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Mental states in brains and computers

Understanding how the brain accomplishes its numerous functions, and using this understanding to develop artificial intelligence algorithms, is the final frontier in science. Full understanding of brain/mental processes requires multi-level phenomics. We would like to create maps between brain states to mental states [1]. Such modeling attempts have been made in the past at high levels of abstraction [2]. Attractor network models are a step closer to neurobiological reality. Using neural models that incorporate some neurobiological mechanisms gives an insight into putative brain processes. Trajectories of brain activations may be linked to mental events. Transitions between brain states depend on the biophysical properties of neurons. Graphs that show such transitions illustrate associative thinking processes, creation of conceptual networks, and help to understand mental disorders. Dynamics of such networks allows to formulate novel  hypotheses, taking into account processes contributing to activation of neurons on many levels. As an example, I will present a simulation showing how autism or ADHD can result from properties of individual neurons changed by genetic mutations [3], and a sketch of learning mechanisms leading to the formation of conspiracy theories [4].

Progress in understanding mechanisms of spatial navigation in hippocampus formation (Nobel 2014) shows the way to create abstract cognitive maps. Analysis of real brain signals to discover fingerprints of brain cognitive activity is still a challenge, but has a great potential to create better brain-computer interfaces, neurofeedback and therapeutic procedures. Using functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) data, it is possible to assess changes in the activation of large-scale brain networks and to develop biomarkers useful for the diagnosis of mental diseases. These networks change as a result of cognitive load and working memory training, modifying the network of potentially accessible brain states [5].  The challenge is to recognize brain fingerprints using EEG and develop practical methods to extract relevant information from the EEG signals in real time, and use it to parameterize neuromodulation methods (DCS, TMS) that act directly on the structure of brain connections, repairing or optimizing their performance. We develop our own methods of signal source localization and reconstruction by solving the inverse problem in EEG [6], new spectral methods for analysis of EEG / MEG signals [7], and try to understand oscillatory dynamics of the brain using recurrence analysis [8].

  • [1] Duch W (1996) Computational physics of the mind. Computer Physics Communication 97: 136-153
  • [2] Duch W and Diercksen GHF (1995) Feature Space Mapping as a universal adaptive system. Computer Physics Communications 87: 341-371
  • [3] Duch W. (2019), Autism Spectrum Disorder and deep attractors in neurodynamics.  Ch. 13, Springer Handbook of Multi-Scale Models of Brain Disorders, pp.135-148.
  • [4] Duch W. (2021). Memetics and Neural Models of Conspiracy Theories. Patterns. Cell Press
  • [5] Finc, K, Bonna, K, He, X, Lydon-Staley, D.M, Kühn, S, Duch, W, & Bassett, D. S. (2020). Dynamic reconfiguration of functional brain networks during working memory training. Nature Communications 11, 2435 (IF 11.8)
  • [6] Rykaczewski, K, Nikadon, J, Duch, W, Piotrowski, T. (2021). SupFunSim: spatial filtering toolbox for EEG. Neuroinformatics 19, 107–125
  • [7] M.K. Komorowski, K. Rykaczewski, T. Piotrowski, K. Jurewicz, J. Wojciechowski, A. Keitel, J. Dreszer, W. Duch (2021). ToFFi – Toolbox for Frequency-based Fingerprinting of Brain Signals. Neurocomputing (rev. 5/2022).
  • [8] Duch W, Furman Ł, Tołpa K, Minati L. Short-Time Fourier Transform and Embedding Method for Recurrence Quantification Analysis of EEG Time Series. The European Physical Journal Special Topics (sub. 4/2022)
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Wlodzislaw Duch is the head of the Neurocognitive Laboratory in the Center of Modern Interdisciplinary Technologies, and the Neuroinformatics and Artificial Intelligence group in the University Centre of Excellence Dynamics, Mathematical Analysis and Artificial Intelligence. For many years he has been running the Department of Informatics, both at Nicolaus Copernicus UniversityTorun, Poland. Currently his laboratory is hosting Polish node of the International Neuroinformatics Coordination Facility (INCF). In 2014-15 he has served as a deputy minister for science and higher education in Poland, and in 2011-14 as the Vice-President for Research and ICT Infrastructure at his University. Before that he has worked as the Nanyang Visiting Professor (2010-12) in the School of Computer EngineeringNanyang Technological University, Singapore where he also worked as a visiting professor in 2003-07. MSc (1977) in theoretical physics, Ph.D. in quantum chemistry (1980), postdoc at Univ. of Southern California, Los Angeles (1980-82), D.Sc. in applied math (1987); worked at the University of Florida; Max-Planck-Institute, Munich, Germany, Kyushu Institute of Technology, Meiji and Rikkyo University in Japan, and several other institutions. He is/was on the editorial board of IEEE TNN, CPC, NIP-LR, Journal of Mind and Behavior, and 14 other journals; was co-founder & scientific editor of the “Polish Cognitive Science” journal; for two terms has served as the President of the European Neural Networks Society executive committee (2006-2008-2011), is an active member of IEEE CIS Technical committee; International Neural Network Society Board of Governors elected him to their most prestigious College of Fellows, and elected member of the Complex Systems Committee of the Polish Academy of Arts and Letters. Expert of the European Union science programs (FP4 to Horizon 2020), member of the high-level expert group of European Institute of Innovation & Technology (EIT). He has published about 360 peer-reviewed scientific papers, has written or co-authored 5 books and co-edited 21 books, and published over 150 conference abstracts and 125 popular articles on diverse subjects. His DuchSoft company has made GhostMiner datamining software package for many years marketed by Fujitsu.

Wlodek Duch is well known for development of computational intelligence (CI) methods that facilitate understanding of data, general CI theory based on similarity evaluation and composition of transformations, meta-learning schemes that automatically discover the best model for a given data. He is working on development of neurocognitive informatics, focusing on algorithms inspired by cognitive functions, information flow in the brain, learning and neuroplasticity, understanding of attention, integrating genetic, molecular, neural and behavioral levels to understand attention deficit disorders in autism and other diseases, infant learning and toys that facilitate mental development, creativity, intuition, insight and mental imagery, geometrical theories that allow for visualization of mental events in relation to the underlying neurodynamics. He has also written several papers in the philosophy of mind, and was one of the founders of cognitive sciences in Poland. (

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Włodzisław Duch
Fellow, International Neural Network Society
Past President, European Neural Network Society Head, Neurocognitive Laboratory, CMIT NCU, Poland


Monday, 23th May 2022, 2:00-3:30 PM (CEST)

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