Dissertations / Theses on the topic 'Spike'
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Ervin, Brian. "Neural Spike Detection and Classification Using Massively Parallel Graphics Processing." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868773.
Full textFisch, Karin. "The contribution of spike-frequency adaptation to the variability of spike responses in a sensory neuron." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-135111.
Full textBergheim, Thomas Stian, and Arve Aleksander Nymo Skogvold. "Parallel Algorithms for Neuronal Spike Sorting." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14199.
Full textBarsakcioglu, Deren. "Resource efficient on-node spike sorting." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/34385.
Full textEmhemmed, Yousef Mohammed. "Maximum likelihood analysis of neuronal spike trains." Thesis, University of Glasgow, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326019.
Full textZhao, Chenyuan. "Spike Processing Circuit Design for Neuromorphic Computing." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/93591.
Full textDoctor of Philosophy
Neuromorphic computing is a kind of specific electronic system that could mimic biological bodies’ behavior. In most cases, neuromorphic computing system is built with analog circuits which have benefits in power efficient and low thermal radiation. Among neuromorphic computing system, one of the most important components is the signal processing interface, i.e. encoder/decoder. To increase the whole system’s performance, novel encoders and decoders have been proposed in this dissertation. In this dissertation, three kinds of temporal encoders, one rate encoder, one latency encoder, one temporal decoder, and one general spike decoder have been proposed. These designs could be combined together to build high efficient spike-based data link which guarantee the processing performance of whole neuromorphic computing system.
Guido, Rodrigo Capobianco. "Spikelet: uma nova transformada wavelet aplicada ao reconhecimento digital de padrões, em tempo real, de spikes e overlaps em sinais neurofisiológicos do campo visual da mosca." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-11092008-172109/.
Full textThis thesis describes the construction of a new wavelet transform, that is called SPIKELET, which is used together with a proposed algorithm, for spikes and overlaps pattern recognition, in a digitalized signal corresponding to the H1 visual neuron action potential from a Diptera\'s fly brain. The algorithm provides both the shape of the identified signal and the \'\'instant\'\' of time it happened. The implementation is also done in real time, using a DSP.
Kwag, Jeehyun. "Synaptic control of spike timing and spike timing-dependent plasticity during theta frequency oscillation in hippocampal CA1 pyramidal neurons." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487275.
Full textOzturk, Ibrahim. "Learning spatio-temporal spike train encodings with ReSuMe, DelReSuMe, and Reward-modulated Spike-timing Dependent Plasticity in Spiking Neural Networks." Thesis, University of York, 2017. http://etheses.whiterose.ac.uk/21978/.
Full textEchtermeyer, Christoph. "Causal pattern inference from neural spike train data." Thesis, St Andrews, 2009. http://hdl.handle.net/10023/843.
Full textEsnaola, Acebes Jose M. "Patterns of spike synchrony in neural field models." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663871.
Full textNeural field models are phenomenological descriptions of the activity of spatially organized, recurrently coupled neuronal networks. Due to their mathematical simplicity, such models are extremely useful for the analysis of spatiotemporal phenomena in networks of spiking neurons, and are largely used in computational neuroscience. Nevertheless, it is well known that traditional neural field descriptions fail to describe the collective dynamics of networks of synchronously spiking neurons. Yet, numerical simulations of networks of spiking neurons show that, even in the case of highly asynchronous activity, fast fluctuations in the common external inputs drive transient episodes of spike synchrony. Moreover, synchronization may also be generated by the network itself, resulting in the appearance of robust large-scale, self-sustained oscillations. In this thesis, we investigate the emergence of synchrony-induced spatiotemporal patterns in spatially distributed networks of heterogeneous spiking neurons. These patterns are not observed in traditional neural field theories and have been largely overlooked in the literature. To investigate synchrony-induced phenomena in neuronal networks, we use a novel neural field model which is exactly derived from a large population of quadratic integrate-and-fire model neurons. The simplicity of the neural field model allows us to analyze the stability of the network in terms of the spatial profile of the synaptic connectivity, and to obtain exact formulas for the stability boundaries characterizing the dynamics of the original spiking neuronal network. Remarkably, the analysis also reveals the existence of a collection of oscillation modes, which are exclusively due to spike-synchronization. We believe that the results presented in this thesis will foster theoretical advances on the collective dynamics of neuronal networks, upgrading the mathematical basis of computational neuroscience.
Hehl, Ulrich. "Embedding of synchronous spike activity in cortical networks." [S.l.] : [s.n.], 2001. http://www.freidok.uni-freiburg.de/volltexte/340/.
Full textLundqvist, Mikael. "Oscillations and spike statistics in biophysical attractor networks." Doctoral thesis, Stockholms universitet, Numerisk analys och datalogi (NADA), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-93316.
Full textAt the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper8: In press.
Avila, Akerberg Oscar. "Spike patterns optimize information transmission in neural populations." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=104710.
Full textEn présence d'un stimulus, tel que la lumière ou le son, les neurones sensoriels répondent par des séquences temporelles d'impulsions électriques, appelées également des potentiels d'action. Il est généralement accepté que ces séquences temporelles de potentiels d'action acheminent des informations concernant le stimulus, cependant, la façon dont les neurones transmettent ces informations est difficile à comprendre, car les neurones agissent selon une dynamique complexe. C'est le cas, par exemple, lorsqu'ils répondent par des groupes de potentiels d'action serrés suivis d'intervalles de calme -- phénomène connu sous le nom de bouffée -- ou lorsque les potentiels d'actions sont alternativement entrecoupés d'intervalles de temps courts et longs -- ce que l'on appelle motifs de mémoire. Quoiqu'on comprenne les mécanismes impliqués dans la production de ces séquences temporelles (modèles temporels), leur rôle fonctionnel est moins bien compris.Dans ce texte, nous avançons l'hypothèse que ces deux types de séquences de potentiels d'action pourraient optimiser la transmission d'information dans des populations de neurones. Pour vérifier cela, nous avons eu recours à des modèles numêriques de neurones, à la théorie mathématique et à des expériences électrophysiologiques. Nous avons étudié les bouffées au niveau des neurones uniques. Ensuite, nous avons comparé la transmission d'information dans des groupes de neurones qui démontrent des motifs de mémoire avec celle dans des groupes qui n'en démontrent pas. Nous avons constaté que la transmission d'information peut être régulée par des séquences de potentiels d'action : dans des réseaux de neurones couplés, l'addition de motifs de mémoire peut augmenter la transmission d'information lorsque les neurones sont couplés avec de l'excitation. Nous avons également constaté que les bouffées régulaient l'activité corrélée de neurones qui reçoivent un stimulus commun avec un contraste qui varie selon le temps. Nos résultats suggèrent que les séquences de pic pourraient jouer un rôle important dans la modulation de la transmission d'information dans des populations de neurones.
Monk, Scott. "Neural response modelling and spike rate estimation techniques." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=123255.
Full textUn processus de point pour modeler des séquences de piques neuraux permet l'application des techniques d'estimation classiques dans leur analyse. L'estimation du taux variable de temps auquel les piques ont lieu est souvent faite afin de trouver l'inférence sur le stimulus qui déclenche la réaction. Ces schémas d'estimation sont souvent basés sur la suppositionque la fréquence de piques élevés suit les statistiques Poisson. Cependant, le taux depiques est un produit du stimulus et des propriétés biophysiques du neurone. Un modèle de processus de point pour les données neuraux doit intégrer la dépendance du stimulus etdes propriétés intrinsèques de la cellule. À cet effet, on modifie le modèle Poisson pour qu'il inclue le phénomène réfractaire observé dans le comportement piquant. Selon ce modèle ajusté, on présente la technique d'estimation Maximum de Vraisemblance (MV) pour letaux de tir qui provoque la réaction piquante. On propose et justifie un modèle paramétrique pour représenter des taux de tir arbitraireset extensifs. L'équation de vraisemblance correspondante pour les paramètres detaux de tir se produit quand une séquence piquante est dérivée. Néanmoins, plusieurs méthodes numériques sont requises pour trouver l'estimation du MV. Ces techniques sont présentées en détail et incluent la sélection d'ordre modèle et l'optimisation non convexe. Une étude empirique, menée afin de déterminer quelle règle de sélection de modèle etinspirée de plusieurs approches trouvées dans la littérature, est la plus exacte. La maximisation globale de l'équation de vraisemblance non convexe est menée en se servant d'une méthode de transformation qui est connue comme une fonction de remplissage. Des simulations informatiques montrent que notre estimateur proposé livre des estimations de taux de tir plus exactes qu'un schéma semblable de Poisson quand les données sont affectées par une période réfractaire. Les résultats démontrent que l'erreur est relativement constante à travers les ensembles de données influencés par plusieurs périodes réfractaires,ce qui indique un estimateur robuste. Les estimations de taux de tir sur des réelles données prises de plusieurs cortex montrent aussi une bonté de convenance (goodness of fit)lorsqu'elles sont contrastées avec les résultats de l'estimateur Poisson. Une comparaison de performance avec d'autres schémas d'estimation populaires suggère que des estimations supérieures sont produites par notre schéma proposé.
Liu, Daqi. "Deep visual learning with spike-timing dependent plasticity." Thesis, University of Lincoln, 2017. http://eprints.lincoln.ac.uk/28660/.
Full textPagin, Matteo [Verfasser]. "Data compression of neural spike signals / Matteo Pagin." Ulm : Universität Ulm, 2021. http://d-nb.info/1232815179/34.
Full textAgarwal, Anjali. "Bayesian variable selection with spike-and-slab priors." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461940937.
Full textCarey, Howard J. III. "EEG Interictal Spike Detection Using Artificial Neural Networks." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4648.
Full textDoig, Henry Ross. "An investigation of the pre-saccadic spike potential." Thesis, Aston University, 1990. http://publications.aston.ac.uk/14625/.
Full textMehboob, Zareen. "Information quantification for spike trains and field potentials." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/information-quantification-for-spike-trains-and-field-potentials(41093f37-7838-41bc-aeb4-1d45f34e2bb8).html.
Full textBorel, Melodie Jeanine Marie. "Spike phase control of mouse hippocampal pyramidal cells." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708146.
Full textMusick, James R. "Mechanisms of spike-frequency adaptation in hypoglossal motoneurons /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/10550.
Full textSchleimer, Jan Hendrik. "Spike statistics and coding properties of phase models." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2013. http://dx.doi.org/10.18452/16788.
Full textThe goal of the thesis is to establish quantitative, analytical relations between the biophysical properties of nerve membranes and the performed neuronal computations for neurons in a tonically spiking regime and in the presence of intrinsic noise. For this purpose, two major lines of investigation are followed. Firstly, microscopic noise caused by the stochastic opening and closing of ion channels is mapped to the macroscopic spike jitter that affects neural coding. The method is generic enough to allow one to treat Markov channel models with complicated, high-dimensional state spaces and calculate from them the noise in the coding variable, i.e., the spike time. Secondly, the suprathreshold filtering properties of neurons are derived, based on the phase response curves (PRCs) by perturbing the associated Fokker-Planck equations. It turns out that key characteristics of the filter, such as the DC component of the gain and the behaviour near the fundamental frequency and its harmonics are related to the particular Fourier components of the PRC and hence the bifurcation type of the neuron. With the help of the derived filter and further approximations one is able to calculate the frequency resolved signal-to-noise ration and finally the total information transmission rate of a conductance based model. Using the method of numerical continuation it is possible to calculate the change in spike time noise level as well as the filtering properties for arbitrary changes in biophysical parameter such as varying channel densities or mean input to the cell. We extend the phase reduction to include correction terms from the amplitude dynamics that are related to the curvature of the isochrons and provide a method to identify the required amplitude sensitivities numerically. It can be shown that the curvature of the isochron has a direct consequence for the noise induced frequency shift.
Fisch, Karin [Verfasser], and Andreas [Akademischer Betreuer] Herz. "The contribution of spike-frequency adaptation to the variability of spike responses in a sensory neuron / Karin Fisch. Betreuer: Andreas Herz." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2011. http://d-nb.info/1015925200/34.
Full textMalvestio, Irene. "Detection of directional interactions between neurons from spike trains." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666226.
Full textUn problema important en la neurociència és determinar la connexió entre neurones utilitzant dades dels seus trens d’impulsos. Un mètode recent que afronta la detecció de connexions direccionals entre dinàmiques utilitzant processos puntuals és la mesura d’interdependència no lineal L. En aquesta tesi, utilitzem el model de Hindmarsh-Rose per testejar L en presència de soroll i per diferents règims dinàmics. Després comparem el desempenyorament de L en comparació al correlograma lineal i a dues mesures de trens d’impulsos. Finalment, apliquem totes aquestes mesures a dades d’impulsos de neurones obtingudes de senyals intracranials electroencefalogràfiques gravades durant una nit a un pacient amb epilèpsia. Quan utilitzem dades simulades, L demostra que és versàtil, robusta i més sensible que les mesures lineals. En canvi, utilitzant dades reals, les mesures lineals troben més connexions que L, especialment entre neurones en la mateixa àrea del cervell i durant la fase de son d’ones lentes.
Miller, Christopher L. "Variation in single kernel hardness within the wheat spike." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/925.
Full textDavie, Jennifer Thorpe. "Generation of the complex spike in cerebellar Purkinje cells." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/1445224/.
Full textMaraš, Mirjana. "Learning efficient signal representation in sparse spike-coding networks." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE023.
Full textThe complexity of sensory input is paralleled by the complexity of its representation in the neural activity of biological systems. Starting from the hypothesis that biological networks are tuned to achieve maximal efficiency and robustness, we investigate how efficient representation can be accomplished in networks with experimentally observed local connection probabilities and synaptic dynamics. We develop a Lasso regularized local synaptic rule, which optimizes the number and efficacy of recurrent connections. The connections that impact the efficiency the least are pruned, and the strength of the remaining ones is optimized for efficient signal representation. Our theory predicts that the local connection probability determines the trade-off between the number of population spikes and the number of recurrent synapses, which are developed and maintained in the network. The more sparsely connected networks represent signals with higher firing rates than those with denser connectivity. The variability of observed connection probabilities in biological networks could then be seen as a consequence of this trade-off, and related to different operating conditions of the circuits. The learned recurrent connections are structured, with most connections being reciprocal. The dimensionality of the recurrent weights can be inferred from the network’s connection probability and the dimensionality of the feedforward input. The optimal connectivity of a network with synaptic delays is somewhere at an intermediate level, neither too sparse nor too dense. Furthermore, when we add another biological constraint, adaptive regulation of firing rates, our learning rule leads to an experimentally observed scaling of the recurrent weights. Our work supports the notion that biological micro-circuits are highly organized and principled. A detailed examination of the local circuit organization can help us uncover the finer aspects of the principles which govern sensory representation
Roy, Dipanjan. "Phase representation of Spike-Burst neurons in a network." Thesis, Aix-Marseille 2, 2011. http://www.theses.fr/2011AIX22057.
Full textThe important relationship between structure and function has always been a fundamental question in neuroscience research. In particular in the case of movement, brain controls large groups of muscles and combines it with sensory informations from the environment to execute purposeful motor behavior. Mapping dynamics encoded in a high dimensional neural space onto low-dimensional behavioral space has always been a difficult challenge as far as theory is concerned. Here, we develope a framework to study spike/burst dynamics having low dimensional phase description, which can readily be extended under certain biological constraints on the coupling to low dimensional functional descriptions. In general, phase models are amongst the simplest of neuron models reproducing spike-burst behavior, excitability and bifurcations towards periodic firing. However, the coupling among neurons has only been considered using generic arguments valid close to the bifurcation point, and the distinction between electric and synaptic coupling remains an open question. In this thesis we aim to address this question and derive a mathematical formulation for the various forms of biologically realistic coupling. We begin by constructing a mathematical model based on a planar simplification of the Morris-Lecar model. Using geometric arguments we then derive a phase description of a network of neurons with biologically realistic electric coupling and subsequently with chemical coupling under the fast synapse approximation. We then demonstrate that electric and synaptic coupling are expressed differently on the level of the network’s phase description, exhibiting qualitatively different dynamics. Our numerical investigations confirm these findings and show excellent correspondence between the dynamics of the full network and the network’s phase description. Following the success of the phase description of the spiking neural network, we extend this approach in order to propose a generating mechanism for parabolic bursting captured by only a single phase variable. This is the first model in the literature which captures bursting dynamics in one dimension. In order to study the emergent behavior we extend this to a network of bursters with global coupling and analytically reduce a high dimensional system to only two dimensions. Further, we investigate the bifurcation properties numerically as well as analytically. One of the key conclusion is that the stability states remain invariant to the increasing number of spikes per burst. Finally we investigate a spikeburst neuron network coupled via mean field type of fast synapses developed in this thesis and systematically carry out a detailed bifurcation analysis of the model, for a tractable special case. Numerical simulations investigate this mean field model beyond special case and clearly reveals qualitative correspondence with the full network model. Moreover, these network displays rich collective dynamics as a function of two parameters, mainly the synaptic coupling strength and the width of the distribution in applied stimulus. Besides incoherence, frequency locking, and oscillator death (a total cessation of firing caused by excessively strong coupling), there exist multistable solutions in the full and the phase network of neurons
Taylor, Peter. "Development of compartment models of epileptic spike-wave discharges." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/development-of-compartment-models-of-epileptic-spikewave-discharges(4f6f4ff6-f5cd-451f-a806-39590b58468e).html.
Full textLinderman, Scott Warren. "Bayesian Methods for Discovering Structure in Neural Spike Trains." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493391.
Full textEngineering and Applied Sciences - Computer Science
Chen, Yan. "Spike pattern analysis of slowly adapting pulmonary stretch receptors." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 135 p, 2009. http://proquest.umi.com/pqdweb?did=1818417461&sid=7&Fmt=2&clientId=8331&RQT=309&VName=PQD.
Full textCao, Shiyan Burdick Joel Wakeman. "Spike train characterization and decoding for neural prosthetic devices /." Diss., Pasadena, Calif. : California Institute of Technology, 2004. http://resolver.caltech.edu/CaltechETD:etd-07232003-012018.
Full textWu, Shang-Rung. "Activation of the spike proteins of alpha- and retroviruses." Stockholm, 2009. http://diss.kib.ki.se/2009/978-91-7409-736-8/.
Full textWaddington, Amelia. "Growing synfire chains with triphasic spike-time-dependent plasticity." Thesis, University of Leeds, 2011. http://etheses.whiterose.ac.uk/1758/.
Full textCui, Yihui. "The many faces of corticostriatal spike-timing dependent plasticity." Paris 6, 2013. http://www.theses.fr/2013PA066398.
Full textThe corticostriatal plasticity is thought to be the neuronal substrate of procedural learning. We first investigated non-hebbian plasticity and found that both depolarization-induced suppression of excitation (DSE) and low-frequency stimulation (LFS) protocols induced LTD and are both mediated by endocannabinoid (eCB)-signaling. We then focused on corticostriatal spike timing-dependent plasticity (STDP) characterization and robustness. We found that with 100 STDP pairings, corticostriatal LTP was NMDA-dependent while LTD involved eCB-signaling. We then tested the robustness of corticostriatal STDP. We uncovered that LTP was even inducible with 5 pairings. Thanks to a model-driven experiment strategy, we demonstrated that this LTP relies on eCB-signaling. This eCB-LTP is homosynaptic, depends on cannabinoid-type-1 receptor (CB1R) and transient receptor potential vanilloid-type-1 (TRPV1) activation and is supported by presynaptic PKA and calcineurin. Our results considerably enlarge the spectrum of action of eCBs since they show that eCBs promote not only depression but also potentiation. To investigate the limits of corticostriatal STDP, we varied the STDP rate. We observed a transition from timing- to rate-dependent plasticity. This rate-dependency exists with both 100 and 10 pairings, in which LTP is respectively NMDA-dependent and CB1 and NMDA receptors. We then applied a randomized jitter within STDP protocol. We showed that NMDA-LTP is highly sensitive to jitter while eCB-LTP is not. These results showed novel forms of corticostriatal plasticity and demonstrated that the multiple learning rules at play for governing the corticostriatal information processing
Oakley, John Christopher. "The role of calcium spikes in neocortical pyramidal cell dendrites : implications for the transduction of dendritic current into spike output /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/10525.
Full textKoppolu, Ravi [Verfasser], Andreas [Akademischer Betreuer] Graner, and Takao [Akademischer Betreuer] Komatsuda. "Six-rowed spike 4 (Vrs4) regulates spike architecture and lateral spikelet fertility in barley (Hordeum vulgare L.) / Ravi Koppolu. Betreuer: Andreas Graner ; Takao Komatsuda." Halle, Saale : Universitäts- und Landesbibliothek Sachsen-Anhalt, 2014. http://d-nb.info/1067842543/34.
Full textPazienti, Antonio. "Manipulations of spike trains and their impact on synchrony analysis." Phd thesis, Universität Potsdam, 2007. http://opus.kobv.de/ubp/volltexte/2008/1744/.
Full textDie Informationsverarbeitung im Gehirn erfolgt maßgeblich durch interaktive Prozesse von Nervenzellen, sogenannten Neuronen. Diese zeigen eine komplexe Dynamik ihrer chemischen und elektrischen Eigenschaften. Es gibt deutliche Hinweise darauf, dass Gruppen synchronisierter Neurone letztlich die Funktionsweise des Gehirns auf allen Ebenen erklären können. Um die schwierige Frage nach der genauen Funktionsweise des Gehirns zu beantworten, ist es daher notwendig, die Aktivität vieler Neuronen gleichzeitig zu messen. Die technischen Voraussetzungen hierfür sind in den letzten Jahrzehnten durch Multielektrodensyteme geschaffen worden, die heute eine breite Anwendung finden. Sie ermöglichen die simultane extrazelluläre Ableitung von bis zu mehreren hunderten Kanälen. Die Voraussetzung für die Korrelationsanalyse von vielen parallelen Messungen ist zunächst die korrekte Erkennung und Zuordnung der Aktionspotentiale einzelner Neurone, ein Verfahren, das als Spikesortierung bezeichnet wird. Eine weitere Herausforderung ist die statistisch korrekte Bewertung von empirisch beobachteten Korrelationen. Mit dieser Dissertationsschrift lege ich eine theoretische Arbeit vor, die sich der Vorverarbeitung der Daten durch Spikesortierung und ihrem Einfluss auf die Genauigkeit der statistischen Auswertungsverfahren, sowie der Effektivität zur Erstellung von Surrogatdaten für die statistische Signifikanzabschätzung auf Korrelationen widmet. Ich verwende zwei komplementäre Strategien, die beide auf der analytischen Berechnung von Punktprozessmanipulationen basieren. In einer ausführlichen Studie habe ich den Effekt von Spikesortierung in mit realistischen Fehlern behafteten korrelierten Spikefolgen modeliert. Zum Vergleich der Ergebnisse zweier unterschiedlicher Methoden zur Korrelationsanalyse auf den gestörten, sowie auf den ungestörten Prozessen, leite ich die entsprechenden analytischen Formeln her. Meine Ergebnisse zeigen, dass koinzidente Aktivitätsmuster multipler Spikefolgen durch Spikeklassifikation erheblich beeinflusst werden. Das ist der Fall, wenn Neuronen nur fälschlicherweise Spikes zugeordnet werden, obwohl diese anderen Neuronen zugehörig sind oder Rauschartefakte sind (falsch positive Fehler). Jedoch haben falsch-negative Fehler (fälschlicherweise nicht-klassifizierte oder missklassifizierte Spikes) einen weitaus grösseren Einfluss auf die Signifikanz der Korrelationen. In einer weiteren Studie untersuche ich die Effektivität einer Klasse von Surrogatmethoden, sogenannte Ditheringverfahren, welche paarweise Korrelationen zerstören, in dem sie koinzidente Spikes von ihrer ursprünglichen Position in einem kleinen Zeitfenster verrücken. Es zeigt sich, dass die Effektivität von Spike-Dithering zur Erzeugung von Surrogatdaten sowohl von der Dithermethode als auch von der Methode zur Koinzidenzzählung abhängt. Für die Wahrscheinlichkeit der Koinzidenzerkennung nach dem Dithern stelle ich analytische Formeln zur Verfügung. Die vorliegende Arbeit bietet neue Einblicke in die Methoden zur Korrelationsanalyse auf multi-variaten Punktprozessen mit einer genauen Untersuchung von unterschiedlichen statistischen Einflüssen auf die Signifikanzabschätzung. Für die praktische Anwendung ergeben sich Leitlinien für den Umgang mit Daten zur Synchronizitätsanalyse.
Burroughs, Amelia Caroline. "Electrophysiological and computational studies of Purkinje cell complex spike dynamics." Thesis, University of Bristol, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.720839.
Full textStreet, Sarah Elizabeth Manis Paul B. "Spike timing in pyramidal cells of the dorsal cochlear nucleus." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2007. http://dc.lib.unc.edu/u?/etd,1012.
Full textTitle from electronic title page (viewed Dec. 18, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Cell and Molecular Physiology." Discipline: Cell and Molecular Physiology; Department/School: Medicine.
Na, Yu. "Stochastic phase dynamics in neuron models and spike time reliability." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/7383.
Full textSomerville, Jared. "The exploration of neurophysiological spike train data using visual analytics." Thesis, University of Plymouth, 2011. http://hdl.handle.net/10026.1/897.
Full textJenkner, Carolin [Verfasser], and Martin [Akademischer Betreuer] Schumacher. "Multivariable modeling of continuous covariates with a spike at zero." Freiburg : Universität, 2018. http://d-nb.info/1162054719/34.
Full textIzadkhasti, Sousan. "Generation of recombinant infectious bronchitis viruses with chimaeric spike proteins." Thesis, Royal Veterinary College (University of London), 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.441413.
Full textHill, Raymond Andrew IV. "Simulation of spike stall inception in a radial vanted diffuser." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42048.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 83-85).
In turbocharger application bleed air at impeller exit is typically used to seal bearing compartments and to balance axial thrust in the rotor. It was previously shown that this bleed air can have a significant impact on both compressor performance and stability. Experiments suggest that spike stall inception in centrifugal compressors can be formed by a vaned diffuser. To address these issues, a numerical study on an advanced, vaned-diffuser centrifugal compressor was conducted to investigate stall inception. A steady three-dimensional Reynolds-averaged Navier-Stokes simulation using a mixing plane was carried out first to evaluate the effects of bleed air at impeller exit on stage and diffuser subcomponent performance. The steady simulation was compared with experimental measurements and did not show significant changes in stage and subcomponent performance due to leakage flow as observed in the experiments, indicating the importance of unsteady flow effects in the vaneless space and adjacent bleed cavity. Next, an unsteady three-dimensional Reynolds-averaged Navier-stokes simulation was carried out on four vaned diffuser passages to investigate the response of the diffuser flow field to short wavelength inlet disturbances in total pressure. The simulation employed a new approach, using circumferentially-averaged diffuser inlet conditions obtained from the steady stage simulation, eliminating the impeller and significantly reducing the computational time. This method was capable of simulating spike-like stall precursors rotating at 66% rotor speed which formed in response to inlet flow disturbances. The results represent a first numerical simulation of rotating spike-like flow disturbances in a radial vaned diffuser, and suggest that the spike stall precursors are formed by the vaned diffuser in absence of a tip leakage flow as it can occur in the rotors of axial compressors.
by Raymond Andrew Hill, IV.
S.M.
Monzon, Joshua Jen C. "Analog VLSI circuit design of spike-timing-dependent synaptic plasticity." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/54636.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 61-63).
Synaptic plasticity is the ability of a synaptic connection to change in strength and is believed to be the basis for learning and memory. Currently, two types of synaptic plasticity exist. First is the spike-timing-dependent-plasticity (STDP), a timing-based protocol that suggests that the efficacy of synaptic connections is modulated by the relative timing between presynaptic and postsynaptic stimuli. The second type is the Bienenstock-Cooper-Munro (BCM) learning rule, a classical ratebased protocol which states that the rate of presynaptic stimulation modulates the synaptic strength. Several theoretical models were developed to explain the two forms of plasticity but none of these models came close in identifying the biophysical mechanism of plasticity. Other studies focused instead on developing neuromorphic systems of synaptic plasticity. These systems used simple curve fitting methods that were able to reproduce some types of STDP but still failed to shed light on the biophysical basis of STDP. Furthermore, none of these neuromorphic systems were able to reproduce the various forms of STDP and relate them to the BCM rule. However, a recent discovery resulted in a new unified model that explains the general biophysical process governing synaptic plasticity using fundamental ideas regarding the biochemical reactions and kinetics within the synapse. This brilliant model considers all types of STDP and relates them to the BCM rule, giving us a fresh new approach to construct a unique system that overcomes all the challenges that existing neuromorphic systems faced. Here, we propose a novel analog verylarge- scale-integration (aVLSI) circuit that successfully and accurately captures the whole picture of synaptic plasticity based from the results of this latest unified model. Our circuit was tested for all types of STDP and for each of these tests, our design was able to reproduce the results predicted by the new-found model. Two inputs are required by the system, a glutamate signal that carries information about the presynaptic stimuli and a dendritic action potential signal that contains information about the postsynaptic stimuli. These two inputs give rise to changes in the excitatory postsynaptic current which represents the modifiable synaptic efficacy output. Finally, we also present several techniques and alternative circuit designs that will further improve the performance of our neuromorphic system.
by Joshua Jen C. Monzon.
M.Eng.
Oliveira, Alexandre (Alexandre S. ). "Finding patterns in timed data with spike timing dependent plasticity." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/77031.
Full textCataloged from PDF version of thesis.
My research focuses on finding patterns in events - in sequences of data that happen over time. It takes inspiration from a neuroscience phenomena believed to be deeply involved in learning. I propose a machine learning algorithm that finds patterns in timed data and is highly robust to noise and missing data. It can find both coincident relationships, where two events tend to happen together; as well as causal relationships, where one event appears to be caused by another. I analyze stock price information using this algorithm and strong relationships are found between companies within the same industry. In particular, I worked with 12 stocks taken from the banking, information technology, healthcare, and oil industries. The relationships are almost exclusively coincidental, rather than causal.
by Alexandre Oliveira.
M.Eng.
Xie, Xiaohui 1972. "Spike-based learning rules and stabilization of persistent neural activity." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86625.
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