Understanding how mind emerges from matter is one of the great remaining questions in science. The Artificial Cognitive Systems lab studies the principles that underly natural intelligence and uses these principles in the development of artificial intelligence.
Our lab is physically located at the Spinoza building, Montessorilaan 3, Nijmegen, The Netherlands. When entering the Spinoza building, you should proceed to room B.00.93A at the ground floor of the B wing (low-rise part of the building). Please contact the front desk for more specific directions once you arrive. You can reach the Spinoza building via public transport.
I am interested in the computational principles that underly adaptive behaviour. The questions that I focus on are how the brain is able to extract information from its environment and use this information in order to generate optimal actions. My main goal is to develop biologically plausible neural network models that further our understanding of natural intelligence and provide a route towards solving the strong AI problem. You may find my curriculum vitae here.
My primary research interest is in developing computational models of (ultra-high-field) fMRI and MEG data to characterize the relationship between cognitive processes and brain connectivity. Besides brain connectivity, I am also interested in neural coding, unsupervised feature learning and deep learning.
I am working on the NEuronal STimulatiOn for Recovery of function (NESTOR) project with Richard van Wezel and Marcel van Gerven for developing cortical implants to restore sight in blind. In this project, I am developing computer vision models that transform camera input into meaningful phosphene patterns, as well as developing AR/VR simulations of phosphene vision, and running psychophysical experiments.
I am interested in structural and functional brain connectivity. In particular I study different (probabilistic) generative models and develop techniques efficiently compute them. Two central themes in my research are integration of different imaging modalities (e.g. fMRI and dMRI) and explicit modeling of uncertainty in connectivity estimates.
I am working on my Veni project together with with Marcel van Gerven, Pieter Medendorp and Roy Kessels. My work focuses on the relation between functional and structural changes in the aging brain and their joint association with cognition.
My research interest is in the application of AI and ML techniques to self-driving vehicles. Applications include scenario-based safety assessment using naturalistic driving data, prediction of road user behaviour, environmental perception, and working towards controllable, explainable and responsible AI.
I am a coordinating postdoctoral researcher in the language and interaction consortium. My areas of expertise are probabilistic machine learning and theoretical neuroscience. In my work I design probabilistic models of the human brain based on deep neural networks. I am also active in pure machine learning research, especially in the field of variational inference and optimal transport.
I use computational models on (high-field) fMRI and MEG to investigate the neural mechanisms underlying perception and memory. Specifically, I am interested in how these bottom-up and top-down processes are implemented and interact in the human brain.
Can we find a generic method to recognize behavior in multi-modal online data streams, independent of domain, species and sensors? Can we tune learned models to perform reliably in specific setups where only limited amount of training data is available? These are the questions I will address during my PhD research.
I am mainly interested in visual experience in the absence of visual input. During my PhD I will investigate to what extent visual imagery relies on the same neural mechanisms as visual perception. Besides neuroscience, I also have a strong interest in philosophy of mind and consciousness.
How does the brain extract complex features and concepts from the information entering our senses? I am particularly interested in how individual neurons, forming a complex network, can perform this task. I apply computational models to experimental data of high temporal resolution to unravel the dynamics of the mechanisms involved in this process.
I apply deep neural networks to affective computing for use in robotics, focusing on methods for the interpretability of black-box machine learning algorithms and aiming to improve human-robot interaction.
I am focusing on encoding and decoding models. One major topic is identifying biologically plausible feature transformations by investigating to what extent deep learning can predict human perceptual processing. I also work on the optimisation of this mapping and its inversion using statistical machine learning techniques.
Visual sensory information, as we receive it on our retina, mainly contains partially hidden objects. Instead of perceiving them as fragmented, we perceive them as completed objects. I am interested in how the brain achieves this amodal completion, and how it (and its underlying neural mechanism) is related to other phenomena such as modal completion and imagery.
We are interested in the mechanisms that allow humans to solve complex problems that require closing of the perception-action cycle. We design cognitively challenging tasks and have human participants execute them to investigate ensuing neurobehavioural responses. For the analysis of neurobehavioural data, we develop new computational models of human brain funciton and sophisticated machine learning techniques for large-scale neural data analysis.
Next to understanding the empirical basis of complex problem solving, we are interested in the theoretical underpinnings of adaptive behaviour. Specifically, we ask whether computational models that are rooted in AI can provide an account of the learning, inference and control problems that are solved by the human brain. To address this question, we develop new learning algorithms and investigate adaptive behaviour in artificial agents.
Machine learning is a key component in the development of new assistive technology and neurotechnology. We push the state of the art by developing new algorithms that are able to monitor and interpret neurobehavioural data. We also develop new algorithms that allow the manipulation of neural processes to restore or augment cognitive function.
We have several projects available that can be theoretical, empirical or applied in flavor. Theoretical projects focus on new developments in machine learning (neural networks, Bayesian statistics, reinforcement learning) and computational modeling of human brain function. Empirical projects focus on developing experiments to study the brain at work in naturalistic settings, where data is acquired using sophisticated recording techniques. To interpret these data, we develop and make use of sophisticated models and algorithms for large-scale data analysis. Applied projects focus on the development and testing of new algorithms that drive the development of the next generation of intelligent machines. Example application domains are neurotechnology, healthcare, artificial creativity, gameplay and cognitive robotics. For more details on our work, please consult the various pages on ww.artcogsys.com.
We welcome motivated students with an exact mindset and an interest in human cognition. In case you are interested just send us an e-mail stating your background and research interest.
During the lectures, the formal concepts underlying modern neural networks will be developed, including deep neural networks and recurrent neural networks. Also, various classic neural network models will be discussed like the (multi-layer) Perceptron, Hopfield networks and Boltzmann machines. During the practicals, students will get to immers themselves in the theoretical and practical aspects of neural networks. Students will get to implement various models using Python, a programming language which they will learn to use during the course.
A main objective of artificial intelligence is to build machines whose cognitive abilities match (or surpass) those of humans. This is also referred to as strong AI. One way to achieve this goal is by developing cognitive architectures that implement the algorithms used by our own brains. This success of such an approach relies on a continuous interplay between AI and neuroscience.
In this course, we will explore how computational models, particularly neural networks, can yield new insights about the mechanisms that give rise to natural intelligence and provide us with the tools to model cognitive processes in artificial systems.
The course consists of different components: