Thesis projects
I welcome students that want to do a thesis project with me as internal supervisor. Below you can find a number of tentative master projects that are closely related to the work in my research group and that can be tailored to your own interests. They range from conceptual to formal and from programming to experiments; some projects are suitable for students in Cognitive Neuroscience or Computer Science as well as Artificial Intelligence. Feel free to contact me if you want to brainstorm.
Open master projects
Neuronally plausible implementation of level-of-detail modulation (AI or CNS project)
Recent conceptual studies of the predictive processing framework distinguished the precision (uncertainty) and the level-of-detail (granularity) of generative models and predictions (Kwisthout et al., 2017). It has been previously proposed that dopamine plays an important role in precision-weighting of prediction errors (Friston et al., 2012). Insights from psychedelics literature suggests that serotonin might play a role in the modulation of level-of-detail of predictions. We postulated (Haskes et al., 2017) that psychedelics (being partial serotonin agonists) lead to overly detailed predictions, i.e., breaking up of established categories of prediction, by de-synchronizing ensembles of neurons. In this project we want to further investigate this idea and establish a neuronally plausible explanation of how level-of-detail modulation is implemented in the brain. Keywords: predictive processing, computational neuroscience, conceptual analysis and computer simulation.

Computational framework for counterfactual predictive processing (AI or CNS or CS project)
In a recent paper we introduced a computational framework, based on causal Bayesian networks, to computationally flesh out the predictive processing framework (Kwisthout et al., 2017). In this project we want to extend this to so-called counterfactually rich generative models in predictive processing (Seth, 2014: A predictive processing theory of sensorimotor contingencies: explaining the puzzle of perceptual presence and its absence in synaesthesia. Cognitive Neuroscience 5, 97–118.). Such models encode sensorimotor contingencies, that is, they represent 'what-if' relations between actions and sensory inputs. We aim to further operationalize this account using Pearl's intervention and counterfactual semantics (Pearl, 2000: Causality: Models, Reasoning, and Inference. Cambridge University Press, New York.). Keywords: predictive processing, computational modeling, conceptual analysis, causal Bayesian networks.

Attention, precision, and level-of-detail (AI or CNS project)
In this project we study how attention relates to level-of-detail. Increased attention or "alertness" is thought to interact with precision in the framework (see e.g. the work of Peter Kok and Floris de Lange); however, it may in fact modulate the level-of-detail of your predictions, rather than increasing the post-synaptic gain of prediction errors. This conceptual/computational/literature study will propose a neuro-cognitive theory on how attention, precision, and level-of-detail interact and how both theories can be conceptually and possibly experimentally disentangled. Keywords: Bayesian networks, theoretical neuroscience, conceptual analysis. Extensions towards a-typical development (e.g., autism spectrum disorders or ADD/ADHD) are possible.

Cognitive aspects of Most Frugal Explanations (AI or Psy project)
Most Frugal Explanation (MFE) is a heuristic approach to the computationally intractable Most Probable Explanation problem in Bayesian networks. In this project we want to compare this theory with cognitive theories on stereotyping, exemplars, and other cognitive approaches towards efficient decision making. In particular we want to study whether MFE can act as the underlying computational framework that supports cognitive theories based on such heuristics. Keywords: cognitive modeling, Bayesian networks, conceptual and computational analysis. Extensions towards experimentation, complexity analysis, etc. possible. Prerequisite course: Theoretical Foundations for Cognitive Agents.

Benchmark experiments on Most Frugal Explanations (AI or CS project)
Most Frugal Explanation (MFE) is a heuristic approach to the computationally intractable Most Probable Explanation problem in Bayesian networks. In this project we experimentally comapre this heuristic with state-of-the-art approximation algorithms on a number of benchmark problems to see whether insight in what variables are relevant can speed up MAP computations. Keywords: Bayesian networks, programming, experimental algorithm comparison. Prerequisite course: Theoretical Foundations for Cognitive Agents and/or Bayesian Networks. Knowledge of C++ is necessary!

Neuromorphic Algorithm Design (AI or CS project)
Neuromorphic chips, such as Intel's new Loihi chip, are not based on the traditional Von Neumann-architecture but represent both data and algorithm in spiking behavior of spiking neural networks. These new architectures allow for a totally different view on how to process information, e.g., encode data in temporal differences between spikes or in synaptic delays. We have received funding from Intel to investigate the runtime behavior of such algorithms on the Loihi. Keywords: Neuromorphic computing, algorithm design and analysis, complexity. Prerequisite course: Neuromorphic Computing (or the Research Seminar).

Complexity of Bayesian inferences (AI or CS project)
I did my PhD research on the computational complexity of various problems in Bayesian networks, such as monotonicity, sensitivity and parameter tuning, and finding the k-th best explanation. In the p-CABI project we study the complexity of approximate inferences, in particular with respect to the predictive processing account. Are you interested in collaboration within this project and/or study (parameterized) complexity of some computational problem in Bayesian networks, this might define an interesting thesis project to you. Keywords: Bayesian networks, approximate inference, (parameterized) computational complexity.