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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.
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Fisch, 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.
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Bergheim, 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.
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Neurons communicate through electrophysiological signals, which may be recorded using electrodes inserted into living tissue.When a neuron emits a signal, it is referred to as a spike, and an electrode can detect these from multiple neurons.Neuronal spike sorting is the process of classifying the spike activity based on which neuron each spike signal is emitted from.Advances in technology have introduced better recording equipment, which allows the recording of many neurons at the same time.However, clustering software is lagging behind.Currently, spike sorting is often performed semi-manually by experts, with computer assistance, in a drastically reduced feature space.This makes the clustering prone to subjectivity.Automating the process will make classification much more efficient, and may produce better results.Implementing accurate and efficient spike sorting algorithms is therefore increasingly important.We have developed parallel implementations of superparamagnetic clustering, a novel clustering algorithm, as well as k-means clustering, serving as a useful comparison.Several feature extraction methods have been implemented to test various input distributions with the clustering algorithms. To assess the quality of the results from the algorithms, we have also implemented different cluster quality algorithms.Our implementations have been benchmarked, and found to scale well both with increased problem sizes and when run on multi-core processors.The results from our cluster quality measurements are inconclusive, and we identify this as a problem related to the subjectivity in the manually classified datasets.To better assess the utility of the algorithms, comparisons with intracellular recordings should be performed.
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Barsakcioglu, Deren. "Resource efficient on-node spike sorting." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/34385.
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Current implantable brain-machine interfaces are recording multi-neuron activity by utilising multi-channel, multi-electrode micro-electrodes. With the rapid increase in recording capability has come more stringent constraints on implantable system power consumption and size. This is even more so with the increasing demand for wireless systems to increase the number of channels being monitored whilst overcoming the communication bottleneck (in transmitting raw data) via transcutaneous bio-telemetries. For systems observing unit activity, real-time spike sorting within an implantable device offers a unique solution to this problem. However, achieving such data compression prior to transmission via an on-node spike sorting system has several challenges. The inherent complexity of the spike sorting problem arising from various factors (such as signal variability, local field potentials, background and multi-unit activity) have required computationally intensive algorithms (e.g. PCA, wavelet transform, superparamagnetic clustering). Hence spike sorting systems have traditionally been implemented off-line, usually run on work-stations. Owing to their complexity and not-so-well scalability, these algorithms cannot be simply transformed into a resource efficient hardware. On the contrary, although there have been several attempts in implantable hardware, an implementation to match comparable accuracy to off-line within the required power and area requirements for future BMIs have yet to be proposed. Within this context, this research aims to fill in the gaps in the design towards a resource efficient implantable real-time spike sorter which achieves performance comparable to off-line methods. The research covered in this thesis target: 1) Identifying and quantifying the trade-offs on subsequent signal processing performance and hardware resource utilisation of the parameters associated with analogue-front-end. Following the development of a behavioural model of the analogue-front-end and an optimisation tool, the sensitivity of the spike sorting accuracy to different front-end parameters are quantified. 2) Identifying and quantifying the trade-offs associated with a two-stage hybrid solution to realising real-time on-node spike sorting. Initial part of the work focuses from the perspective of template matching only, while the second part of the work considers these parameters from the point of whole system including detection, sorting, and off-line training (template building). A set of minimum requirements are established which ensure robust, accurate and resource efficient operation. 3) Developing new feature extraction and spike sorting algorithms towards highly scalable systems. Based on waveform dynamics of the observed action potentials, a derivative based feature extraction and a spike sorting algorithm are proposed. These are compared with most commonly used methods of spike sorting under varying noise levels using realistic datasets to confirm their merits. The latter is implemented and demonstrated in real-time through an MCU based platform.
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Emhemmed, 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.
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Zhao, Chenyuan. "Spike Processing Circuit Design for Neuromorphic Computing." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/93591.
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Von Neumann Bottleneck, which refers to the limited throughput between the CPU and memory, has already become the major factor hindering the technical advances of computing systems. In recent years, neuromorphic systems started to gain increasing attention as compact and energy-efficient computing platforms. Spike based-neuromorphic computing systems require high performance and low power neural encoder and decoder to emulate the spiking behavior of neurons. These two spike-analog signals converting interface determine the whole spiking neuromorphic computing system's performance, especially the highest performance. Many state-of-the-art neuromorphic systems typically operate in the frequency range between 〖10〗^0KHz and 〖10〗^2KHz due to the limitation of encoding/decoding speed. In this dissertation, all these popular encoding and decoding schemes, i.e. rate encoding, latency encoding, ISI encoding, together with related hardware implementations have been discussed and analyzed. The contributions included in this dissertation can be classified into three main parts: neuron improvement, three kinds of ISI encoder design, two types of ISI decoder design. Two-path leakage LIF neuron has been fabricated and modular design methodology is invented. Three kinds of ISI encoding schemes including parallel signal encoding, full signal iteration encoding, and partial signal encoding are discussed. The first two types ISI encoders have been fabricated successfully and the last ISI encoder will be taped out by the end of 2019. Two types of ISI decoders adopted different techniques which are sample-and-hold based mixed-signal design and spike-timing-dependent-plasticity (STDP) based analog design respectively. Both these two ISI encoders have been evaluated through post-layout simulations successfully. The STDP based ISI encoder will be taped out by the end of 2019. A test bench based on correlation inspection has been built to evaluate the information recovery capability of the proposed spiking processing link.Doctor 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.
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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/.
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A presente tese descreve a construção de uma nova transformada wavelet, aqui chamada de SPIKELET, que, combinada com um algoritmo proposto, é aplicada no reconhecimento computacional de padrões em spikes (picos) e spikes sobrepostos (overlaps) encontrados no sinal digitalizado correspondente às reações do neurônio H1 do cérebro de uma mosca de ordem Diptera, que é sensível aos estímulos visuais do meio externo. O algoritmo fornece, além do formato do sinal encontrado, o \'\'instante\'\' em que ele ocorreu, sendo que a implementação é feita, inclusive, em tempo-real, com o uso de um DSP.This 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.
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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.
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Spike timing during oscillation has been suggested to play an important role in hippocampal processing. However, how the hippocampal network and the individual neurons interact to precisely control spike timing when they receive synaptic inputs from two major excitatory input pathways - Schaffer collateral and perforant path - during natural network oscillation is yet unknown. Investigation of spike timing control mechanism would shed light on how the local STDP learning rule could be influenced by different cortical inputs during theta oscillation. Here I used whole-cell path-clamp recording of CAl pyramidal neurons in vitro and dynamic clamp to simulate in vivo-like theta frequency oscillation at the soma to characterise the spike timing responses of CAl pyramidal neurons to Schaffer collateral and perforant path inputs during theta oscillation and present them as phase response curves (PRCs), Analysis of PRCs revealed that postsynaptic spike times could not only be advanced but also be delayed depending on the timing of excitatory inputs relative to the oscillation. Such control of spike timing during theta oscillation was dependent on the synaptic weight of the input and the frequency of the oscillation. Ih and GABAB receptor-mediated inhibition were identified as an intrinsic and synaptic mechanism, respectively, underlying spike time delay during oscillation. Activation of both Ih and GABAi3 receptor-mediated inhibition by perforant path stimulation contributed to greater spike time delay compared to that with Schaf.:fer collateral input stimulation which was only mediated by Ih. Such different spike timing characteristics were important in STDP induction at the Schaffer collateral-CAl pyramidal cell synapse, Depending on the timing of the perforant path activation during theta oscillation, perforant path input could control the timing of the postsynaptic spike during STDP induction which could reverse the sign of the synaptic modification, Thus, during natural network oscillation with multiple synaptic inputs active, timing of the heterosynaptic inputs from entorhinal cortex to the hippocampus could control the outcome of the homosynaptic plasticity in the CAL These results may have implications for how the external information could be encoded and stored in the hippocampal network.
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Ozturk, 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/.
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SNNs are referred to as the third generation of ANNs. Inspired from biological observations and recent advances in neuroscience, proposed methods increase the power of SNNs. Today, the main challenge is to discover efficient plasticity rules for SNNs. Our research aims are to explore/extend computational models of plasticity. We make various achievements using ReSuMe, DelReSuMe, and R-STDP based on the fundamental plasticity of STDP. The information in SNNs is encoded in the patterns of firing activities. For biological plausibility, it is necessary to use multi-spike learning instead of single-spike. Therefore, we focus on encoding inputs/outputs using multiple spikes. ReSuMe is capable of generating desired patterns with multiple spikes. The trained neuron in ReSuMe can fire at desired times in response to spatio-temporal inputs. We propose alternative architecture for ReSuMe dealing with heterogeneous synapses. It is demonstrated that the proposed topology exactly mimic the ReSuMe. A novel extension of ReSuMe, called DelReSuMe, has better accuracy using less iteration by using multi-delay plasticity in addition to weight learning under noiseless and noisy conditions. The proposed heterogeneous topology is also used for DelReSuMe. Another plasticity extension based on STDP takes into account reward to modulate synaptic strength named R-STDP. We use dopamine-inspired STDP in SNNs to demonstrate improvements in mapping spatio-temporal patterns of spike trains with the multi-delay mechanism versus single connection. From the viewpoint of Machine Learning, Reinforcement Learning is outlined through a maze task in order to investigate the mechanisms of reward and eligibility trace which are the fundamental in R-STDP. To develop the approach we implement Temporal-Difference learning and novel knowledge-based RL techniques on the maze task. We develop rule extractions which are combined with RL and wall follower algorithms. We demonstrate the improvements on the exploration efficiency of TD learning for maze navigation tasks.
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Echtermeyer, Christoph. "Causal pattern inference from neural spike train data." Thesis, St Andrews, 2009. http://hdl.handle.net/10023/843.
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Esnaola, Acebes Jose M. "Patterns of spike synchrony in neural field models." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663871.
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Els models neuronals de camp mig són descripcions fenomenològiques de
l'activitat de xarxes de neurones espacialment organitzades. Gràcies a
la seva simplicitat, aquests models són unes eines extremadament útils
per a l'anàlisi dels patrons espai-temporals que apareixen a les
xarxes neuronals, i s'utilitzen àmpliament en neurociència
computacional. És ben sabut que els models de camp mig tradicionals no
descriuen adequadament la dinàmica de les xarxes de neurones si
aquestes actuen de manera síncrona. No obstant això, les simulacions
computacionals de xarxes neuronals demostren que, fins i tot en estats
d'alta asincronia, fluctuacions ràpides dels inputs comuns que arriben a
les neurones poden provocar períodes transitoris en els quals les
neurones de la xarxa es comporten de manera síncrona. A més a més, la
sincronització també pot ser generada per la mateixa xarxa, donant
lloc a oscil·lacions auto-sostingudes.
En aquesta tesi investiguem la presència de patrons espai-temporals deguts a la
sincronització en xarxes de neurones heterogènies i espacialment distribuïdes.
Aquests patrons no s'observen en els models tradicionals de camp mig,
i per aquest motiu han estat àmpliament ignorats en la literatura.
Per poder investigar la dinàmica induïda per l'activitat sincronitzada de les neurones,
fem servir un nou model de camp mig que es deriva exactament d'una població de
neurones de tipus quadratic integrate-and-fire. La
simplicitat del model ens permet analitzar l'estabilitat de la xarxa
en termes del perfil espacial de la connectivitat sinàptica, i obtenir
fórmules exactes per les fronteres d'estabilitat que caracteritzen la
dinàmica de la xarxa neuronal original. Aquest mateix anàlisi també
revela l'existència d'un conjunt de modes d'oscil·lació que es deuen
exclusivament a l'activitat sincronitzada de les neurones. Creiem que
els resultats presentats en aquesta tesi inspiraran nous avenços
teòrics relacionats amb la dinàmica col·lectiva de les xarxes neuronals,
contribuïnt així en el desenvolupament de la neurociència computacional.Neural 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.
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Hehl, Ulrich. "Embedding of synchronous spike activity in cortical networks." [S.l.] : [s.n.], 2001. http://www.freidok.uni-freiburg.de/volltexte/340/.
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Lundqvist, 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.
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The work of this thesis concerns how cortical memories are stored and retrieved. In particular, large-scale simulations are used to investigate the extent to which associative attractor theory is compliant with known physiology and in vivo dynamics. The first question we ask is whether dynamical attractors can be stored in a network with realistic connectivity and activity levels. Using estimates of biological connectivity we demonstrated that attractor memories can be stored and retrieved in biologically realistic networks, operating on psychophysical timescales and displaying firing rate patterns similar to in vivo layer 2/3 cells. This was achieved in the presence of additional complexity such as synaptic depression and cellular adaptation. Fast transitions into attractor memory states were related to the self-balancing inhibitory and excitatory currents in the network. In order to obtain realistic firing rates in the network, strong feedback inhibition was used, dynamically maintaining balance for a wide range of excitation levels. The balanced currents also led to high spike train variability commonly observed in vivo. The feedback inhibition in addition resulted in emergent gamma oscillations associated with attractor retrieval. This is congruent with the view of gamma as accompanying active cortical processing. While dynamics during retrieval of attractor memories did not depend on the size of the simulated network, above a certain size the model displayed the presence of an emergent attractor state, not coding for any memory but active as a default state of the network. This default state was accompanied by oscillations in the alpha frequency band. Such alpha oscillations are correlated with idling and cortical inhibition in vivo and have similar functional correlates in the model. Both inhibitory and excitatory, as well as phase effects of ongoing alpha observed in vivo was reproduced in the model in a simulated threshold-stimulus detection task.At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper8: In press.
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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.
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Sensory neurons respond to a stimulus, such as light or sound with temporal sequences of electric pulses known as action potentials. Action potentials carry information about a stimulus in temporal sequences. Little is known, however, about how groups of neurons convey information, in part because of the complexity of natural stimuli and also because of neurons' complex dynamics that make them respond with spike patterns. Spike patterns, a tightly packed group of action potentials followed by a quiet period, are also known as a burst. Another kind of pattern is alternating time intervals between action potentials: a long interval followed by a short one, followed by a long one, knownas a nonrenewal pattern. Here we hypothesize that both bursts and nonrenewalpatterns may optimize information transmission in populations of neurons. To testthis, we used a combination of numerical neuron models, mathematical theory andelectrophysiology experiments. We investigated burst firing at the single neuronlevel as well as correlations between the activity of bursting neurons. In addition, weexplored information transmission by nonrenewal patterns in populations of neurons.We found that bursts of action potentials can act as single units of information.Also, bursts regulate correlated activity in neurons. Moreover, nonrenewal patternsincrease information transmission in groups of neurons coupled with excitation. Ourresults have implications for information coding by neural populations. In particular,our results suggest that spike patterns may optimize information transmission inpopulations of neurons.En 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.
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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.
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Using point processes to model neural spike sequences allows the application of classical estimation techniques in their analysis. Estimation of the time varying rate at which spikes occur is often conducted to draw inference on the stimulus which triggered the response. Such estimation schemes are often founded on the assumption that spiking follows Poisson statistics, however, the observed firing rate is a product of both the stimulus and bio-physical properties of the neuron. A point process model for neural data must then incorporate dependency both on stimulus and intrinsic characteristics of the cell. To achieve this we modify the Poisson model such that it includes the refractory phenomenon observed in spiking behaviour. This results in a modified firing rate which is free from distortion caused by refractory effects. A Maximum Likelihood (ML) estimation technique for this adjusted firing rate which better represents some relation to the stimulus is presented. We propose and justify a parametric model to represent a broad class of arbitrary firing rates. The corresponding likelihood equation for the firing rate parameters given an observed spike sequence is derived, however, several numerical methods are required to findthe ML estimate. These techniques are presented in detail and include model order selection and non-convex optimization. An empirical study is conducted to determine which model selection rule, from several approaches found in the literature, is most accurate. Global maximization of the non-convex likelihood equation is carried out using a transformation method known as a filled function. Computer simulations show that our proposed estimator can potentially lead to more accurate estimates of firing rates, as opposed to a Poisson scheme, when the data is affected by a refractory period. Results demonstrate that the error is relatively constant across datasets influenced by a range of refractory periods, indicating the estimator is robust. Rate estimates on real neural data taken from various cortices also show improved goodness of fit when contrasted with results from the Poisson estimator. A brief performance comparison with other popular estimation schemes suggests superior estimates are produced by our proposed scheme.Un 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.
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Liu, Daqi. "Deep visual learning with spike-timing dependent plasticity." Thesis, University of Lincoln, 2017. http://eprints.lincoln.ac.uk/28660/.
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For most animal species, reliable and fast visual pattern recognition is vital for their survival. Ventral stream, a primary pathway within visual cortex, plays an important role in object representation and form recognition. It is a hierarchical system consisting of various visual areas, in which each visual area extracts different level of abstractions. It is known that the neurons within ventral stream use spikes to represent these abstractions. To increase the level of realism in a neural simulation, spiking neural network (SNN) is often used as the neural network model. From SNN point of view, the analog output values generated by traditional artificial neural network (ANN) can be considered as the average spiking firing rates. Unlike traditional ANN, SNN can not only use spiking rates but also specific spiking timing sequences to represent the structural information of the input visual stimuli, which greatly increases the distinguishability. To simulate the learning procedure of the ventral stream, various research questions need to be resolved. In most cases, traditional methods use winner-take-all strategy to distinguish different classes. However, such strategy works not well for overlapped classes within decision space. Moreover, neurons within ventral stream tends to recognize new input visual stimuli in a limited time window, which requires a fast learning procedure. Furthermore, within ventral stream, neurons receive continuous input visual stimuli and can only access local information during the learning procedure. However, most traditional methods use separated visual stimuli as the input and incorporate global information within the learning period. Finally, to verify the universality of the proposed SNN framework, it is necessary to investigate its classification performance for complex real world tasks such as video-based face disguise recognition. To address the above problems, a novel classification method inspired by the soft I winner-take-all strategy has been proposed firstly, in which each associated class will be assigned with a possibility and the input visual stimulus will be classified as the class with the highest possibility. Moreover, to achieve a fast learning procedure, a novel feed-forward SNN framework equipped with an unsupervised spike-timing dependent plasticity (STDP) learning rule has been proposed. Furthermore, an eventdriven continuous STDP (ECS) learning method has been proposed, in which two novel continuous input mechanisms have been used to generate a continuous input visual stimuli and a new event-driven STDP learning rule based on the local information has been applied within the training procedure. Finally, such methodologies have also been extended to the video-based disguise face recognition (VDFR) task in which human identities are recognized not just on a few images but the sequences of video stream showing facial muscle movements while speaking.
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Pagin, Matteo [Verfasser]. "Data compression of neural spike signals / Matteo Pagin." Ulm : Universität Ulm, 2021. http://d-nb.info/1232815179/34.
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Agarwal, Anjali. "Bayesian variable selection with spike-and-slab priors." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461940937.
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Carey, Howard J. III. "EEG Interictal Spike Detection Using Artificial Neural Networks." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4648.
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Epilepsy is a neurological disease causing seizures in its victims and affects approximately 50 million people worldwide. Successful treatment is dependent upon correct identification of the origin of the seizures within the brain. To achieve this, electroencephalograms (EEGs) are used to measure a patient’s brainwaves. This EEG data must be manually analyzed to identify interictal spikes that emanate from the afflicted region of the brain. This process can take a neurologist more than a week and a half per patient. This thesis presents a method to extract and process the interictal spikes in a patient, and use them to reduce the amount of data for a neurologist to manually analyze. The effectiveness of multiple neural network implementations is compared, and a data reduction of 3-4 orders of magnitude, or upwards of 99%, is achieved.
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Doig, Henry Ross. "An investigation of the pre-saccadic spike potential." Thesis, Aston University, 1990. http://publications.aston.ac.uk/14625/.
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A large negative spike potential, which is closely related to the onset of saccadic eyemovements, can be recorded from electrodes adjacent to the orbits. This potential, thepresaccadic spike potential, has often been regarded as an artefact related to eyemovement recordings and little work has been performed to establish its normal waveformand parameters. A positive spike potential, exactly coincident with the frontal negativespike, has also been recorded from electrodes positioned over the posterior scalp andthere has been some debate regarding any possible relationship between the twopotentials. The frontal spike potential has been associated with motor unit activity in theextraocular muscles prior to the saccade. This thesis investigates both the large anteriorand smaller posterior spike potentials and relates these recordings to the saccadic eyemovements associated with them. The anterior spike potential has been recorded from normal subjects to ascertain its normallatency and amplitude parameters for both horizontal and vertical saccades. A relationshipbetween saccade size and spike potential amplitude is described, the spike potentialamplitude reducing with smaller saccades. The potential amplitude also reduces withadvancing age. Studying the topographical distribution of the spike potential across thescalp shows the posterior spike activity may arise from potential spread of the larger frontalspike potential. Spike potential recordings from subjects with anomalous eye movements further implicate the extraocular muscles and their innervation in the generation of the spike potential. These recordings indicate that the spike potential may have some use as a clinical recording from patients with disease conditions affecting either their extraocular muscles or the innervational pathways to these muscles. Further recordings of the potential are necessary, however, to determine the exact nature of the changes which may occur with such conditions.
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Mehboob, 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.
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Neural signals are recorded from various regions of the brain and are analysed to understand the working mechanism of neurons and how they interpret external environment. The aim is to understand how this nature's supercomputer works. This helps in exploring human systems and intelligence, treat mental conditions and develop smart machines. Neural data recordings are collected from individual neurons and from populations of neurons. The single neuronal activity recordings are spike train and the activity generated from multiple neurons are field potentials. The data obtained are in enormous amount and of millisecond precision, as a consequence their processing is not a trivial task and efficient techniques are required for decoding these datasets. This work proposes several methods for the analysis of spike train and field potentials. A self-organising map based clustering is applied to synchronous spike train and generates topographically ordered and information-preserving clusters that help interpret how stimuli features are encoded by the neurons. An information-coupled empirical mode decomposition framework is developed for field potentials. It extracts informative oscillatory functions and information coding frequency bands in the recordings. This has several applications. The informative modes reveal underlying neuronal activities w.r.t stimuli, which otherwise have to be extracted by bandpass filters, followed by Fourier or wavelets analysis. It can also be used to analyse neuronal population activity under a medical condition or to understand neuronal interactions by information-connectivity analysis among electrodes. The proposed framework is developed into the form of a toolbox which can be used for educational and research purposes.
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Borel, 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.
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Musick, 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.
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Schleimer, 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.
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Ziel dieser Arbeit ist es eine Beziehung zwischen den biophysikalischen Eigenschaften der Nervenmembran, und den ausgeführten Berechnungen und Filtereigenschaften eines tonisch feuernden Neurons, unter Einbeziehen intrinsischer Fluktuationen, herzustellen. Zu diesem Zweck werden zu erst die mikroskopischen Fluktuationen, die durch das stochastische Öffnen und Schließen der Ionenkanäle verursacht werden, zu makroskopischer Varibilität in den Zeitpunkten des Auftretens der Aktionspotentiale übersetzt, denn es sind diese Spikezeiten die in vielen sensorischen Systemen informationstragenden sind. Die Methode erlaubt es das stochastischer Verhalten komplizierter Ionenkanalstrukturen mit einer großen Zahl an Untereinheiten, in Spikezeitenvariabilität zu übersetzen. Als weiteres werden die Filtereigenschaften der Nervenzellen in der überschwelligen Dynamik, also bei Existenz eines stabilen Grenzzyklus, aus ihren Phasenantwortkurven (PAK), einer Eigenschaft des linearisierten adjungierten Flusses auf dem Grenzzyklus, in einem stöhrungstheoretischen Ansatz berechnet. Es ergibt sich, dass Charakteristika des Filter, wie beispielsweise die DC Komponente und die Eigenschaften des Filters um die Fundamentalfrequenz und ihrer Harmonien, von den Fourierkomponenten der PAK abhängen. Unter Verwendung der hergeleiteten Filter und weiterer Annahmen ist es möglich das frequenzabhängige Signal-zu-Rauschen Verhältnis zu berechnen, und damit eine untere Schranke für die Informationstransferrate eines Leitfähigkeitsmodells zu berechnen. Unter Zuhilfenahme der numerischen Kontinuierungsmethode ist es möglich die Veränderungen in der Spikevariabilität und den Filtern für jeden biophysikalischen Parameter des System zu verfolgen. Weiterhin wurde die verwendete Phasenreduktion durch eine Korrektur ergänzt, die die Radialdynamik einbezieht. Es zeigt sich, dass die Krümmung der Isochronen einen Einfluss darauf hat ob das Rauschen einen positiven oder negativen Frequenzschift hervorruft.The 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.
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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.
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Malvestio, Irene. "Detection of directional interactions between neurons from spike trains." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666226.
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An important problem in neuroscience is the assessment of the connectivity
between neurons from their spike trains. One recent approach developed for
the detection of directional couplings between dynamics based on recorded
point processes is the nonlinear interdependence measure L. In this thesis we
first use the Hindmarsh-Rose model system to test L in the presence of noise
and for different spiking regimes of the dynamics. We then compare the
performance of L against the linear cross-correlogram and two spike train
distances. Finally, we apply all measures to neuronal spiking data from an
intracranial whole-night recording of a patient with epilepsy. When applied
to simulated data, L proves to be versatile, robust and more sensitive than
the linear measures. Instead, in the real data the linear measures find more
connections than L, in particular for neurons in the same brain region and
during slow wave sleep.Un 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.
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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.
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Davie, 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/.
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Each neuron of the nervous system is a machine specialised to appropriately transform its synaptic inputs into a pattern of spiking output. This is achieved through the combination of specialisations in synaptic properties and location, passive cell geometry and placement of particular active ion channels. The challenge presented to the neuroscientist is to, within each cell type, identify such specialisations in input distribution and resulting active events, and assess their relative importance in the generation of action potential output patterns. The Purkinje cell, in particular its response to climbing fibre (CF) input, is an excellent setting in which to attempt to meet this challenge. The Purkinje cell receives a single, easily isolated CF axon, which makes hundreds of synapses across the cell's highly branched, active dendritic tree, resulting in the generation of prominent dendritic calcium spikes and a distinctive, reproducible burst of fast action potentials (the complex spike) at the soma. In this thesis I have separated out the importance of the size of this input, its location and the active dendritic spikes it triggers in the generation of the complex spike. I have found that, to a large extent, the complex spike pattern is determined by the size of the CF input alone. I have characterised the complex spike (its number of spikes, their timing, height and reliability) at both constant physiological frequency and across a range of paired- pulse depression causing intervals. By alternating between whole cell current and voltage clamp in the same cell, I have recorded both the complex spikes and EPSCs generated at certain paired pulse intervals. In this way I have been able to construct the EPSC - complex spike 'input - output' relationship. This demonstrated that there is a straightforward linear transformation between the EPSC input amplitude and the number and timing of spikes in the complex spike. This applies across cells, explaining a large amount of the inter-cell variability in complex spike pattern. Input location and dendritic spikes have surprisingly little influence over the Purkinje cell complex spike. I found that complex spikes generated by dendritically distributed CF input can be reproduced by using conductance clamp to inject CF-like synaptic conductance at the soma. Both CF input and somatic EPSG injection produced complex spike waveforms that can only be easily explained by a model in which spikelets are initiated at a distant site and variably propagated to the soma. By using simultaneous somatic and dendritic recording I have demonstrated that this distant site initiation site is not in the dendrites. Somatic EPSG injection reproduced complex spikes independently of dendritic spikes, and extra dendritic spikes triggered by CF stimulation were associated with only 0.24 0.09 extra somatic spikelets in the complex spike. Rather, I have found that dendritic spikes, generated reliably by the dendritic location of CF inputs, have a role in regulating the post-complex spike pause. An extra dendritic spike generates a 3.4 0.7 mV deeper AHP and a 52 11 % longer pause before spontaneous spiking resumed. In this way, I have identified specialisations that encode the size, and thus timing, of CF inputs in the complex spike burst, whilst allowing the dendritic excitation of Purkinje cells (which is strongly associated synaptic and intrinsic plasticity) to be simultaneously encoded in the post-complex spike pause. This may reflect the complex spike's proposed dual role in both controlling ongoing movement and correcting for motor errors.
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Maraš, Mirjana. "Learning efficient signal representation in sparse spike-coding networks." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE023.
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La complexité de l’entrée sensorielle est parallèle à la complexité de sa représentation dans l’activité neurale des systèmes biologiques. Partant de l’hypothèse que les réseaux biologiques sont réglés pour atteindre une efficacité et une robustesse maximales, nous étudions comment une représentation efficace peut être réalisée dans des réseaux avec des probabilités de connexion locale et une dynamique synaptique observée de manière expérimentale. Nous développons une règle synaptique locale régularisée de type Lasso, qui optimise le nombre et l’efficacité des connexions récurrentes. Les connexions qui affectent le moins le rendement sont élaguées, et la force des connexions restantes est optimisée pour une meilleure représentation du signal. Notre théorie prédit que la probabilité de connexion locale détermine le compromis entre le nombre de potentiels d’action de la population et le nombre de connexions synaptiques qui sont développées et maintenues dans le réseau. Les réseaux plus faiblement connectés représentent des signaux avec des fréquences de déclenchement plus élevées que ceux avec une connectivité plus dense. La variabilité des probabilités de connexion observées dans les réseaux biologiques pourrait alors être considérée comme une conséquence de ce compromis et serait liée à différentes conditions de fonctionnement des circuits. Les connexions récurrentes apprises sont structurées et la plupart des connexions sont réciproques. La dimensionnalité des poids synaptiques récurrents peut être déduite de la probabilité de connexion du réseau et de la dimensionnalité du stimulus. La connectivité optimale d’un réseau avec des délais synaptiques se situe quelque part à un niveau intermédiaire, ni trop faible ni trop dense. De plus, lorsque nous ajoutons une autre contrainte biologique comme la régulation des taux de décharge par adaptation, notre règle d’apprentissage conduit à une mise à l’échelle observée de manière expérimentale des poids synaptiques. Nos travaux soutiennent l’idée que les micro-circuits biologiques sont hautement organisés et qu’une étude détaillée de leur organisation nous aidera à découvrir les principes de la représentation sensorielleThe 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
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Roy, Dipanjan. "Phase representation of Spike-Burst neurons in a network." Thesis, Aix-Marseille 2, 2011. http://www.theses.fr/2011AIX22057.
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[résumé trop long]The 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
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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.
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Background: Despite the so-called "generalised" nature of many epileptic seizures, patient specific spatio-temporal properties have been shown using imaging data at the macroscopic level of the cortex. Previous computational models have failed to account for spatial heterogeneities at the scale of the entire cortex. Furthermore, one of they key benefits of developing a model is the ability to easily test stimulation protocols. Previous studies of generalised spike-wave (the hallmark of absence epilepsy) have abstracted away from this.METHODSIn this work we develop a set of models of epileptic activity, one of which is at the scale of the entire cortex and incorporates anatomically relevant connectivity from human subjects. A similar model incorporating physiologically relevant thalamocortical circuitry is developed in order to test hypotheses regarding stimulation protocols.RESULTSWe show that the model can account for large-scale spatio-temporal dynamics similar to those seen in epileptic patients. We demonstrate, using the model of thalamocortical interaction, that such a modelling approach can be used for the evaluation of stimulation protocols which are shown to successfully abort the seizure prematurely.CONCLUSIONThis work highlights the importance of computational modelling to support existing data and to make specific predictions regarding testable hypotheses. For example, a stimulus given at the correct time with the correct amplitude will stop the seizure.
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Linderman, Scott Warren. "Bayesian Methods for Discovering Structure in Neural Spike Trains." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493391.
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Neuroscience is entering an exciting new age. Modern recording technologies enable simultaneous measurements of thousands of neurons in organisms performing complex behaviors. Such recordings offer an unprecedented opportunity to glean insight into the mechanistic underpinnings of intelligence, but they also present an extraordinary statistical and computational challenge: how do we make sense of these large scale recordings? This thesis develops a suite of tools that instantiate hypotheses about neural computation in the form of probabilistic models and a corresponding set of Bayesian inference algorithms that efficiently fit these models to neural spike trains. From the posterior distribution of model parameters and variables, we seek to advance our understanding of how the brain works.
Concretely, the challenge is to hypothesize latent structure in neural populations, encode that structure in a probabilistic model, and efficiently fit the model to neural spike trains. To surmount this challenge, we introduce a collection of structural motifs, the design patterns from which we construct interpretable models. In particular, we focus on random network models, which provide an intuitive bridge between latent types and features of neurons and the temporal dynamics of neural populations. In order to reconcile these models with the discrete nature of spike trains, we build on the Hawkes process — a multivariate generalization of the Poisson process — and its discrete time analogue, the linear autoregressive Poisson model. By leveraging the linear nature of these models and the Poisson superposition principle, we derive elegant auxiliary variable formulations and efficient inference algorithms. We then generalize these to nonlinear and nonstationary models of neural spike trains and take advantage of the Pólya-gamma augmentation to develop novel Markov chain Monte Carlo (MCMC) inference algorithms. In a variety of real neural recordings, we show how our methods reveal interpretable structure underlying neural spike trains.
In the latter chapters, we shift our focus from autoregressive models to latent state space models of neural activity. We perform an empirical study of Bayesian nonparametric methods for hidden Markov models of neural spike trains. Then, we develop an MCMC algorithm for switching linear dynamical systems with discrete observations and a novel algorithm for sampling Pólya-gamma random variables that enables efficient annealed importance sampling for model comparison.
Finally, we consider the “Bayesian brain” hypothesis — the hypothesis that neural circuits are themselves performing Bayesian inference. We show how one particular implementation of this hypothesis implies autoregressive dynamics of the form studied in earlier chapters, thereby providing a theoretical interpretation of our probabilistic models. This closes the loop, connecting top-down theory with bottom-up inferences, and suggests a path toward translating large scale recording capabilities into new insights about neural computation.Engineering and Applied Sciences - Computer Science
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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.
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Cao, 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.
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Wu, Shang-Rung. "Activation of the spike proteins of alpha- and retroviruses." Stockholm, 2009. http://diss.kib.ki.se/2009/978-91-7409-736-8/.
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Waddington, Amelia. "Growing synfire chains with triphasic spike-time-dependent plasticity." Thesis, University of Leeds, 2011. http://etheses.whiterose.ac.uk/1758/.
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How collections of neurons combine into functional networks capable of intricate and accurate information processing is one of the biggest and most interesting challenges in neuroscience today. To approach this challenge, it is necessary to address the problem one structure at a time. In this thesis the focus is the development of synfire chains. Synfire chains are feed-forward neural structures which have long been suggested as a possible mechanism by which precisely timed sequences of neural activity could be generated. Precise spatiotemporal firing patterns are known to occur in the brains of many animals including, rats, mice, song birds, monkeys and humans. Such firing patterns have been linked with a wide range of behaviours including motor responses and sensory encoding. There have been many previous computational studies which address the development of synfire chains. However, they have all required either initial sparse connectivity or strong topological constraints in addition to any synaptic learning rules. Here, it is shown that this necessity can be removed. In this model, development is guided by an experimentally reported spike-timing-dependent plasticity (STDP) rule, triphasic STDP, plus activity-dependent excitability. This STDP rule, which has not been previously used in computational studies, is shown to successfully develop a synfire chain in a network of binary neurons. The width and length of the final chain can be controlled through model parameters. In addition, it is possible to embed multiple chains within one neural network. Next, the effect of triphasic STDP is investigated in a network of more realistic leaky integrate and fire neurons. Here, synfire chain development is shown to be robust in the presence of heterogeneous delays. Finally, the development is described as a random walk, creating a concrete relationship between the model parameters and final network structure.
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Cui, Yihui. "The many faces of corticostriatal spike-timing dependent plasticity." Paris 6, 2013. http://www.theses.fr/2013PA066398.
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La plasticité corticostriatale est le substrat de l’apprentissage procédural. Nous avons caractérisé l’implication des endocannabinoides (eCBs) dans des formes de plasticité non-hebiennes telle que la depolarization-induced suppression of excitation (DSE) ou les stimulations à basse fréquence. Nous nous sommes ensuite concentré sur la caractérisation de la spike timing-dependent plasticity (STDP). Nous avons démontré que la LTP-STDP etait NMDAR dépendante alors que la LTD de��pendait des eCBs. En baissant le nombre de stimulations nous avons mis en évidence une nouvelle forme de plasticité: une eCB-LTP induite par un très faible nombre de stimulations appariées (5-10). Cette eCB-LTP est homosynaptique, médiée par l’activation des récepteurs CB1 et TRPV1 et par le couple présynaptique PKA-calcineurine. Nos résultats démontrent le large spectre d’action des eCBs puisque ceux-ci sous-tendent non seulement les phénomènes de dépression synaptique mais aussi de potentiation. Nous avons alors exploré les limites de la STDP en variant la fréquence des stimulations corticostriatales et observé une transition entre la dépendance au timing vs fréquence de la STDP dans laquelle la LTP est médiée par les NMDAR (pour de faibles fréquences) puis par un mélange NMDAR / CB1R (pour des fréquences plus élevées). Enfin, nous avons montré une très sensibilité des plasticités NMDA-dépendantes face au jitter alors que celles médiées par les eCBs sont beaucoup plus résistantes au jitter. Ces résultats montrent de nouvelles formes de plasticités corticostriatales et la grande complexité des règles d’apprentissage synaptiques qui gouvernent le traitement des informations corticostriatalesThe 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
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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.
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Koppolu, 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.
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Pazienti, Antonio. "Manipulations of spike trains and their impact on synchrony analysis." Phd thesis, Universität Potsdam, 2007. http://opus.kobv.de/ubp/volltexte/2008/1744/.
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The interaction between neuronal cells can be identified as the computing mechanism of the brain. Neurons are complex cells that do not operate in isolation, but they are organized in a highly connected network structure. There is experimental evidence that groups of neurons dynamically synchronize their activity and process brain functions at all levels of complexity. A fundamental step to prove this hypothesis is to analyze large sets of single neurons recorded in parallel. Techniques to obtain these data are meanwhile available, but advancements are needed in the pre-processing of the large volumes of acquired data and in data analysis techniques. Major issues include extracting the signal of single neurons from the noisy recordings (referred to as spike sorting) and assessing the significance of the synchrony.
This dissertation addresses these issues with two complementary strategies, both founded on the manipulation of point processes under rigorous analytical control. On the one hand I modeled the effect of spike sorting errors on correlated spike trains by corrupting them with realistic failures, and studied the corresponding impact on correlation analysis. The results show that correlations between multiple parallel spike trains are severely affected by spike sorting, especially by erroneously missing spikes. When this happens sorting strategies characterized by classifying only good'' spikes (conservative strategies) lead to less accurate results than tolerant'' strategies. On the other hand, I investigated the effectiveness of methods for assessing significance that create surrogate data by displacing spikes around their original position (referred to as dithering). I provide analytical expressions of the probability of coincidence detection after dithering. The effectiveness of spike dithering in creating surrogate data strongly depends on the dithering method and on the method of counting coincidences. Closed-form expressions and bounds are derived for the case where the dither equals the allowed coincidence interval.
This work provides new insights into the methodologies of identifying synchrony in large-scale neuronal recordings, and of assessing its significance.Die 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.
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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.
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Street, 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.
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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2007.Title 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.
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Na, Yu. "Stochastic phase dynamics in neuron models and spike time reliability." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/7383.
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The present thesis is concerned with the stochastic phase dynamics of neuron models and spike time reliability. It is well known that noise exists in all natural systems, and some beneficial effects of noise, such as coherence resonance and noise-induced synchrony, have been observed. However, it is usually difficult to separate the effect of the nonlinear system itself from the effect of noise on the system's phase dynamics. In this thesis, we present a stochastic theory to investigate the stochastic phase dynamics of a nonlinear system. The method we use here, called ``the stochastic multi-scale method'', allows a stochastic phase description of a system, in which the contributions from the deterministic system itself and from the noise are clearly seen. Firstly, we use this method to study the noise-induced coherence resonance of a single quiescent ``neuron" (i.e. an oscillator) near a Hopf bifurcation. By calculating the expected values of the neuron's stochastic amplitude and phase, we derive analytically the dependence of the frequency of coherent oscillations on the noise level for different types of models. These analytical results are in good agreement with numerical results we obtained. The analysis provides an explanation for the occurrence of a peak in coherence measured at an intermediate noise level, which is a defining feature of the coherence resonance. Secondly, this work is extended to study the interaction and competition of the coupling and noise on the synchrony in two weakly coupled neurons. Through numerical simulations, we demonstrate that noise-induced mixed-mode oscillations occur due to the existence of multistability states for the deterministic oscillators with weak coupling. We also use the standard multi-scale method to approximate the multistability states of a normal form of such a weakly coupled system. Finally we focus on the spike time reliability that refers to the phenomenon: the repetitive application of a stochastic stimulus to a neuron generates spikes with remarkably reliable timing whereas repetitive injection of a constant current fails to do so. In contrast to many numerical and experimental studies in which parameter ranges corresponding to repetitive spiking, we show that the intrinsic frequency of extrinsic noise has no direct relationship with spike time reliability for parameters corresponding to quiescent states in the underlying system. We also present an ``energy" concept to explain the mechanism of spike time reliability. ``Energy" is defined as the integration of the waveform of the input preceding a spike. The comparison of ``energy" of reliable and unreliable spikes suggests that the fluctuation stimuli with higher ''energy" generate reliable spikes. The investigation of individual spike-evoking epochs demonstrates that they have a more favorable time profile capable of triggering reliably timed spike with relatively lower energy levels.
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Somerville, Jared. "The exploration of neurophysiological spike train data using visual analytics." Thesis, University of Plymouth, 2011. http://hdl.handle.net/10026.1/897.
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Neuroscientists are increasingly overwhelmed by new recordings of the nervous system. These recordings are significantly increasing in size due to new electrophysiological techniques, such as multi-electrode arrays. These techniques can simultaneously record the electrical activity (or spike trains) from thousands of neurons. These new datasets are larger than the traditional datasets recorded from single electrodes where fewer than ten spike trains are usually recorded. Consequently, new tools are now required to effectively analyse these new datasets. This thesis describes how techniques from the field of Visual Analytics can be applied to detect specific patterns in spike train data. These techniques are realised in a software tool called Neurigma. Neurigma is a collection of visual representations of spike train data that are unified to provide a coordinated representation of the data. The visual representations within Neurigma include: an interactive raster plot, an improved correlation grid, a novel representation called the correlation plot (which includes a novel coupling estimation algorithm), and a novel network diagram. These views provide insight into spike train data, and particularly, they identify correlated patterns, called functional connectivity. Within this thesis Neurigma is used to analyse synthetically generated datasets and experimental recordings. Three main findings are presented. First, propagating spiral patterns are identified within recordings from the neonatal mouse retina. Second, functional connectivity is identified within the cat visual cortex. Finally, the functional connectivity of a large synthetic dataset, of 1000 spike trains, is accurately classified into direct, indirect and common input coupling.
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Jenkner, 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.
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Izadkhasti, 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.
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Hill, 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.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.This 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.
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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.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Cataloged 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.
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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.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged 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.
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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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