Relevant bibliographies by topics / Model "integrate-and-fire"

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Model "integrate-and-fire"'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Model "integrate-and-fire".'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Model "integrate-and-fire""

1

Ashida, Go, and Waldo Nogueira. "Spike-Conducting Integrate-and-Fire Model." eneuro 5, no. 4 (July 2018): ENEURO.0112–18.2018. http://dx.doi.org/10.1523/eneuro.0112-18.2018.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Gerstner, Wulfram, and Romain Brette. "Adaptive exponential integrate-and-fire model." Scholarpedia 4, no. 6 (2009): 8427. http://dx.doi.org/10.4249/scholarpedia.8427.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Destexhe, Alain. "Conductance-Based Integrate-and-Fire Models." Neural Computation 9, no. 3 (March 1, 1997): 503–14. http://dx.doi.org/10.1162/neco.1997.9.3.503.

Full text
Abstract:
A conductance-based model of Na+ and K+ currents underlying action potential generation is introduced by simplifying the quantitative model of Hodgkin and Huxley (HH). If the time course of rate constants can be approximated by a pulse, HH equations can be solved analytically. Pulse-based (PB) models generate action potentials very similar to the HH model but are computationally faster. Unlike the classical integrate-and fire (IAF) approach, they take into account the changes of conductances during and after the spike, which have a determinant influence in shaping neuronal responses. Similarities and differences among PB, IAF, and HH models are illustrated for three cases: high-frequency repetitive firing, spike timing following random synaptic inputs, and network behavior in the presence of intrinsic currents.
APA, Harvard, Vancouver, ISO, and other styles
4

Górski, Tomasz, Damien Depannemaecker, and Alain Destexhe. "Conductance-Based Adaptive Exponential Integrate-and-Fire Model." Neural Computation 33, no. 1 (January 2021): 41–66. http://dx.doi.org/10.1162/neco_a_01342.

Full text
Abstract:
The intrinsic electrophysiological properties of single neurons can be described by a broad spectrum of models, from realistic Hodgkin-Huxley-type models with numerous detailed mechanisms to the phenomenological models. The adaptive exponential integrate-and-fire (AdEx) model has emerged as a convenient middle-ground model. With a low computational cost but keeping biophysical interpretation of the parameters, it has been extensively used for simulations of large neural networks. However, because of its current-based adaptation, it can generate unrealistic behaviors. We show the limitations of the AdEx model, and to avoid them, we introduce the conductance-based adaptive exponential integrate-and-fire model (CAdEx). We give an analysis of the dynamics of the CAdEx model and show the variety of firing patterns it can produce. We propose the CAdEx model as a richer alternative to perform network simulations with simplified models reproducing neuronal intrinsic properties.
APA, Harvard, Vancouver, ISO, and other styles
5

Van Pottelbergh, Tomas, Guillaume Drion, and Rodolphe Sepulchre. "Robust Modulation of Integrate-and-Fire Models." Neural Computation 30, no. 4 (April 2018): 987–1011. http://dx.doi.org/10.1162/neco_a_01065.

Full text
Abstract:
By controlling the state of neuronal populations, neuromodulators ultimately affect behavior. A key neuromodulation mechanism is the alteration of neuronal excitability via the modulation of ion channel expression. This type of neuromodulation is normally studied with conductance-based models, but those models are computationally challenging for large-scale network simulations needed in population studies. This article studies the modulation properties of the multiquadratic integrate-and-fire model, a generalization of the classical quadratic integrate-and-fire model. The model is shown to combine the computational economy of integrate-and-fire modeling and the physiological interpretability of conductance-based modeling. It is therefore a good candidate for affordable computational studies of neuromodulation in large networks.
APA, Harvard, Vancouver, ISO, and other styles
6

Ascione, Giacomo, and Bruno Toaldo. "A Semi-Markov Leaky Integrate-and-Fire Model." Mathematics 7, no. 11 (October 29, 2019): 1022. http://dx.doi.org/10.3390/math7111022.

Full text
Abstract:
In this paper, a Leaky Integrate-and-Fire (LIF) model for the membrane potential of a neuron is considered, in case the potential process is a semi-Markov process. Semi-Markov property is obtained here by means of the time-change of a Gauss-Markov process. This model has some merits, including heavy-tailed distribution of the waiting times between spikes. This and other properties of the process, such as the mean, variance and autocovariance, are discussed.
APA, Harvard, Vancouver, ISO, and other styles
7

Tonnelier, Arnaud, Hana Belmabrouk, and Dominique Martinez. "Event-Driven Simulations of Nonlinear Integrate-and-Fire Neurons." Neural Computation 19, no. 12 (December 2007): 3226–38. http://dx.doi.org/10.1162/neco.2007.19.12.3226.

Full text
Abstract:
Event-driven strategies have been used to simulate spiking neural networks exactly. Previous work is limited to linear integrate-and-fire neurons. In this note, we extend event-driven schemes to a class of nonlinear integrate-and-fire models. Results are presented for the quadratic integrate-and-fire model with instantaneous or exponential synaptic currents. Extensions to conductance-based currents and exponential integrate-and-fire neurons are discussed.
APA, Harvard, Vancouver, ISO, and other styles
8

Zador, Anthony M., and Barak A. Pearlmutter. "VC Dimension of an Integrate-and-Fire Neuron Model." Neural Computation 8, no. 3 (April 1996): 611–24. http://dx.doi.org/10.1162/neco.1996.8.3.611.

Full text
Abstract:
We compute the VC dimension of a leaky integrate-and-fire neuron model. The VC dimension quantifies the ability of a function class to partition an input pattern space, and can be considered a measure of computational capacity. In this case, the function class is the class of integrate-and-fire models generated by varying the integration time constant T and the threshold θ, the input space they partition is the space of continuous-time signals, and the binary partition is specified by whether or not the model reaches threshold at some specified time. We show that the VC dimension diverges only logarithmically with the input signal bandwidth N. We also extend this approach to arbitrary passive dendritic trees. The main contributions of this work are (1) it offers a novel treatment of computational capacity of this class of dynamic system; and (2) it provides a framework for analyzing the computational capabilities of the dynamic systems defined by networks of spiking neurons.
APA, Harvard, Vancouver, ISO, and other styles
9

Breen, Barbara J., William C. Gerken, and Robert J. Butera. "Hybrid Integrate-and-Fire Model of a Bursting Neuron." Neural Computation 15, no. 12 (December 1, 2003): 2843–62. http://dx.doi.org/10.1162/089976603322518768.

Full text
Abstract:
We present a reduction of a Hodgkin-Huxley (HH)—style bursting model to a hybridized integrate-and-fire (IF) formalism based on a thorough bifurcation analysis of the neuron's dynamics. The model incorporates HH-style equations to evolve the subthreshold currents and includes IF mechanisms to characterize spike events and mediate interactions between the subthreshold and spiking currents. The hybrid IF model successfully reproduces the dynamic behavior and temporal characteristics of the full model over a wide range of activity, including bursting and tonic firing. Comparisons of timed computer simulations of the reduced model and the original model for both single neurons and moderate lysized networks (n ≤ 500) show that this model offers improvement in computational speed over the HH-style bursting model.
APA, Harvard, Vancouver, ISO, and other styles
10

Robert, M. E. "Integrate-and-Fire Model for Electrically Stimulated Nerve Cell." IEEE Transactions on Biomedical Engineering 53, no. 4 (April 2006): 756–58. http://dx.doi.org/10.1109/tbme.2006.870209.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Model "integrate-and-fire""

1

Russo, Elena Tea. "Fluctuation properties in random walks on networks and simple integrate and fire models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9565/.

Full text
Abstract:
In questa tesi si è studiato l’insorgere di eventi critici in un semplice modello neurale del tipo Integrate and Fire, basato su processi dinamici stocastici markoviani definiti su una rete. Il segnale neurale elettrico è stato modellato da un flusso di particelle. Si è concentrata l’attenzione sulla fase transiente del sistema, cercando di identificare fenomeni simili alla sincronizzazione neurale, la quale può essere considerata un evento critico. Sono state studiate reti particolarmente semplici, trovando che il modello proposto ha la capacità di produrre effetti "a cascata" nell’attività neurale, dovuti a Self Organized Criticality (auto organizzazione del sistema in stati instabili); questi effetti non vengono invece osservati in Random Walks sulle stesse reti. Si è visto che un piccolo stimolo random è capace di generare nell’attività della rete delle fluttuazioni notevoli, in particolar modo se il sistema si trova in una fase al limite dell’equilibrio. I picchi di attività così rilevati sono stati interpretati come valanghe di segnale neurale, fenomeno riconducibile alla sincronizzazione.
APA, Harvard, Vancouver, ISO, and other styles
2

Bernardi, Davide. "Detecting Single-Cell Stimulation in Recurrent Networks of Integrate-and-Fire Neurons." Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20560.

Full text
Abstract:
Diese Arbeit ist ein erster Versuch, mit Modellbildung und mathematischer Analyse die Experimente zu verstehen, die zeigten, dass die Stimulation eines einzelnen Neurons im Cortex eine Verhaltensreaktion auslösen kann. Dieser Befund stellt die verbreitete Ansicht infrage, dass viele Neurone nötig sind, um Information zuverlässig kodieren zu können. Der Ausgangspunkt der vorliegenden Untersuchung ist die Stimulation einer zufällig ausgewählten Zelle in einem Zufallsnetzwerk exzitatorischer und inhibitorischer Neuronmodelle. Es wird dann nach einem plausiblen Ausleseverfahren gesucht, das die Einzelzellstimulation mit einer mit den Experimenten vergleichbaren Zuverlässigkeit detektieren kann. Das erste Ausleseschema reagiert auf Abweichungen vom spontanen Zustand in der Aktivität einer Auslesepopulation. Die Stimulation wird detektiert, wenn bei der Auswahl der Auslesepopulation denjenigen Neuronen ein Vorzug gegeben wird, die eine direkte Verbindung von der stimulierten Zelle bekommen. Im zweiten Teil der Arbeit wird das Ausleseschema erweitert, indem ein zweites Netzwerk als Ausleseschaltkreis dient. Interessanterweise erweist sich dieses Ausleseschema nicht nur als plausibler, sondern auch als effektiver. Diese Resultate basieren sowohl auf Simulationen als auch auf analytischen Rechnungen. Weitere Experimente zeigten, dass eine konstante Strominjektion einen Effekt auslöst, der kaum von Dauer und Intensität der Stimulation abhängt, der aber bei unregelmäßiger Stimulation zunimmt. Der letzte Teil der Arbeit befasst sich mit einer theoretischen Erklärung für diese Ergebnisse. Hierzu werden die biologischen Eigenschaften des Systems im Modell detaillierter beschrieben. Weiterhin wird die Funktionsweise des Ausleseschemas so modifiziert, dass es auf Veränderungen reagiert, anstatt den Input zu integrieren. Dieser Differenzierdetektor liefert Ergebnisse, die mit den Experimenten übereinstimmen, und könnte bei nichtstationärem Input vorteilhaft sein.
This thesis is a first attempt at developing a theoretical model of the experiments which show that the stimulation of a single cell in the cortex can trigger a behavioral reaction and that challenge the common belief that many neurons are needed to reliably encode information. As a starting point of the present work, one neuron selected at random within a random network of excitatory and inhibitory integrate-and-fire neurons is stimulated. One important goal of this thesis is to seek a readout scheme that can detect the single-cell stimulation in a plausible way with a reliability compatible with the experiments. The first readout scheme reacts to deviations from the spontaneous state in the activity of a readout population. When the choice of readout neurons is sufficiently biased towards those receiving direct links from the stimulated cell, the stimulation can be detected. In the second part of the thesis, the readout scheme is extended by employing a second network as a readout circuit. Interestingly, this new readout scheme is not only more plausible, but also more effective. These results are based both on numerical simulations of the network and on analytical approximations. Further experiments showed that the probability of the behavioral reaction is substantially independent of the length and intensity of the stimulation, but it increases when an irregular current is used. The last part of this thesis seeks a theoretical explanation for these findings. To this end, a recurrent network including more biological details of the system is considered. Furthermore, the functioning principle of the readout is modified to react to changes in the activity of the local network (a differentiator readout), instead of integrating the input. This differentiator readout yields results in accordance with the experiments and could be advantageous in the presence of nonstationarities.
APA, Harvard, Vancouver, ISO, and other styles
3

Mahat, Aarati. "Dynamic features of neural activity in primary auditory cortex captured by an integrate-and-fire network model for auditory streaming." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6609.

Full text
Abstract:
Past decades of auditory research have identified several acoustic features that influence perceptual organization of sound, in particular, the frequency of tones and the rate of presentation. One class of stimuli that have been intensively studied are sequences of tones that alternate in frequency. They are typically presented in patterns of repeating doublets ABAB… or repeating triplets ABA-ABA-... where the symbol “-” stands for a gap of silence between triplets repeats. The duration of each tone or silence is typically tens to hundreds of milliseconds, and listeners hearing the sequence perceive either one auditory object ("stream integration") or two separate auditory objects (“stream segregation”). Animal studies have characterized single- and multi- unit neural activity and event-related local field potentials while systematically varying frequency separation between tones (ΔF) or the presentation rate (PR). They found that the B tone responses in doublets were differentially suppressed with increasing PR and that the B tones responses in triplets decreased with larger ΔF. However, the neural mechanisms underlying these animal data have yet to be explained. In this study, we built an integrate-and-fire network model of the primary auditory cortex (AC) that accurately reproduced the experimental results. Then, we extended the model to account for basic spectro-temporal features of electrocorticography (ECoG) recordings from the posteriomedial part of the Heschl's gyrus (HGPM; cortical area equivalent to the AC of monkeys), obtained from humans listening to sequences of triplets ABA-. Finally, we constructed a firing rate reduced model of the proposed integrate-and-fire network and analyzed its dynamics as function of parameters. A large network of voltage-dependent leaky integrate-and-fire neurons (3600 excitatory, 900 inhibitory) was constructed to simulate neural activity from layers 3/4 of AC during streaming of tone triplets. Parameters describing synaptic and membrane properties were based on experimental data from early studies of AC. Network structure assumed spatially-dependent probability of connections and tonotopic organization. Subpopulations of neurons were tuned to different frequencies along the tonotopic map. In-silico recordings were performed during the presentation of long sequences of triplets and/or doublets. The network’s output was derived with two types of measurements in mind: spiking activity of individual neurons and/or local populations of neurons, and local field potentials. The network spiking neural activity reproduced reliably data reports, including dependence of responses to the B tone in triplets ABA- on stimulus parameter ΔF. Approximations of average evoked potentials (AEPs) from ECoG signals recorded at four depth contacts placed over human HGPM during auditory streaming of triplets were also obtained.
APA, Harvard, Vancouver, ISO, and other styles
4

Iolov, Alexandre V. "Parameter Estimation, Optimal Control and Optimal Design in Stochastic Neural Models." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34866.

Full text
Abstract:
This thesis solves estimation and control problems in computational neuroscience, mathematically dealing with the first-passage times of diffusion stochastic processes. We first derive estimation algorithms for model parameters from first-passage time observations, and then we derive algorithms for the control of first-passage times. Finally, we solve an optimal design problem which combines elements of the first two: we ask how to elicit first-passage times such as to facilitate model estimation based on said first-passage observations. The main mathematical tools used are the Fokker-Planck partial differential equation for evolution of probability densities, the Hamilton-Jacobi-Bellman equation of optimal control and the adjoint optimization principle from optimal control theory. The focus is on developing computational schemes for the solution of the problems. The schemes are implemented and are tested for a wide range of parameters.
APA, Harvard, Vancouver, ISO, and other styles
5

Bahrami, Abdorrahim. "Modelling and Verifying Dynamic Properties of Neuronal Networks in Coq." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42643.

Full text
Abstract:
Since the mid-1990s, formal verification has become increasingly important because it can provide guarantees that a software system is free of bugs and working correctly based on a provided model. Verification of biological and medical systems is a promising application of formal verification. Human neural networks have recently been emulated and studied as a biological system. Some recent research has been done on modelling some crucial neuronal circuits and using model checking techniques to verify their temporal properties. In large case studies, model checkers often cannot prove the given property at the desired level of generality. In this thesis, we provide a model using the Coq proof assistant and prove some properties concerning the dynamic behavior of some basic neuronal structures. Understanding the behavior of these modules is crucial because they constitute the elementary building blocks of bigger neuronal circuits. By using a proof assistant, we guarantee that the properties are true in the general case, that is, true for any input values, any length of input, and any amount of time. In this thesis, we define a model of human neural networks. We verify some properties of this model starting with properties of neurons. Neurons are the smallest unit in a human neuronal network. In the next step, we prove properties about functional structures of human neural networks which are called archetypes. Archetypes consist of two or more neurons connected in a suitable way. They are known for displaying some particular classes of behaviours, and their compositions govern several important functions such as walking, breathing, etc. The next step is verifying properties about structures that couple different archetypes to perform more complicated actions. We prove a property about one of these kinds of compositions. With such a model, there is the potential to detect inactive regions of the human brain and to treat mental disorders. Furthermore, our approach can be generalized to the verification of other kinds of networks, such as regulatory, metabolic, or environmental networks.
APA, Harvard, Vancouver, ISO, and other styles
6

Cieniak, Jakub. "Stimulus Coding and Synchrony in Stochastic Neuron Models." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20004.

Full text
Abstract:
A stochastic leaky integrate-and-fire neuron model was implemented in this study to simulate the spiking activity of the electrosensory "P-unit" receptor neurons of the weakly electric fish Apteronotus leptorhynchus. In the context of sensory coding, these cells have been previously shown to respond in experiment to natural random narrowband signals with either a linear or nonlinear coding scheme, depending on the intrinsic firing rate of the cell in the absence of external stimulation. It was hypothesised in this study that this duality is due to the relation of the stimulus to the neuron's excitation threshold. This hypothesis was validated with the model by lowering the threshold of the neuron or increasing its intrinsic noise, or randomness, either of which made the relation between firing rate and input strength more linear. Furthermore, synchronous P-unit firing to a common input also plays a role in decoding the stimulus at deeper levels of the neural pathways. Synchronisation and desynchronisation between multiple model responses for different types of natural communication signals were shown to agree with experimental observations. A novel result of resonance-induced synchrony enhancement of P-units to certain communication frequencies was also found.
APA, Harvard, Vancouver, ISO, and other styles
7

Schwalger, Tilo. "The interspike-interval statistics of non-renewal neuron models." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2013. http://dx.doi.org/10.18452/16824.

Full text
Abstract:
Um die komplexe Dynamik von Neuronen und deren Informationsverarbeitung mittels Pulssequenzen zu verstehen, ist es wichtig, die stationäre Puls-Aktivität zu charakterisieren. Die statistischen Eigenschaften von Pulssequenzen können durch vereinfachte stochastische Neuronenmodelle verstanden werden. Eine gut ausgearbeitete Theorie existiert für die Klasse der Erneuerungsmodelle, welche die statistische Unabhängigkeit der Interspike-Intervalle (ISI) annimmt. Experimente haben jedoch gezeigt, dass viele Neuronen Korrelationen zwischen ISIs aufweisen und daher nicht gut durch einen Erneuerungsprozess beschrieben werden. Solche Korrelationen können durch Nichterneuerungs-Modelle erfasst werden, welche jedoch theoretisch schlecht verstanden sind. Diese Arbeit ist eine analytische Studie von Nichterneuerungs-Modellen, die zwei bedeutende Korrelationsmechanismen untersucht: farbiges Rauschen, welches zeitlich-korrelierten Input darstellt, und negative Puls-Rückkopplung, welche Feuerraten-Adaption realisiert. Für das "Perfect-Integrate-and-Fire" (PIF) Modell, welchen durch ein allgemeines Gauss''sches farbiges Rauschen getrieben ist, werden die Statistiken höherer Ordnung der Output-Pulssequenz hergeleitet, insbesondere der Koeffizient der Variation, der serielle Korrelationskoeffizient (SCC), die ISI-Dichte und der Fano-Faktor. Weiterhin wird die Dynamik des PIF Modells mit Puls-getriggertem Adaptionsstrom und weissem Stromrauschen im Detail analysiert. Die Theorie liefert einen Ausdruck für den SCC, der für schwaches Rauschen aber beliebige Adaptions-Stärke und Zeitskale gültig ist, sowie die lineare Antwortfunktion und das Leistungsspektrum der Pulssequenz. Ausserdem wird gezeigt, dass ein stochastischer Adaptionsstrom wie ein langsames farbiges Rauschen wirkt, was ermöglicht, die dominierende Quellen des Rauschen in einer auditorischen Rezeptorzelle zu bestimmen. Schliesslich wird der SCC für das fluktuations-getriebene Feuerregime berechnet.
To understand the complex dynamics of neurons and its ability to process information using a sequence of spikes, it is vital to characterize its stationary spontaneous spiking activity. The statistical properties of spike trains can be explained by reduced stochastic neuron models that account for various sources of noise. A well-developed theory exists for the class of renewal models, in which the interspike intervals (ISIs) are statistically independent. However, experimental studies show that many neurons are not well described by a renewal process because of correlations between ISIs. Such correlations can be captured by generalized, non-renewal models, which are, however, poorly understood theoretically. This thesis represents an analytical study of non-renewal models, focusing on two prominent correlation mechanisms: colored-noise driving representing temporally correlated inputs, and negative feedback currents realizing spike-frequency adaptation. For the perfect integrate-and-fire (PIF) model driven by a general Gaussian colored noise input, the higher-order statistics of the output spike train is derived using a weak-noise analysis of the Fokker-Planck equation. This includes formulas for the coefficient of variation, the serial correlation coefficient (SCC), the ISI density and the Fano factor. Then, the dynamics of a PIF model with a spike-triggered adaptation and a white-noise current is analyzed in detail. The theory yields an expression for the SCC valid for weak noise but arbitrary adaptation strengths and time scale, and also provides the linear response to time-dependent stimuli and the spike train power spectrum. Furthermore, it is shown that a stochastic adaptation current acts like a slow colored noise, which permits to determine the source of spiking variability observed in an auditory receptor neuron. Finally, the SCC is calculated for the fluctuation-driven spiking regime by assuming discrete states of colored noise or adaptation current.
APA, Harvard, Vancouver, ISO, and other styles
8

Devalle, Federico. "Collective phenomena in networks of spiking neurons with synaptic delays." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666912.

Full text
Abstract:
A prominent feature of the dynamics of large neuronal networks are the synchrony-driven collective oscillations generated by the interplay between synaptic coupling and synaptic delays. This thesis investigates the emergence of delay-induced oscillations in networks of heterogeneous spiking neurons. Building on recent theoretical advances in exact mean field reductions for neuronal networks, this work explores the dynamics and bifurcations of an exact firing rate model with various forms of synaptic delays. In parallel, the results obtained using the novel firing rate model are compared with extensive numerical simulations of large networks of spiking neurons, which confirm the existence of numerous synchrony-based oscillatory states. Some of these states are novel and display complex forms of partial synchronization and collective chaos. Given the well-known limitation of traditional firing rate models to describe synchrony-based oscillations, previous studies greatly overlooked many of the oscillatory states found here. Therefore, this thesis provides a unique exploration of the oscillatory scenarios found in neuronal networks due to the presence of delays, and may substantially extend the mathematical tools available for modeling the plethora of oscillations detected in electrical recordings of brain activity.
Una característica fonamental de la dinàmica d'una xarxa neuronal és l'emergència d'oscil·lacions degudes a sincronització. L'origen d'aquestes oscil·lacions és molt sovint degut les interaccions sinàptiques i als seus retards temporals inherents. Aquesta tesi analitza la emergència d'oscil·lacions produïdes per retards sinàptics en xarxes neuronals heterogènies. A partir de troballes recents en teories de camp mig per xarxes neuronals, aquest treball explora la dinàmica i les bifurcacions d'un model de {\it rate} amb diferents tipus de retards sinàptics. En paral·lel els resultats obtinguts mitjançant el nou model de rate són comparats amb simulacions numèriques de grans xarxes neuronals. Aquestes simulacions confirmen l'existència de nombrosos estats oscil·latoris produïts per sincronització. Alguns d'aquests estats són nous I mostren formes complexes de sincronització parcial i de caos col·lectiu. Gran part d'aquestes oscil·lacions han estat àmpliament ignorades a la literatura, degut a la limitació dels models tradicionals de rate per descriure estats amb un alt nivell de sincronització. Així doncs aquesta tesi ofereix una exploració única dels possibles escenaris oscil·latoris en xarxes neuronals amb retards sinàptics, i amplia significativament les eines matemàtiques disponibles per a la modelització de la gran diversitat d'oscil·lacions neuronals presents en les mesures elèctriques de l'activitat cerebral.
APA, Harvard, Vancouver, ISO, and other styles
9

Esnaola, Acebes Jose M. "Patterns of spike synchrony in neural field models." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663871.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
10

Pressley, Joanna. "Response dynamics of integrate-and-fire neuron models." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8521.

Full text
Abstract:
Thesis (Ph. D.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Applied Mathematics and Scientific Computation Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Model "integrate-and-fire""

1

Feng, Jianfeng. "Integrate-and-fire model with correlated inputs." In Lecture Notes in Computer Science, 258–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0098181.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Risinger, Lon, and Khosrow Kaikhah. "Modified Bifurcating Neuron with Leaky-Integrate-and-Fire Model." In Innovations in Applied Artificial Intelligence, 1033–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24677-0_106.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Zhao, Liang, and Feng Qian. "CRPSO-Based Integrate-and-Fire Neuron Model for Time Series Prediction." In Lecture Notes in Computer Science, 100–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13498-2_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Buonocore, Aniello, Luigia Caputo, Enrica Pirozzi, and Luigi M. Ricciardi. "On a Generalized Leaky Integrate–and–Fire Model for Single Neuron Activity." In Computer Aided Systems Theory - EUROCAST 2009, 152–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04772-5_21.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Bove, Marco, Michele Giugliano, and Massimo Grattarola. "Regulatory effects of long term biochemical processes in integrate-and-fire model neurons." In Neural Circuits and Networks, 189–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58955-3_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Naud, Richard, and Wulfram Gerstner. "The Performance (and Limits) of Simple Neuron Models: Generalizations of the Leaky Integrate-and-Fire Model." In Computational Systems Neurobiology, 163–92. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3858-4_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Giugliano, Michele, Giancarlo La Camera, Alexander Rauch, Hans-Rudolf Lüscher, and Stefano Fusi. "Non-monotonic Current-to-Rate Response Function in a Novel Integrate-and-Fire Model Neuron." In Artificial Neural Networks — ICANN 2002, 141–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_24.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Lin, Min, and Gang Wang. "Complex Behavior in an Integrate-and-Fire Neuron Model Based on Assortative Scale-Free Networks." In Lecture Notes in Electrical Engineering, 457–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-26001-8_60.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Akhmet, Marat. "Integrate-and-Fire Biological Oscillators." In Nonlinear Hybrid Continuous/Discrete-Time Models, 175–99. Paris: Atlantis Press, 2011. http://dx.doi.org/10.2991/978-94-91216-03-9_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Fourcaud-Trocmé, Nicolas. "Integrate and Fire Models, Deterministic." In Encyclopedia of Computational Neuroscience, 1–9. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_148-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Model "integrate-and-fire""

1

Zador, Anthony M., and Barak A. Pearlmutter. "VC dimension of an integrate-and-fire neuron model." In the ninth annual conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/238061.238064.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Tomas, Pedro, and Leonel Sousa. "Feature Selection for the Stochastic Integrate and Fire Model." In 2007 IEEE International Symposium on Intelligent Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/wisp.2007.4447639.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Mishra, Abhilash, and Santosh Kumar Majhi. "Design and Analysis of Modified Leaky Integrate and Fire Model." In TENCON 2018 - 2018 IEEE Region 10 Conference. IEEE, 2018. http://dx.doi.org/10.1109/tencon.2018.8650527.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Hamilton, Tara Julia, and Andre van Schaik. "Silicon implementation of the generalized integrate-and-fire neuron model." In 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2011. http://dx.doi.org/10.1109/issnip.2011.6146585.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Zhenzhong Wang, Lilin Guo, and Malek Adjouadi. "A biological plausible Generalized Leaky Integrate-and-Fire neuron model." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6945192.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Zhang, Li, and Da-zheng Feng. "The Properties and Stability Analysis of an Integrate-and-Fire Model." In 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2009. http://dx.doi.org/10.1109/wicom.2009.5302917.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Zohora, Fatima Tuz, Sutapa Debnath, and A. B. M. Harun-ur Rashid. "Memristor-CMOS Hybrid Implementation of Leaky Integrate and Fire Neuron Model." In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2019. http://dx.doi.org/10.1109/ecace.2019.8679259.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Liu, Zhi-hong, Yu-rong Zhou, and Xiao-feng Pang. "Coherence Resonance in a Noise-Driven Nonlinear Integrate-and-Fire Model." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '08). IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.690.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Maranhao, Gabriel, and Janaina G. Guimaraes. "Integrate and Fire Neuron Implementation using CMOS Predictive Technology Model for 32nm." In 2019 34th Symposium on Microelectronics Technology and Devices (SBMicro). IEEE, 2019. http://dx.doi.org/10.1109/sbmicro.2019.8919380.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Chen, Chong, and Zhuo Chen. "Parameter estimation of an integrate-and-fire model based on symbolic analysis." In 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2012. http://dx.doi.org/10.1109/bmei.2012.6513110.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Model "integrate-and-fire"' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography