Books by Nikola K. Kasabov
From to , he was a faculty member and lecturer at Osaka University. For the last 30 years, he has been working in computational neuroscience. Since the dawn of AI in the s, inspiration from the brain has helped researchers make computers more intelligent. In turn, AI is now helping to accelerate research on understanding the brain. A prime example is the application of artifical neural networks to brain images from 3D electron microscopy.
It is starting to become possible to reconstruct the brain's wiring diagram, or "connectome.
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Further progress is expected to yield connectomic information from the cerebral cortex, regarded by many neuroscientists as the brain region most crucial for human intelligence. Sebastian Seung is Anthony B. Seung has done influential research in both computer science and neuroscience. Over the past decade, he has helped pioneer the new field of connectomics, developing new computational technologies for mapping the connections between neurons. His lab created EyeWire. Before joining the Princeton faculty in , Seung studied at Harvard University, worked at Bell Laboratories, and taught at the Massachusetts Institute of Technology.
Abstract: Non-Gaussianity is a key concept in several machine learning techniques. In the 's, its importance was understood in the framework of independent component analysis ICA. Related models have been developed for time series as well, for example as non-Gaussian autoregressive models, or non-Gaussian state-space models. While the theory for such linear models is well understood by now, extending the theory to nonlinear models is an important question in current and future research.
In particular, some recent efforts in deep unsupervised learning can be seen as attempts to accomplish such a nonlinear extension, but they often resort to heuristic criteria instead of justified probabilistic models. I will discuss some of my very recent results extending the ICA framework to nonlinear models, in order to accomplish principled nonlinear deep feature extraction. After post-doctoral work at the Helsinki University of Technology, he moved to the University of Helsinki in , where he is currently Professor of Computer Science, especially Machine Learning. Aapo Hyvarinen is the main author of the books "Independent Component Analysis" and "Natural Image Statistics" , and author or coauthor of more than scientific articles.
His current work concentrates on applications of unsupervised machine learning methods to neuroscience. The current development of the third generation of artificial neural networks - the spiking neural networks SNN along with the technological development of highly parallel neuromorphic hardware systems of millions of artificial spiking neurons as processing elements, makes it possible to model complex data streams in a more efficient, brain-like way [1,2].
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NeuCube was first proposed for brain data modelling [3,4]. A STDM has modules for: preliminary data analysis, data encoding into spike sequences, unsupervised learning of temporal or spatio-temporal patterns, classification, regression, prediction, optimisation, visualisation and knowledge discovery. A STDM can be used to predict early and accurately events and outcomes through the ability of SNN to be trained to spike early, when only a part of a new pattern is presented as input data.
The methodology is illustrated on benchmark data with different characteristics, such as: financial data streams; brain data for brain computer interfaces; personalised and climate date for individual stroke occurrence prediction ; ecological and environmental disaster prediction, such as earthquakes. The talk discusses implementation on highly parallel neuromorphic hardware platforms such as the Manchester SpiNNaker  and the ETH Zurich chip [8,9].
These STDM are not only significantly more accurate and faster than traditional machine learning methods and systems, but they lead to a significantly better understanding of the data and the processes that generated it. New directions for the development of SNN and STDM are pointed towards a further integration of principles from the science areas of computational intelligence, bioinformatics and neuroinformatics and new applications across domain areas [10,11].
His main research interests are in the areas of neural networks, intelligent information systems, soft computing, bioinformatics, neuroinformatics. He has published more than publications that include 15 books, journal papers, 80 book chapters, 28 patents and numerous conference papers. He has supervised to completion 38 PhD students. More information of Prof. Since ancient times, it has remained a mystery how our brain generates our mind and intelligence. Recent advancement of brain measurement and data analysis allows for addressing this long-standing question.
In the field of human brain science, brain activities related to behaviors, perception and higher cognitions are investigated using non-invasive measurements such as fMRI, magneto-enchepharography MEG and electro-enchepharography EEG. These methods have been employed to comprehensively describe macro-scale organization of human brain functions such as functional brain mapping  and human connectome , .
Human brain imaging data is typically low quality and high dimensional. Combinations of signal processing, image processing and machine learning methods are indispensable to discover meaningful patterns hidden in high dimensional multivariate data. In this tutorial, I review several studies using human brain imaging methods with particular focus on analysis methodological aspects.
The first half focuses on analysis of fMRI data including functional brain mapping, decoding studies —, and recent human connectome studies . The latter half focuses on analysis of MEG data which is magnetic field measurement generated by population neuronal activities on the brain. MEG has high temporal resolution millisecond , allowing for investigation of human brain dynamics in behavioral time scale which cannot be done with fMRI. One of big challenges of MEG data analysis is to reconstruct brain activities from MEG measurements outside the head, which is an inverse problem referred to as the source localization problem.
We have been attempting to solve the problem by multi-data integration approach. I will introduce series of studies in our laboratories including the high-spatio-temporal source imaging by MEG-fMRI integration with the hierarchical Bayesian model — and whole-brain network dynamics identification with the high dimensional state space methods —.ningpickgrilas.tk
Persistent Memory in Single Node Delay-Coupled Reservoir Computing
He has received M. His research interest is to develop novel data analysis methodology for human brain science with multi-modal data integration approach. We take for granted the ease that we can move and interact in the environment, as it takes little conscious effort to reach out to grab an object of interest or use a fork to pick up food.
The present talk will describe two lines of research that exploit the use of robotic technology to quantify upper limb motor function and dysfunction. My basic research program explores the neural, mechanical and behavioural aspects of sensorimotor function. Inspired by optimal control theory, we have performed a series of studies that illustrate the surprising sophistication of the human motor system to rapidly respond to small mechanical disturbances of the arm during goal-directed motor actions.
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The ability of robots to quantify motor performance makes them also potentially useful as a next generation technology for neurological assessment. Most assessment scales for sensorimotor function are subjective in nature with relatively coarse rating systems, reflecting that it is difficult for even experienced observers to discriminate consistently small changes in performance using only the naked eye. Received B. Sc and M. Winter , and a Ph. He completed his post-doctoral training at the University of Montreal from to under the supervision of Dr.
John Kalaska. His research program includes technology development, basic research on voluntary motor control and clinical research on the use of robots for neurological assessment. He has published over refereed journal articles and given over invited talks. He is the inventor of the KINARM robot and is actively involved in the continued development of advanced technologies for use in basic and clinical research.
Deep Learning has been under the focus of neural network research and industrial communities due to its proven ability to scale well into difficult problems and due to its performance breakthroughs over other architectural and learning techniques in important benchmarking problems. This was mainly in the form of improved data representation in supervised learning tasks. Reinforcement learning RL is considered the model of choice for problems that involve learning from interaction, where the target is to optimize a long term control strategy or to learn to formulate an optimal policy.
Typically these applications involve processing a stream of data coming from different sources, ranging from central massive databases to pervasive smart sensors. RL does not lend itself naturally to deep learning and currently there is no uniformed approach to combine deep learning with reinforcement learning despite good attempts. Examples of important open questions are: How to make the state-action learning process deep? How to make the architecture of an RL system appropriate to deep learning without compromising the interactivity of the system? Although recently there have been important advances in dealing with these issues, they are still scattered and with no overarching framework that promote then in a well-defined and natural way.
This special session will provide a unique platform for researchers from the Deep Learning and Reinforcement Learning communities to share their research experience towards a uniformed Deep Reinforcement Learning DRL framework in order to allow this important interdisciplinary branch to take-off on solid grounds. It will focus on the potential benefits of the different approaches to combine RL and DL. The aim is to bring more focus onto the potential of infusing reinforcement learning framework with deep learning capabilities that could allow it to deal more efficiently with current learning applications including but not restricted to online streamed data processing that involves actions.
Neural networks in the biological brain realize highly flexible and energy efficient information processing based on pattern-processing mechanisms and architectures different from the modern symbol-logic computers. Future computers will employ the principles, structures and devices that integrate these two streams in information processing with a variety of possible hardware. Koprinkova-Hristova, Mladenov, V. Sandamirskaya, Y. Bell, C. Hamburg, Germany. Bello, Guarini, M. Richter, M. Kazerounian, S. Frontiers in Neuroscience , 7 , New Ideas in Psychology , 31 3 , — Autonomous reinforcement of behavioral sequences in neural dynamics Kazerounian, S.
Autonomous reinforcement of behavioral sequences in neural dynamics. Villa, Duch, W.
Duran, B. Springer Berlin Heidelberg.
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