Global ETD Search

1	Brain network determinants of fear memory strength following unpredictable fear conditioning Burgess, JoColl Alexis 24 January 2024 (has links) In traditional Pavlovian fear conditioning paradigms, animals associate a neutral sensory cue, such as a sound or light, with an aversive stimulus like a mild electric shock. Over time, they develop a conditioned fear response to the cue alone. However, in the real world, cues that predict danger usually lack temporal predictability. Environmental unpredictability is known to enhance aversive memory. The basolateral amygdala (BLA) plays a critical role in forming aversive memories, mediating the convergence of multimodal sensory information. Previous research on the BLA has shown that different neuronal populations within this area encode valence-associative memories during fear conditioning. Our study aims to explore neuronal network activity within the BLA between predictable versus unpredictable fear conditioning. We employed time-lapse microendoscopic recording techniques to observe BLA neurons' somal calcium activity during fear conditioning and an auditory fear recall test (Chapter 3). We identified neurons with different patterns during these paradigms. 'Memory Winners' showed successful convergence of sensory information consistent with retention of the fear memory, while 'Memory Losers' failed to display conditioned stimulus (CS)-evoked calcium responses during fear recall, indicating a loss of fear memory. A further group, the 'Fear Expression' neurons lacked learning-related plasticity for the tone and shock but showed early CS-evoked activity during fear recall. When we introduced unpredictability during fear conditioning, we observed that the distinct functional classes of neurons remained consistent across paradigms. However, the tone and footshock evoked activity did differ within these neuronal classifications. 'Memory Winners' showed early tone- and shock-evoked increased responsivity, while 'Memory Losers' displayed varying shock responsivity depending on whether the conditioning was predictable or unpredictable. Additionally, we identified an extinction-related functional sub-classification of neurons within the BLA. These included neurons that became less responsive during late extinction trials ('extinction-sensitive'), neurons that showed increased CS-evoked activity during late extinction ('extinction learning'), and neurons that maintained consistent activity levels during fear recall and late extinction trials ('fear sustained'). In a departure from most studies that focus on unexpected stimuli or outcomes, we also investigated whether attention signals, defined by transient changes in BLA neuronal calcium activity, are generated when expected stimuli are omitted (Chapter 4). Using neuronal calcium imaging in the BLA, we found that many amygdala neurons displayed attention signals in a stochastic manner during omitted punishment. These neurons showed enhanced sensory processing and plasticity compared to neurons without error signals. Finally, in Chapter 5, we found that unpredictable fear conditioning affected fear-related freezing behaviors and increased the cFos expression in both the BLA and the lateral septum (LS). This supports the notion that the amygdala is strategically positioned to perceive unpredictable aversive cues during conditioning. Collectively, our findings suggest that unpredictability of aversive cues results in specific alterations in the BLA and other brain areas. / 2025-01-24T00:00:00Z Neurosciences Amygdala Calcium signaling Fear conditioning Prediction errors PTSD
2	Some methods for reducing the total consumption and production prediction errors of electricity: Adaptive Linear Regression of Original Predictions and Modeling of Prediction Errors Oleksandra, Shovkun January 2014 (has links) Balance between energy consumption and production of electricityis a very important for the electric power system operation and planning. Itprovides a good principle of effective operation, reduces the generation costin a power system and saves money. Two novel approaches to reduce thetotal errors between forecast and real electricity consumption wereproposed. An Adaptive Linear Regression of Original Predictions (ALROP)was constructed to modify the existing predictions by using simple linearregression with estimation by the Ordinary Least Square (OLS) method.The Weighted Least Square (WLS) method was also used as an alternativeto OLS. The Modeling of Prediction Errors (MPE) was constructed in orderto predict errors for the existing predictions by using the Autoregression(AR) and the Autoregressive-Moving-Average (ARMA) models. For thefirst approach it is observed that the last reported value is of mainimportance. An attempt was made to improve the performance and to getbetter parameter estimates. The separation of concerns and the combinationof concerns were suggested in order to extend the constructed approachesand raise the efficacy of them. Both methods were tested on data for thefourth region of Sweden (“elområde 4”) provided by Bixia. The obtainedresults indicate that all suggested approaches reduce the total percentageerrors of prediction consumption approximately by one half. Resultsindicate that use of the ARMA model slightly better reduces the total errorsthan the other suggested approaches. The most effective way to reduce thetotal consumption prediction errors seems to be obtained by reducing thetotal errors for each subregion. Modeling of Prediction Errors ARMA OLS WLS TECP TECC TPE MAPE MPE
3	Obesity is associated with insufficient behavioral adaptation Mathar, David 20 November 2018 (has links) Obesity is one of the major health concerns nowadays according to the World Health Organisation (WHO global status report on noncommunicable diseases 2010). Thus, there is an urgent need for understanding obesity-associated alterations in food-related and general cognition and their underlying structural and functional correlates within the central nervous system (CNS). Neuroscientific research of the past decade has mainly focussed on obesity-related differences within homeostatic and hedonic processing of food stimuli. Therein, alterations during anticipation and consumption of food-reward stimuli in obese compared with lean subjects have been highlighted. This points at an altered adaptation of eating behavior in obese individuals. This thesis investigates if adaptation of behavior is attenuated in obese compared to lean individuals in learning-related processes beyond the food domain. In five consecutive experimental studies, we show that obese participants reveal reduced adaptation of behavior within and outside the food context. With the help of MRI, we relate these behavioral findings to alterations in structure and function of the fronto-striatal dopaminergic system in obesity. In more detail, reduced behavioral adaptation seems to be associated with attenuated utilization of negative prediction errors in obese individuals. Within the brain, this relates to reduced functional coupling between subcortical dopaminergic target regions (ventral striatum) and executive cortical structures (supplementary motor area) in obesity, as revealed by fMRI analysis. info:eu-repo/classification/ddc/610 ddc:610
4	Effekten av IAS 19 för värderingsmodellernas prognostiseringsförmåga och det observerade aktiepriset Andersson, Jesper, Söderqvist, Joakim January 2019 (has links) Denna kandidatuppsats testar förmånsredovisningen IAS 19 på marknadens observerade aktiepris för företag listade på OMX30. Syftet är att analysera effekten av IAS 19R på tre absoluta aktievärderingsmodeller, diskonterade kassaflödesmodellen, utdelningsdiskonteringsmodellen och residualvinstmodellen. Dessutom, om löner och annan ersättning samt avsättningar till pension inom IAS 19 har haft en positiv effekt på de observerade aktiepriserna. Metoderna som har använts för att testa precisionen av modellerna är reella och absoluta prognosfeltermsberäkningar. Vidare, för att testa effekten av anställningsförmåner, aktievärderingsmodellerna och IAS 19 på det observerade aktiepriset genomförs en multipel regressionsanalys med paneldata mellan åren 2009–2017. Regressionsmodellen inkluderar 22 företag listade på OMX30 per den 1a juli 2009. Inom det ekonometriska ramverket, har fyra stycken regressioner, med fasta effekter testats. Resultaten tyder på att förmånsredovisningen, IAS 19, inte har någon signifikant påverkan på det observerade aktiepriset. Däremot, i motsats med tidigare forskning, visar resultaten att löner och bonusar har en positiv effekt på de observerade aktiepriserna för företag listade på OMX30. / This Bachelor thesis examines the employee benefits accounting IAS 19 on market share prices for companies listed on OMX30. The purpose is to analyze the effect of IAS 19R on three absolute valuation methods, Discounted Cash Flow, Dividend Discount and Residual Income valuation models. Also, what effect salaries, wages and defined benefits obligations in firms consolidated financial statements have had a positive effect on the market share price. The models which have been used to examine the predictability in the stock price valuations in the thesis are estimated using signed and absolute prediction errors. Furthermore, to examine the effect of employee benefits, share valuation models and IAS 19 on market share price a panel data between 2009-2017 have been used. The model includes 22 listed companies on OMX30 as of the 1stof July 2009. Within the econometric framework, four regressions have been applied, all with fixed effects. The results suggest that the employee benefits accounting have no significant impact on market share prices. However, in contrast to previous research, results show that salaries and wages have a positive impact on market share price for companies listed on OMX30. IAS 19 Employee benefits accounting Stock price valuation models Prediction errors IAS 19 Förmånsredovisning Aktievärderingsmodeller Prognostiseringsfeltermer Business Administration Företagsekonomi
5	近單根模型之最小平方估計量的預測誤差 / Mean-squared prediction errors of the least squares predictors in near-integrated models 張凱君, Chang, Kai-Jiun Unknown Date (has links) The asymptotic expression for the mean-squared prediction error is discussed for the near-unit-root models. We find the mean-squared prediction error based on the ordinary least square estimator is smaller than the one using pretest estimating under some certain conditions. 近單根模型方均預測誤差 near-integrated models mean-squared prediction errors
6	Changing Perspective : Expanding cognitive models as a result of prediction errors and information seeking Neuman, Erica January 2024 (has links) To be able to make accurate predictions and adapt, we sometimes need to adjust our understanding of the world, yet what incentivizes expansion of our mental world models remains poorly understood. The aim of this study was to investigate what motivates people to update their world models – here referred to as the ontological model structure, and how updating is related to uncertainty. The study is of experimental design and uses a digital game divided into two conditions (ambiguous and unambiguous) that vary the expectations for the game’s causal structure. The goal of the game was to gain points by accurately predicting on what food item a fly will land. To make accurate predictions, the participant should adjust their cognitive model when encountering new information. Data from 84 participants was collected online, using Prolific.co. It was hypothesized that initial ambiguity would affect the likelihood of information seeking by increasing the frequency of prediction errors and would result in a faster switch to an optimal cognitive model. The study found that participants in the more ambiguous condition sought information earlier, gained more prediction errors and changed to an optimal model faster than participants in the less ambiguous condition. However, both participant groups seemed equally as equipped to change models as a result of prediction errors. This might indicate that despite similar support for an initial model, it is the prediction errors and our recent history that affects our tendency to adjust our cognitive models. / För att kunna göra korrekta prediktioner och anpassa oss behöver vi ibland justera vår förståelse av världen, vad som motiverar en revidering av våra mentala modeller är dock fortfarande oklart. Studiens syfte var att undersöka vad som motiverar människor att uppdatera sina modeller – benämnd här som den ontologiska modellstrukturen, och hur uppdateringen är relaterad till osäkerhet. Studien är av experimentell design och använder ett digitalt spel uppdelat i två betingelser (tvetydig och entydig), som varierar förväntningarna på spelets ontologiska struktur. Spelets mål var att samla poäng genom att korrekt predicera på vilken matvara en fluga kommer att landa. För att kunna göra korrekta prediktioner bör deltagaren justera sin kognitiva modell när ny information fås. Data från 84 deltagare samlades in online, med hjälp av Prolific.co. Det antogs att den initiala tvetydigheten skulle påverka sannolikheten för informationssökning genom att öka frekvensen av prediktionsfel och att det skulle resultera i en snabbare övergång till en optimal kognitiv modell. Studien fann att deltagare i den mer tvetydiga betingelsen sökte information tidigare, fick fler prediktionsfel och ändrade till en optimal modell snabbare än deltagare i den mindre tvetydiga betingelsen. Däremot verkade båda deltagargrupperna lika väl utrustade att byta modell till följd av prediktionsfel. Det kan tyda på att trots liknande stöd för en initialmodell är det prediktionsfel och vår närhistoria som påverkar vår tendens att justera våra kognitiva modeller. cognitive model prediction errors changed worldview information seeking cognitive dissonance learning knowledge acquisition kognitiv modell prediktionsfel förändrad världsbild informationssökning kognitiv dissonans inlärning kunskapsinhämtning Psychology Psykologi
7	Inférence et apprentissage perceptifs dans l’autisme : une approche comportementale et neurophysiologique / Perceptual inference and learning in autism : a behavioral and neurophysiological approach Sapey-Triomphe, Laurie-Anne 04 July 2017 (has links) La perception de notre environnement repose sur les informations sensorielles reçues, mais aussi sur nos a priori. Dans le cadre Bayésien, ces a priori capturent les régularités de notre environnement et sont essentiels pour inférer les causes de nos sensations. Récemment, les théories du cerveau Bayésien ont été appliquées à l'autisme pour tenter d'en expliquer les symptômes. Les troubles du spectre de l'autisme (TSA) sont caractérisés par des difficultés de compréhension des interactions sociales, par des comportements restreints et répétitifs, et par une perception sensorielle atypique.Cette thèse vise à caractériser l'inférence et l'apprentissage perceptifs dans les TSA, en étudiant la sensorialité et la construction d'a priori. Nous avons utilisé des tests comportementaux, des modèles computationnels, des questionnaires, de l'imagerie fonctionnelle et de la spectroscopie par résonnance magnétique chez des adultes avec ou sans TSA. La définition des profils sensoriels de personnes avec des hauts quotients autistiques a été affinée grâce à un questionnaire dont nous avons validé la traduction française. En explorant les stratégies d'apprentissage perceptif, nous avons ensuite montré que les personnes avec TSA étaient moins enclines à spontanément utiliser une mode d'apprentissage permettant de généraliser. L'étude de la construction implicite des a priori a montré que les personnes avec TSA étaient capables d'apprendre un a priori, mais l'ajustaient difficilement suite à un changement de contexte. Enfin, l'étude des corrélats neurophysiologiques de l'inférence perceptive a révélé un réseau cérébral et une neuromodulation différents dans les TSA.L'ensemble de ces résultats met en lumière une perception atypique dans les TSA, marquée par un apprentissage et une pondération anormale des a priori. Une approche Bayésienne des TSA pourrait améliorer leur caractérisation, diagnostics et prises en charge / How we perceive our environment relies both on sensory information and on our priors or expectations. Within the Baysian framework, these priors capture the underlying statistical regularities of our environment and allow inferring sensation causes. Recently, Bayesian brain theories suggested that autistic symptoms could arise from an atypical weighting of sensory information and priors. Autism spectrum disorders (ASD) is characterized defined by difficulties in social interactions, by restricted and repetitive patterns of behaviors, and by an atypical sensory perception.This thesis aims at characterizing perceptual inference and learning in ASD, and studies sensory sensitivity and prior learning. This was investigated using behavioral tasks, computational models, questionnaires, functional magnetic resonance imaging and magnetic resonance spectroscopy in adults with or without ASD. Sensory profiles in people with high autism spectrum quotients were first refined, using a questionnaire that we validated in French. The study of perceptual learning strategies then revealed that subjects with ASD were less inclined to spontaneously use a learning style enabling generalization. The implicit learning of priors was explored and showed that subjects with ASD were able to build up a prior but had difficulties adjusting it in changing contexts. Finally, the investigation of the neurophysiological correlates and molecular underpinnings of a similar task showed that perceptual decisions biased by priors relied on a distinct neural network in ASD, and was not related to the same modulation by the glutamate/GABA ratio.The overall results shed light on an atypical learning and weighting of priors in ASD, resulting in an abnormal perceptual inference. A Bayesian approach could help characterizing ASD and could contribute to ASD diagnosis and care Troubles du spectre de l’autisme Inférence perceptive Apprentissage Cerveau Bayésien A priori Erreur de prédiction Sensibilité sensorielle Autism spectrum disorders Perceptual inference Learning Bayesian brain Priors Prediction errors Sensory sensitivity 612.8
8	Predictive coding in auditory processing : insights from advanced modeling of EEG and MEG mismatch responses / Principe du codage prédictif pour le traitement de l'information auditive : apports de l'EEG et de la MEG pour la modélisation de réponses à la déviance Lecaignard, Françoise 28 September 2016 (has links) Cette thèse porte sur le codage prédictif comme principe général pour la perception et vise à en étayer les mécanismes computationnels et neurophysiologiques dans la modalité auditive. Ce codage repose sur des erreurs de prédictions se propageant dans une hiérarchie, et qui pourraient se refléter dans des réponses cérébrales au changement (ou déviance) telles que la Négativité de discordance (mismatch negativity, MMN). Nous avons manipulé la prédictibilité de sons déviants et utilisé des approches de modélisation computationnelle et dynamique causale (DCM) appliquées à des enregistrements électrophysiologiques (EEG, MEG) simultanés.Une modulation des réponses à la déviance par la prédictibilité a été observée, permettant d'établir un lien avec les erreurs de prédictions. Cet effet implique un apprentissage implicite des régularités acoustiques, dont l'influence sur le traitement auditif a pu être caractérisée par notre approche de modélisation. Sur le plan computationnel, un apprentissage a été mis en évidence au cours de ce traitement auditif, reposant sur une fenêtre d'intégration temporelle dont la taille varie avec la prédictibilité des déviants. Cet effet pourrait également moduler la connectivité synaptique sous-tendant le traitement auditif, comme le suggère l'analyse DCM.Nos résultats mettent en évidence la mise en œuvre d'un apprentissage perceptif au sein d'une hiérarchie auditive soumis à une modulation par la prédictibilité du contexte acoustique, conformément aux prédictions du codage prédictif. Ils soulignent également l'intérêt de ce cadre théorique pour émettre et tester expérimentalement des hypothèses mécanistiques précises / This thesis aims at testing the predictive coding account of auditory perception. This framework rests on precision-weighted prediction errors elicited by unexpected sounds that propagate along a hierarchical organization in order to maintain the brain adapted to a varying acoustic environment. Using the mismatch negativity (MMN), a brain response to unexpected stimuli (deviants) that could reflect such errors, we could address the computational and neurophysiological underpinnings of predictive coding. Precisely, we manipulated the predictability of deviants and applied computational learning models and dynamic causal models (DCM) to electrophysiological responses (EEG, MEG) measured simultaneously. Deviant predictability was found to modulate deviance responses, a result supporting their interpretation as prediction errors. Such effect might involve the (high-level) implicit learning of sound sequence regularities that would in turn influence auditory processing in lower hierarchical levels. Computational modeling revealed the perceptual learning of sounds, resting on temporal integration exhibiting differences induced by our predictability manipulation. In addition, DCM analysis indicated predictability changes in the synaptic connectivity established by deviance processing. These results conform predictive coding predictions regarding both deviance processing and its modulation by deviant predictability and strongly support perceptual learning of auditory regularities achieved within an auditory hierarchy. Our findings also highlight the power of this mechanistic framework to elaborate and test new hypothesis enabling to improve our understanding of auditory processing Cerveau bayésien Précision des erreurs de prédictions Inférence perceptive Électroencéphalographie Magnétoencéphalographie Potentiels évoqués Reconstruction de sources Fusion multimodale Bayesian brain Precision-weighted prediction errors Perceptual inference Electroencephalography Magnetoencephalography Evoked potentials Source reconstruction Fused inversion 612.8
9	Channel Probing for an Indoor Wireless Communications Channel Hunter, Brandon 13 March 2003 (has links) (PDF) The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of this parameter allows spatial diversity at the transmitter along with the receiver to be simulated. The process of going from raw measurement data to discrete arrivals and then to clustered arrivals is analyzed. Many possible errors associated with discrete arrival processing are discussed along with possible solutions. Four clustering methods are compared and their relative strengths and weaknesses are pointed out. The effects that errors in the clustering process have on parameter estimation and model performance are also simulated. multipath multipath propagation propagation environment indoor propagation indoor multipath indoor multipath propagation channel probing channel probing indoor wireless communications channel indoor wireless communications indoor wireless communications wireless communications wireless communications channel MIMO SISO multiple input multiple output single input single output antenna diversity antenna diversity antenna array channel modeling modeling model wireless propagation clustering methods clustering algorithms clustering parameter estimation parameter channel matrix h matrix ray tracing point spread function psf deconvolution clean algorithm clean algorithm antenna pattern network analyzer temporal pattern 2.4 ghz system 6 ghz system channel capacity capacity estimate channel capacity estimate propagation environment scattering transfer matrix matrix water filling solution water filling method water filling Saleh Valenzuela Saleh and Valenzuela Model arrival time of arrival angle of arrival direction of arrival arrival amplitude arrivals antenna positioner data simulations measurements angle of departure direction of departure correlation convolution errors error CFA CFAD constant false alarm constant false alarm detection signal signal dependent clutter clutter returns transmitter transmit receive receiver transmit antenna receive antenna spatial correlation system PSF system point spread function mapping fuzzy c-means clustering fuzzy clustering c-means clustering c-means fuzzy k-means euclidean distance time amplitude delay performance prediction performance prediction errors fuzzy weighted distance method rectangular method weighted rectangular method statistics statistical statistical model Electrical and Computer Engineering

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