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Principal Investigator

Marcel van Gerven

Principal Investigator - Donders Institute

I am interested in the theoretical and computational principles that allow the brain to generate optimal behavior based on sparse reward signals provided by the environment. We create biologically plausible neural network models that further our understanding of natural intelligence and provide a route towards general-purpose intelligent machines. You may find my curriculum vitae...

Abstract taken from Google Scholar:

Deep neural network (DNN) is an indispensable machine learning tool for achieving human-level performance on many learning tasks. Yet, due to its black-box nature, it is inherently difficult to understand which aspects of the input data drive the decisions of the network. There are various real-world scenarios in which humans need to make actionable decisions based on the output DNNs. Such decision support systems can be found in critical domains, such as legislation, law enforcement, etc. It is important that the humans making high-level decisions can be sure that the DNN decisions are driven by combinations of data features that are appropriate in the context of the deployment of the decision support system and that the decisions made are legally or ethically defensible. Due to the incredible pace at which DNN technology is being developed, the development of new methods and studies on explaining the …

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Abstract taken from Google Scholar:

Issues regarding explainable AI involve four components: users, laws and regulations, explanations and algorithms. Together these components provide a context in which explanation methods can be evaluated regarding their adequacy. The goal of this chapter is to bridge the gap between expert users and lay users. Different kinds of users are identified and their concerns revealed, relevant statements from the General Data Protection Regulation are analyzed in the context of Deep Neural Networks (DNNs), a taxonomy for the classification of existing explanation methods is introduced, and finally, the various classes of explanation methods are analyzed to verify if user concerns are justified. Overall, it is clear that (visual) explanations can be given about various aspects of the influence of the input on the output. However, it is noted that explanation methods or interfaces for lay users are missing and we …

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Abstract taken from Google Scholar:

ConclusionNeural networks are experiencing a revival that not only transforms AI but also provides new insights about neural computation in biological systems. The contributions in this special issue describe new advances in neural networks that increase their efficacy or plausibility from a biological point of view. A closer interaction between the AI and neuroscience communities is expected to lead to various other theoretical and practical breakthroughs in the years to come.

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Abstract taken from Google Scholar:

Research into the neural correlates of individual differences in imagery vividness point to an important role of the early visual cortex. However, there is also great fluctuation of vividness within individuals, such that only looking at differences between people necessarily obscures the picture. In this study, we show that variation in moment-to-moment experienced vividness of visual imagery, within human subjects, depends on the activity of a large network of brain areas, including frontal, parietal, and visual areas. Furthermore, using a novel multivariate analysis technique, we show that the neural overlap between imagery and perception in the entire visual system correlates with experienced imagery vividness. This shows that the neural basis of imagery vividness is much more complicated than studies of individual differences seemed to suggest.SIGNIFICANCE STATEMENT Visual imagery is the ability to visualize …

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Abstract taken from Google Scholar:

Research suggests that perception and imagination engage neuronal representations in the same visual areas. However, the underlying mechanisms that differentiate sensory perception from imagination remain unclear. Here, we examine the directed coupling (effective connectivity) between fronto-parietal and visual areas during perception and imagery. We found an increase in bottom-up coupling during perception relative to baseline and an increase in top-down coupling during both perception and imagery, with a much stronger increase during imagery. Modulation of the coupling from frontal to early visual areas was common to both perception and imagery. Furthermore, we show that the experienced vividness during imagery was selectively associated with increases in top-down connectivity to early visual cortex. These results highlight the importance of top-down processing in internally as well as externally …

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Abstract taken from Google Scholar:

Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a …

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Abstract taken from Google Scholar:

Perception is strongly influenced by expectations. Accordingly, perception has sometimes been cast as a process of inference, whereby sensory inputs are combined with prior knowledge. However, despite a wealth of behavioral literature supporting an account of perception as probabilistic inference, the neural mechanisms underlying this process remain largely unknown. One important question is whether top-down expectation biases stimulus representations in early sensory cortex, i.e., whether the integration of prior knowledge and bottom-up inputs is already observable at the earliest levels of sensory processing. Alternatively, early sensory processing may be unaffected by top-down expectations, and integration of prior knowledge and bottom-up input may take place in downstream association areas that are proposed to be involved in perceptual decision-making. Here, we implicitly manipulated human …

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Abstract taken from Google Scholar:

Multivariate pattern analysis is a technique that allows the decoding of conceptual information such as the semantic category of a perceived object from neuroimaging data. Impressive single-trial classification results have been reported in studies that used fMRI. Here, we investigate the possibility to identify conceptual representations from event-related EEG based on the presentation of an object in different modalities: its spoken name, its visual representation and its written name. We used Bayesian logistic regression with a multivariate Laplace prior for classification. Marked differences in classification performance were observed for the tested modalities. Highest accuracies (89% correctly classified trials) were attained when classifying object drawings. In auditory and orthographical modalities, results were lower though still significant for some subjects. The employed classification method allowed for a precise temporal localization of the features that contributed to the performance of the classifier for three modalities. These findings could help to further understand the mechanisms underlying conceptual representations. The study also provides a first step towards the use of concept decoding in the context of real-time brain-computer interface applications.

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Abstract taken from Google Scholar:

Brain–computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, ie, the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing …

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Abstract taken from Google Scholar:

Research on brain–computer interfaces (BCIs) is gaining strong interest. This is motivated by BCIs being applicable for helping disabled, for gaming, and as a tool in cognitive neuroscience. Often, motor imagery is used to produce (binary) control signals. However, finding other types of control signals that allow the discrimination of multiple classes would help to increase the applicability of BCIs. We have investigated if modulation of posterior alpha activity by means of covert spatial attention in two dimensions can be reliably classified in single trials. Magnetoencephalography (MEG) data were collected for 15 subjects who were engaged in a task where they covertly had to visually attend left, right, up or down during a period of 2500 ms. We then classified the trials using support vector machines. The four orientations of covert attention could be reliably classified up to a maximum of 69% correctly classified trials …

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