Eye movements explain decodability during perception and cued attention in MEG

Contains fulltext : 202750.pdf (publisher's version ) (Open Access)Eye movements are an integral part of human perception, but can induce artifacts in many magneto-encephalography (MEG) and electroencephalography (EEG) studies. For this reason, investigators try to minimize eye movements and remove these artifacts from their data using different techniques. When these artifacts are not purely random, but consistent regarding certain stimuli or conditions, the possibility arises that eye movements are actually inducing effects in the MEG signal. It remains unclear how much of an influence eye movements can have on observed effects in MEG, since most MEG studies lack a control analysis to verify whether an effect found in the MEG signal is induced by eye movements. Here, we find that we can decode stimulus location from eye movements in two different stages of a working memory match-to-sample task that encompass different areas of research typically done with MEG. This means that the observed MEG effect might be (partly) due to eye movements instead of any true neural correlate. We suggest how to check for eye movement effects in the data and make suggestions on how to minimize eye movement artifacts from occurring in the first place.10 p

Circulating immunoglobulins are not associated with intraplaque mast cell number and other vulnerable plaque characteristics in patients with carotid artery stenosis.

BACKGROUND\nRecently, we have shown that intraplaque mast cell numbers are associated with atherosclerotic plaque vulnerability and with future cardiovascular events, which renders inhibition of mast cell activation of interest for future therapeutic interventions. However, the endogenous triggers that activate mast cells during the progression and destabilization of atherosclerotic lesions remain unidentified. Mast cells can be activated by immunoglobulins and in the present study, we aimed to establish whether specific immunoglobulins in plasma of patients scheduled for carotid endarterectomy were related to (activated) intraplaque mast cell numbers and plasma tryptase levels. In addition, the levels were related to other vulnerable plaque characteristics and baseline clinical data.\nMETHODS AND RESULTS\nOxLDL-IgG, total IgG and total IgE levels were measured in 135 patients who underwent carotid endarterectomy. No associations were observed between the tested plasma immunoglobulin levels and total mast cell numbers in atherosclerotic plaques. Furthermore, no associations were found between IgG levels and the following plaque characteristics: lipid core size, degree of calcification, number of macrophages or smooth muscle cells, amount of collagen and number of microvessels. Interestingly, statin use was negatively associated with plasma IgE and oxLDL-IgG levels.\nCONCLUSIONS\nIn patients suffering from carotid artery disease, total IgE, total IgG and oxLDL-IgG levels do not associate with plaque mast cell numbers or other vulnerable plaque histopathological characteristics. This study thus does not provide evidence that the immunoglobulins tested in our cohort play a role in intraplaque mast cell activation or grade of atherosclerosis.Biopharmaceutic

Mechanisms of active perception: A neural network approach

Contains fulltext : 219671.pdf (pub ) (Open Access)Radboud University, 25 juni 2020Promotor : Gerven, M.A.J. van Co-promotor : Bosch, S.E.VI, 203 p

Emergent mechanisms of evidence integration in recurrent neural networks

Contains fulltext : 595095.pdf (publisher's version ) (Open Access)Recent advances in machine learning have enabled neural networks to solve tasks humans typically perform. These networks offer an exciting new tool for neuroscience that can give us insight in the emergence of neural and behavioral mechanisms. A big gap remains though between the very deep neural networks that have risen in popularity and outperformed many existing shallow networks in the field of computer vision and the highly recurrently connected human brain. This trend towards ever-deeper architectures raises the question why the brain has not developed such an architecture. Besides wiring constraints we argue that the brain operates under different circumstances when performing object recognition, being confronted with noisy and ambiguous sensory input. The role of time in the process of object recognition is investigated, showing that a recurrent network trained through reinforcement learning is able to learn the amount of time needed to arrive at an accurate estimate of the stimulus and develops behavioral and neural mechanisms similar to those found in the human and non-human primate literature.22 p

Adaptive time scales in recurrent neural networks

Contains fulltext : 221366.pdf (publisher's version ) (Open Access)Recent experiments have revealed a hierarchy of time scales in the visual cortex, where different stages of the visual system process information at different time scales. Recurrent neural networks are ideal models to gain insight in how information is processed by such a hierarchy of time scales and have become widely used to model temporal dynamics both in machine learning and computational neuroscience. However, in the derivation of such models as discrete time approximations of the firing rate of a population of neurons, the time constants of the neuronal process are generally ignored. Learning these time constants could inform us about the time scales underlying temporal processes in the brain and enhance the expressive capacity of the network. To investigate the potential of adaptive time constants, we compare the standard approximations to a more lenient one that accounts for the time scales at which processes unfold. We show that such a model performs better on predicting simulated neural data and allows recovery of the time scales at which the underlying processes unfold. A hierarchy of time scales emerges when adapting to data with multiple underlying time scales, underscoring the importance of such a hierarchy in processing complex temporal information.14 p

Task-dependent attention allocation through uncertainty minimization in deep recurrent generative models

Item does not contain fulltextAllocating visual attention through saccadic eye movements is a key ability of intelligent agents. Attention is both influenced through bottom-up stimulus properties as well as top-down task demands. The interaction of these two attention mechanisms is not yet fully understood. A parsimonious reconciliation posits that both processes serve the minimization of predictive uncertainty. We propose a recurrent generative neural network model that predicts a visual scene based on foveated glimpses. The model shifts its attention in order to minimize the uncertainty in its predictions. We show that the proposed model produces naturalistic eye-movements focusing on salient stimulus regions. Introducing the additional task of classifying the stimulus, modulates the saccade patterns and enables effective image classification. Given otherwise equal conditions, we show that different task requirements cause the model to focus on distinct, task-relevant regions. The results provide evidence that uncertainty minimization could be a fundamental mechanisms for the allocation of visual attention.Conference on Cognitive Computational Neuroscience (Berlin, Germany, 13-16 September 2019

Population codes of prior knowledge learned through environmental regularities

Contains fulltext : 228672.pdf (publisher's version ) (Open Access)How the brain makes correct inferences about its environment based on noisy and ambiguous observations is one of the fundamental questions in Neuroscience. Prior knowledge about the probability with which certain events occur in the environment plays an important role in this process. Humans are able to incorporate such prior knowledge in an efficient, Bayes optimal, way in many situations, but it remains an open question how the brain acquires and represents this prior knowledge. The long time spans over which prior knowledge is acquired make it a challenging question to investigate experimentally. In order to guide future experiments with clear empirical predictions, we used a neural network model to learn two commonly used tasks in the experimental literature (i.e. orientation classification and orientation estimation) where the prior probability of observing a certain stimulus is manipulated. We show that a population of neurons learns to correctly represent and incorporate prior knowledge, by only receiving feedback about the accuracy of their inference from trial-to-trial and without any probabilistic feedback. We identify different factors that can influence the neural responses to unexpected or expected stimuli, and find a novel mechanism that changes the activation threshold of neurons, depending on the prior probability of the encoded stimulus. In a task where estimating the exact stimulus value is important, more likely stimuli also led to denser tuning curve distributions and narrower tuning curves, allocating computational resources such that information processing is enhanced for more likely stimuli. These results can explain several different experimental findings, clarify why some contradicting observations concerning the neural responses to expected versus unexpected stimuli have been reported and pose some clear and testable predictions about the neural representation of prior knowledge that can guide future experiments.16 p

Eye movement effects in MEG

Eye movements are an integral part of human perception, yet in many magneto-encephalography (MEG) and electroencephalography (EEG) studies people try to minimize them, because of the large artifacts they can induce in the signal. Most studies lack a good control check to verify whether eye movements are causing an effect found in the MEG signal. Therefore, it remains unclear how much of an influence eye movements can have on observed effects in MEG. We find that we can decode stimulus location from eye movements in two different stages of a working memory match-to-sample task that encompass different areas of research typically done with MEG. This means that the observed MEG effect might be (partly) due to eye movements instead of any true neural correlate. We suggest how to check for eye movement effects in your data and make suggestions on how to minimize eye movement artifacts from occurring in the first place

Eye movement effects in MEG

Item does not contain fulltextEye movements are an integral part of human perception, yet in many magneto-encephalography (MEG) and electroencephalography (EEG) studies people try to minimize them, because of the large artifacts they can induce in the signal. Most studies lack a good control check to verify whether eye movements are causing an effect found in the MEG signal. Therefore, it remains unclear how much of an influence eye movements can have on observed effects in MEG. We find that we can decode stimulus location from eye movements in two different stages of a working memory match-to-sample task that encompass different areas of research typically done with MEG. This means that the observed MEG effect might be (partly) due to eye movements instead of any true neural correlate. We suggest how to check for eye movement effects in your data and make suggestions on how to minimize eye movement artifacts from occurring in the first place