To see the other types of publications on this topic, follow the link: COMPUTATIONAL NEUROSCIENCE MODELS.
Dissertations / Theses on the topic 'COMPUTATIONAL NEUROSCIENCE MODELS'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'COMPUTATIONAL NEUROSCIENCE MODELS.'
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.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Marsh, Steven Joseph Thomas. "Efficient programming models for neurocomputation." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709268.
Full text
2
Zhu, Mengchen. "Sparse coding models of neural response in the primary visual cortex." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53868.
Full textAbstract:
Sparse coding is an influential unsupervised learning approach proposed as a theoretical model of the encoding process in the primary visual cortex (V1). While sparse coding has been successful in explaining classical receptive field properties of simple cells, it was unclear whether it can account for more complex response properties in a variety of cell types. In this dissertation, we demonstrate that sparse coding and its variants are consistent with key aspects of neural response in V1, including many contextual and nonlinear effects, a number of inhibitory interneuron properties, as well as the variance and correlation distributions in the population response. The results suggest that important response properties in V1 can be interpreted as emergent effects of a neural population efficiently representing the statistical structures of natural scenes under resource constraints. Based on the models, we make predictions of the circuit structure and response properties in V1 that can be verified by future experiments.
3
Földiak, Peter. "Models of sensory coding." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239097.
Full text
4
Woldman, Wessel. "Emergent phenomena from dynamic network models : mathematical analysis of EEG from people with IGE." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/23297.
Full textAbstract:
In this thesis mathematical techniques and models are applied to electroencephalographic (EEG) recordings to study mechanisms of idiopathic generalised epilepsy (IGE). First, we compare network structures derived from resting-state EEG from people with IGE, their unaffected relatives, and healthy controls. Next, these static networks are combined with a dynamical model describing the ac- tivity of a cortical region as a population of phase-oscillators. We then examine the potential of the differences found in the static networks and the emergent properties of the dynamic network as individual biomarkers of IGE. The emphasis of this approach is on discerning the potential of these markers at the level of an indi- vidual subject rather than their ability to identify differences at a group level. Finally, we extend a dynamic model of seizure onset to investigate how epileptiform discharges vary over the course of the day in ambulatory EEG recordings from people with IGE. By per- turbing the dynamics describing the excitability of the system, we demonstrate the model can reproduce discharge distributions on an individual level which are shown to express a circadian tone. The emphasis of the model approach is on understanding how changes in excitability within brain regions, modulated by sleep, metabolism, endocrine axes, or anti-epileptic drugs (AEDs), can drive the emer- gence of epileptiform activity in large-scale brain networks. Our results demonstrate that studying EEG recordings from peo- ple with IGE can lead to new mechanistic insight on the idiopathic nature of IGE, and may eventually lead to clinical applications. We show that biomarkers derived from dynamic network models perform significantly better as classifiers than biomarkers based on static network properties. Hence, our results provide additional ev- idence that the interplay between the dynamics of specific brain re- gions, and the network topology governing the interactions between these regions, is crucial in the generation of emergent epileptiform activity. Pathological activity may emerge due to abnormalities in either of those factors, or a combination of both, and hence it is essential to develop new techniques to characterise this interplay theoretically and to validate predictions experimentally.
5
Mender, Bedeho M. W. "Models of primate supraretinal visual representations." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:ce1fff8e-db5c-46e4-b5aa-7439465c2a77.
Full textAbstract:
This thesis investigates a set of non-classical visual receptive field properties observed in the primate brain. Two main phenomena were explored. The first phenomenon was neurons with head-centered visual receptive fields, in which a neuron responds maximally to a visual stimulus in the same head-centered location across all eye positions. The second phenomenon was perisaccadic receptive field dynamics, which involves a range of experimentally observed response behaviours of an eye-centered neuron associated with the advent of a saccade that relocates the neuron's receptive field. For each of these two phenomena, a hypothesis was proposed for how a neural circuit with a suitable initial architecture and synaptic learning rules could, when subjected to visually-guided training, develop the receptive field properties in question. Corresponding neural network models were first trained as hypothesized, and subsequently tested in conditions similar to experimental tasks used to interrogate the physiology of the relevant primate neural circuits. The behaviour of the models was compared to neurophysiological observations as a metric for their explanatory power. In both cases the neural network models were in broad agreement with experimental observations, and the operation of these models was studied to shed light on the neural processing behind these neural phenomena in the brain.
6
Shepardson, Dylan. "Algorithms for inverting Hodgkin-Huxley type neuron models." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31686.
Full textAbstract:
Thesis (Ph.D)--Algorithms, Combinatorics, and Optimization, Georgia Institute of Technology, 2010.Committee Chair: Tovey, Craig; Committee Member: Butera, Rob; Committee Member: Nemirovski, Arkadi; Committee Member: Prinz, Astrid; Committee Member: Sokol, Joel. Part of the SMARTech Electronic Thesis and Dissertation Collection.
7
Boatin, William. "Characterization of neuron models." Thesis, Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-04182005-181732/.
Full textAbstract:
Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006.Dr. Robert H. Lee, Committee Member ; Dr. Kurt Wiesenfeld, Committee Member ; Dr Robert J. Butera, Committee Member.
8
BIDDELL, KEVIN MICHAEL. "CREATION OF A BIOPHYSICAL MODEL OF A STRIATAL DORSAL LATERAL MEDIUM SPINY NEURON INCORPORATING DENDRITIC EXCITATION BY NMDA AND AMPA RECEPTOR MODELS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196211076.
Full text
9
Vellmer, Sebastian. "Applications of the Fokker-Planck Equation in Computational and Cognitive Neuroscience." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21597.
Full textAbstract:
In dieser Arbeit werden mithilfe der Fokker-Planck-Gleichung die Statistiken, vor allem die Leistungsspektren, von Punktprozessen berechnet, die von mehrdimensionalen Integratorneuronen [Engl. integrate-and-fire (IF) neuron],
Netzwerken von IF Neuronen und Entscheidungsfindungsmodellen erzeugt werden.
Im Gehirn werden Informationen durch Pulszüge von Aktionspotentialen kodiert.
IF Neurone mit radikal vereinfachter Erzeugung von Aktionspotentialen haben sich in Studien die auf Pulszeiten fokussiert sind als Standardmodelle etabliert. Eindimensionale IF Modelle können jedoch beobachtetes Pulsverhalten oft nicht beschreiben und müssen dazu erweitert werden. Im erste Teil dieser Arbeit wird eine Theorie zur Berechnung der Pulszugleistungsspektren von stochastischen, multidimensionalen IF Neuronen entwickelt. Ausgehend von der zugehörigen Fokker-Planck-Gleichung werden partiellen Differentialgleichung abgeleitet, deren Lösung sowohl die stationäre Wahrscheinlichkeitsverteilung und Feuerrate, als auch das Pulszugleistungsspektrum beschreibt.
Im zweiten Teil wird eine Theorie für große, spärlich verbundene und homogene Netzwerke aus IF Neuronen entwickelt, in der berücksichtigt wird, dass die zeitlichen Korrelationen von Pulszügen selbstkonsistent sind. Neuronale Eingangströme werden durch farbiges Gaußsches Rauschen modelliert, das von einem mehrdimensionalen Ornstein-Uhlenbeck Prozess (OUP) erzeugt wird. Die Koeffizienten des OUP sind vorerst unbekannt und sind als Lösung der Theorie definiert. Um heterogene Netzwerke zu untersuchen, wird eine iterative Methode erweitert.
Im dritten Teil wird die Fokker-Planck-Gleichung auf Binärentscheidungen von Diffusionsentscheidungsmodellen [Engl. diffusion-decision models (DDM)] angewendet. Explizite Gleichungen für die Entscheidungszugstatistiken werden für den einfachsten und analytisch lösbaren Fall von der Fokker-Planck-Gleichung hergeleitet. Für nichtliniear Modelle wird die Schwellwertintegrationsmethode erweitert.This thesis is concerned with the calculation of statistics, in particular the power spectra, of point processes generated by stochastic multidimensional integrate-and-fire (IF) neurons, networks of IF neurons and decision-making models from the corresponding Fokker-Planck equations. In the brain, information is encoded by sequences of action potentials. In studies that focus on spike timing, IF neurons that drastically simplify the spike generation have become the standard model. One-dimensional IF neurons do not suffice to accurately model neural dynamics, however, the extension towards multiple dimensions yields realistic behavior at the price of growing complexity. The first part of this work develops a theory of spike-train power spectra for stochastic, multidimensional IF neurons. From the Fokker-Planck equation, a set of partial differential equations is derived that describes the stationary probability density, the firing rate and the spike-train power spectrum. In the second part of this work, a mean-field theory of large and sparsely connected homogeneous networks of spiking neurons is developed that takes into account the self-consistent temporal correlations of spike trains. Neural input is approximated by colored Gaussian noise generated by a multidimensional Ornstein-Uhlenbeck process of which the coefficients are initially unknown but determined by the self-consistency condition and define the solution of the theory. To explore heterogeneous networks, an iterative scheme is extended to determine the distribution of spectra. In the third part, the Fokker-Planck equation is applied to calculate the statistics of sequences of binary decisions from diffusion-decision models (DDM). For the analytically tractable DDM, the statistics are calculated from the corresponding Fokker-Planck equation. To determine the statistics for nonlinear models, the threshold-integration method is generalized.
10
Hendrickson, Eric B. "Morphologically simplified conductance based neuron models: principles of construction and use in parameter optimization." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33905.
Full textAbstract:
The dynamics of biological neural networks are of great interest to neuroscientists and are frequently studied using conductance-based compartmental neuron models. For speed and ease of use, neuron models are often reduced in morphological complexity. This reduction may affect input processing and prevent the accurate reproduction of neural dynamics. However, such effects are not yet well understood. Therefore, for my first aim I analyzed the processing capabilities of 'branched' or 'unbranched' reduced models by collapsing the dendritic tree of a morphologically realistic 'full' globus pallidus neuron model while maintaining all other model parameters. Branched models maintained the original detailed branching structure of the full model while the unbranched models did not. I found that full model responses to somatic inputs were generally preserved by both types of reduced model but that branched reduced models were better able to maintain responses to dendritic inputs. However, inputs that caused dendritic sodium spikes, for instance, could not be accurately reproduced by any reduced model. Based on my analyses, I provide recommendations on how to construct reduced models and indicate suitable applications for different levels of reduction. In particular, I recommend that unbranched reduced models be used for fast searches of parameter space given somatic input output data.
The intrinsic electrical properties of neurons depend on the modifiable behavior of their ion channels. Obtaining a quality match between recorded voltage traces and the output of a conductance based compartmental neuron model depends on accurate estimates of the kinetic parameters of the channels in the biological neuron. Indeed, mismatches in channel kinetics may be detectable as failures to match somatic neural recordings when tuning model conductance densities. In my first aim, I showed that this is a task for which unbranched reduced models are ideally suited. Therefore, for my second aim I optimized unbranched reduced model parameters to match three experimentally characterized globus pallidus neurons by performing two stages of automated searches. In the first stage, I set conductance densities free and found that even the best matches to experimental data exhibited unavoidable problems. I hypothesized that these mismatches were due to limitations in channel model kinetics. To test this hypothesis, I performed a second stage of searches with free channel kinetics and observed decreases in the mismatches from the first stage. Additionally, some kinetic parameters consistently shifted to new values in multiple cells, suggesting the possibility for tailored improvements to channel models. Given my results and the potential for cell specific modulation of channel kinetics, I recommend that experimental kinetic data be considered as a starting point rather than as a gold standard for the development of neuron models.
11
Liu, Siqi. "Automating the Reconstruction of Neuron Morphological Models: the Rivulet Algorithm Suite." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/18167.
Full textAbstract:
The automatic reconstruction of single neuron cells is essential to enable large-scale data-driven investigations in computational neuroscience. The problem remains an open challenge due to various imaging artefacts that are caused by the fundamental limits of light microscopic imaging. Few previous methods were able to generate satisfactory neuron reconstruction models automatically without human intervention. The manual tracing of neuron models is labour heavy and time-consuming, making the collection of large-scale neuron morphology database one of the major bottlenecks in morphological neuroscience. This thesis presents a suite of algorithms that are developed to target the challenge of automatically reconstructing neuron morphological models with minimum human intervention. We first propose the Rivulet algorithm that iteratively backtracks the neuron fibres from the termini points back to the soma centre. By refining many details of the Rivulet algorithm, we later propose the Rivulet2 algorithm which not only eliminates a few hyper-parameters but also improves the robustness against noisy images. A soma surface reconstruction method was also proposed to make the neuron models biologically plausible around the soma body. The tracing algorithms, including Rivulet and Rivulet2, normally need one or more hyper-parameters for segmenting the neuron body out of the noisy background. To make this pipeline fully automatic, we propose to use 2.5D neural network to train a model to enhance the curvilinear structures of the neuron fibres. The trained neural networks can quickly highlight the fibres of interests and suppress the noise points in the background for the neuron tracing algorithms. We evaluated the proposed methods in the data released by both the DIADEM and the BigNeuron challenge. The experimental results show that our proposed tracing algorithms achieve the state-of-the-art results.
12
Endres, Dominik M. "Bayesian and information-theoretic tools for neuroscience." Thesis, St Andrews, 2006. http://hdl.handle.net/10023/162.
Full text
13
Lin, Risa J. "Real-time methods in neural electrophysiology to improve efficacy of dynamic clamp." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/49016.
Full textAbstract:
In the central nervous system, most of the processes ranging from ion channels to neuronal networks occur in a closed loop, where the input to the system depends on its output. In contrast, most experimental preparations and protocols operate autonomously in an open loop and do not depend on the output of the system. Real-time software technology can be an essential tool for understanding the dynamics of many biological processes by providing the ability to precisely control the spatiotemporal aspects of a stimulus and to build activity-dependent stimulus-response closed loops. So far, application of this technology in biological experiments has been limited primarily to the dynamic clamp, an increasingly popular electrophysiology technique for introducing artificial conductances into living cells. Since the dynamic clamp combines mathematical modeling with electrophysiology experiments, it inherits the limitations of both, as well as issues concerning accuracy and stability that are determined by the chosen software and hardware. In addition, most dynamic clamp systems to date are designed for specific experimental paradigms and are not easily extensible to general real-time protocols and analyses. The long-term goal of this research is to develop a suite of real-time tools to evaluate the performance, improve the efficacy, and extend the capabilities of the dynamic clamp technique and real-time neural electrophysiology. We demonstrate a combined dynamic clamp and modeling approach for studying synaptic integration, a software platform for implementing flexible real-time closed-loop protocols, and the potential and limitations of Kalman filter-based techniques for online state and parameter estimation of neuron models.
14
Matos, Pinto Thiago. "Computational models of intracellular signalling and synaptic plasticity induction in the cerebellum." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/11560.
Full textAbstract:
Many molecules and the complex interactions between them underlie plasticity in the cerebellum. However, the exact relationship between cerebellar plasticity and the different signalling cascades remains unclear. Calcium-calmodulin dependent protein kinase II (CaMKII) regulates many forms of synaptic plasticity, but very little is known about its function during plasticity induction in the cerebellum. The aim of this thesis is to contribute to a better understanding of the molecular mechanisms that regulate the induction of synaptic plasticity in cerebellar Purkinje cells (PCs). The focus of the thesis is to investigate the role of CaMKII isoforms in the bidirectional modulation of plasticity induction at parallel fibre (PF)-PC synapses. For this investigation, computational models that represent the CaMKII activation and the signalling network that mediates plasticity induction at these synapses were constructed. The model of CaMKII activation by calcium-calmodulin developed by Dupont et al (2003) replicates the experiments by De Koninck and Schulman (1998). Both theoretical and experimental studies have argued that the phosphorylation and activation of CaMKII depends on the frequency of calcium oscillations. Using a simplified version of the Dupont model, it was demonstrated that the CaMKII phosphorylation is mostly determined by the average calcium-calmodulin concentration, and therefore depends only indirectly on the actual frequency of calcium oscillations. I have shown that a pulsed application of calcium-calmodulin is, in fact, not required at all. These findings strongly indicate that the activation of CaMKII depends on the average calcium-calmodulin concentration and not on the oscillation frequency per se as asserted in those studies. This thesis also presents the first model of AMPA receptor phosphorylation that simulates the induction of long-term depression (LTD) and potentiation (LTP) at the PF-PC synapse. The results of computer simulations of a simple mathematical model suggest that the balance of CaMKII-mediated phosphorylation and protein phosphatase 2B (PP2B)-mediated dephosphorylation of AMPA receptors determines whether LTD or LTP occurs in cerebellar PCs. This model replicates the experimental observations by Van Woerden et al (2009) that indicate that CaMKII controls the direction of plasticity at PF-PC synapses. My computer simulations support Van Woerden et al’s original suggestion that filamentous actin binding can enable CaMKII to regulate bidirectional plasticity at these synapses. The computational models of intracellular signalling constructed in this thesis advance the understanding of the mechanisms of synaptic plasticity induction in the cerebellum. These simple models are significant tools for future research by the scientific community.
15
Voils, Danny. "Scale Invariant Object Recognition Using Cortical Computational Models and a Robotic Platform." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/632.
Full textAbstract:
This paper proposes an end-to-end, scale invariant, visual object recognition system, composed of computational components that mimic the cortex in the brain. The system uses a two stage process. The first stage is a filter that extracts scale invariant features from the visual field. The second stage uses inference based spacio-temporal analysis of these features to identify objects in the visual field. The proposed model combines Numenta's Hierarchical Temporal Memory (HTM), with HMAX developed by MIT's Brain and Cognitive Science Department. While these two biologically inspired paradigms are based on what is known about the visual cortex, HTM and HMAX tackle the overall object recognition problem from different directions. Image pyramid based methods like HMAX make explicit use of scale, but have no sense of time. HTM, on the other hand, only indirectly tackles scale, but makes explicit use of time. By combining HTM and HMAX, both scale and time are addressed. In this paper, I show that HTM and HMAX can be combined to make a com- plete cortex inspired object recognition model that explicitly uses both scale and time to recognize objects in temporal sequences of images. Additionally, through experimentation, I examine several variations of HMAX and its
16
McClure, Patrick. "Adapting deep neural networks as models of human visual perception." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/278073.
Full textAbstract:
Deep neural networks (DNNs) have recently been used to solve complex perceptual and decision tasks. In particular, convolutional neural networks (CNN) have been extremely successful for visual perception. In addition to performing well on the trained object recognition task, these CNNs also model brain data throughout the visual hierarchy better than previous models. However, these DNNs are still far from completely explaining visual perception in the human brain. In this thesis, we investigated two methods with the goal of improving DNNs’ capabilities to model human visual perception: (1) deep representational distance learning (RDL), a method for driving representational spaces in deep nets into alignment with other (e.g. brain) representational spaces and (2) variational DNNs that use sampling to perform approximate Bayesian inference. In the first investigation, RDL successfully transferred information from a teacher model to a student DNN. This was achieved by driving the student DNN’s representational distance matrix (RDM), which characterises the representational geometry, into alignment with that of the teacher. This led to a significant increase in test accuracy on machine learning benchmarks. In the future, we plan to use this method to simultaneously train DNNs to perform complex tasks and to predict neural data. In the second investigation, we showed that sampling during learning and inference using simple Bernoulli- and Gaussian-based noise improved a CNN’s representation of its own uncertainty for object recognition. We also found that sampling during learning and inference with Gaussian noise improved how well CNNs predict human behavioural data for image classification. While these methods alone do not fully explain human vision, they allow for training CNNs that better model several features of human visual perception.
17
Salimi-Khorshidi, Gholamreza. "Statistical models for neuroimaging meta-analytic inference." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:40a10327-7f36-42e7-8120-ae04bd8be1d4.
Full textAbstract:
A statistical meta-analysis combines the results of several studies that address a set of related research hypotheses, thus increasing the power and reliability of the inference. Meta-analytic methods are over 50 years old and play an important role in science; pooling evidence from many trials to provide answers that any one trial would have insufficient samples to address. On the other hand, the number of neuroimaging studies is growing dramatically, with many of these publications containing conflicting results, or being based on only a small number of subjects. Hence there has been increasing interest in using meta-analysis methods to find consistent results for a specific functional task, or for predicting the results of a study that has not been performed directly. Current state of neuroimaging meta-analysis is limited to coordinate-based meta-analysis (CBMA), i.e., using only the coordinates of activation peaks that are reported by a group of studies, in order to "localize" the brain regions that respond to a certain type of stimulus. This class of meta-analysis suffers from a series of problems and hence cannot result in as accurate results as desired. In this research, we describe the problems that existing CBMA methods are suffering from and introduce a hierarchical mixed-effects image-based metaanalysis (IBMA) solution that incorporates the sufficient statistics (i.e., voxel-wise effect size and its associated uncertainty) from each study. In order to improve the statistical-inference stage of our proposed IBMA method, we introduce a nonparametric technique that is capable of adjusting such an inference for spatial nonstationarity. Given that in common practice, neuroimaging studies rarely provide the full image data, in an attempt to improve the existing CBMA techniques we introduce a fully automatic model-based approach that employs Gaussian-process regression (GPR) for estimating the meta-analytic statistic image from its corresponding sparse and noisy observations (i.e., the collected foci). To conclude, we introduce a new way to approach neuroimaging meta-analysis that enables the analysis to result in information such as “functional connectivity” and networks of the brain regions’ interactions, rather than just localizing the functions.
18
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 textAbstract:
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.
19
Kulkarni, Anirudh. "Dynamics of neuronal networks." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066377/document.
Full textAbstract:
Dans cette thèse, nous étudions le vaste domaine des neurosciences à travers des outils théoriques, numériques et expérimentaux. Nous étudions comment les modèles à taux de décharge peuvent être utilisés pour capturer différents phénomènes observés dans le cerveau. Nous étudions les régimes dynamiques des réseaux couplés de neurones excitateurs (E) et inhibiteurs (I): Nous utilisons une description fournie par un modèle à taux de décharge et la comparons avec les simulations numériques des réseaux de neurones à potentiel d'action décrits par le modèle EIF. Nous nous concentrons sur le régime où le réseau EI présente des oscillations, puis nous couplons deux de ces réseaux oscillants pour étudier la dynamique résultante. La description des différents régimes pour le cas de deux populations est utile pour comprendre la synchronisation d'une chaine de modules E-I et la propagation d'ondes observées dans le cerveau. Nous examinons également les modèles à taux de décharge pour décrire l'adaptation sensorielle: Nous proposons un modèle de ce type pour décrire l'illusion du mouvement consécutif («motion after effect», (MAE)) dans la larve du poisson zèbre. Nous comparons le modèle à taux de décharge avec des données neuronales et comportementales nouvellesIn this thesis, we investigate the vast field of neuroscience through theoretical, numerical and experimental tools. We study how rate models can be used to capture various phenomena observed in the brain. We study the dynamical regimes of coupled networks of excitatory (E) and inhibitory neurons (I) using a rate model description and compare with numerical simulations of networks of neurons described by the EIF model. We focus on the regime where the EI network exhibits oscillations and then couple two of these oscillating networks to study the resulting dynamics. The description of the different regimes for the case of two populations is helpful to understand the synchronization of a chain of E-I modules and propagation of waves observed in the brain. We also look at rate models of sensory adaptation. We propose one such model to describe the illusion of motion after effect in the zebrafish larva. We compare this rate model with newly obtained behavioural and neuronal data in the zebrafish larva
20
Rehn, Martin. "Aspects of memory and representation in cortical computation." Doctoral thesis, KTH, Numerisk Analys och Datalogi, NADA, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4161.
Full textAbstract:
Denna avhandling i datalogi föreslår modeller för hur vissa beräkningsmässiga uppgifter kan utföras av hjärnbarken. Utgångspunkten är dels kända fakta om hur en area i hjärnbarken är uppbyggd och fungerar, dels etablerade modellklasser inom beräkningsneurobiologi, såsom attraktorminnen och system för gles kodning. Ett neuralt nätverk som producerar en effektiv gles kod i binär mening för sensoriska, särskilt visuella, intryck presenteras. Jag visar att detta nätverk, när det har tränats med naturliga bilder, reproducerar vissa egenskaper (receptiva fält) hos nervceller i lager IV i den primära synbarken och att de koder som det producerar är lämpliga för lagring i associativa minnesmodeller. Vidare visar jag hur ett enkelt autoassociativt minne kan modifieras till att fungera som ett generellt sekvenslärande system genom att utrustas med synapsdynamik. Jag undersöker hur ett abstrakt attraktorminnessystem kan implementeras i en detaljerad modell baserad på data om hjärnbarken. Denna modell kan sedan analyseras med verktyg som simulerar experiment som kan utföras på en riktig hjärnbark. Hypotesen att hjärnbarken till avsevärd del fungerar som ett attraktorminne undersöks och visar sig leda till prediktioner för dess kopplingsstruktur. Jag diskuterar också metodologiska aspekter på beräkningsneurobiologin idag.In this thesis I take a modular approach to cortical function. I investigate how the cerebral cortex may realise a number of basic computational tasks, within the framework of its generic architecture. I present novel mechanisms for certain assumed computational capabilities of the cerebral cortex, building on the established notions of attractor memory and sparse coding. A sparse binary coding network for generating efficient representations of sensory input is presented. It is demonstrated that this network model well reproduces the simple cell receptive field shapes seen in the primary visual cortex and that its representations are efficient with respect to storage in associative memory. I show how an autoassociative memory, augmented with dynamical synapses, can function as a general sequence learning network. I demonstrate how an abstract attractor memory system may be realised on the microcircuit level -- and how it may be analysed using tools similar to those used experimentally. I outline some predictions from the hypothesis that the macroscopic connectivity of the cortex is optimised for attractor memory function. I also discuss methodological aspects of modelling in computational neuroscience.
QC 20100916
21
Elijah, Daniel. "Neural encoding by bursts of spikes." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/neural-encoding-by-bursts-of-spikes(56f4cf97-3887-4e89-bc0d-8db183ce9ce1).html.
Full textAbstract:
Neurons can respond to input by firing isolated action potentials or spikes. Sequences of spikes have been linked to the encoding of neuron input. However, many neurons also fire bursts; mechanistically distinct responses consisting of brief high-frequency spike firing. Bursts form separate response symbols but historically have not been thought to encode input. However, recent experimental evidence suggests that bursts can encode input in parallel with tonic spikes. The recognition of bursts as distinct encoding symbols raises important questions; these form the basic aims of this thesis: (1) What inputs do bursts encode? (2) Does burst structure provide extra information about different inputs. (3) Is burst coding robust against the presence of noise; an inherent property of all neural systems? (4) What mechanisms are responsible for burst input encoding? (5) How does burst coding manifest in in-vivo neurons. To answer these questions, bursting is studied using a combination of neuron models and in-vivo hippocampal neuron recordings. Models ranged from neuron-specific cell models to models belonging to three fundamentally different burst dynamic classes (unspecific to any neural region). These classes are defined using concepts from non-linear system theory. Together, analysing these model types with in-vivo recordings provides a specific and general analysis of burst encoding. For neuron-specific and unspecific models, a number of model types expressing different levels of biological realism are analysed. For the study of thalamic encoding, two models containing either a single simplified burst-generating current or multiple currents are used. For models simulating three burst dynamic classes, three further models of different biological complexity are used. The bursts generated by models and real neurons were analysed by assessing the input they encode using methods such as information theory, and reverse correlation. Modelled bursts were also analysed for their resilience to simulated neural noise. In all cases, inputs evoking bursts and tonic spikes were distinct. The structure of burst-evoking input depended on burst dynamic class rather than the biological complexity of models. Different n-spike bursts encoded different inputs that, if read by downstream cells, could discriminate complex input structure. In the thalamus, this n-spike burst code explains informative responses that were not due to tonic spikes. In-vivo hippocampal neurons and a pyramidal cell model both use the n-spike code to mark different LFP features. This n-spike burst may therefore be a general feature of bursting relevant to both model and in-vivo neurons. Bursts can also encode input corrupted by neural noise, often outperforming the encoding of single spikes. Both burst timing and internal structure are informative even when driven by strongly noise-corrupted input. Also, bursts induce input-dependent spike correlations that remain informative despite strong added noise. As a result, bursts endow their constituent spikes with extra information that would be lost if tonic spikes were considered the only informative responses.
22
Allen, John Michael. "Effects of Abstraction and Assumptions on Modeling Motoneuron Pool Output." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1495538117787703.
Full text
23
Lundh, Dan. "A computational neuroscientific model for short-term memory." Thesis, University of Exeter, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324742.
Full text
24
Harkin, Emerson. "A Simplified Serotonin Neuron Model." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38533.
Full text
25
Ging-Jehli, Nadja Rita. "On the implementation of Computational Psychiatry within the framework of Cognitive Psychology and Neuroscience." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555338342285251.
Full text
26
Strack, Beata. "Multi-column multi-layer computational model of neocortex." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3279.
Full textAbstract:
We present a multi-layer multi-column computational model of neocortex that is built based on the activity and connections of known neuronal cell types and includes activity-dependent short term plasticity. This model, a network of spiking neurons, is validated by showing that it exhibits activity close to biology in terms of several characteristics: (1) proper laminar flow of activity; (2) columnar organization with focality of inputs; (3) low-threshold-spiking (LTS) and fast-spiking (FS) neurons function as observed in normal cortical circuits; and (4) different stages of epileptiform activity can be obtained with either increasing the level of inhibitory blockade, or simulation of NMDA receptor enhancement. The aim of this research is to provide insight into the fundamental properties of vertical and horizontal inhibition in neocortex and their influence on epileptiform activity. The developed model was used to test novel ideas about modulation of inhibitory neuronal types in a developmentally malformed cortex. The novelty of the proposed research includes: (1) design and implementation of a multi-layer multi-column model of the cortex with multiple neuronal types and short-time plasticity, (2) modification of the Izhikevich neuron model in order to model biological maximum firing rate property, (3) generating local field potential (LFP) and EEG signals without modeling multiple neuronal compartments, (4) modeling several known conditions to validate that the cortex model matches the biology in several aspects,(5) modeling different abnormalities in malformed cortex to test existing and to generate novel hypotheses.
27
Chaturvedi, Ashutosh. "Development of Accurate Computational Models for Patient-Specific Deep Brain Stimulation." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1323392558.
Full text
28
Ximing, LI. "Insights into Delivery of Somatic Calcium Signals to the Nucleus During LTP Revealed by Computational Modeling." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou152236301476345.
Full text
29
Xie, Danke. "A computational biologically-plausible model of working memory for serial order, repetition and binding." Diss., [La Jolla, Calif.] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3344748.
Full textAbstract:
Thesis (Ph. D.)--University of California, San Diego, 2009.Title from first page of PDF file (viewed April 1, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 150-163).
30
Adhikari, Sombudha. "IDENTIFICATION OF PROTEIN PARTNERS FOR NIBP, A NOVEL NIK-AND IKKB-BINDING PROTEIN THROUGH EXPERIMENTAL, COMPUTATIONAL AND BIOINFORMATICS TECHNIQUES." Master's thesis, Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/216569.
Full textAbstract:
BioengineeringM.S.
NIBP is a prototype member of a novel protein family. It forms a novel subcomplex of NIK-NIBP-IKKB and enhances cytokine-induced IKKB-mediated NFKB activation. It is also named TRAPPC9 as a key member of trafficking particle protein (TRAPP) complex II, which is essential in trans-Golgi networking (TGN). The signaling pathways and molecular mechanisms for NIBP actions remain largely unknown. The aim of this research is to identify potential proteins interacting with NIBP, resulting in the regulation of NFKB signaling pathways and other unknown signaling pathways. At the laboratory of Dr. Wenhui Hu in the Department of Neuroscience, Temple University, sixteen partner proteins were experimentally identified that potentially bind to NIBP. NIBP is a novel protein with no entry in the Protein Data Bank. From a computational and bioinformatics standpoint, we use prediction of secondary structure and protein disorder as well as homology-based structural modeling approaches to create a hypothesis on protein-protein interaction between NIBP and the partner proteins. Structurally, NIBP contains three distinct regions. The first region, consisting of 200 amino acids, forms a hybrid helix and beta sheet-based domain possibly similar to Sybindin domain. The second region comprised of approximately 310 residues, forms a tetratrico peptide repeat (TPR) zone. The third region is a 675 residue long all beta sheet and loops zone with as many as 35 strands and only 2 helices, shared by Gryzun-domain containing proteins. It is likely to form two or three beta sheet sandwiches. The TPR regions of many proteins tend to bind to the peptides from disordered regions of other proteins. Many of the 16 potential binding proteins have high levels of disorder. These data suggest that the TPR region in NIBP most likely binds with many of these 16 proteins through peptides and other domains. It is also possible that the Sybindin-like domain and the Gryzun-like domain containing beta sheet sandwiches bind to some of these proteins.
Temple University--Theses
31
Agmon, Eran. "A Computational Model of Adaptive Sensory Processing in the Electroreception of Mormyrid Electric Fish." PDXScholar, 2011. https://pdxscholar.library.pdx.edu/open_access_etds/291.
Full textAbstract:
Electroreception is a sensory modality found in some fish, which enables them to sense the environment through the detection of electric fields. Biological experimentation on this ability has built an intricate framework that has identified many of the components involved in electroreception's production, but lack the framework for bringing the details back together into a system-level model of how they operate together. This thesis builds and tests a computational model of the Electrosensory Lateral Line Lobe (ELL) in mormyrid electric fish in an attempt to bring some of electroreception's structural details together to help explain its function. The ELL is a brain region that functions as a primary processing area of electroreception. It acts as an adaptive filter that learns to predict reoccurring stimuli and removes them from its sensory stream, passing only novel inputs to other brain regions for further processing. By creating a model of the ELL, the relevant components which underlie the ELL's functional, electrophysiological patterns can be identified and scientific hypotheses regarding their behavior can be tested. Systems science's approach is adopted to identify the ELL's relevant components and bring them together into a unified conceptual framework. The methodological framework of computational neuroscience is used to create a computational model of this structure of relevant components and to simulate their interactions. Individual activation tendencies of the different included cell types are modeled with dynamical systems equations and are interconnected according to the connectivity of the real ELL. Several of the ELL's input patterns are modeled and incorporated in the model. The computational approach claims that if all of the relevant components of a system are captured and interconnected accurately in a computer program, then when provided with accurate representations of the inputs a simulation should produce functional patterns similar to those of the real system. These simulated patterns generated by the ELL model are compared to recordings from real mormyrid ELLs and their correspondences validate or nullify the model's integrity. By building a computation model that can capture the relevant components of the ELL's structure and through simulation reproduces its function, a systems-level understanding begins to emerge and leads to a description of how the ELL's structure, along with relevant inputs, generate its function. The model can be manipulated more easily than a biological ELL, and allows us to test hypotheses regarding how changes in the structures affect the function, and how different inputs propagate through the structure in a way that produces complex functional patterns.
32
Herrera-Valdez, Marco Arieli. "Geometry and nonlinear dynamics underlying excitability phenotypes in biophysical models of membrane potential." Diss., The University of Arizona, 2014. http://hdl.handle.net/10150/312741.
Full textAbstract:
The main goal of this dissertation was to study the bifurcation structure underlying families of low dimensional dynamical systems that model cellular excitability. One of the main contributions of this work is a mathematical characterization of profiles of electrophysiological activity in excitable cells of the same identified type, and across cell types, as a function of the relative levels of expression of ion channels coded by specific genes. In doing so, a generic formulation for transmembrane transport was derived from first principles in two different ways, expanding previous work by other researchers. The relationship between the expression of specific membrane proteins mediating transmembrane transport and the electrophysiological profile of excitable cells is well reproduced by electrodiffusion models of membrane potential involving as few as 2 state variables and as little as 2 transmembrane currents. Different forms of the generic electrodiffusion model presented here can be used to study the geometry underlying different forms of excitability in cardiocytes, neurons, and other excitable cells, and to simulate different patterns of response to constant, time-dependent, and (stochastic) time- and voltage-dependent stimuli. In all cases, an initial analysis performed on a deterministic, autonoumous version of the system of interest is presented to develop basic intuition that can be used to guide analyses of non-autonomous or stochastic versions of the model. Modifications of the biophysical models presented here can be used to study complex physiological systems involving single cells with specific membrane proteins, possibly linking different levels of biological organization and spatio-temporal scales.
33
Iyer, Laxmi R. "CANDID - A Neurodynamical Model of Idea Generation." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1326828617.
Full text
34
Friedman, Anika J. "A Computational Model of Neurofilament Kinetics Relating Axonal Caliber Growth and the Neurofilament Slowing Phenomenon." Ohio University Honors Tutorial College / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1556283731141422.
Full text
35
Arruda, Denise de. "A célula periglomerular do bulbo olfatório e seu papel no processamento de odores: um modelo computacional." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-23092010-171519/.
Full textAbstract:
Os interneurônios do bulbo olfatório são elementos chave para o entendimento do processamento de odores. O papel funcional desses neurônios ainda não é bem compreendido, em especial o papel da célula periglomerular (PG). O presente trabalho consiste em construir um modelo biologicamente plausível da célula PG e investigar os efeitos dessa célula em conjunto com modelos da célula mitral e da célula granular. Esses modelos são acoplados através de conexões sinápticas inspiradas nas conexões existentes no bulbo olfatório, formando uma pequena rede simplificada. A rede é usada para analisar o efeito da inibição inicial da célula mitral por parte da célula PG e os mecanismos que podem influenciar o padrão de atividade da célula mitral. Através deste estudo, verifica-se que a célula PG pode influenciar na frequência, no tempo de disparo e gerar atrasos na propagação do potencial da célula mitral, agindo como um mecanismo de controle nas camadas iniciais do processamento de odores do bulbo olfatório.Interneurons of the olfactory bulb are key elements for understanding odor processing. The functional role of these cells are not yet well understood, in particular the role of periglomerular cell (PG). This work aims at constructing a biologically plausible model of the PG cell to study effects of the coupling of this cell with model of mitral and granule cells of the olfactory bulb. Single cell models of these three cell types coupled by synaptic connections inspired on existing connections in the olfactory bulb, constituting a small and simple network. This network is used to investigate the effect of early lateral inhibition of the mitral cell by PG cell and the mechanisms witch can influence the output activity pattern of mitral cell. The study shows that the PG cell may influence the spike frequency and the spike timing of the mitral cell, as well as provoke delays in the propagation of action potential along this cell. Therefore, the PG cell may act as a control mechanism in the early odor processing stages in the olfactory bulb.
36
Souza, Fábio Marques Simões de. ""Estudo da origem e do papel das oscilações elétricas em um modelo computacional do sistema olfativo de vertebrados"." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/59/59134/tde-07112005-152337/.
Full textAbstract:
Esse trabalho consiste no estudo de alguns mecanismos responsáveis pela geração das oscilações elétricas observadas no sistema olfativo de vertebrados e das possíveis funções que essas oscilações possam ter no processamento da informação olfativa. Da-se especial atenção ao papel desempenhado pelo ritmo respiratório e pelas sinapses químicas e elétricas nesse processo. Para realizar essa investigação, foram utilizados modelos computacionais que reproduzem aspectos da anatomia e da fisiologia do epitélio olfativo, do bulbo olfativo e do córtex piriforme. Os modelos foram desenvolvidos e simulados no neurossimulador GENESIS, funcionando no sistema operacional LINUX. A análise dos resultados foi feita no programa MATLAB (Mathworks). Inicialmente, a tese faz uma descrição do substrato neurobiológico que compõe as camadas iniciais do sistema olfativo, incluindo o epitélio, bulbo e córtex olfativo, e de como a informação olfativa é processada por cada camada, discutindo a importância do sentido olfativo e a relevância da neurociência computacional no estudo da origem e do papel das oscilações elétricas existentes nesse sistema (Capítulo 1). O capítulo 2 descreve os materiais e métodos utilizados para a construção dos modelos computacionais e para análise dos resultados. O capítulo 3 faz uma descrição detalhada do modelo computacional utilizado e dos experimentos realizados com o modelo. Finalmente, o capítulo 4 apresenta e discute os resultados das simulações realizadas e o capítulo 5 estende essa discussão, concluindo a tese. O capítulo 6 contém as referências bibliográficas utilizadas no trabalho. Os resultados do trabalho sugerem que as oscilações elétricas no sistema olfativo poderiam ser geradas em várias estruturas e níveis de organização, abrangendo os níveis moleculares, celulares e de sistemas neurais. E que as sinapses químicas e elétricas, assim como os ritmos respiratórios, podem ter um papel fundamental na geração dessas oscilações. Assim, o modelo construído propõe uma explicação plausível para a origem das oscilações elétricas no sistema olfativo de vertebrados e discute as possíveis funções que essas oscilações teriam no contexto do processamento da informação sensorial.This work is a study of some mechanisms associated with the generation of electric oscillations in the vertebrate olfactory system. Special attention is given for the role of the respiratory rhythm, chemical synapses and electrical synapses in this process. The possible functions of the electric oscillations in olfactory information processing are explored. A computational model that reproduces aspects of the anatomy and physiology of the olfactory epithelium, bulb and piriform cortex was utilized to realize this investigation. The models were developed and simulated in the GENESIS neurosimulator, running under the LINUX operational system. The analysis of the results was made in the software MATLAB (Mathworks). In the beginning, the thesis describe the neurobiological substracts of the initial layers of the olfactory system, including the olfactory epithelium, bulb and piriform cortex, and explore how the olfactory information is processed by each layer. The chapter 1 presents the importance of the olfactory sense and the use of computational neuroscience to study the role of the electric oscillations in this system. The chapter 2 explains the material and methods utilized to develop the computational model and to analyse the data generated by the model. The chapter 3 describes the used computational model and the experiments realized with the model. Finally, the chapter 4 presents and discusses the results of the simulations. The chapter 5 extends the discussion and concludes the thesis. The chapter 6 contains the bibliographic references. The results of the work suggest that electric oscillations in the olfactory system could be generated in several structures and organizational levels, including the molecular level, the cellular and neural systems level. In particular, the results shown that chemical and electric synapses, as well as the respiratory rhythm, may have a fundamental role in the generation of these oscillations. Indeed, the constructed model proposes a plausible explanation for the origin of the electrical oscillations in the vertebrate olfactory system and discusses the possible function of these oscillations in the context of sensorial information processing.
37
Facchini, Denise Arruda. "O papel dos interneurônios inibitórios do bulbo olfatório no processamento de odores: um estudo computacional." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-10112015-173452/.
Full textAbstract:
O entendimento dos mecanismos de representação e processamento de odores pelo sistema olfatório é uma das questões centrais da neurociência moderna. Os odores são codificados pela circuitaria interna do bulbo olfatório em padrões espaço-temporais refletidos pela atividade de suas células de saída, as células mitrais e tufosas, que transmitem os resultados das computações dessa estrutura inicial de processamento a regiões corticais superiores. A arquitetura das conexões existentes no bulbo olfatório apresenta inibição lateral em duas camadas diferentes de sua estrutura laminar, intermediadas por dois tipos distintos de interneurônios. Na camada glomerular, mais externa, a inibição lateral é mediada pelas células periglomerulares e na camada plexiforme externa, mais interna, a inibição lateral é mediada pelas células granulares. O papel desses dois níveis distintos de inibição lateral e os mecanismos segundo os quais eles atuam moldando os padrões espaço-temporais de resposta do bulbo olfatório a odores diferentes são ainda pouco conhecidos. O objetivo deste trabalho foi construir um modelo de rede neural biologicamente plausível do bulbo olfatório para investigar como dois tipos diferentes de interneurônios, atuando em estágios distintos de processamento, podem contribuir para a discriminação de odores e a coordenação dos padrões de disparo das células mitrais. O modelo de rede construído, com representação de odores pela atividade das células mitrais e baseado nas interações recíprocas entre essas células e os interneurônios inibitórios, mostrou que a inibição gerada pelas células periglomerulares pode melhorar o contraste entre odores similares, facilitando a discriminação de odores, enquanto que a inibição das células granulares atua no refinamento da resposta de saída da informação olfatória.The understanding of odor representation and processing mechanisms by the olfactory system is one of the central questions of modern neuroscience. Odors are encoded by the olfactory bulb circuitry in terms of spatiotemporal spiking patterns. These are reflected in the activity of the mitral cells, which are the output cells of the olfactory bulb that transmit the information processed in this early structure to higher cortical regions. The architecture of the olfactory bulb connections presents lateral inhibition at two different layers of its laminar structure, mediated by two distinct types of interneurons. In the glomerular layer, lateral inhibition is mediated by periglomerular cells. In the external plexiform layer, lateral inhibition is mediated by granule cells. The role of these two different lateral inhibition levels and the mechanisms whereby they shape the spatial and temporal patterns of the olfactory bulb response to different odors is not well known. The aim of this work was to build a biologically plausible neural network model of the olfactory bulb to investigate how two different types of interneurons, acting at different processing stages, could contribute to odor discrimination and the coordination of the mitral cells spiking patterns. The results of simulations of the network model shown that the inhibition generated by periglomerular cells can provide contrast enhancement and odors discrimination, while the granule cell inhibition can refine the output response of the olfactory information.
38
McGuinness, James. "Implications of potassium channel heterogeneity for model vestibulo-ocular reflex response fidelity." Thesis, University of Stirling, 2014. http://hdl.handle.net/1893/21844.
Full textAbstract:
The Vestibulo-Ocular Reflex (VOR) produces compensatory eye movements in response to head and body rotations movements, over a wide range of frequencies and in a variety of dimensions. The individual components of the VOR are separated into parallel pathways, each dealing with rotations or movements in individual planes or axes. The Horizontal VOR (hVOR) compensates for eye movements in the Horizontal plane, and comprises a linear and non-linear pathway. The linear pathway of the hVOR provides fast and accurate compensation for rotations, the response being produced through 3-neuron arc, producing a direct translation of detected head velocity to compensatory eye velocity. However, single neurons involved in the middle stage of this 3-neuron arc cannot account for the wide frequency over which the reflex compensates, and the response is produced through the population response of the Medial Vestibular Nucleus (MVN) neurons involved. Population Heterogeneity likely plays a role in the production of high fidelity population response, especially for high frequency rotations. Here we present evidence that, in populations of bio-physical compartmental models of the MVN neurons involved, Heterogeneity across the population, in the form of diverse spontaneous firing rates, improves the response fidelity of the population over Homogeneous populations. Further, we show that the specific intrinsic membrane properties that give rise to this Heterogeneity may be the diversity of certain slow voltage activated Potassium conductances of the neurons. We show that Heterogeneous populations perform significantly better than Homogeneous populations, for a wide range of input amplitudes and frequencies, producing a much higher fidelity response. We propose that variance of Potassium conductances provides a plausible biological means by which Heterogeneity arises, and that the Heterogeneity plays an important functional role in MVN neuron population responses. We discuss our findings in relation to the specific mechanism of Desynchronisation through which the benfits of Heterogeneity may arise, and place those findings in the context of previous work on Heterogeneity both in general neural processing, and the VOR in particular. Interesting findings regarding the emergence of phase leads are also discussed, as well as suggestions for future work, looking further at Heterogeneity of MVN neuron populations.
39
Vieira, Diogo Porfirio de Castro. "Estudo sobre atividade auto sustentada em modelos de redes neurais corticais." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-10022014-153843/.
Full textAbstract:
O entendimento de como a informação é representada e processada no cérebro e quais são os mecanismos necessários para que isto seja possível é um dos grandes desafios da neurociência. A atividade populacional das células corticais possui dinâmica emergente bastante complexa, apresentando padrões auto-sustentados mesmo na ausência de estímulos externos. Esses padrões de atividade podem representar estados internos de auto-organização da rede neural cortical. Porém, quais características da rede cortical seriam essenciais para o entendimento deste tipo de atividade? Podemos elencar duas características fundamentais: a organização topológica da rede e as características dinâmicas das unidades funcionais da rede (os neurônios). Neste trabalho estudamos a influência da topologia e da dinâmica dos neurônios sobre a atividade auto-sustentada de dois modelos corticais diferentes. O primeiro modelo possui arquitetura hierárquica e modular construída segundo uma estratégia top-down. As simulações com este modelo mostram que criação hierárquica de módulos favorece a atividade auto-sustentada em concordância com trabalhos anteriores de outros autores. Também observamos que diferentes classes funcionais de neurônios influenciam de maneiras distintas a atividade auto-sustentada da rede. O segundo modelo possui arquitetura em camadas com regras intra- e inter-laminares específicas baseadas em dados anatômicos do córtex visual primário de gatos. As simulações com este modelo mostram um importante papel das condutâncias sinápticas excitatórias e inibitórias sobre o início da atividade auto-sustentada na rede, especialmente sobre a largura (intervalo de valores da condutância excitatória) da zona de transição entre as regiões com e sem atividade auto-sustentada no diagrama de condutâncias sinápticas. Conclui-se que a topologia da rede cortical e sua composição em termos de combinações de neurônios de diferentes tipos têm importante papel sobre a existência e as propriedades da atividade auto-sustentada na rede.To understand how information is represented and processed in the brain and the necessary mechanisms for this is one of the major challenges in neuroscience. The population activity of cortical cells has complex and emergent dynamics, showing self-sustained activity patterns even in the absence of external stimuli. These activity patterns may represent internal self-organizing states of the cortical network. Which characteristics that make up the cortical network would be essential to understand this type of activity? We can list two basic characteristics: the topological organization of the network and the dynamic characteristics of its functional units (the neurons). In this work we studied the influence of topology and neuronal dynamics on self-sustained activity in two different cortical network models. The first model has hierarchical and modular architecture constructed according to a top-down strategy. Simulations with this model show that the hierarchical creation of modules favors self-sustained activity in agreement with results from other authors. We also observed that different functional neuronal classes influence in distict ways the self-sustained activity. The second model has a layered architecture with specific intra- and inter-laminar rules based on anatomical evidence from the primary visual cortex of cats. Simulations with this model show an important role of excitatory and inhibitory synaptic conductances on the beginning of self-sustained network activity, specially on the width of the border (range of excitatory conductance values) between regions with and without self-sustained activity in the excitatory-inhibitory synaptic conductances diagram. We conclude that network topology and its composition in terms of combinations of neurons with different dynamics have an important role on the existence and properties of self-sustained activity in the network.
40
Hunt, Laurence T. "Modelling human decision under risk and uncertainty." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:244ce799-7397-4698-8dac-c8ca5d0b3e28.
Full textAbstract:
Humans are unique in their ability to flexibly and rapidly adapt their behaviour and select courses of action that lead to future reward. Several ‘component processes’ must be implemented by the human brain in order to facilitate this behaviour. This thesis examines two such components; (i) the neural substrates supporting action selection during value- guided choice using magnetoencephalography (MEG), and (ii) learning the value of environmental stimuli and other people’s actions using functional magnetic resonance imaging (fMRI). In both situations, it is helpful to formally model the underlying component process, as this generates predictions of trial-to-trial variability in the signal from a brain region involved in its implementation. In the case of value-guided action selection, a biophysically realistic implementation of a drift diffusion model is used. Using this model, it is predicted that there are specific times and frequency bands at which correlates of value are seen. Firstly, there are correlates of the overall value of the two presented options, and secondly the difference in value between the options. Both correlates should be observed in the local field potential, which is closely related to the signal measured using MEG. Importantly, the content of these predictions is quite distinct from the function of the model circuit, which is to transform inputs relating to the value of each option into a categorical decision. In the case of social learning, the same reinforcement learning model is used to track both the value of two stimuli that the subject can choose between, and the advice of a confederate who is playing alongside them. As the confederate advice is actually delivered by a computer, it is possible to keep prediction error and learning rate terms for stimuli and advice orthogonal to one another, and so look for neural correlates of both social and non-social learning in the same fMRI data. Correlates of intentional inference are found in a network of brain regions previously implicated in social cognition, notably the dorsomedial prefrontal cortex, the right temporoparietal junction, and the anterior cingulate gyrus.
41
Monté, Rubio Gemma C. "Computational analysis of schizophrenia: Implementation of a multivariate model of anatomical differences." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/348264.
Full textAbstract:
Chronic schizophrenia has been widely studied, consistent findings have shown the anatomical pattern associated with this disease, but the clinical picture is often undifferentiated at first presentation. Finding morphometric alterations associated with a disease is a widespread goal in neuroimaging. It has been performed in hundreds of studies from applying Voxel-Based Morphometry (VBM). However, VBM is a mass-univariate approach, assumes that voxels are independent and this may not be the most biologically plausible assumption to make. Many neuroimaging advances are focused in multivariate framework, like pattern recognition approaches. Such applications reveal complex associations and prediction models that provide greater accuracy for characterizing differences, also in schizophrenia. Such techniques require some form of characterization of inter-subject neuroanatomical variability, where the registration plays a significant role. If data are imprecisely modeled or the characterization used does not incorporate key information, this may result in poor predictions. The use of suboptimal features limits the accuracy with which predictions may be made. This scenario makes necessary exploring features from images and use the most informative to optimise pattern recognition in clinical research. Regarding modeling, accuracy is being established during VBM-type preprocessing. If segmentation does not work accurately, the next normalisation step cannot be accurate either. Hence the interest in the accuracy of automated computational tools is also increasing.
To adress these issues, the current thesis was divided into three studies. First study was focused on the comparison between segmentation algorithms by SPM (http://www.fil.ion.ucl.ac.uk/spm/): “Unified segmentation” (US) and “New Segmentation” (NS), and FSL (http://fsl.fmrib.ox.ac.uk/fsl/): “FMRIB’s Automated Segmentation tool” (FAST). The IBSR dataset (http://vivo.cornell.edu/display/individual5017) that includes segmented classes by experts was used to establish a ground truth. A detailed comparison between algorithms was conducted using different methods. In study 2 a Gaussian Process machine learning approach was used for predicting age, gender and body mass index (BMI) using the IXI dataset (http://biomedic.doc.ic.ac.uk/brain-development/index.php?n=Main.Datasets). MRI data were segmented using NS and registered with the “Shooting Geodesic toolbox”. Proper characterizations from VBM-type preprocessed data (linear kernels) and its dependence on the smoothing (FWHM from 0 to 20mm) were evaluated. Study 3 consisted in an application to Schizophrenia (Sample involved 111 patients and 111 controls provided by FIDMAG: http://www.fidmag.com/fidmag/index.php) with the optimal features from study 2. Our hypothesis was that image features that worked well in study 2 would also work well for predicting schizophrenia.
Results from study 1 showed that US was the most sensitive algorithm, and FAST the most specific, NS was found the most balanced of the three, no significant differences w.r.t. the sensitivity of US and the specificity of FAST were detected. Moreover, NS obtained the highest Jaccard coefficient, becoming the most similar to the ground truth. FAST was found the last in this ranking. In study 2, results from predicting age, gender and BMI pointed that scalar momentum was the best feature. Interestingly, grey matter (GM) was not the best feature for predicting age, and whithe matter was the best feature for predicting BMI. In general, performances were highly dependent on the smoothing, although scalar momentum was not among the most dependent. Findings from study 3 showed that scalar momentum provided best feature than GM for predicting schizophrenia, this results confirmed the hypothesis a priori.
Main conclusion is that multivariate pattern recognition analyses using scalar momentum provide an excellent strategy for classifying schizophrenia. This approach might potentially be extended to other psychiatric and neurodegenerative diseases both in research and as an aid to differential diagnosis in routine clinical practice.Aunque el patrón anatómico asociado a la esquizofrenia es conocido, el cuadro clínico es a menudo difícil de diferenciar en su debut. Un extendido objetivo en neurociencias es encontrar alteraciones morfológicas asociadas a una enfermedad. Muchos estudios han aplicado la Morfología Basada en Voxel (VBM), pero es univariante y no cumple la asunción biológicamente más plausible. Esto conduce a la neuroimagen hacia entornos multivariantes como el reconocimiento de patrones. Estas técnicas requieren de una caracterización de los datos que cuantifique la variabilidad neuroanatomica. Si los datos no están modelados con precisión y/o las caracterizaciones no incorporan información clave, la precisión de las predicciones será limitada. Es necesario explorar características a partir de imágenes y seleccionar las más informativas. También es esencial el preprocesado tipo-VBM requerido. Si la segmentación no es precisa, la normalización tampoco puede serlo. Para abordar estos aspectos, esta tesis se divide en tres estudios. El primero compara algoritmos de segmentación de SPM: “Unified segmentation” (US) y “New Segmentation” (NS), y de FSL: “FMRIB’s Automated Segmentation tool” (FAST). Se realizó una comparación entre algoritmos usando diferentes métodos con las imágenes IBSR (vivo.cornell.edu/display/individual5017), por incluir tejidos segmentados por expertos. En el estudio 2, una máquina de aprendizaje de Procesos Gaussianos fue aplicada para predecir edad, género e índice de masa corporal (IMC) usando los datos IXI (biomedic.doc.ic.ac.uk/brain-development/index.php?n=Main.Datasets). Las imágenes fueron preprocesadas con SPM12. Después, caracterizaciones de éstos datos fueron evaluadas, así como su relación con el suavizado (FWHM: 0-20mm). El estudio 3 consistió en aplicar la metodología del estudio 2 a la esquizofrenia (datos FIDMAG). Nuestra hipótesis fue que las características óptimas del estudio 2 también lo serían en éste. Los primeros resultados mostraron que NS fue la herramienta más equilibrada en cuanto a sensibilidad y especificidad. También NS obtuvo el coeficiente Jaccard más alto, dando segmentaciones más similares a las realizadas por expertos. FAST obtuvo el índice menor. En el estudio 2, los resultados de las predicciones señalaron los momentos escalares como la mejor característica. Curiosamente, la sustancia gris (GM) no fue la óptima para predecir la edad, y la sustancia blanca fue la mejor para predecir IMC. Se observó alta dependencia del suavizado. En el estudio 3 los momentos escalares aportaron mejor caracterización para predecir esquizofrenia que la GM, confirmado la hipótesis a priori. En conclusión los momentos escalares proveen de características que alcanzan mayor precisión en el reconocimiento de patrones para predecir la esquizofrenia. Éste enfoque podría extenderse a otras enfermedades tanto en investigación y como ayuda al diagnóstico diferencial en la clínica diaria.
42
Freitas, Josiane da Silva. "Estudo computacional de efeitos de alterações nas condutâncias de canais iônicos sobre a atividade elétrica de modelos morfologicamente realistas de células granulares do giro denteado do hipocampo de ratos." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/59/59134/tde-01072016-141400/.
Full textAbstract:
A ocorrência de status epileticus (SE) desencadeia algumas alterações no sistema nervoso central. O giro denteado (GD) do hipocampo sofre com modificações na expressão gênica dos canais iônicos das células granulares (CGs) e essas células sofrem alterações morfológicas. Essas alterações se manifestam com o brotamento de fibras musgosas, redução no número de espinhas dendríticas, encurtamento e estreitamento da arborização dendrítica. As modificações na expressão gênica dos canais iônicos afetam suas densidades máximas de condutância. Este estudo utilizou 40 modelos computacionais realistas para simular alterações nas condutâncias de canais iônicos e seus efeitos sobre dois grupos de CGs do GD. Os modelos foram construídos com base em reconstruções tridimensionais de 20 CGS com morfologia alterada após SE induzido por pilocarpina (CG-PILO) e 20 de morfologia normal (CG-controle). Foram dotados dos canais iônicos de sódio rápido (Na), canal de potássio de retificação tardia rápido (fKdr), canal de potássio de retificação tardia lento (fKdr), canal de potássio de tipo A (KA), canal de potássio dependente de cálcio e de voltagem de alta condutância (BK), canal de potássio dependente de cálcio de baixa condutância (SK) e canais de cálcio dos tipos T, N e L. As simulações foram realizadas no software Neuron. Foram realizados test t para detectar se ocorre diferenças significativas entre os grupos CG-controle e CG-PILO As alterações nas densidades máximas de condutância provocaram mudanças nos parâmetros de excitabilidade dos grupos CG-PILO e CG- controle, alterando valores de frequência de disparos, reobase e cronaxia. Os grupos apresentam respostas significativamente diferentes para as médias de reobase para a maioria dos valores de densidade máxima de condutância,, porém para cronaxia a maioria dos grupo não apresentou diferenças significativas. O grupo CG-controle apresentou médias maiores de frequência de disparos que o CG-PILO e o grupo CG-PILO apresentou valores de reobase maior para as alterações de densidade de condutância da maioria dos canais, sendo essas diferenças significativas.The occurrence of status epilepticus (SE) triggers some changes in the central nervous system. The dentate gyrus (DG) of the hippocampus suffers from changes in gene expression of ion channels of granule cells (GCs) and these cells undergo morphological changes. These changes manifest themselves with mossy fiber sprouting, reduction in the number of dendritic spines, shortening and narrowing of dendritic branching. Changes in gene expression of ion channels affect their maximum densities of conductance. This study used 40 realistic computer models to simulate changes in conductance of ion channels and its effect on two groups of CGs of the GD. The models were built based on three-dimensional reconstructions of 20 CGS with morphology changed after pilocarpine-induced SE (CG-PILO) and 20 normal morphology (CG-control). The models were equipped with the ion channels of fast sodium (Na), fast delayed rectifying potassium channel (fKDR), slow delayed rectifying potassium channel (fKdr), potassium channel type A (KA), potassium channel dependent calcium and high voltage conductance (BK), potassium channel dependent calcium low conductance (SK) and the calcium channel types T, N and L. The simulations were performed at Neuron software.T tests were performed to p-values <0.05 for detecting significant differences between the GC-control group and GC-PILO. Changes in maximum densities conductance caused changes in excitability parameters CG-PILO and GC- control groups, by changing frequency values of spikes, rheobase and chronaxie. The groups have significantly different responses to the averages for the most rheobase maximum density values of conductance, but these differences were shortly found for chronaxie values. The CG-control group had higher average frequency of spikes than the CG-PILO group. The CG-PILO group had rheobase values higher for conductance density changes the most channels. These differences are significant.
43
Kelly, Sean T. "Neural population coding of visual motion." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/54840.
Full textAbstract:
Motion in the outside world forms one of the primary uses of visual information for many animals. The ability to interpret motion quickly and accurately permits interaction with and response to events in the outside world. While much is known about some aspects of motion perception, there is less agreement about how feature selectivity leading to motion perception is actually formed in the convergent and divergent pathways of the visual system. It is even less clear how these classical understandings of motion processing, often driven by artificial stimuli with little resemblance to the outside world, correspond to responses of neurons when using more natural stimuli. In this thesis, we probe these gaps, first by demonstrating that synchronization within the visual thalamus leads to efficient representations of motion (through tuning properties) in primary visual cortex, exploiting precise timing across populations in a unique manner compared to traditional models. We then create a novel “minimally-natural” stimulus with the appearance of an infinite hallway wallpapered with sinusoidal gratings, to probe how such minimally natural features modulate our predictions of neural responses based upon feature tuning properties. Through encoding and decoding models we find that measuring a restricted tuning parameter space limits our ability to capture all response properties but preserves relevant information for decoding. We finish with an exploration of ethologically relevant natural features, perspective and complex motion, and show that even moderate amounts of each feature within or near the classical V1 receptive field changes the neural response from what classical feature tuning would predict and improves stimulus classification tremendously. Together all of these results indicate that capturing information about motion in the outside world through visual stimuli requires a more advanced model of feature selectivity that incorporates parameters based on more complex spatial relationships.
44
Pallarés, Picazo Vicente. "Individual traits versus invariances of cognitive functions: a model-based study of brain connectivity." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666806.
Full textAbstract:
Es conocido en la literatura de neuroimagen que las redes cerebrales funcionales reflejan rasgos personales. Estas características individuales, podrían interferir al caracterizar la cognición entendida como la manera en que se coordinan las redes para realizar una tarea, como mantener la atención, recordar, o procesar información visual. Cómo estos aspectos individuales coexisten con mecanismos generales es, por tanto, una pregunta clave en investigación sobre conectividad cerebral. Este trabajo estudia la relación entre marcadores de conectividad específicos tanto de sujetos, como de tareas. Se centra en dos escalas temporales distintas: la variabilidad entre sesiones, y las fluctuaciones rápidas producidas durante una sesión de adquisición. Utilizamos técnicas de machine learning para separar cuantitativamente las contribuciones de información del sujeto y del estado cognitivo a la conectividad. La metodología presentada nos permite extraer aquellas redes representativas de ambas dimensiones, así como profundizar en su evolución, sugiriendo las escalas temporales relevantes en la cognición.És conegut en la literatura de neuroimatge que les xarxes cerebrals funcionals reflecteixen trets personals. Aquestes característiques individuals podrien interferir en caracteritzar la cognició entesa com la manera en què les xarxes es coordinen per realitzar una tasca, com mantenir l'atenció, recordar o processar informació visual. Cóm aquests aspectes individuals coexisteixen amb mecanismes generals, és, per tant, una pregunta clau en recerca sobre connectivitat cerebral. Aquest treball estudia la relació entre marcadors de connectivitat específics tant de subjectes, com de tasques. Se centra en dues escales temporals: la variabilitat entre sessions, i les fluctuacions ràpides produïdes durant una sessió d'adquisició. Utilitzem tècniques de machine learning per separar quantitativament les contribucions d'informació del subjecte i de l'estat cognitiu a la connectivitat. La metodologia presentada ens permet extreure aquelles xarxes representatives d'ambdues dimensions, així com aprofundir en la seva evolució, suggerint les escales temporals rellevants en la cognició.
There is consistent evidence in the neuroimaging literature that functional brain networks reflect personal traits. Individual specificity may interfere with the characterization of cognition, in terms of coordination of brain networks to perform a task, such as sustained attention, memory retrieval or visual information processing. How individual traits coexist with invariant mechanisms is, therefore, a key question in brain connectivity research. This work aims to examine the relationship between subject- and task-specific connectivity signatures. It focuses on two different timescales: day-to-day variability and faster fluctuations exhibited within a scanning session. We adopt a machine learning approach to quantitatively disentangle the contribution of subject information and cognitive state to the connectivity patterns. The proposed methodology allows us to extract the specific brain networks that support each of the two dimensions, as well as to delve into their changes over time, suggesting the relevant timescales for cognition.
45
Brooks, Matthew Bryan. "Multistability in bursting patterns in a model of a multifunctional central pattern generator." Atlanta, Ga. : Georgia State University, 2009. http://digitalarchive.gsu.edu/math_theses/73/.
Full textAbstract:
Thesis (M.S.)--Georgia State University, 2009.Title from title page (Digital Archive@GSU, viewed July 20, 2010) Andrey Shilnikov, Robert Clewley, Gennady Cymbalyuk, committee co-chairs; Igor Belykh, Vladimir Bondarenko, Mukesh Dhamala, Michael Stewart, committee members. Includes bibliographical references (p. 65-67).
46
Vieira, Diogo Porfirio de Castro. "Análises de estabilidade e de sensibilidade de modelos biologicamente plausíveis do córtex visual primário." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-18032009-163830/.
Full textAbstract:
A neurociência computacional é uma vasta área que tem como objeto de estudo o entendimento ou a emulação da dinâmica cerebral em diversos níveis. Neste trabalho atenta-se ao estudo da dinâmica de neurônios, os quais, no consenso atual, acredita-se serem as unidades fundamentais do processamento cerebral. A importância do estudo sobre o comportamento de neurônios se encontra na diversidade de propriedades que eles podem apresentar. O estudo se torna mais rico quando há interações de sistemas internos ao neurônio em diferentes escalas de tempo, criando propriedades como adaptação, latência e comportamento em rajada, o que pode acarretar em diferentes papéis que os neurônios podem ter na rede. Nesta dissertação é feita uma análise sob o ponto de vista de sistemas dinâmicos e de análise de sensibilidade de seis modelos ao estilo de Hodgkin-Huxley e compartimentais de neurônios encontrados no córtex visual primário de mamíferos. Esses modelos correspondem a seis classes eletrofisiológicas de neurônios corticais e o estudo feito nesta dissertação oferece uma contribuição ao entendimento dos princípios de sistemas dinâmicos subjacentes a essa classificação.Computational neuroscience is a vast scientific area which has as subject of study the unsderstanding or emulation of brain dynamics at different levels. This work studies the dynamics of neurons, which are believed, according to present consensus, to be the fundamental processing units of the brain. The importance of studying neuronal behavior comes from the diversity of properties they may have. This study becomes richer when there are interactions between distintic neuronal internal systems, in different time scales, creating properties like adaptation, latency and bursting, resulting in different roles that neurons may have in the network. This dissertation contains a study of six reduced compartmental conductance-based models of neurons found in the primary visual cortex of mammals under the dynamical systems and sensitivity analysis viewpoints. These models correspond to six eletrophysiological classes of cortical neurons and this dissertation offers a contribution to the understanding of the dynamical-systems principles underlying such classification.
47
Bothma, Adel. "A model-based statistical approach to functional MRI group studies." Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:7d52e314-39f7-41b7-bdd3-6e5c30d4940a.
Full textAbstract:
Functional Magnetic Resonance Imaging (fMRI) is a noninvasive imaging method that reflects local changes in brain activity. FMRI group studies involves the analysis of the functional images acquired for each of a group of subjects under the same experimental conditions. We propose a spatial marked point-process model for the activation patterns of the subjects in a group study. Each pattern is described as the sum of individual centres of activation. The marked point-process that we propose allows the researcher to enforce repulsion between all pairs of centres of an individual subject that are within a specified minimum distance of each other. It also allows the researcher to enforce attraction between similarly-located centres from different subjects. This attraction helps to compensate for the misalignment of corresponding functional areas across subjects and is a novel method of addressing the problem of imperfect inter-subject registration of functional images. We use a Bayesian framework and choose prior distributions according to current understanding of brain activity. Simulation studies and exploratory studies of our reference dataset are used to fine-tune the prior distributions. We perform inference via Markov chain Monte Carlo. The fitted model gives a summary of the activation in terms of its location, height and size. We use this summary both to identify brain regions that were activated in response to the stimuli under study and to quantify the discrepancies between the activation maps of subjects. Applied to our reference dataset, our measure is successful in separating out those subjects with activation patterns that do not agree with the overall group pattern. In addition, our measure is sensitive to subjects with a large number of activation centres relative to the other subjects in the group. The activation summary given by our model makes it possible to pursue a range of inferential questions that cannot be addressed with ease by current model-based approaches.
48
Daouzli, Adel Mohamed. "Systèmes neuromorphiques : étude et implantation de fonctions d'apprentissage et de plasticité." Thesis, Bordeaux 1, 2009. http://www.theses.fr/2009BOR13806/document.
Full textAbstract:
Dans ces travaux de thèse, nous nous sommes intéressés à l'influence du bruit synaptique sur la plasticité synaptique dans un réseau de neurones biophysiquement réalistes. Le simulateur utilisé est un système électronique neuromorphique. Nous avons implanté un modèle de neurones à conductances basé sur le formalisme de Hodgkin et Huxley, et un modèle biophysique de plasticité. Ces travaux ont inclus la configuration du système, le développement d'outils pour l'exploiter, son utilisation ainsi que la mise en place d'une plateforme le rendant accessible à la communauté scientifique via Internet et l'utilisation de scripts PyNN (langage de description de simulations en neurosciences computationnelles)In this work, we have investigated the effect of input noise patterns on synaptic plasticity applied to a neural network. The study was realised using a neuromorphic hardware simulation system. We have implemented a neural conductance model based on Hodgkin and Huxley formalism, and a biophysical model for plasticity. The tasks performed during this thesis project included the configuration of the system, the development of software tools, the analysis tools to explore experimental results, and the development of the software modules for the remote access to the system via Internet using PyNN scripts (PyNN is a neural network description language commonly used in computational neurosciences)
49
Cisi, Rogério Rodrigues Lima. "Sistema de simulação de circuitos neuronais da medula espinhal desenvolvido em arquitetura web." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-31032008-173530/.
Full textAbstract:
Este trabalho descreve o desenvolvimento de um sistema de simulação de circuitos neuronais, com interface de utilização amigável e arquitetura baseada em web. O sistema é direcionado ao estudo de redes de neurônios da medula espinhal, responsáveis pelo controle motor, sujeitas à ativação por vias superiores e periféricas ou por estímulos elétricos. Sua utilidade está relacionada à criação de hipóteses ou teorias sobre o processamento neuronal realizado no caso são ou patológico, a atividades como a interpretação de resultados de experimentos eletrofisiológicos realizados em humanos e no direcionamento e validação de procedimentos experimentais. Para os propósitos deste projeto, a simulação computacional é o recurso mais indicado a se utilizar, considerando o grande número de variáveis envolvidas e o caráter não-linear dos elementos constituintes. As simulações devem retratar de maneira fidedigna as principais propriedades que caracterizam os núcleos neuronais a se estudar. Essas propriedades estão associadas ao recrutamento de unidades motoras, às relações de entrada-saída dos conjuntos neuronais, à influência das vias aferentes sobre os motoneurônios, ao papel da inibição recorrente e da inibição recíproca, à geração de força e do sinal eletromiográfico, entre outros. A simulação do reflexo H, que é uma técnica muito importante utilizada em estudos neurofisiológicos, está presente neste trabalho. Pretende-se que o sistema de simulação aqui proposto seja uma ferramenta útil para pesquisa e ensino da neurofisiologia do controle motor, provendo subsídios que levem a um melhor entendimento dos circuitos neuronais modelados.This work describes the development of a simulation system of neuronal circuitry, having a user-friendly interface and based on web architecture. The system is intended for studying spinal cord neuronal networks responsible for muscle control, subjected to descending drive or electrical stimulation. It is potentially useful in many activities, such as the interpretation of electrophysiological experiments conducted with humans, the proposition of hypotheses or theories on neuronal processing. Computer simulation is the most indicated approach to attain the objectives of this project because of the huge number of variables and the non-linear characteristics of the constituting elements. The simulations should mimic in a faithful way the main properties related to the modeled neuronal nuclei. These properties are associated with: i) motor-unit recruitment, ii) neuronal nuclei input-output relations, iii) afferent tract influence on motoneurons, iv) effects of recurrent inhibition and reciprocal inhibition, v) generation of force and electromyogram, and others. The generation of the H-reflex by the Ia-motoneuron pool system, which is an important tool in human neurophysiology, is included in the simulation system. The biological reality obtained with the present simulator and its web-based implementation make it a powerful tool for researchers in neurophysiology.
50
Guimarães, Karine Damásio. "Influência da nicotina no foco de atenção : um modelo neurocomputacional para os circuitos da recompensa e tálamo-cortical." Laboratório Nacional de Computação Científica, 2015. https://tede.lncc.br/handle/tede/217.
Full textAbstract:
Submitted by Maria Cristina (library@lncc.br) on 2015-10-08T14:40:37Z
No. of bitstreams: 1
thesiskarine.pdf: 6490137 bytes, checksum: 5cfc4bafd419dffab8b58cc3783124ae (MD5)Approved for entry into archive by Maria Cristina (library@lncc.br) on 2015-10-08T14:40:53Z (GMT) No. of bitstreams: 1 thesiskarine.pdf: 6490137 bytes, checksum: 5cfc4bafd419dffab8b58cc3783124ae (MD5)
Made available in DSpace on 2015-10-08T14:41:02Z (GMT). No. of bitstreams: 1 thesiskarine.pdf: 6490137 bytes, checksum: 5cfc4bafd419dffab8b58cc3783124ae (MD5) Previous issue date: 2015-03-30
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
In this work we develop a neurocomputational model based on ordinary differential equations which describes the interaction between the reward circuit and the thalamocortical circuit, taking into account the influence of astrocyte. The physiology for these circuits is studied by a coupled model, used to obtain numerical results that describe the action potential behavior associated to each neuron in the neural network. The initial value equations of the proposed models are discretized using classical numerical methods. Thus, it is possible to study the attentional focus behavior when an exogenous substance is added to the system, in particular, to study the influence of nicotine on the attentional focus. The proposed modeling is applied on problems arising in medicine, specifically, in neuropsychiatry. The study cases refer to patients with chemical dependence in nicotine and attention deficit hyperactivity disorder (ADHD)
Neste trabalho desenvolvemos um modelo neurocomputacional baseado em equações diferenciais ordinárias, que descreve a interação entre o circuito da recompensa e o circuito tálamo-cortical, considerando a influência do astrócito. O estudo da fisiologia destes circuitos inspira a construção de um modelo acoplado para ser usado na obtenção de resultados numéricos que descrevem o comportamento do potencial de ação associado a cada neurônio da rede neural. Os problemas de valor inicial que representam os modelos estudados são discretizados usando métodos numéricos clássicos. Desta forma, é possível estudar o comportamento do foco de atenção quando uma substância exógena é adicionada ao sistema, em particular, estudar a influência da nicotina no foco de atenção. A modelagem aqui proposta é aplicada em problemas advindos da medicina, especificamente, da área de neuropsiquiatria. Os casos de estudos estudo estão restritos a pacientes com problemas de dependência química em nicotina e pacientes com transtorno de déficit de atenção e hiperatividade (TDAH).