COST Action B27 , WG1 Theoretical Study on Oscillation & Cognition, Polish contributions

1. COST Action B27, WG1Theoretical Study on Oscillation & Cognition, Polish contributions Reported by Włodzisław Duch (Google: Duch) Department of Informatics, Nicolaus Copernicus University, Torun, Poland School of Computer Engineering, Nanyang Technological University, Singapore

2. Notes Summer time ... not all responded on a short notice. Most people do theory and applications and work in several places ... I will talk about activities of 4 groups: • Andrzej Cichocki, Warsaw Univ. Technology & RIKEN Brain Science Institute, Wako-shi • Rafał Bogacz,Bristol, Uni. Wrocław & Princeton • Wiesław Kamiński, Maria Curie-Skłodowska University, Lublin • Włodzisław Duch, Nicolaus Copernicus University & Nanyang Technological University, Singapore

3. Laboratory for Advanced Brain Signal Processing Andrzej Cichocki http://www.bsp.brain.riken.jp/~cia/ RIKEN, Brain Science Institute, JAPAN & Warsaw University of Technology, POLAND

4. Laboratory for Advanced Brain Signal ProcessingRiken Brain Science Institute, Japan Research mission and central research interest: The laboratory for Advanced Brain Signal Processing is focused on developing novel and state of the art methods to: • extract, detect, recognize, • find functional connectivity • classify brain signals and to use the insights gained to build intelligent feature extraction systems for Early Detection and Classification of Dementia, especially Alzheimer Disease (AD), evaluation of aging of the brain using Blind Signal Processing (BSP) and Time Frequency Representation (TFR) of EEG and fMRI/PET.

5. Research Projects of the Laboratory for Advanced Brain Signal Processing Database Experiments, collecting and preprocessing EEG, EOG, EMG , PET, fMRI, MUR data Modeling Olfactory, Auditory S Diagnosis of Dementia, AD BLIND SIGNAL PROCESSING MACHINE LEARNING DATA MINING Electronic Nose Electronic Ears Brain Computer Interface Analysis of EEG/ERP Analysis MUR Intelligent Communication Human with machine Detection, Enhancement Classification, Extraction. Functional Connectivity Spike Sorting Information Retrieval Clustering

6. Research Objectives One of the main objective of the Laboratory is to develop and apply novel blind signal processing (BSP) and Machine Learning (ML) algorithms and methods including: Sparse Components Analysis (SCA), Time-Frequency Component Analyzer (TFCA), Independent Components Analysis (ICA), Blind Deconvolution - Equalization and Hierarchical Clusteringto analyze multi-sensory, multi-modal biomedical signals, especially high density array EEG signals.

7. High Density Array EEG Recording/Analysis Systems in LABSP, RIKEN BSI

8. LABSP Research Projects 1. Developments and Implementation of Novel Blind Signal Processing and Machine Learning Techniques for Analysis, Finding Functional Relationships and Modeling of Brain Signals. 2. Intelligent Communication between Human Brain and Machine - Development of Software/Hardware for Human/Brain Computer Interface (H/BCI) and Classification of Various Mental States. 3. Early Detection and Classification of Dementia, especially Alzheimer Disease (AD) using Blind Signal Processing (BSP) and Time-Frequency Representation (TFR) of EEG and Other Neuroimaging Techniques. 4. Modeling Some Aspects of Auditory System and Olfactory System: Contribution to Development of Electronic Ears and Electronic Nose – Artificial Olfaction.

9. Unique Results • Development of novel models for BSP (State space, Kalman filter, multilayer, recurrent NN, BSE NN using linear predictability). • Development, implementation, integration and theoretical analysis of new associative learning algorithms for ICA, SCA, BSE, MBD, NMF and SPCA. • Applications of BSP algorithms to real-world problems • Early detection of Alzheimer’s disease (Clinical Neurophysiology) • Analysis of high density array EEG data (extraction of unique components and elimination of dependent artifacts, investigation validity and reliability, improvements in source localization) • Reduction of artifacts in simultaneous recording EEG and fMRI • Speech separation, enhancement and modeling auditory cortex • Support clinical diagnosis of brain death using ICA.

10. Procedure for extracting markers of AD Processing flow of the developed method. The main novelty lies in ordering and selection of only few significant AD markers (components), back-projecting (deflation) of these components on the scalp level and processing them in the time frequency domain using approximated sparsification. Advanced pattern recognition and machine learning techniques are applied for classification and analysis of the data.

11. Rafał BogaczBristol/Princeton/Wrocław Theory of Event Related Potentials (ERP) • ERPs are computed by averaging EEG signals over many trials, time locked to an event in psychological experiment (e.g. stimulus presentation). • Should ERP’s be regarded as uncorrelated with the background EEG, or generated by the event-related reorganization of this ongoing rhythmic activity? • Detection of phase resetting in electroencephalogram; paper with Nick Yeung, Clay Holroyd, Jonathan D. Cohen.

12. “Classical view” (phasic peak) Phase resetting and enhancement Pure phase resetting Individual EEG epochs … … … Averaged ERP Theories of ERP origin

13. Evaluation of methods • We evaluated a number of methods previously used to support the phase-resetting theory of ERP origin. • We generated artificial EEG signals by superimposing phasic peak on noise (according to classical view). • When applied to the simulated data, the methods in question produced results that have been previously interpreted as evidence of synchronized oscillations, even though no such synchrony was present.

14. Wiesław Kamiński, Grzegorz WójcikDivision of Complex Systemsand NeurodynamicsInstitute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland • Neurocomputing • Brain and Visual System Modelling • Parallel Processing • Physical analysis • Software development and Visualisation kaminski@neuron.umcs.lublin.plgmwojcik@gmail.com

15. Brain and Visual System Modelling • Modeling and investigation of large biological neural networks • Visual systems simulations and models of cortex • Dynamical analysis and applications of Artificial Neural Networks (ANN) • Liquid State Machines (LSM), etc. • Analysis Based on Physics • Thermodynamic and statistical physics methods in network’s dynamics analysis • Analysis based on informational theory • Self Organising Criticality (SOC) investigations • Chaos theory and applications

16. Parallel Processing/Visualization • Grids and large clusters for simulations • Adaptation of GENESIS/MPGENESIS simulators for MPI environment • Development of visualisation methods for the cortex dynamics and comparison with experimental results. • Participation in the CLUSTERIX project (National Linux Cluster, more than 800 Itanium processors).

17. Włodzisław Duch & Co (Google: Duch) Department of Informatics, Nicolaus Copernicus University, Torun, Poland, and School of Computer Engineering, Nanyang Technological University (NTU), Singapore Done many smaller projects on: Hebbian associative memories with chaotic itinerancy and large Lyapunow exponents for mixed pattern separation (P. Matykiewicz). Visualization of trajectories in such networks and stability analysis of locally Hopfield nets with highly correlated patterns (F. Piękniewski, L. Rybicki) Fuzzy symbolic dynamics for simplification of neurodynamics. A-life biots based on Boltzman machines (L. Rybicki) Global Brain Simulations – just starting ... Cognitive architectures, integration of perception with cognition – just starting ...

18. Attention-Based Artificial Cognitive Control Understanding System (ABACCUS) First attempt: large EU integrated project, with 9 participants: King’s College London (John G. Taylor, coordinator). New version: BRAin as Complex System (BRACS), on a smaller scale, more focused on simulations and understanding the principles of complex brain-like information processing. The time of large scale global brain simulations has come! Computer speeds have just reached brain power (about 1016 binop/s), but computers are far from brain’s complexity/style. Science: understand how high-level cognition arises from low-level interactions between neurons, build powerful research tool; to understand complex systems is to be able to build them. Practical: humanized, cognitive computer applications require a brain-like architecture (either software or hardware) to deal with such problems efficiently; it is at the center of cognitive robotics.

19. Scheme of the brain ... High-level sketch of the brain structures, with connections based on different types of neurotransmiters marked in different colors.

20. BRACS Assumptions & Goals • Assumption: gross neuroanatomical brain structure is critical for its function, therefore it should be preserved. • Should be founded on neuro-scientific understanding of attention and the sensory and motor systems it controls, development in children, simplified modeling, computer power. • Fusion of the appropriate brain-based models, guided by the overall architecture of the brain and developmental learning stages should lead to high-level cognitive processing. • Develop an attention control systems for focusing in sensory surveillance tasks, and for image searching. • Development of control structures for autonomous machines. • Create its own goals in an autonomous fashion. • Darwin VII small robot (G. Edelman) works with 53K mean firing +phase neurons, 1.7 M synapses, modeling 28 brain areas and achieving sensorimotor integration; our project is larger and more structured, hopefully higher cognitive functions emerge ...

21. Neuroscience and Development Vision Speech Tactile Learning of PFC goals Memory System Working Memory Motor Control Feedback Attention Control Atomization system Reasoning System Value Maps Drive and Intrinsic reward system Action/Object reward system Sketch of the BRACS system Computational Platform, Simulation Environment and Integration Rough sketch of the BRACS system, based on simplified spiking neurons.