large exciter (LE). When in oscillatory state, CP in turn excites the LE neuron. This positive feedback may maintain long episodes of CP oscillations. 4. The pyloric pacemaker neurons follow the CPO rhythm with variable coordination modes (i.e., 1:1, 1:2) and switch among these modes when their membrane potential is modified. The CPO inputs strongly constrain the pyloric period, which as a result may adopt only a few discrete values. This effect is based on mechanisms of entrainment between the CPO and the pyloric oscillator. 5. Pyloric constrictor neurons show differential sensitivity from the pyloric pacemaker neurons with respect to the CPO inputs. Consequently, their bursting period can be a shorter harmonic of the bursting period of the pyloric pacemakers neurons. 6. The CPO neurons seem to be the first example of modulatory gating neurons that also give timing cues to a rhythmic pattern generating network.
5
Stiefel, Klaus M., and G. Bard Ermentrout. "Neurons as oscillators." Journal of Neurophysiology 116, no. 6 (December 1, 2016): 2950–60. http://dx.doi.org/10.1152/jn.00525.2015.
Full textAbstract:
Regularly spiking neurons can be described as oscillators. In this article we review some of the insights gained from this conceptualization and their relevance for systems neuroscience. First, we explain how a regularly spiking neuron can be viewed as an oscillator and how the phase-response curve (PRC) describes the response of the neuron's spike times to small perturbations. We then discuss the meaning of the PRC for a single neuron's spiking behavior and review the PRCs measured from a variety of neurons in a range of spiking regimes. Next, we show how the PRC can be related to a number of common measures used to quantify neuronal firing, such as the spike-triggered average and the peristimulus histogram. We further show that the response of a neuron to correlated inputs depends on the shape of the PRC. We then explain how the PRC of single neurons can be used to predict neural network behavior. Given the PRC, conduction delays, and the waveform and time course of the synaptic potentials, it is possible to predict neural population behavior such as synchronization. The PRC also allows us to quantify the robustness of the synchronization to heterogeneity and noise. We finally ask how to combine the measured PRCs and the predictions based on PRC to further the understanding of systems neuroscience. As an example, we discuss how the change of the PRC by the neuromodulator acetylcholine could lead to a destabilization of cortical network dynamics. Although all of these studies are grounded in mathematical abstractions that do not strictly hold in biology, they provide good estimates for the emergence of the brain's network activity from the properties of individual neurons. The study of neurons as oscillators can provide testable hypotheses and mechanistic explanations for systems neuroscience.
6
Ruff, Douglas A., and Richard T. Born. "Feature attention for binocular disparity in primate area MT depends on tuning strength." Journal of Neurophysiology 113, no. 5 (March 1, 2015): 1545–55. http://dx.doi.org/10.1152/jn.00772.2014.
Full textAbstract:
Attending to a stimulus modulates the responses of sensory neurons that represent features of that stimulus, a phenomenon named “feature attention.” For example, attending to a stimulus containing upward motion enhances the responses of upward-preferring direction-selective neurons in the middle temporal area (MT) and suppresses the responses of downward-preferring neurons, even when the attended stimulus is outside of the spatial receptive fields of the recorded neurons (Treue S, Martinez-Trujillo JC. Nature 399: 575–579, 1999). This modulation renders the representation of sensory information across a neuronal population more selective for the features present in the attended stimulus (Martinez-Trujillo JC, Treue S. Curr Biol 14: 744–751, 2004). We hypothesized that if feature attention modulates neurons according to their tuning preferences, it should also be sensitive to their tuning strength, which is the magnitude of the difference in responses to preferred and null stimuli. We measured how the effects of feature attention on MT neurons in rhesus monkeys ( Macaca mulatta) depended on the relationship between features—in our case, direction of motion and binocular disparity—of the attended stimulus and a neuron's tuning for those features. We found that, as for direction, attention to stimuli containing binocular disparity cues modulated the responses of MT neurons and that the magnitude of the modulation depended on both a neuron's tuning preferences and its tuning strength. Our results suggest that modulation by feature attention may depend not just on which features a neuron represents but also on how well the neuron represents those features.
7
JOHNSTON, DAVID, SIMON PETER MEKHAIL, MARY ANN GO, and VINCENT R. DARIA. "MODELING NEURONAL RESPONSE TO SIMULTANEOUS AND SEQUENTIAL MULTI-SITE SYNAPTIC STIMULATION." International Journal of Modern Physics: Conference Series 17 (January 2012): 1–8. http://dx.doi.org/10.1142/s2010194512007878.
Full textAbstract:
The flow of information in the brain theorizes that each neuron in a network receives synaptic inputs and sends off its processed signals to neighboring neurons. Here, we model these synaptic inputs to understand how each neuron processes these inputs and transmits neurotransmitters to neighboring neurons. We use the NEURON simulation package to stimulate a neuron at multiple synaptic locations along its dendritic tree. Accumulation of multiple synaptic inputs causes changes in the neuron's membrane potential leading to firing of an action potential. Our simulations show that simultaneous synaptic stimulation approaches firing of an action potential at lesser inputs compared to sequential stimulation at multiple sites distributed along several dendritic branches.
8
Baker, Curtis L. "Spatial- and temporal-frequency selectivity as a basis for velocity preference in cat striate cortex neurons." Visual Neuroscience 4, no. 02 (February 1990): 101–13. http://dx.doi.org/10.1017/s0952523800002273.
Full textAbstract:
AbstractMeasurements were made of the optimal velocity for drifting bar-shaped stimuli to excite striate cortex neurons of the cat. These data were compared to the optimal spatial and temporal frequencies of the same neurons, as determined with drifting sine-wave grating stimuli. A systematic relationship was revealed, whereby those neurons preferring higher velocities of bar motion also preferred lower spatial and higher temporal frequencies of gratings. The optimal bar velocity for a given neuron could be quantitatively predicted from the ratio of that neuron's optimal temporal frequency to its optimal spatial frequency.
9
Wright, Nathaniel C., Mahmood S. Hoseini, Tansel Baran Yasar, and Ralf Wessel. "Coupling of synaptic inputs to local cortical activity differs among neurons and adapts after stimulus onset." Journal of Neurophysiology 118, no. 6 (December 1, 2017): 3345–59. http://dx.doi.org/10.1152/jn.00398.2017.
Full textAbstract:
Cortical activity contributes significantly to the high variability of sensory responses of interconnected pyramidal neurons, which has crucial implications for sensory coding. Yet, largely because of technical limitations of in vivo intracellular recordings, the coupling of a pyramidal neuron’s synaptic inputs to the local cortical activity has evaded full understanding. Here we obtained excitatory synaptic conductance ( g) measurements from putative pyramidal neurons and local field potential (LFP) recordings from adjacent cortical circuits during visual processing in the turtle whole brain ex vivo preparation. We found a range of g-LFP coupling across neurons. Importantly, for a given neuron, g-LFP coupling increased at stimulus onset and then relaxed toward intermediate values during continued visual stimulation. A model network with clustered connectivity and synaptic depression reproduced both the diversity and the dynamics of g-LFP coupling. In conclusion, these results establish a rich dependence of single-neuron responses on anatomical, synaptic, and emergent network properties. NEW & NOTEWORTHY Cortical neurons are strongly influenced by the networks in which they are embedded. To understand sensory processing, we must identify the nature of this influence and its underlying mechanisms. Here we investigate synaptic inputs to cortical neurons, and the nearby local field potential, during visual processing. We find a range of neuron-to-network coupling across cortical neurons. This coupling is dynamically modulated during visual processing via biophysical and emergent network properties.
10
Hong, En, Fatma Gurel Kazanci, and Astrid A. Prinz. "Different Roles of Related Currents in Fast and Slow Spiking of Model Neurons From Two Phyla." Journal of Neurophysiology 100, no. 4 (October 2008): 2048–61. http://dx.doi.org/10.1152/jn.90567.2008.
Full textAbstract:
Neuronal activity arises from the interplay of membrane and synaptic currents. Although many channel proteins conducting these currents are phylogenetically conserved, channels of the same type in different animals can have different voltage dependencies and dynamics. What does this mean for our ability to derive rules about the role of different types of ion channels in neuronal activity? Can results about the role of a particular channel type in a particular type of neuron be generalized to other neuron types? We compare spiking model neurons in two databases constructed by exploring the maximal conductance spaces of two models. The first is a model of crustacean stomatogastric neurons, and the second is a model of rodent thalamocortical neurons, but both models contain similar types of membrane currents. Spiking neurons in both databases show distinct fast and slow subpopulations, but our analysis reveals that related currents play different roles in fast and slow spiking in the stomatogastric versus thalamocortical neurons. This analysis involved conductance-space visualization and comparison of voltage traces, current traces, and frequency-current relationships from all spiker subpopulations. Our results are consistent with previous work indicating that the role a membrane current plays in shaping a neuron's behavior depends on the voltage dependence and dynamics of that current and may be different in different neuron types depending on the properties of other currents it is interacting with. Conclusions about the function of a type of membrane current based on experiments or simulations in one type of neuron may therefore not generalize to other neuron types.
Dissertations / Theses on the topic "Neurons"
1
Moonens, Sofie. "Mirror Neurons : The human mirror neuron system." Thesis, Högskolan i Skövde, Institutionen för kommunikation och information, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-6103.
Full textAbstract:
This literature review explores human mirror neurons from several angles. First it retells mirror neuron history, from the initial discovery in the macaque monkey research through to the experiments determining if there is a human brain homologue. Then the merits of two opposing evolutionary views – mirror neurons as an adaptation or an association, here referring to an adaptation’s byproduct – are discussed. Lastly the autistic mirror neuron dysfunction hypothesis – stating that a faulty mirror neuron system is at the basis of autistic behavioral patterns – is examined for its validity but ultimately found lacking and in need of further development.
2
Moubarak, Estelle. "Constraints imposed by morphological and biophysical properties of axon and dendrites on the electrical behaviour of rat substantia nigra pars compacta dopaminergic neurons." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0746.
Full textAbstract:
L’activité électrique des neurones est déterminée par des interactions complexes entre leurs propriétés morphologiques et biophysiques. Les neurones dopaminergiques (DA) de la substance noire compacte (SNc) présentent une caractéristique morphologique peu commune parmi les neurones de mammifères: leur axone émerge fréquemment d’une dendrite à une distance très variable du soma. Malgré cette importante variabilité dans la localisation de l’axone, peu d’articles ont étudié un lien potentiel entre morphologie neuronale et activité électrique dans ces cellules. Dans un premier article, nous avons exploré l’importante variabilité observée dans les neurones DA en caractérisant de nombreux paramètres morphologiques et biophysiques. Nos résultats suggèrent que la géométrie de l’AIS n’affecte pas significativement la forme du potentiel d’action ni l’activité pacemaker. En revanche, l’activité électrique est influencée par la morphologie et les conductances somatodendritiques. Dans une seconde étude, nous avons caractérisé le développement morphologique des neurones DA au cours des trois premières semaines post-natales. Nous avons observé une croissance asymétrique de l’arbre dendritique: la dendrite portant l’axone semble se complexifier plus que les autres dendrites. Cette asymétrie est associée à une contribution différente de la dendrite portant l’axone et des dendrites ne portant pas l’axone à la forme du potentiel d’action. Ces résultats suggèrent que les neurones DA de la SNc sont robustes aux variations morphologiques de l’axone et que les particularités morphologiques et biophysiques de leur arbre dendritique minimisent l’influence de l’AIS sur leur activité électriqueNeuronal output is defined by the complex interplay between the biophysical and morphological properties of neurons. Dopaminergic (DA) neurons of the substantia nigra pars compacta (SNc) are spontaneously active and generate a regular pacemaking activity. While most mammalian neurons have an axon emerging from the soma, the axon of DA neurons often arises from a dendrite at highly variable distances from the soma. Despite this large cell-to-cell variation in axon location, few studies have tried to unravel the potential link between neuronal morphology and electrical behaviour in this cell type. In a first article, we explored the high degree of cell-to-cell variability found in DA neurons by characterising several morphological and biophysical parameters. While AIS geometry did not seem to significantly affect action potential shape or pacemaking activity, we found that the electrical behaviour of DA neurons was particularly sensitive to somatodendritic morphology and conductances. In a second study, we characterised the morphological development of DA neurons during the first three post-natal weeks. We observed an asymmetric development of the dendritic tree, favouring the elongation and complexity of the axon-bearing dendrite. This asymmetry is associated with different contributions of the axon-bearing and non-axon bearing dendrites to action potential shape. Overall, the two studies suggest that DA neurons of the SNc are highly robust to cell-to-cell variations in axonal morphology. The peculiar morphological and biophysical profile of the dendritic arborization attenuates the role of the AIS in shaping electrical behaviour in this neuronal type
3
Steinbush, H. W. M. "Het neuron als bruggenbouwer "bridging disciplines by neurons" /." Maastricht : Maastricht : Instituut hersenen en gedrag ; University Library, Universiteit Maastricht [host], 1999. http://arno.unimaas.nl/show.cgi?fid=12984.
Full text
4
Serrat, Reñé Román. "Papel de Alex3 en la vía de señalización de Wnt y en la dinámica mitocondrial." Doctoral thesis, Universitat de Barcelona, 2012. http://hdl.handle.net/10803/83338.
Full textAbstract:
La proteína Alex3 forma parte de la familia de genes exclusiva de los mamíferos euterios Armcx, caracterizada por presentar una alta expresión en el SNC, por encontrarse localizada en clúster en el cromosoma X y porque se originaron a partir de la retrotransposición del gen Armc10 y una rápida duplicación en tándem en una evolución temprana de los mamíferos euterios. Las proteínas Armcx/Armc10 poseen primariamente una localización subcelular bimodal, encontrándose asociadas a la membrana externa mitocondrial y en el núcleo celular, localización que concuerda con sus secuencias proteicas que poseen putativos dominios de localización en estos compartimentos.
La sobreexpresión de las proteínas Armcx/Armc10 produce una profunda alteración de la red mitocondrial, demostrando que esta familia de proteínas juega un papel importante en la regulación de la dinámica y agregación mitocondrial y al menos, la sobreexpresión de la proteína Alex3, no induce cambios en los parámetros bio-energéticos mitocondriales, tales como el consumo de oxígeno, el potencial de membrana, el contenido de DNA mitocondrial, la actividad de la citocromo c oxidasa o la recaptación de Ca2+, ni alteran el balance de fisión/fusión mitocondrial.
Tanto la sobreexpresión como el silenciamiento de las proteínas Alex3 y Armc10 en neuronas hipocampales se ha visto alteran la distribución y transporte mitocondrial. Las proteínas Alex3 y Armc10 interaccionan con el complejo Kinesina/Miro/Trak2, regulador del transporte mitocondrial, lo cual sugiere que esta familia de proteínas regularían el transporte y dinámica mitocondrial a través de este complejo de proteínas. La interacción de Alex3 con este complejo también se ha visto es dependiente de los niveles de Ca2+, reduciéndose la interacción de estas proteínas cuando los niveles de Ca2+ son elevados.
Por otra parte, la vía de señalización asociada a proteínas Wnt se ha visto induce la degradación de la proteína Alex3 por un proceso independiente del proteosoma. Esta degradación no depende de los componentes de la vía canónica Dishevelled, GSK3-β y β-catenina ni de los componentes no canónicos JNK, CAMKII y calcineurina, habiéndose demostrado que la PKC y la CK2 juegan un papel principal en el control y degradación de los niveles de la proteína Alex3 de forma dependiente e independiente de las vías de señalización de Wnt. De manera similar, la depleción de los niveles intracelulares de Ca2+ también reproduce la degradación de Alex3. Además, la degradación de Alex3 a través de las vías de señalización asociadas a las proteínas Wnt revierte los fenotipos de agregación mitocondrial inducidos por la sobreexpresión de Alex3 y es evitado por la activación de la PKC, lo que sugiere que las proteínas Wnt podrían jugar un papel en el control de la dinámica mitocondrial mediante la regulación de las proteínas Armcx.Alex3 protein belongs to the eutherian specific family of genes Armcx, characterized by a high expression on the CNS, to be localized in a cluster on the X chromosome and to be originated by retrotransposition of Armc10 gene in a fast duplication in tandem. The Armcx/Armc10 proteins have a primary bimodal localization, both in nucleus and mitochondria as indicate their putative domains. Overexpression of Armcx/Armc10 proteins causes a profound alteration on the mitochondrial net showing that this family of proteins plays an important role in the regulation of the mitochondrial dynamics and at least, the overexpression of Alex3 protein neither change the bioenergetic parameters of mitochondria such as respiration, mitochondrial DNA content or calcium uptake nor alters the mitochondrial fusion/fission rate. Both the overexpression and knock-down of Alex3 and Armc10 proteins in hippocampal neurons alters the mitochondrial distribution and transport. Alex3 and Armc10 interact with the Kinesin/Miro/Trak2 mitochondrial transport regulator complex, suggesting that the Armcx protein family regulates mitochondrial dynamics through this complex. Moreover the interaction of Alex3 with this complex is dependent of calcium levels, diminishing the interaction when calcium levels are high. On the other hand, the Wnt signalling pathway induces the degradation of Alex3 protein in a proteosome independent process. This degradation is independent of the Wnt canonical and non-canonical members Dishevelled, GSK3β, β-catenin, JNK, calcineurin and CAMKII, but showing that the PKC and CKII members play a principal role in the control and degradation of Alex3 protein levels dependently and independently of Wnt pathways. Moreover, Alex3 degradation through Wnt signalling pathways, reverts the mitochondrial aggregation phenotypes and is avoided by PKC activation, suggesting that Wnt proteins can play a role in the control of mitochondrial dynamics through the regulation of Armcx proteins.
5
Wilson, Jennifer M. M. "Mechanisms of neuronal integration in adrenomedullary sympathetic preganglionic neurons." Thesis, University of Ottawa (Canada), 2002. http://hdl.handle.net/10393/6334.
Full textAbstract:
Sympathetic preganglionic neurons innervating the adrenal medulla (AD-SPN) regulate the release of adrenal catecholamines into the bloodstream. This research was undertaken to investigate the intrinsic properties and synaptic pathways characteristic of AD-SPN in neonatal rat spinal cord slice preparation. The presence of Lucifer Yellow from the patch pipette and Rhodamine-Dextran-Lysine from the adrenal medulla in the same neuron post recording identified AD-SPN. Active intrinsic properties revealed and characterised include: a potassium-mediated transient outward rectification present in 96% of AD-SPN and separable into a short 4-aminopyridine- and a long barium-sensitive component; a potassium-mediated sustained outward rectification revealed in TTx, activated positive to -50mV and blocked with quinine. These conductances contribute to the repolarising phase of the action potential. 89% of AD-SPN possessed potassium-mediated anomalous inward rectification. All AD-SPN displayed a high voltage-activated calcium spike that prolongs the action potential. The addition of internal caesium (140mM) revealed a low threshold spike mediated by T-type calcium channels that serve to facilitate burst firing. 75% of AD-SPN exhibited evidence of electrotonic coupling, indicated by characteristic oscillations in membrane potential and confirmed with dual recordings from electrotonically coupled AD-SPN. Electrotonic coupling promoted synchronous activity. An enhanced afterhyperpolarising potential facilitated transient termination of action potential firing forming bursts of activity. A role for calcium in the regulation of neuronal activity via action on electrotonic coupling was suggested by caffeine (10mM) decreasing, BAPTA-AM (15muM) and calcium free aCSF increasing the junctional conductance. Electrical stimulation of the descending fibres in both the ipsi- and contralateral funiculi evoked fast EPSPs in all AD-SPN that were mediated by both NMDA and non-NMDA receptors. A subpopulation of AD-SPN received fast IPSPs mediated by GABA acting via GABAA receptors. A train of stimuli (4 x 10Hz) in ipsi- and contralateral funiculi also evoked a slow IPSP mediated by noradrenaline acting via alpha 2-adrenergic receptors to increase a potassium conductance. The results provide insight into central mechanisms that contribute to the regulation of adrenomedullary catecholamine secretion.
6
Ganguly, Karunesh. "Activity-dependent regulation of neuronal excitability in hippocampal neurons /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2002. http://wwwlib.umi.com/cr/ucsd/fullcit?p3059903.
Full text
7
Avó, Freixo Francisco Duque Projecto. "Novel roles for the mitotic kinase Nek7 in hippocampal neurons." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/399540.
Full textAbstract:
The microtubule cytoskeleton plays essential roles during cell division, migration, differentiation, defining cell morphology and organizing intracellular transport. The properties of microtubules, such as their stability, polarity and dynamics, are spatially and temporally regulated by several factors, including post-translational modifications, stabilizing/destabilizing MAPs, motors, kinases, phosphatases, etc.
Many of these factors were identified in cycling cells and particularly during mitosis. Nevertheless, some bona-fide mitotic microtubule regulators are also expressed in differentiated cells such as neurons. In a neuron, the microtubule cytoskeleton is organized differently in axons and dendrites, to guarantee a unidirectional transmission of the signal in a neuronal network. In axons, microtubules are generally more stable and are oriented with their plus-ends growing towards the axon cone, while in dendrites microtubules have mixed polarity.
In the work described in this thesis, we performed an RNAi screen in neurons with a short list of mitotic/microtubule — related genes that were found upregulated or constantly expressed in a microarray during hippocampal neuron differentiation in vitro.
In this screen, we found that the mitotic kinase Nek7 regulated axon length in immature neurons (5/6DIV). Nek7 depletion generates longer axons, and interestingly depletion/absence of Nek6, a kinase that works together with Nek7 in mitosis to phosphorylate the kinesin Eg5, generated the same phenotype. Eg5 pharmacological inhibition also increased axon length, as described by others, suggesting that these kinases are regulating axon length through Eg5. However, depletion of Nek9, another kinase form the same mitotic module, gave rise to shorter axons, indicating that the whole module is not conserved in neurons.
In mature neurons (14DIV) Nek7 depletion decreased the total length and branching of dendrites and affected dendritic spines, in a kinase-dependent way. Nek6 and Nek9 had no effect on these morphological parameters, but Eg5 inhibition also decreased dendrite length and branching, and spine density. Indeed, co-expression of an Eg5 S1033D phosphomimetic but not of a S1033A phosphor-null mutant, rescued the effects of Nek7 depletion. Furthermore, Nek7 controls Eg5 accumulation in dendrites, in a S1033 phosphorylation-dependent way. To explore the mechanisms behind these dendritic phenotypes, we analyzed microtubule polarity and stability in these dendrites, and observed that Nek7 depletion/Eg5 inhibition increases the percentage of retrograde microtubule EB3 comets in the distal parts of the dendrite. Additionally, Eg5 inhibition with STLC also increased EB3 comet density and decreased tubulin acetylation in dendrites. Ectopic generation of excess of microtubules and of minus-end distal microtubules in the distal regions of the dendrites by expression of the CM1 domain of CDK5Rap2 also gave rise to similar dendritic phenotypes, suggesting that these observations are correlated.
I also observed that Eg5 inhibition with STLC can counteract the effects of KIF23 depletion in terms of dendritic microtubule polarity, a motor kinesin that is involved in establishing the mixed polarity microtubule array in dendrites. Furthermore, this depends on Eg5 binding to microtubules and on its motor function, since FCPT treatment did not rescue KIF23 —depletion phenotypes.
We suggest a model where Nek7 phosphorylates Eg5 S1033 in dendrites, thus mediating Eg5 transport by dynein and accumulation in dendritic microtubules via TPX2, by analogy with mechanisms existent during mitosis. As expected, I observed that depletion of TPX2 also decreased total dendrite length. In dendrites, immobile Eg5 likely crosslinks and stabilizes microtubules in parallel bundles, and mobile Eg5 may also help to guide and sort microtubules into parallel bundles, and to mediates sliding of antiparallel microtubules. It is also possible that Eg5 can regulate the rate of short microtubule transport in dendrites, as demonstrated by others in axons. Altogether, these functions would promote dendritic growth and branching and correct spine formation.Los microtúbulos son una componente importante del citoesqueleto, esenciales en la división celular, migración, transporte intracelular y diferenciación. La polaridad, estabilidad y dinámica de los microtúbulos son reguladas por muchos factores, como MAPs (proteínas asociadas a microtúbulos), quinesinas, dineínas, quinasas, fosfatasas, entre otros. Muchos de estos reguladores fueron descubiertos y caracterizados por su función durante la mitosis, pero algunos también están presentes en células diferenciadas, como por ejemplo neuronas. Las neuronas dependen mucho de la organización de los microtúbulos para su función. En una neurona, el axón tiene microtúbulos de polaridad uniforme, mientras que en las dendritas la polaridad es mixta, y esto es esencial para la transmisión unidireccional de la señal nerviosa. El sistema de diferenciación de neuronas hipocampales in vitro se utiliza para estudiar la morfología neuronal y funciones del citoesqueleto. En mi trabajo de tesis doctoral, he caracterizado la función de una quinasa mitótica, Nek7, como reguladora de la diferenciación de neuronas hipocampales. He observado que Nek7, junto con Nek6, regula el crecimiento axonal en neuronas inmaduras (5/6DIV). En ausencia de Nek7 o Nek6 los axones son más largos, mientras que la depleción de Nek9, otra quinasa que funciona en conjunto con Nek6/7 en mitosis, genera axones más cortos. En neuronas maduras (14DIV), Nek7 controla la morfología de dendritas y espinas a través de la regulación de la quinesina Eg5, que también es su substrato en mitosis. Los defectos generados por la depleción de Nek7 se rescatan con un mutante fosfo-mimético de Eg5 (S1033D) pero no con un mutante no fosforilable (S1033A). Además, Nek7 controla el reclutamiento y acumulación de Eg5 en la parte distal de las dendritas, a través de esta fosforilación. En la base de estos fenotipos encontramos problemas en la estabilidad y polaridad de microtúbulos en las dendritas. Tanto la depleción de Nek7 como la inactivación de Eg5 aumentan el porcentaje de microtúbulos de polaridad reversa en la parte distal de la dendrita, y disminuyen la acetilación de microtúbulos, un indicador de estabilidad. Finalmente se presenta un modelo en lo cual Eg5 regula la estabilidad, polaridad y deslizamiento de los microtúbulos dendríticos para favorecer el crecimiento dendrítico.
8
Stanke, Jennifer J. "Beyond Neuronal Replacement: Embryonic Retinal Cells Protect Mature Retinal Neurons." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250820277.
Full text
9
Bonifazi, Paolo. "Information processing in dissociated neuronal cultures of rat hippocampal neurons." Doctoral thesis, SISSA, 2005. http://hdl.handle.net/20.500.11767/4080.
Full textAbstract:
One of the major aims of Systems Neuroscience is to understand how the nervous system
transforms sensory inputs into appropriate motor reactions. In very simple cases sensory neurons
are immediately coupled to motoneurons and the entire transformation becomes a simple reflex, in
which a noxious signal is immediately transformed into an escape reaction. However, in the most complex behaviours, the nervous system seems to analyse in detail the sensory inputs and is performing some kind of information processing (IP). IP takes place at many different levels of the nervous system: from the peripheral nervous system, where sensory stimuli are detected and converted into electrical pulses, to the central nervous system, where features of sensory stimuli are extracted, perception takes place and actions and motions are coordinated. Moreover, understanding the basic computational properties of the nervous system, besides being at the core of Neuroscience,
also arouses great interest even in the field of Neuroengineering and in the field of Computer
Science. In fact, being able to decode the neural activity can lead to the development of a new
generation of neuroprosthetic devices aimed, for example, at restoring motor functions in severely
paralysed patients (Chapin, 2004). On the other side, the development of Artificial Neural Networks (ANNs) (Marr, 1982; Rumelhart & McClelland, 1988; Herz et al., 1981; Hopfield, 1982; Minsky & Papert, 1988) has already proved that the study of biological neural networks may lead to the development and to the design of new computing algorithms and devices. All nervous systems are based on the same elements, the neurons, which are computing devices which, compared to silicon components, are much slower and much less reliable. How are nervous systems of all living species able to survive being based on slow and poorly reliable components? This obvious and naïve question is equivalent to characterizing IP in a more quantitative way.
In order to study IP and to capture the basic computational properties of the nervous system,
two major questions seem to arise. Firstly, which is the fundamental unit of information processing:
2 single neurons or neuronal ensembles? Secondly, how is information encoded in the neuronal firing? These questions - in my view - summarize the problem of the neural code.
The subject of my PhD research was to study information processing in dissociated neuronal
cultures of rat hippocampal neurons. These cultures, with random connections, provide a more general view of neuronal networks and assemblies, not depending on the circuitry of a neuronal network in vivo, and allow a more detailed and careful experimental investigation. In order to record the activity of a large ensemble of neurons, these neurons were cultured on multielectrode arrays (MEAs) and multi-site stimulation was used to activate different neurons and pathways of the
network. In this way, it was possible to vary the properties of the stimulus applied under a
controlled extracellular environment. Given this experimental system, my investigation had two
major approaches. On one side, I focused my studies on the problem of the neural code, where I studied in particular information processing at the single neuron level and at an ensemble level,
investigating also putative neural coding mechanisms. On the other side, I tried to explore the possibility of using biological neurons as computing elements in a task commonly solved by conventional silicon devices: image processing and pattern recognition. The results reported in the first two chapters of my thesis have been published in two
separate articles. The third chapter of my thesis represents an article in preparation.
10
Albrecht, David. "Efectos de la proteína SPARC sobre la maduración de las sinapsis autápticas colinérgicas." Doctoral thesis, Universitat de Barcelona, 2012. http://hdl.handle.net/10803/107673.
Full textAbstract:
Las sinapsis son un elemento clave para el funcionamiento del sistema nervioso y la formación de las sinapsis es un proceso decisivo a lo largo de la vida. El establecimiento de los contactos sinápticos ocurre tanto en el sistema nervioso en desarrollo como en el cerebro adulto. Durante todo este proceso las neuronas y las células gliales se encuentran íntimamente acopladas por todo el sistema nervioso. Las investigaciones de las últimas décadas han evidenciado que las células gliales poseen un rol que va más allá del clásicamente atribuido, demostrando que la glía posee una función relevante en la formación y en el funcionamiento de las sinapsis mediante la secreción de diversas moléculas, las cuales han sido caracterizadas principalmente desde un punto de vista postsináptico. Para el estudio de la interacción neurona-glía, el laboratorio de Neurobiología ha implementado microcultivos de neuronas aisladas del ganglio cervical superior, sistema en el cual una neurona individual crece sobre una gota de colágeno haciendo contacto consigo misma. Los microcultivos establecidos rutinariamente en el laboratorio, consisten en una sola neurona que crece en ausencia de otros tipos celulares, denominados SCM; y una sola neurona que crece en presencia de células gliales o GM. Con este sistema, estudios previos realizados en el laboratorio demostraron que las células gliales incrementan la frecuencia de la neurotransmisión espontánea y modifica la plasticidad a corto plazo. Durante el desarrollo de esta tesis doctoral, se estableció que la glía periférica inmadura in vitro e in vivo secretan la proteína matricelular SPARC, proteína que se expresa ampliamente en el sistema nervioso en desarrollo y en áreas sinaptogénicas, a pesar de lo cual se desconoce la función que tiene en el desarrollo y en la plasticidad. En este trabajo se caracterizan las funciones de SPARC sobre la neurotransmisión y la maduración de las sinapsis colinérgicas autápticas, observándose que concentraciones nanomolares de SPARC durante el desarrollo sináptico aumentan la frecuencia de la neurotransmisión espontánea e incrementan la depresión a corto plazo. La secreción local de SPARC sobre neuronas maduras durante 24-36 horas, incrementa solamente la depresión a corto plazo. Finalmente, mediante electrofisiológica y microscopía electrónica correlativa, demostramos que los terminales presinápticos desarrollados en presencia de concentraciones nanomolares de SPARC, presentan dos a tres veces menos vesículas sinápticas totales y vesículas sinápticas ancladas a las zonas activas. Las evidencias experimentales obtenidas en este trabajo señalan que la proteína matricelular SPARC retiene las sinapsis autápticas colinérgicas en un estado inmaduro.The synapses are a key element for the Nervous system functions. They are established mainly during the nervous system development, but they can be formed in the adult nervous system as well. Neurons and glial cells are intimately coupled during all these processes. During the last decade it has been described that glial cells participate in the synapses establishment and information processing, they do so secreting factors. To study the neuron-glía interaction, our laboratory has set up neuronal microcultures, where a single neuron grows in a drop of collagen, a permissive substracte, surrounded by agarose, a non permissive substrate, forcing the neuron to develop inside the collagen drop, forcing it to have contact only with itself. These kind of synapses are called autapses. During this PhD thesis, we found that immature glial cells secrete SPARC in vitro and in vivo. We proved that SPARC has an effect over the neurotransmission and the presynaptic terminal maturations in cholinergic autapses; nanomolar concentration of SPARC applied during the neuronal development enhances the spontaneous neurotransmission and the short-term plasticity. We have also characterized that nanomolar concentration of SPARC decreases the total vesicular number and the docked vesicles number in presynaptic terminal. These experimental results obtained during the thesis lead us to propose that SPARC arrest the synapses in an immature stage.
Books on the topic "Neurons"
1
Gerstner, Wulfram. Spiking neuron models: Single neurons, populations, plasticity. Cambridge, U.K: Cambridge University Press, 2002.
Find full text
2
Fillenz, Marianne. Noradrenergic neurons. Cambridge [England]: Cambridge University Press, 1990.
Find full text
3
Taylor, John Gerald. Coupled Oscillating Neurons. London: Springer London, 1992.
Find full text
4
Colombo, Bruno, ed. The Musical Neurons. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08132-3.
Full text
5
Taylor, John Gerald, and C. L. T. Mannion, eds. Coupled Oscillating Neurons. London: Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-1965-4.
Full text
6
Sifr, Ken Al. Too many neurons. New York: Vantage Press, 1997.
Find full text
7
1931-, Taylor John Gerald, and Mannion C. L. T, eds. Coupled oscillating neurons. London: Springer-Verlag, 1992.
Find full text
8
Armstrong, William E., and Jeffrey G. Tasker, eds. Neurophysiology of Neuroendocrine Neurons. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118606803.
Full text
9
Augenbraun, Eliene. Acidified compartments in neurons. [New York]: [Columbia University], 1992.
Find full text
10
J, Bean Andrew, ed. Protein trafficking in neurons. Amsterdam: Elsevier/Academic Press, 2007.
Find full textBook chapters on the topic "Neurons"
1
Cheng, Xiaoyan, Sebastian Simmich, Finn Zahari, Tom Birkoben, Maximiliane Noll, Tobias Wolfer, Eckhard Hennig, Robert Rieger, Hermann Kohlstedt, and Andreas Bahr. "Biologically Inspired and Energy-Efficient Neurons." In Springer Series on Bio- and Neurosystems, 357–84. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36705-2_15.
Full textAbstract:
AbstractSilicon neurons represent different levels of biological details and accuracies as a trade-off between complexity and power consumption. With respect to this trade-off and high similarity to neuron behaviour models, relaxation-type oscillator circuits often yield a good compromise to emulate neurons. In this chapter, two exemplified relaxation-type silicon neurons are presented that emulate neural behaviour with energy consumption under the scale of nJ/spike. The first proposed fully CMOS relaxation SiN is based on mathematical Izhikevich model and can mimic a broad range of physiologically observable spike patterns. The results of kinds of biologically plausible output patterns and coupling process of two SiNs are presented in 0.35 $$\upmu $$ μ m CMOS technology. The second type is a novel ultra-low-frequency hybrid CMOS-memristive SiN based on relaxation oscillators and analog memristive devices. The hybrid SiN directly emulates neuron behaviour in the range of physiological spiking frequencies (less than 100 Hz). The relaxation oscillator is implemented and fabricated in 0.13 $$\upmu $$ μ m CMOS technology. An autonomous neuronal synchronization process is demonstrated with two relaxation oscillators coupled by an analog memristive device in the measurement to emulate the synchronous behaviour between spiking neurons.
2
Akiyama, Haruhiko. "Neurons." In Neuroinflammatory Mechanisms in Alzheimer’s Disease Basic and Clinical Research, 225–36. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8350-4_12.
Full text
3
Abe, Koji. "Neurons." In Cerebral Ischemia, 217–32. Totowa, NJ: Humana Press, 1999. http://dx.doi.org/10.1007/978-1-59259-479-5_8.
Full text
4
Lyle, Randall R. "Neurons." In Encyclopedia of Child Behavior and Development, 1012–13. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_1958.
Full text
5
Sabah, Nassir H. "Neurons." In Neuromuscular Fundamentals, 231–74. First edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9781003024798-7.
Full text
6
Saucedo, Leslie. "Neurons." In Getting to Know Your Cells, 37–42. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30146-9_7.
Full text
7
Stetter, Martin. "Neurons and Neuronal Signal Propagation." In Exploration of Cortical Function, 5–22. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0430-5_2.
Full text
8
Strisciuglio, Nicola, and Nicolai Petkov. "Brain-Inspired Algorithms for Processing of Visual Data." In Lecture Notes in Computer Science, 105–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_8.
Full textAbstract:
AbstractThe study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing.In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.
9
Dieudonné, Stéphane. "Golgi Neurons." In Essentials of Cerebellum and Cerebellar Disorders, 201–5. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24551-5_24.
Full text
10
Pietrajtis, Katarzyna, and Stéphane Dieudonné. "Golgi Neurons." In Handbook of the Cerebellum and Cerebellar Disorders, 829–52. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-1333-8_34.
Full textConference papers on the topic "Neurons"
1
Cao, Guoxin, You Zhou, Jeong Soon Lee, Jung Yul Lim, and Namas Chandra. "Mechanical Model of Neuronal Function Loss." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-39447.
Full textAbstract:
The mechanism of mild traumatic brain injury (mTBI) is directly related to the relationship between the mechanical response of neurons and their biological/chemical functions since the neuron is the main functional component of brain.1 The hypotheses is that the external mechanical load will firstly cause the mechanical deformation of neurons, and then, when the mechanical deformation of neurons reaches to a critical point (the mechanical deformation threshold), it will initiate the chemical/biological response (e.g. neuronal function loss). Therefore, defining and measuring the mechanical deformation threshold for the neuronal cell injury is an important first step to understand the mechanism of mTBI. Typically, the mechanical response of neurons is investigated based on the deformation of in vitro model, in which the neurons are cultured on the elastic substrate (e.g. PDMS membranes). The elastic membrane is deformed by the external load, e.g. equibiaxial stretching. The substrate deformation is considered to be the deformation of neurons since the substrate is several orders stiffer than the neurons and the neurons are perfectly bonded with the substrate. The fluoresce method is typically used to test the cell injury, e.g. the cell vitality and the neuron internal ROS level.1, 2
2
Previtera, Michelle L., Mason Hui, Malav Desai, Devendra Verma, Rene Schloss, and Noshir A. Langrana. "Neuronal Precursor Cell Proliferation on Elastic Substrates." In ASME 2011 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2011. http://dx.doi.org/10.1115/sbc2011-53246.
Full textAbstract:
Numerous stem cells therapies have been studied for the replacement of damaged neurons due to spinal cord injury. Our laboratory’s goal is to design an implantable platform for spinal cord neuron (SCN) proliferation and differentiation in order to replace damaged neurons in the injured spinal cord. Based on previous literature, we suspect we can promote neuronal precursor cell (NPC) proliferation and differentiation utilizing elastic matrices.
3
Rohlev, Anton, Christian Radehaus, Jacques I. Pankove, R. F. Carson, and G. Borglis. "Optoelectronic Neuron." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/optcomp.1991.me5.
Full textAbstract:
In analogy to biological neurons that interact with other neurons both electrically and chemically, the optoelectronic neurons interact with other optoelectronic neurons electrically and/or optically. Like a biological neuron, the optoelectronic neuron can have multiple input and thresholding. While the chemical interaction between biological neurons can employ different neurotransmitters, the optical interaction between optoelectronic neurons can employ photons of different wavelengths.
4
Taylor, V. J., and Thomas F. Krile. "Electrooptical implementation of the Huberman-Hogg neural model." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.fb3.
Full textAbstract:
The proposed hybrid electrooptical implementation of the Huberman-Hogg (H-H) model has several major components.1 The H-H model specifies that the neuron connections are between nearest diagonal neighbors and that the output of a neuron is a nonlinear function of the neuron memory and a difference operation performed on the neuron inputs. The first component is a liquid crystal TV which serves as an input device that converts electrical signals to a 2-D array of optical signals. This 2-D array simulates one layer of neurons. The second component is an optical system that specifies the interconnections between the neurons that are appropriate for the H-H model. The third component is a CCD camera that serves as an output device which transforms the 2-D optical interconnection signals to electrical signals. The fourth component is a digitizer processor that stores the internal state of each neuron in digital form and performs the output calculations and logic operations required for each neuron. The system is arranged in a loop so that the outputs of one 2-D array, or layer of neurons, become the inputs to the next layer of neurons.
5
Ball, John M., Clarence C. Franklin, David J. Schulz, and Satish S. Nair. "Co-Regulation of Calcium and Delayed Rectifier Currents Maintains Neuronal Output in a Model of a Crustacean Cardiac Motor Neuron." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2299.
Full textAbstract:
Homeostatic processes are widespread throughout all living systems, and the nervous system is no exception. Individual neurons as well as neuronal networks must maintain levels of excitability and connectivity to ensure that consistent functional output is achieved. Possible mechanisms for maintaining functional output using co-regulation of channel conductances is studied for a motor neuron, using a computational model. The results are both consistent with and extend the biological observations.
6
Lee, Yun-Jhu, Mehmet Berkay On, Luis El Srouji, Li Zhang, Mahmoud Abdelghany, and S. J. Ben Yoo. "Demonstration of Neural Heterogeneity with Programmable Brain-Inspired Optoelectronic Spiking Neurons." In Optical Fiber Communication Conference. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/ofc.2024.tu3f.4.
Full textAbstract:
Neural heterogeneity enables spiking neural networks to implement complex functions with fewer neurons. We designed, simulated, and demonstrated programmable optoelectronic spiking neurons that can achieve multiple neuron characteristics based on external tuning voltages.
7
Howard, R. V., W. K. Chai, and H. S. Tzou. "Modal Voltages of Linear and Nonlinear Structures Using Distributed Artificial Neurons." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0547.
Full textAbstract:
Abstract Laminated or embedded distributed neurons on structural components serve as in-situ sensors monitoring structure’s dynamic state and health status. Thin film piezoelectric patches are perfect candidates for this purpose. A generic piezoelectric neuron concept is introduced first, followed by definitions of neural signals generated by an arbitrary neuron laminated on a generic nonlinear double-curvature elastic shell. This generic neuron theory can be applied to a large class of linear and nonlinear common geometries, e.g., spheres, cylindrical shells, plates, etc. To demonstrate the neuron concept, an Euler-Bernoulli beam laminated with segmented neurons is studied. Neural signals and modal voltages are presented. Theoretical results are compared with experimental data favorably.
8
Lin, Steven, and Demetri Psaltis. "GaAs optoelectronic neurons." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.mk1.
Full textAbstract:
Monolithically integrated optoelectronic circuits have emerged as a viable solution for implementing nonlinear operations and providing gain in optical neural networks. However, before they can be incorporated into a practical system, these devices must be in the form of large arrays and have sufficient optical gain to compensate for the optical losses due to the low efficiency of the interconnection medium. Furthermore, they much dissipate little electrical power so that the performance will not be limited by heat dissipation and at the same time exhibit high input sensitivity to accommodate the low input power received by the neurons. In this paper, several integration schemes utilizing GaAs light emitting diodes (LEDs), heterojunction bipolar phototransistors. metal-semiconductor field-effect transistors (MESFETs) and optical field-effect transistors (OPFETs) are presented. Typical results in optoelectronic neurons that incorporate phototransistors as the detectors show a differential optical gain of 10-40, which the neurons that incorporate OPFETs as detectors show a gain of approximately 80. The electrical power dissipations in these neurons are less than 2 mW/neuron and the switching energy is measured to be between 10 and 40 pJ. While these results generate optimism, issues like scalability, processing limitations and compatibility, device integrability, input-output isolation, and tradeoff between power dissipation and optical gain still need to be addressed before high density arrays can be practically incorporated in neural networks.
9
Krishnamoorthy, Ashok V., Gökçe Yayla, Gary C. Marsden, and Sadik Esener. "Free-space optoelectronic neural system prototype." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.mqq2.
Full textAbstract:
We have previously designed a freespace optoelectronic neural system1 based on the D-STOP architecture.2 The neural architecture minimizes the number of required light transmitters and provides full connectivity between neurons, flexible functionality neurons and synapses, low signal timing skew, and accurate electronic fan-in with dendritic processing capability. Neural signals are encoded by using a combination of pulse-width modulating optical neurons and pulse-amplitude modulating electronic synapses. The neural system prototype consists of a 16-node input layer, a four-neuron hidden layer, and a single neuron output layer. The input layer consists of a 4 × 4 array of PLZT modulators. An 8 × 8 synapse scalable prototype of the neural chip and the space-invariant optical interconnection system have been fabricated. The image of the modulator array is distributed optically to the 64 synapses of the four hidden layer neurons, which are electrically connected to the output neuron. One differencing dendrite per four synapses is used. Measured synapse and dendrite unit characteristics exhibit high linearity with low power consumption. Test results allow a 100 ns minimum pulse width for neural outputs.
10
Pitta, Marina Galdino da Rocha, Jordy Silva de Carvalho, Luzilene Pereira de Lima, and Ivan da Rocha Pitta. "iPSC therapies applied to rehabilitation in parkinson’s disease." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.022.
Full textAbstract:
Background: Parkinson’s disease (PD) is a neurological disorder that affects movement, mainly due to damage and degeneration of the nigrostriatal dopaminergic pathway. The diagnosis is made through a clinical neurological analysis where motor characteristics are considered. There is still no cure, and treatment strategies are focused on symptoms control. Cell replacement therapies emerge as an alternative. Objective: This review focused on current techniques of induced pluripotent stem cells (iPSCs). Methods: The search terms used were: “Parkinson’s Disease”, “Stem cells” and “iPSC”. Open articles written in English, from 2016-21 were selected in the Pubmed database, 10 publications were identified. Results: With the modernization of iPSC, it was possible to reprogram pluripotent human somatic cells and generate dopaminergic neurons and individual-specific glial cells. To understand the molecular basis, cell and animal models of neurons and organelles are currently being employed. Organoids are derived from stem cells in a three-dimensional matrix, such as matrigel or hydrogels derived from animals. The neuronal models are: α-synuclein (SNCA), leucine-rich repeat kinase2 (LRRK2), PARK2, putative kinase1 induced by phosphatase and tensin homolog (PINK1), DJ-1. Both models offer opportunities to investigate pathogenic mechanisms of PD and test compounds on human neurons. Conclusions: Cell replacement therapy is promising and has great capacity for the treatment of neurodegenerative diseases. Studies using iPSC neuron and PD organoid modeling is highly valuable in elucidating relevants neuronal pathways and therapeutic targets, moreover providing important models for testing future therapies.
Reports on the topic "Neurons"
1
Orendurff, Dody. Consciousness, neurons, and laughing gas. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.837.
Full text
2
Rothganger, Fredrick, James Aimone, Christina Warrender, and Derek Trumbo. Neurons to algorithms LDRD final report. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1096471.
Full text
3
Almassian, Amin. Information Representation and Computation of Spike Trains in Reservoir Computing Systems with Spiking Neurons and Analog Neurons. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2720.
Full text
4
Brown, Thomas H. Self-Organization of Hebbian Synapses on Hippocampal Neurons. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada299559.
Full text
5
Brown, Thomas H. Self-Organization of Hebbian Synapses on Hippocampal Neurons. Fort Belvoir, VA: Defense Technical Information Center, January 1996. http://dx.doi.org/10.21236/ada309810.
Full text
6
Carvey, Paul M. Cytokine Induction of Dopamine Neurons from Progenitor Cells. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada391417.
Full text
7
Gothilf, Yoav, Yonathan Zohar, Susan Wray, and Hanna Rosenfeld. Inducing sterility in farmed fish by disrupting the development of the GnRH System. United States Department of Agriculture, October 2007. http://dx.doi.org/10.32747/2007.7696512.bard.
Full textAbstract:
Hypothalamic gonadotropinreleasing hormone (GnRH1) is the key hormone in the control of gametogenesis and gonadal growth in vertebrates. Developmentally, hypothalamic GnRHproducing neurons originate from the olfactory placode, migrate along olfactory axons into the forebrain, and continue to the preoptic area and hypothalamus where they function to stimulate gonadotropin secretion from the pituitary gland. An appropriate location of GnRH neurons within the hypothalamus is necessary for normal reproductive function in the adult; abnormal migration and targeting of GnRH neurons during embryogenesis results in hypogonadism and infertility. The developmental migration of GnRH neurons and axonal pathfinding in mammals are modulated by a plethora of factors, including receptors, secreted molecules, adhesion molecules, etc. Yet the exact mechanism that controls these developmental events is still unknown. We investigated these developmental events and the underlying mechanisms using a transgenic zebrafish model, Tg(gnrh1: EGFP), in which GnRH1 neurons and axons are fluorescently labeled. The role of factors that potentially affect the development of this system was investigated by testing the effect of their knockdown and mutation on the development of the GnRH1 system. In addition, their localization in relation to GnRH1 was described during development. These studies are expected to generate the scientific foundation that will lead to developing innovative technologies, based on the disruption of the early establishment of the GnRH system, for inducing sterility in farmed fish, which is highly desirable for economical and environmental reasons.
8
Ori, Naomi, and Sarah Hake. Similarities and differences in KNOX function. United States Department of Agriculture, March 2008. http://dx.doi.org/10.32747/2008.7696516.bard.
Full textAbstract:
Hypothalamic gonadotropinreleasing hormone (GnRH1) is the key hormone in the control of gametogenesis and gonadal growth in vertebrates. Developmentally, hypothalamic GnRHproducing neurons originate from the olfactory placode, migrate along olfactory axons into the forebrain, and continue to the preoptic area and hypothalamus where they function to stimulate gonadotropin secretion from the pituitary gland. An appropriate location of GnRH neurons within the hypothalamus is necessary for normal reproductive function in the adult; abnormal migration and targeting of GnRH neurons during embryogenesis results in hypogonadism and infertility. The developmental migration of GnRH neurons and axonal pathfinding in mammals are modulated by a plethora of factors, including receptors, secreted molecules, adhesion molecules, etc. Yet the exact mechanism that controls these developmental events is still unknown. We investigated these developmental events and the underlying mechanisms using a transgenic zebrafish model, Tg(gnrh1: EGFP), in which GnRH1 neurons and axons are fluorescently labeled. The role of factors that potentially affect the development of this system was investigated by testing the effect of their knockdown and mutation on the development of the GnRH1 system. In addition, their localization in relation to GnRH1 was described during development. These studies are expected to generate the scientific foundation that will lead to developing innovative technologies, based on the disruption of the early establishment of the GnRH system, for inducing sterility in farmed fish, which is highly desirable for economical and environmental reasons.
9
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.
Full text
10
Johnson, Don H. Simulation of Excitatory/Inhibitory Interactions in Single Auditory Neurons. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada253614.
Full text