Dr. Dimitrije Markovic

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© Sven Ellger

PostDoc

Name

Dr. Dimitrije Markovic

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Organisationsname

Professur für Kognitive Computationale Neurowissenschaft

Professur für Kognitive Computationale Neurowissenschaft

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Besuchsadresse:

Falkenbrunnen, Raum 138 Chemnitzer Str. 46b

01187 Dresden

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Postadresse:

Technische Universität Dresden Bereich Mathematik und Naturwissenschaften
Professur für Kognitive Computationale Neurowissenschaft

01062 Dresden

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Veröffentlichungen

Schwöbel, S., Marković, D., Smolka, M. N., Kiebel, S. (2024) Joint modeling of choices and reaction times based on Bayesian contextual behavioral control. PLOS Computational Biology 20(7): e1012228. doi:10.1371/journal.pcbi.1012228

Marković, D., Friston, K. J., Kiebel, S. J. (2024).  Bayesian sparsification for deep neural networks with Bayesian model reduction. IEEE Access, doi:10.1109/ACCESS.2024.3417219

Markovic, D., Reiter, A. M. F., & Kiebel, S. J. (2022). Revealing human sensitivity to a latent temporal structure of changes. Frontiers in Behavioral Neuroscience, 16. doi:10.3389/fnbeh.2022.962494

Markovic, D., Stojic, H., Schwobel, S., & Kiebel, S. J. (2021). An empirical evaluation of active inference in multi-armed bandits. Neural Networks, 144, 229-246. doi:10.1016/j.neunet.2021.08.018

Markovic, D., Goschke, T., & Kiebel, S. J. (2021). Meta-control of the exploration-exploitation dilemma emerges from probabilistic inference over a hierarchy of time scales. Cognitive, Affective & Behavioral Neuroscience, 21(3), 509-533. doi:10.3758/s13415-020-00837-x

Frölich, S., Markovic, D., & Kiebel, S. J. (2021). Neuronal Sequence Models for Bayesian Online Inference. Frontiers in Artificial Intelligence. doi:10.3389/frai.2021.530937

Gros, C., Valenti, R., Schneider, L., Gutsche, B., & Markovic, D. (2021). Predicting the cumulative medical load of COVID-19 outbreaks after the peak in daily fatalities. PLoS One, 16(4), e0247272. doi:10.1371/journal.pone.0247272

Schwöbel, S., Markovic, D., Smolka, M. N., & Kiebel, S. J. (2021). Balancing control: A Bayesian interpretation of habitual and goal-directed behavior. Journal of Mathematical Psychology, 100. doi:10.1016/j.jmp.2020.102472

Frölich, S. & Markovic, D. & Kiebel, S. J. (2020). Neuronal Sequence Models for Bayesian Online Inference. https://arxiv.org/abs/2004.00930

Ott F, Marković D, Strobel A, Kiebel SJ (2020) Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit. PLoS Comput Biol 16(2): e1007685. doi: 10.1371/journal.pcbi.1007685

Parr, T., Markovic, D., Kiebel, S. J., & Friston, K. J. (2019). Neuronal message passing using Mean-field, Bethe, and Marginal approximations. Scientific Reports, 9. doi:10.1038/s41598-018-38246-3

Markovic, D., & Malesevic, N. (2018). Adaptive Interface for Mapping Body Movements to Sounds. Computational Intelligence in Music, Sound, Art and Design, Evomusart 2018, 10783, 194-205. doi:10.1007/978-3-319-77583-8_13

Rivera, D. C., Ott, F., Markovic, D., Strobel, A., & Kiebel, S. J. (2018). Context-Dependent Risk Aversion: A Model-Based Approach. Frontiers in Psychology, 9. doi:10.3389/fpsyg.2018.02053

Mohr H, Zwosta K, Markovic D, Bitzer S, Wolfensteller U, et al. (2018) Deterministic response strategies in a trial-and-error learning task. PLOS Computational Biology 14(11): e1006621. doi:10.1371/journal.pcbi.1006621

Schwobel, S., Kiebel, S., & Markovic, D. (2018). Active Inference, Belief Propagation, and the Bethe Approximation. Neural Computation, 30(9), 2530-2567. doi:10.1162/neco_a_01108

Malesevic, N., Markovic, D., Kanitz, G., Controzzi, M., Cipriani, C., & Antfolk, C. (2018). Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals. Complexity. doi:10.1155/2018/9728264

Malesevic, N., Markovic, D., Kanitz, G., Controzzi, M., Cipriani, C., & Antfolk, C. (2017). Decoding of individual finger movements from surface EMG signals using Vector Autoregressive Hierarchical Hidden Markov Models (VARHHMM). 2017 International Conference on Rehabilitation Robotics (Icorr), 1518-1523.

Markovic, D., & Kiebel, S. J. (2016). Comparative Analysis of Behavioral Models for Adaptive Learning in Changing Environments. Frontiers in Computational Neuroscience, 10. doi:10.3389/fncom.2016.00033

Markovic, D., Glascher, J., Bossaerts, P., O'Doherty, J., & Kiebel, S. J. (2015). Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism. Plos Computational Biology, 11(10). doi:10.1371/journal.pcbi.1004558

Markovic, D., & Gros, C. (2014). Power laws and self-organized criticality in theory and nature. Physics Reports-Review Section of Physics Letters, 536(2), 41-74. doi:10.1016/j.physrep.2013.11.002

Markovic, D., Gros, C., & Schuelein, A. (2013). Criticality in conserved dynamical systems: Experimental observation vs. exact properties. Chaos, 23(1). doi:10.1063/1.4773003

Markovic, D., & Gros, C. (2012). Intrinsic Adaptation in Autonomous Recurrent Neural Networks. Neural Computation, 24(2), 523-540. doi:10.1162/NECO_a_00232

Gros, C., Kaczor, G., & Markovic, D. (2012). Neuropsychological constraints to human data production on a global scale. European Physical Journal B, 85(1). doi:10.1140/epjb/e2011-20581-3

Markovic, D., & Gros, C. (2010). Self-Organized Chaos through Polyhomeostatic Optimization. Physical Review Letters, 105(6). doi:10.1103/PhysRevLett.105.068702

Markovic, D., & Gros, C. (2009). Vertex routing models. New Journal of Physics, 11. doi:10.1088/1367-2630/11/7/073002