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Understanding how mind emerges from matter is one of the great remaining questions in science. The Artificial Cognitive Systems lab studies the computational principles that underly natural intelligence and uses these principles to develop more capable and efficient intelligent machines.

Contact

  • Prof. dr. Marcel van Gerven
  • Department of Artificial Intelligence
  • Donders Centre for Cognition
  • Donders Institute for Brain, Cognition and Behaviour
  • Radboud University
  •   m.vangerven@donders.ru.nl
  •   +31 24 365 59 31
  •   +31 24 365 27 28
  •   PO Box 9104 6500 HE Nijmegen the Netherlands
  •   SP B.00.93A Montessorilaan 3 6525 HR Nijmegen the Netherlands

Our lab is 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.

People

Current Members

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    Marcel van Gerven

    Principal Investigator

    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 here.

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    Luca Ambrogioni

    Assistant Professor

    I am an assistant professor in AI. 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.

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    Max Hinne

    Assistant Professor

    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.

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    Nasir Ahmad

    Postdoctoral Researcher

    My research interests relate to spiking and recurrent neural networks and taking inspiration from these models for neuroscientific insights and better ML methods. In particular, thinking about the encoding of information in spike timing, the utility of firing rate trajectories, and more. Beyond this, I'm interested in functional/computational benefits that emerge in systems which are constrained as biology is (e.g. in energy, physical resources etc).

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    Kiki van der Heijden

    Postdoctoral Researcher

    In my research, I investigate the computational mechanisms underlying neural encoding of sound location in naturalistic spatial hearing in normal and hearing-impaired listeners. To achieve this, I use an interdisciplinary approach combining cognitive neuroscience and computational modeling: I develop neurobiological-inspired deep neural network models of processing of sound location in subcortical auditory structures and the auditory cortex. Building on the resulting insights, I aim to optimize signal processing algorithms in cochlear implants to boost neural spatial auditory processing in cochlear implant users.

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    Jan-Pieter Paardekooper

    Research Fellow

    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.

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    Abdullahi Ali

    PhD Student

    I am interested in the biological constraints and environmental pressures that drive organisation and information processing in the visual cortex. What aspects of the biological substrate play a key role in the formation of neural circuits and visual function? What visual representations do agents learn in a context in which they actively have to engage with their environment? In order to answer these questions, I aim to develop biologically-plausible neural network models that can solve visual problems in ecologically valid environments.

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    Thirza Dado

    PhD Student

    Reinforcement learning is the problem where an agent must learn how to make decisions in an unknown world only based on incomplete sensory information. During my PhD project, I focus on constructing and navigating internal representations of procedurally generated environments with "dual variational control". Importantly, the uncertainty of the variational distribution serves as an intrinsic motivation for efficient exploration. That is, uncertainty about the world decreases with (smart) exploration, up until the moment where the agent knows how to act to get what it wants.

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    Sander Dalm

    PhD Student

    I'm an external PhD student who studies biologically plausible approximations of the backpropagation algorithm. My research focuses on activity based learning: a form of learning where error signals are propagated using only information locally available at the synapse. Scaling these algorithms from toy problems to real world applications is one of the central themes in my work.

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    Elsbeth van Dam

    PhD Student

    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.

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    Lex Dingemans

    PhD Student

    I am a physician, working partially in the Human Genetics department of the Radboudumc and partially in this lab. As part of my PhD, I am working on applying artificial intelligence in our clinical practice. I hope to improve clinical care for our patients and make the life of doctors easier this way. I will mostly be working on facial recognition and using machine learning and probabilistic programming on phenotypic data, to help assist in diagnosing patients and see if we can predict the outcome of genetic tests.

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    Burcu Küçükoğlu

    PhD Student

    My interests lie in augmenting human capabilities with the help of intelligent artificial systems to empower people in reaching their full potential. I am especially curious about reinforcement learning, biologically plausible neural network models, and Bayesian methods for machine learning. During my PhD, I will be focusing on developing reinforcement learning algorithms for efficient closed-loop control of neural systems, in the specific context of generating optimal phosphene vision to restore some actionable perception for the visually impaired through neurotechnology.

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    Lynn Le

    PhD Student

    The visual cortex represents stimuli in a complex and nonlinear way. I want to gain a better understanding of the representations and the dynamics of complex neuronal activity in the visual cortex. I aim to do this by developing advanced and biologically plausible computer models that generate predictions of neural activity recorded in awake monkeys.

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    Djamari Oetringer

    PhD Student

    My research focuses on the neural and cognitive mechanisms that underlie the segmentation of information into meaningful events. Do the boundaries between segments reflect a change in representation of the external environment? Do these boundaries occur in a nested temporal hierarchy? Does attention affect the segments and in turn memory performance? To answer these questions, I am looking at fMRI and MEG data that are gathered while participants perceive real-life-like stimuli, and using a data-driven method to establish the segmentations.

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    Gabriëlle Ras

    PhD Student

    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.

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    Jaap de Ruyter van Steveninck

    PhD Student

    Restoring some visual perception in blindness, using intelligent technology? Thanks to current developments in neuro-technology and artificial intelligence, this is becoming a very realistic scenario! My research aims to optimise prosthetic vision by developing clever models that can capture our rather complex visual environment into meaningful stimulation patterns, bridging the gap between human and computer vision.

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    Gianluigi Silvestri

    PhD Student

    My research focuses on the understanding and improvement of variational inference and reinforcement learning algorithms, and their combination to allow the control of complex systems. Practical applications include neurotechnology, personalized preventive health, nutrition, behaviour and agriculture.

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    Jordy Thielen

    PhD Student

    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.

Alumni

Ali Bahramisharif, Sander Bosch, Nadine Dijkstra, Umut Güçlü, Yağmur Güçlütürk, Ronald Janssen, Haiteng Jiang, Pasi Jylänki, Claudia Lüttke, Silvan Quax, Andrew Reid, Anne Schoenmakers, Katja Seeliger, Elena Shumskaya, Irina Simanova, and Erdi Çalli.

Publications

Preprints

  1. Ras, G., Ambrogioni, L., Haselager, P., van Gerven, M. A. J., & Güçlü, U. (2020). Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations. ArXiv arXiv:2006.15983 [cs.CV].
  2. Ahmad, N., van Gerven, M. A. J., & Ambrogioni, L. (2020). GAIT-prop: A biologically plausible learning rule derived from backpropagation of error. ArXiv arXiv:2006.06438 [cs.LG].
  3. Geerligs, L., van Gerven, M., & Güçlü, U. (2020). Detecting neural state transitions underlying event segmentation. BioRxiv (doi: https://doi.org/10.1101/2020.04.30.069989)
  4. Xie, N., Ras, G., van Gerven, M., & Doran, D. (2020). Explainable Deep Learning: A Field Guide for the Uninitiated., ArXiv arXiv:2004.14545 [cs.LG]
  5. Ahmad, N., Ambrogioni, L., & van Gerven, M. A. J. (2020). Spike-Timing-Dependent Inference of Synaptic Weights. ArXiv, arXiv:2003.03988 [q-bio.NC]
  6. Sancaktar, C., van Gerven, M., & Lanillos, P. (2019). End-to-End Pixel-Based Deep Active Inference for Body Perception and Action. ArXiv, arXiv:2001.05847 [cs.CV]
  7. Standvoss, K., Quax, S. C., & van Gerven, M. A. J. (2020). Visual Attention Through Uncertainty Minimization in Recurrent Generative Models. BioRxiv (doi: https://doi.org/10.1101/2020.02.14.948992)
  8. Dallaire, P., Ambrogioni, L., Trottier, L., Güçlü, U., Hinne, M., Giguère, P., Chaib-Draa, B., van Gerven, M., & Laviolette, F. (2020). The Indian Chefs Process ArXiv, arXiv:2001.10657 [cs.LG]
  9. Ras, G., Dotsch, R., Ambrogioni, L., Güçlü, U., & van Gerven, M. A. J. (2019). Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks. ArXiv, arXiv:1912.09831 [cs.LG]
  10. Ras, G., Ambrogioni, L., Güçlü, U., & van Gerven, M. A. J. (2019). Temporal Factorization of 3D Convolutional Kernels. ArXiv, arXiv:1912.04075 [cs.LG]
  11. Hinne, M., van Gerven, M. A. J., & Ambrogioni, L. (2019). Causal inference using Bayesian non-parametric quasi-experimental design. ArXiv, arXiv:1911.06722 [stat.ME]
  12. Seeliger, K., Sommers, R. P., Güçlü, U., Bosch, S. E., & van Gerven, M. A. J. (2019). A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time, BioRxiv (doi: https://doi.org/10.1101/687681).
  13. Ambrogioni, L., Güçlü, U., & van Gerven, M. (2019). k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport ArXiv, arXiv:1907.04050 [stat.ML].
  14. Thielen, J., Güçlü, U., Güçlütürk, Y., Ambrogioni, L., Bosch, S. E., & van Gerven, M. A. J. (2019). DeepRF: Ultrafast population receptive field mapping with deep learning, BioRxiv, (doi: https://doi.org/10.1101/732990), 1-12.
  15. Seeliger, K., Ambrogioni, L., Gucluturk, Y., Guclu, U., & van Gerven, M. A. J. (2019). Neural system identification with neural information flow, BioRxiv, (doi: http://dx.doi.org/10.1101/553255.), 1–14.
  16. Ambrogioni, L., & van Gerven, M. A. J. (2019). Perturbative estimation of stochastic gradients. ArXiv:1904.00469v3 [Stat.ML], 1–10.
  17. Dijkstra, N., Hinne, M., Bosch, S. E., & van Gerven, M. A. J. (2019). Individual differences in the influence of mental imagery on conscious perception, BioRxiv (doi: https://doi.org/10.1101/607770).
  18. Troost, M., Seeliger, K. and van Gerven, M. (2018) Generalization of an Upper Bound on the Number of Nodes Needed to Achieve Linear Separability, ArXiv arXiv:1802.03488 [stat.ML].
  19. Escalante, H. J., Kaya, H., Salah, A. A., Escalera, S., Gucluturk, Y., Guclu, U., … van Lier, R. (2018). Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos., ArXiv. arXiv:1802.00745 [cs.CV].
  20. Ambrogioni, L, Güçlü, U, Güçlütürk, van Gerven, M (2018). Wasserstein variational gradient descent: From semi-discrete optimal transport to ensemble variational inference, arXiv:1811.02827.
  21. Ivanenko, A., Watkins, P., van Gerven, M. A. J., Hammerschmidt, K. and Englitz, B. (2018). Classification of mouse ultrasonic vocalizations using deep learning. BioRxiv, 1–23.
  22. Ambrogioni, L, Güçlü, U, van Gerven, MAJ, Maris, E, 2017. The kernel mixture network: A nonparametric method for conditional density estimation of continuous random variables. arXiv. arXiv:1705.07111 [stat.ML], 1–10.
  23. Güçlü, U, Güçlütürk, Y, Madadi, M, Escalera, S, Baró, X, González, J., van Lier, R., van Gerven, MAJ, 2017. End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks ArXiv. arXiv:1703.03305 [cs.CV]. 1–18.

Journal papers

  1. Quax, S. C., Bosch, S. E., Peelen, M. V., & van Gerven, M. A. J. (2021). Population codes of prior knowledge learned through environmental regularities. Scientific Reports, 11(1), 640. https://doi.org/10.1038/s41598-020-79366-z
  2. Dijkstra, N., Ambrogioni, L., Vidaurre, D., & van Gerven, M. (2020). Neural dynamics of perceptual inference and its reversal during imagery. eLife, 9. https://doi.org/10.7554/eLife.53588
  3. Quax, S. C., D’Asaro, M., & van Gerven, M. A. J. (2020). Adaptive time scales in recurrent neural networks. Scientific Reports, 10(1), 11360. https://doi.org/10.1038/s41598-020-68169-x
  4. Berezutskaya, J., Freudenburg, Z. V., Ambrogioni, L., Güçlü, U., van Gerven, M. A. J., & Ramsey, N. F. (2020). Cortical network responses map onto data-driven features that capture visual semantics of movie fragments. Scientific Reports, 10(1), 12077. https://doi.org/10.1038/s41598-020-68853-y
  5. Berezutskaya, J., Freudenburg, Z. V., Güçlü, U., van Gerven, M. A. J., & Ramsey, N. F. (2020). Brain-optimized extraction of complex sound features that drive continuous auditory perception. PLoS Computational Biology, 16(7), e1007992. https://doi.org/10.1371/journal.pcbi.1007992
  6. Ivanenko, A., Watkins, P., van Gerven, M. A. J., Hammerschmidt, K., & Englitz, B. (2020). Classifying sex and strain from mouse ultrasonic vocalizations using deep learning. PLoS Computational Biology, 16(6), e1007918. https://doi.org/10.1371/journal.pcbi.1007918
  7. Ambrosen, K. S., Eskildsen, S. F., Hinne, M., Krug, K., Lundell, H., Schmidt, M. N., van Gerven, M. A. J., Mørup, M., & Dyrby, T. B. (2020). Validation of structural brain connectivity networks: The impact of scanning parameters. NeuroImage, 204, 116207. https://doi.org/10.1016/j.neuroimage.2019.116207
  8. Thielen, J., Bosch, S. E., van Leeuwen, T. M., van Gerven, M. A. J., & van Lier, R. (2019). Evidence for confounding eye movements under attempted fixation and active viewing in cognitive neuroscience. Scientific Reports, 9(1), 17456. https://doi.org/10.1038/s41598-019-54018-z
  9. Jiang, H., Bahramisharif, A., van Gerven, M. A. J., & Jensen, O. (2019). Distinct directional couplings between slow and fast gamma power to the phase of theta oscillations in the rat hippocampus. The European Journal of Neuroscience. https://doi.org/10.1111/ejn.14644
  10. van Dam, E. A., Noldus, L. P. J. J., & van Gerven, M. A. J. (2019). Deep Learning Improves Automated Rodent Behavior Recognition Within a Specific Experimental Setup. Journal of Neuroscience Methods, 108536. https://doi.org/10.1016/j.jneumeth.2019.108536
  11. Dijkstra, N, Hinne, M, Bosch, SE, van Gerven, MAJ. Between-subject variability in the influence of mental imagery on conscious perception. Scientific Reports, 9:15658 | https://doi.org/10.1038/s41598-019-52072-1
  12. Jacques Junior, J. C. S., Gucluturk, Y., Perez, M., Andujar, C., Baro, X., Escalante, H. J., … Escalera, S. (2019). First impressions: A survey on vision-based apparent personality trait analysis. IEEE Trans Eff Comput, 3045, 1–20. https://doi.org/10.1109/TAFFC.2019.2930058
  13. Quax, SC, Dijkstra, N, van Staveren, MJ, Bosch, SE, & van Gerven, MAJ (2019). Eye movements explain decodability during perception and cued attention in MEG. NeuroImage, 195(March), 444–453.
  14. Thielen, J, Bosch, SE, van Leeuwen, TM, van Gerven, MAJ, van Lier, R (2019). Neuroimaging findings on amodal completion: A review iPerception, 10(2), 1-25.
  15. Dijkstra, N, Bosch, SE, van Gerven, MAJ (2019) Shared neural mechanisms of visual perception and imagery. Trends in Cognitive Sciences, 1–12. https://doi.org/10.1016/j.tics.2019.02.004
  16. Quax, S, van Gerven, MAJ (2018) Emergent mechanisms of evidence integration in recurrent neural networks. PLoS ONE, 13(10): e0205676.
  17. Seeliger K, Güçlü U, Ambrogioni L, Güclutürk Y, van Gerven MAJ (2018) Generative adversarial networks for reconstructing natural images from brain activity. Neuroimage, 181:775-785.
  18. Thielen, J, van Lier, van Gerven, MAJ (2018). No evidence for confounding orientation-dependent fixational eye movements under baseline conditions. Scientific Reports, 8(11644), 1-10
  19. Baker, CI, & van Gerven, MAJ (2018). New advances in encoding and decoding of brain signals. NeuroImage. https://doi.org/10.1016/j.neuroimage.2018.06.064
  20. Dijkstra, N, Mostert, P, de Lange, FP, Bosch, S, & van Gerven, MAJ (2018). Differential temporal dynamics during visual imagery and perception. eLIFE, DOI: https, 1–16. https://doi.org/10.1101/226217
  21. Güçlütürk Y, Güçlü U, van Gerven MAJ, van Lier R. (2018). Representations of naturalistic stimulus complexity in early and associative visual and auditory cortices. Scientific Reports, 8(3439), 1–16.
  22. van Gerven, MAJ, & Bohte, S (2017). Artificial neural networks as models of neural information processing. Frontiers in Computational Neuroscience, (doi: 10.3389/fncom.2017.00114 Editorial:), 1–2. https://doi.org/10.1038/nature14539
  23. van Gerven MAJ, (2017). Computational foundations of natural intelligence. Front Comput Neurosci. 2017, 1–42.
  24. Gucluturk, Y, Güçlü, U, Baro, X, Escalante, HJ, Guyon, I, Escalera, S, …, van Gerven, MAJ, van Lier, R., (2017). Multimodal first impression analysis with deep residual networks. IEEE Trans Eff Comput, 99, 1–14. http://doi.org/10.1007/978-3-319-49409-8
  25. Hirschmann, J, Schoffelen, JM, Schnitzler, A, & van Gerven, MAJ, (2017). Parkinsonian rest tremor can be detected accurately based on neuronal oscillations recorded from the subthalamic nucleus. Clinical Neurophysiology. http://doi.org/10.1016/j.clinph.2017.07.419
  26. Seeliger, K, Fritsche, M, Güçlü, U, Schoenmakers, S, Schoffelen, J, Bosch, SE, & van Gerven, MAJ, (2017). Convolutional neural network-based encoding and decoding of visual object recognition in space and time. NeuroImage, doi: 10.1016/j.neuroimage.2017.07.018.
  27. Berezutskaya, J, Freudenburg, ZV, Güçlü, U, van Gerven, MAJ, & Ramsey, NF (2017). Neural tuning to low-level features of speech throughout the perisylvian cortex. J Neurosci, 37(33), 7906-7920 10.1523/JNEUROSCI.0238-17.2017.
  28. Dijkstra, N, Zeidman, P, Ondobaka, S, van Gerven, MAJ, and Friston, K (2017). Distinct top-down and bottom-up brain connectivity during visual perception and imagery. Scientific Reports, 7(5677), 1–9. http://doi.org/10.1038/s41598-017-05888-8.
  29. Ambrogioni, L, van Gerven, MAJ, Maris, E, 2017. Dynamic decomposition of spatiotemporal neural signals. PLoS Comp. Biol. 13(5), e1005540.
  30. Benozzo, D, Jylanki, P, Olivetti, E, Avesani, P, van Gerven, MAJ, 2017. Bayesian estimation of directed functional coupling from brain recordings. PLoS One 12, e0177359.
  31. Dijkstra, N., Bosch, S., van Gerven, MAJ, 2017. Vividness of visual imagery depends on the neural overlap with perception in visual areas. J. Neurosci. 37(5):1367-1373.
  32. Hinne, M, Meijers, A, Bakker, R, Tiesinga, PHE, Morup, M, van Gerven, MAJ, 2017. The missing link : Predicting connectomes from noisy and partially observed tract tracing data. PLoS Comp. Biol. 1–22. doi:10.1371/journal.pcbi.1005374
  33. Güçlü, U, Gerven, MAJ, 2017. Modeling the dynamics of human brain activity with recurrent neural networks. J. Front. Comput. Neurosci. 11. doi:10.3389/fncom.2017.00007
  34. van Gerven, MAJ, 2017. A primer on encoding models in sensory neuroscience. J Math Psychol. vol. 76, iss. Part B, 172-183.
  35. Güçlü, U, van Gerven, MAJ (2017). Increasingly complex representations of natural movies across the dorsal stream are shared between subjects. Neuroimage. 145(Part B), 329-336.
  36. Janssen, RJ, Jylänki, P, van Gerven, MAJ, 2016. Let’s not waste time: Using temporal information in clustered activity estimation with spatial adjacency restrictions (CAESAR) for parcellating fMRI data. PLoS ONE, 11(12): e0164703.
  37. Shumskaya, E, van Gerven, MAJ, Norris, DG, Vos, PE, Kessels, RPC, 2016. Abnormal connectivity in the sensorimotor network predicts attention deficits in traumatic brain injury. Experimental Brain Research. In Press.
  38. Janssen, MAM, Hinne, M, Janssen, RJ, van Gerven, MAJ, Steens, SC, Góraj, B, Koopmans, PP, Kessels, RPC, 2016. Resting-state subcortical functional connectivity in HIV-infected patients on long-term cART. Brain Imaging Behav. doi:10.1007/s11682-016-9632-4
  39. van de Nieuwenhuijzen, ME, van den Borne, E, Jensen, O, van Gerven, MAJ (2016). Spatiotemporal dynamics of cortical representations during and after stimulus presentation. Frontiers in Systems Neuroscience. DOI: 10.3389/fnsys.2016.00042.
  40. Dijkstra, N, van de Nieuwenhuijzen, ME, van Gerven, MAJ (2016). The spatiotemporal dynamics of binocular rivalry: evidence for increased top-down flow prior to a perceptual switch. Neuroscience of Consciousness. DOI: http://dx.doi.org/10.1093/nc/niw003.
  41. Jiang, H, Popov, T, Jylänki, P, Bi, K, Yao, Z, Lu, Q, Jensen, O, van Gerven, MAJ (2016). Predictability of depression severity based on posterior alpha oscillations. Clinical Neurophysiology, 127(4): 2108–2114.
  42. Hinne, M, Janssen, RJ, Heskes, T, van Gerven, MAJ (2015). Bayesian estimation of conditional independence graphs improves functional connectivity estimates. PLoS Comp Biol, 11(11): e1004534.
  43. Lüttke, C, Ekman, M, van Gerven, MAJ, de Lange, FP (2015). Preference for audiovisual speech congruency in superior temporal cortex. J Cog Neurosci, 28(1), 1-7.
  44. Janssen, RJ, Jylänki, P, Kessels, RPC, van Gerven, MAJ (2015). Probabilistic model-based functional parcellation reveals a robust, fine-grained subdivision of the striatum. NeuroImage, 119, 398–405.
  45. Güçlü, U, van Gerven, MAJ (2015). Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci., 35(27):10005-10014.
  46. Jiang, H, Bahramisharif, A, van Gerven, MAJ, Jensen, O (2015). Measuring directionality between neuronal oscillations of different frequencies. Neuroimage, 118:359-367.
  47. Hinne, M, Ekman, M, Janssen, R, Heskes, T, van Gerven, MAJ (2015). Probabilistic clustering of the human connectome identifies communities and hubs. PLoS ONE, 10(1): e0117179.
  48. Jiang, H, van Gerven, MAJ, Jensen, O. Modality-specific alpha modulations facilitate long-term memory encoding in the presence of distracters. J Cogn Neurosci. 2015. 27(3):583-592
  49. Simanova, I, van Gerven, MAJ, Oostenveld, R, Hagoort, P. Predicting the semantic category of internally generated words from neuromagnetic recordings. J Cogn Neurosci. 2015. 27(1):35-45.
  50. Schoenmakers, S, Güçlü, U, van Gerven, MAJ, Heskes, T. Gaussian mixture models and semantic gating improve reconstructions from human brain activity. Frontiers in Computational Neuroscience. 2014. 8(173).
  51. Lopez-Gordo, MA, Sanchez Morillo, D, van Gerven, MAJ. Spreading codes enables the blind estimation of the hemodynamic response with short-events sequences. Int J Neur Syst. 2014. DOI: 10.1142/S012906571450035X
  52. Hinne, M, Lenkoski, A, Heskes, T, van Gerven, MAJ. Efficient sampling of Gaussian graphical models using conditional Bayes factors. Stat. 2014; DOI:10.1002/sta4.66
  53. Janssen, RJ, Hinne, M, Heskes, T, van Gerven, MAJ. Quantifying uncertainty in brain network measures using Bayesian connectomics. Frontiers in Computational Neuroscience. 2014; DOI:10.3389/fncom.2014.00126
  54. Güçlü, U and van Gerven, MAJ. Unsupervised feature learning improves prediction of human brain activity in response to natural images. PLoS Comput Biol. 2014; DOI: 10.1371/journal.pcbi.1003724.
  55. Simanova, I, Hagoort, P, Oostenveld, R, van Gerven, MAJ. Modality-independent decoding of semantic information from the human brain. Cereb Cortex. 2014; 24:426-434.
  56. Hinne M, Ambrogioni L, Janssen R, Heskes T, van Gerven, MAJ. Structurally-informed Bayesian functional connectivity analysis. NeuroImage. 2014; 86:294-305.
  57. Bahramisharif, A, van Gerven, MAJ, Aarnoutse, E, Mercier, M, Schwartz, T, Foxe, J, Ramsey, N, Jensen, O. Propagating neocortical gamma bursts are coordinated by traveling alpha waves. The Journal of Neuroscience. 2013; 33(48):18849-18854.
  58. Roijendijk, L, Farquhar, J, van Gerven, MAJ, Jensen, O, Gielen, S. Exploring the impact of target eccentricity and task difficulty on covert visual spatial attention and its implications for brain computer interfacing. PLoS ONE. 2013; 8(12):e80489.
  59. Niazi, AM, van den Broek, PLC, Klanke, S, Barth, M, Poel, M, van Gerven, MAJ. Online decoding of object-based attention using real-time fMRI. European Journal of Neuroscience. 2013; 39(2):319-329.
  60. Kok P, Brouwer GJ, van Gerven MAJ, de Lange FP. Prior expectations bias sensory representations in visual cortex. The Journal of Neuroscience. 2013; 33(41):16275-16284.
  61. van de Nieuwenhuijzen, ME, Backus, AR, Bahramisharif, A, Doeller, CF, Jensen, O, van Gerven, MAJ. MEG-based decoding of the spatiotemporal dynamics of visual category perception. Neuroimage. 2013; 83:1063-1073.
  62. Schoenmakers, S, Barth, M, Heskes, T, van Gerven, MAJ. Linear reconstruction of perceived images from human brain activity. Neuroimage. 2013; 83:951-961.
  63. Vidaurre, D, van Gerven MAJ, Bielza, C, Larrañaga, P, Heskes, T. Bayesian sparse partial least squares. Neural Computation. 2013; 25(12):3318-3339.
  64. Brouwer, A-M, Reuderink, B, Vincent, J, van Gerven, MAJ, van Erp, JBF. Distinguishing between target and nontarget fixations in a visual search task using fixation-related potentials. Journal of Vision. 2013; 13(3).
  65. Geuze, J, van Gerven, MAJ, Farquhar, J, Desain P. Detecting semantic priming at the single-trial level. PLoS ONE. 2013; 8(4): e60377
  66. van Gerven, MAJ, Maris, E, Sperling, M, Sharan, A, Litt, B, Anderson, C, Baltuch, G, Jacobs, J. Decoding the memorization of individual stimuli with direct human brain recordings. Neuroimage. 2012; 70:223-232.
  67. Hinne, M, Heskes, T, Beckmann, CF, van Gerven, MAJ. Bayesian inference of structural brain networks. Neuroimage. 2012; 66:543-552.
  68. van Gerven, MAJ, Chao ZC, Heskes, T. On the decoding of intracranial data using sparse orthonormalized partial least squares. J Neural Eng. 2012; 9(2):026017.
  69. Llera A, van Gerven MAJ, Gómez V, Kappen HJ. On the use of interaction error potentials for adaptive brain computer interfaces. Neural Networks. 2011; 24(10):1120-1127.
  70. Jensen O, Oostenveld R, Klanke S, Hadjipapas A, Okazaki Y, van Gerven MAJ. Using brain-computer interfaces and brain-state dependent stimulation as a tool in cognitive neuroscience. Frontiers in Psychology. 2011;2.
  71. van Gerven MAJ, Kok P, de Lange FP, Heskes T. Dynamic decoding of ongoing perception. Neuroimage. 2011; 57:950–957.
  72. Treder MS, Bahramisharif A, Schmidt NM, van Gerven MAJ, Blankertz B. Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention. J. NeuroEngineering and Rehabil. 2011; 8(24).
  73. Bahramisharif A, Heskes T, Jensen O, van Gerven MAJ. Lateralized responses during covert attention are modulated by target eccentricity. Neurosci. Lett. 2011; 491(1):35-39.
  74. Simanova I, van Gerven MAJ, Oostenveld R, Hagoort P. Identifying object categories from event-related EEG: toward decoding of conceptual representations. PLoS ONE. 2010; 5(12):e14465.
  75. van Gerven MAJ, de Lange FP, Heskes T. Neural decoding with hierarchical generative models. Neural Comput. 2010; 22(12):3127-3142
  76. van Gerven MAJ, Cseke B, de Lange FP, Heskes T. Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior. Neuroimage. 2010; 50(1):150-161.
  77. Bahramisharif A, van Gerven MAJ, Heskes T, Jensen O. Covert attention allows for continuous control of brain-computer interfaces. Eur. J. Neurosci. 2010; 31(8):1501-1508.
  78. van Gerven MAJ, Bahramisharif A, Heskes T, Jensen O. Selecting features for BCI control based on a covert spatial attention paradigm. Neural Netw. 2009; 22(9):1271-1277.
  79. van Gerven MAJ, Farquhar J, Schaefer R, Vlek R, Geuze J, Nijholt A, et al. The brain-computer interface cycle. J Neural Eng. 2009; 6(4):041001.
  80. van Gerven MAJ, Hesse C, Jensen O, Heskes T. Interpreting single trial data using groupwise regularisation. Neuroimage. 2009; 46(3):665-676.
  81. van Gerven MAJ, Jensen O. Attention modulations of posterior alpha as a control signal for two-dimensional brain-computer interfaces. J. Neurosci. Methods. 2009; 179(1):78-84.
  82. Willems D, Niels R, van Gerven MAJ, Vuurpijl L. Iconic and multi-stroke gesture recognition. Pattern Recognit. 2009;42(12):3303-3312.
  83. van Gerven MAJ, Taal BG, Lucas PJF. Dynamic Bayesian networks as prognostic models for clinical patient management. J Biomed Inform. 2008; 41(4):515-529.
  84. van Gerven MAJ, Lucas PJF, van der Weide, TP. A generic qualitative characterization of independence of causal influence. Internat J Approx Reas. 2008; 48(1):214-136.
  85. van Gerven MAJ, Jurgelenaite R, Taal BG, Heskes T, Lucas PJF. Predicting carcinoid heart disease with the noisy-threshold classifier. Artif Intell Med. 2007; 40(1):4555.
  86. van Gerven MAJ, Díez FJ, Taal BG, Lucas PJF. Selecting treatment strategies with dynamic limited-memory influence diagrams. Artif Intell Med. 2007; 40(3):171-186.

Selected conference and workshop proceedings

  1. Standvoss, K., Quax, S. C., & van Gerven, M. A. J. (2019). Task-dependent attention allocation through uncertainty minimization in deep recurrent generative models. 2019 Conference on Cognitive Computational Neuroscience, 1022–1025. (doi: https://doi.org/10.32470/ccn.2019.1202-0)
  2. Bollen, C. J. M., van Wezel, R. J. A., van Gerven, M. A. J., & Güçlütürk, Y. (2019). Emotion Recognition with Simulated Phosphene Vision Proceedings of the 2Nd Workshop on Multimedia for Accessible Human Computer Interfaces, 1–8. (doi: https://doi.org/10.1145/3347319.3356836)
  3. Thorat, S., Aldegheri, G., van Gerven, M. A. J., & Peelen, M. V. (2019). Modulation of early visual processing alleviates capacity limits in solving multiple tasks. ArXiv arXiv:1907.12309 [q-bio.NC]. (doi: 10.32470/CCN.2019.1229-0)
  4. Thorat, S., van Gerven, M., & Peelen, M. (2019). The functional role of cue-driven feature-based feedback in object recognition ArXiv, arXiv:1903.10446 [q-bio.NC] (doi: 10.32470/CCN.2018.1044-0).
  5. Ambrogioni, L, Güçlü, U, Berezutskaya, J, van den Borne, E, Güçlütürk, Y, Hinne, M, Maris, E, van Gerven, M (2018). Forward amortized inference for likelihood-free variational marginalization, AIStats, ArXiv:1805.11542, 2019.
  6. Ambrogioni, L, Ebel, P, Max Hinne, Güçlü, U, van Gerven, MAJ, Maris, E (2018). SpikeCaKe: Semi-analytic nonparametric Bayesian inference for spike-spike neuronal connectivity. AIStats, BioRxiv:340489, 2019.
  7. Ambrogioni, L, Güçlü, U, Güçlütürk, Y, Hinne, M, Maris, E, van Gerven, MAJ (2018). Wasserstein variational inference, In Advances in Neural Information Processing Systems (NeurIPS'18) (Vol. 31), ArXiv:1805.11284v2.
  8. Ambrogioni L, Berezutskaya J, Guclu, U, van den Born, E, Gucluturk, Y, van Gerven, MAJ, Maris E. Bayesian model ensembling using meta-trained recurrent neural networks. 2017. NIPS Workshop:1–3.
  9. Güçlü, U, Ambrogioni, L, Maris, E, van Lier, R, van Gerven, MAJ. Algorithmic composition of polyphonic music with the WaveCRF. 2017. NIPS Workshop:1–3.
  10. Güçlütürk, Y, Güçlü, U, Seeliger, K, Bosch, S, Van Lier, R, van Gerven, MAJ, 2017. Deep adversarial neural decoding. NIPS 2017. 1-12.
  11. Berezutskaya, J, Freudenburg, Z, Ramsey, N, Güçlü , U, van Gerven, MAJ. Modeling brain responses to perceived speech with LSTM networks, BENELEARN, 2017.
  12. Ambrogioni, L, Hinne, M, van Gerven, MAJ, Maris, E, 2017. GP CaKe: Effective brain connectivity with causal kernels. arXiv. NIPS 2017, 1–10.
  13. Escalante, HJ, Guyon, I, Escalera, S, Jr, JJ, Ayache, S, Viegas, E., … van Lier, R (2017). Design of an explainable machine learning challenge for video interviews, IJCNN, 3688–3695.
  14. Grant, E, Kohli, P, van Gerven, MAJ Deep disentangled representations for volumetric reconstruction ECCV 2016. Lecture Notes in Computer Science, 9915, 266-279, 2016.
  15. Güçlütürk, Y, Güçlü, U, van Lier, R, van Gerven, MAJ (2016). Convolutional sketch inversion. In: Hua G., Jégou H. (eds) Computer Vision – ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science, vol 9913. Springer, Cham
  16. Güçlü, U, Thielen, J, Hanke, M, & van Gerven, MAJ Brains on beats. NIPS. 2016
  17. Güçlütürk, Y, Güçlü, U, van Gerven, MAJ, van Lier, R, 2016. Deep impression: Audiovisual deep residual networks for multimodal apparent personality trait recognition. ECCV 2016. arXiv:1609.05119 [cs.CV]. 2016.
  18. Grant, E, Sahm, S., Zabihi, M, van Gerven, MAJ
    Predicting and visualizing psychological attributions with a deep neural network. 23rd International Conference on Pattern Recognition. 2016.
  19. Güçlü, U, Knechten, M. and van Gerven, MAJ. A two-stage approach to estimating voxel-specific encoding models improves prediction of hemodynamic responses to natural images. In: Neuroinformatics 2014. Frontiers in Neuroinformatics. 2014.
  20. Schoenmakers, S, van Gerven, MAJ, Heskes, T. Gaussian mixture models improve fMRI-based image reconstruction. In: IEEE Proceedings of Pattern Recognition in Neuroimaging. 2014
  21. van de Nieuwenhuijzen, M, Jensen, O, van Gerven, MAJ. Reading what’s on your mind: Decoding images of different categories from working memory maintenance. In: Biomag. 2014.
  22. Hinne, M, Heskes, T, van Gerven, MAJ. Structural connectivity estimation via Bayesian data fusion. In: Human Brain Mapping. 2013.
  23. Güçlü, U and van Gerven, MAJ. Unsupervised learning of invariant features for encoding fMRI responses to natural images. In: Human Brain Mapping. 2013.
  24. Hinne, M, Eckman, M, Heskes, T, van Gerven, MAJ. Learning parcellated brain networks with an infinite relational model prior. In: Bayesian Non-parametrics 9. 2013
  25. Güçlü, U and van Gerven, MAJ. Unsupervised learning of features for Bayesian decoding in functional magnetic resonance imaging. In: BENELEARN 2013.
  26. Bahramisharif, A, van Gerven, MAJ, Jensen, O. Occipital alpha power and covert visual spatial attention in 2D. In: Biomag2012.
  27. van de Nieuwenhuijzen, M, Backus, A, Bahramisharif, A, Doeller, C, Jensen, O, van Gerven, MAJ. Classifying perceived natural images from MEG data using multivariate methods. In: Biomag2012.
  28. Backus, AR, van Gerven, MAJ, Jensen, O, Doeller, C. Category-specific changes in resting-state brain connectivity on multiple timescales following a sequence encoding task. In: Amsterdam Memory Slam 2012.
  29. Backus AR, Meeuwissen EB, Jensen O, van Gerven MAJ. Investigating the spatiotemporal dynamics of long-term memory retrieval using multivariate pattern analyses on magnetoencephalography Data. In: NIPS 2011 workshop on Machine Learning and Interpretation in Neuroimaging.
  30. Wouters HJP, van Gerven MAJ, Heskes T, Treder MS, Bahramisharif A. Covert attention as a paradigm for subject-independent brain-computer interfacing. In: NIPS 2011 workshop on Machine Learning and Interpretation in Neuroimaging.
  31. Bahramisharif A, van Gerven MAJ, Schoffelen J-M, Ghahramani Z, Heskes T. The dynamic beamformer. In: NIPS 2011 workshop on Machine Learning and Interpretation in Neuroimaging.
  32. van Gerven MAJ, Maris E, Heskes T. A Markov random field approach to neural encoding and decoding. In: ICANN 2011. 2011.
  33. van Gerven MAJ, de Lange FP, Heskes T. A hierarchical generative model for percept reconstruction. In: Human Brain Mapping. 2010.
  34. van Gerven MAJ, Simanova I. Concept classification with Bayesian multi-task learning. In: NAACL-HLT workshop on Computational Neurolinguistics. 2010.
  35. van Gerven MAJ, Heskes T. Sparse orthonormalized partial least squares. In: BNAIC. 2010.
  36. Simanova I, van Gerven MAJ, Oostenveld R, Hagoort P. Identifying object categories from event-related EEG: Toward decoding of conceptual representations. In: Human Brain Mapping. 2010.
  37. van Gerven MAJ, Bahramisharif A, Jensen O. Modulations in alpha activity by covert attention: a new 2D control signal for BCI. In: 8th Dutch Endo-Neuro-Psycho Meeting. Doorwerth, the Netherlands: 2009.
  38. Bahramisharif A, van Gerven MAJ, Heskes T. Exploring the impact of alternative feature representations on BCI classification. In: European Symposium on Artificial Neural Networks. 2009.
  39. Birlutiu A, Dijkstra TMH, van Gerven MAJ, Heskes T. Does the immune system have an influence on malaria parasite gene expression? In: 5th Netherlands Institute for Systems Biology Symposium. 2009.
  40. van Gerven MAJ, Cseke B, Oostenveld R, Heskes T. Bayesian source localization with the multivariate Laplace prior. In: Bengio Y, Schuurmans D, Lafferty J, Williams CKI, Culotta A, editors. Neural Information Processing Systems 23. 2009. p. 1901-1909.
  41. van Gerven MAJ, Takashima A, Heskes T. Selecting and identifying regions of interest using groupwise regularization. In: NIPS Workshop on New Directions in Statistical Learning for Meaningful and Reproducible fMRI Analysis. 2008.
  42. van Gerven MAJ. Tensor decompositions for probabilistic classification. In: Intelligent Data Analysis in bioMedicine and Pharmacology (IDAMAP) 2007. 2007.

Dissertations

  1. van Gerven, MAJ (2018). Menselijke Machines. Inaugural speech.
  2. van Gerven, MAJ (2007). Bayesian Networks for Clinical Decision Support. PhD thesis.

Book chapters

  1. van Gerven, M. A. J., Seeliger, K., Güçlü, U., & Güçlütürk, Y. (2019). Current Advances in Neural Decoding. In W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, & K.-R. Müller (Eds.), Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (pp. 379–394). https://doi.org/10.1007/978-3-030-28954-6_21
  2. Ras, G, van Gerven, MAJ, & Haselager, P (2018). Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges, 1–15. In Explainable and Interpretable Models in Computer Vision and Machine Learning. ArXiv:1803.07517V2
  3. Escalante, HJ, Escalera, S, Guyon, I, Baró, X, Güçlütürk, Y, Güçlü, U, van Gerven, M (eds). Explainable and Interpretable Models in Computer Vision and Machine Learning. Springer, 2018.
  4. Willems, RM and van Gerven, MAJ (2018). New fMRI methods for the study of language. In: Oxford Handbook of Psycholinguistics.
  5. Güçlü, U. and van Gerven, M. (2017). Probing human brain function with artificial neural networks. In: Computational Models of Brain and Behavior (ed. A. Moustafa), pages 413–423. John Wiley & Sons. https://doi.org/10.1002/9781119159193.ch30
  6. Diez, FJ, van Gerven, MAJ. Dynamic LIMIDS. In: Decision Theory Models for Applications in Artificial Intelligence, IGI Global; 2012. p. 164-189.
  7. Vuurpijl L, Willems D, Niels R, van Gerven MAJ. Design issues for pen-centric interactive maps. In: Interactive Collaborative Information Systems. Springer; 2010. p. 273-296.
  8. van Gerven MAJ, Lucas PJF. The role of background knowledge in Bayesian classification. Advances in Probabilistic Graphical Models. In: StudFuzz 213. Berlin Heidelberg: Springer-Verlag; 2007. p. 377-396.

Technical reports

  1. Ambrogioni, L, Umut, G, Maris, E, van Gerven, MAJ. 2017. Estimating nonlinear dynamics with the ConvNet smoother ArXiv. arXiv:1702.05243 [stat.ML]. 1–8.
  2. Quax SC, van Koppen TC, Jylänki P, Dumoulin SO, van Gerven MAJ. Slice-sampled Bayesian PRF mapping. BioRxiv. 2016;1–23.
  3. Solin, A, Jylänki, P, Kauramäki, J, Heskes, T, van Gerven, MAJ, Särkkä, S (2016). Regularizing solutions to the MEG inverse problem using space-time separable covariance functions. arXiv:1604.04931 [stat.AP].
  4. Güçlü, U, & van Gerven, MAJ (2015). Semantic vector space models predict neural responses to complex visual stimuli. arXiv:1510.04738. 2015
  5. van Gerven MAJ, Heskes T (2008) L1/Lp regularization of differences. Radboud University Nijmegen
  6. Heskes T, van Gerven MAJ (2008) Stability conditions for L1/Lp regularization. Nijmegen, The Netherlands: Radboud University Nijmegen
  7. van Gerven, MAJ (2007) Approximate inference in graphical models using tensor decompositions. Radboud University Nijmegen
  8. van Gerven, MAJ (2006) Efficient Bayesian inference by factorizing conditional probability distributions. Nijmegen, The Netherlands: Radboud University
  9. van Gerven, MAJ Taal BG (2006) Structure and parameters of a Bayesian network for carcinoid prognosis. Nijmegen, The Netherlands: Radboud University Nijmegen

Research

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    Cognitive Neuroscience

    Uncovering computational principles of human brain function

    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 function and sophisticated machine learning techniques for large-scale neural data analysis.

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    Artificial Intelligence

    Building machines that think like humans

    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.

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    Human-Machine Interaction

    Creating new interfaces between humans and machines

    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.

Teaching

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    Internships

    Master students are welcome to join us on various projects via a Master thesis project or a lab rotation.

    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.

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    Neural Networks

    B.Sc. Artificial Intelligence (AI-B2)

    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.

    More details.

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    Neural Information Processing Systems

    M.Sc. Artificial Intelligence (MA-AI)

    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:

    • During the lectures, students will get acquainted with the formal aspects and practical development of computational models of cognitive processes. They will learn about the current state of research concerning the modeling and understanding of natural intelligence.
    • Students will present key papers on the state of the art in class themselves.
    • During the practical sessions, students will learn to write computer programs related to specific topics discussed in class. To this end, the Python programming language will be used.
    • In the final part of the course, students will formulate their own research project. The outcome of the research project should be a working computational model accompanied by a NIPS style conference paper that provides original insights about cognitive processing in artificial and/or biological agents.

    More details.

Resources

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    Code

    Gaussian processes with causal kernels

    Gaussian processes for the estimation of effective connectivity using causal kernels.

    Distance-dependent spatial clustering algorithm

    Bayesian clustering algorithm which estimates the number of clusters as well as uncertainty about cluster structure.

    Bayesian connectomics toolbox

    Bayesian approaches for structural and functional connectivity estimation.

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    Data

    FMRI data of visual perception and imagery of three visual categories for 25 subjects. This collection contains betas in MNI space per participant for each category separately and for each trial separately.

    BRAINS dataset

    FMRI measurements of early visual cortex responses to handwritten characters

    69 dataset

    FMRI measurements of early visual cortex responses to handwritten digits