Novel design methods of central nervous system of C. elegans and olfactory bulb model of mammal based on sequential logic and numerical integration

This study proposes a novel design method of a neuromorphic electronic circuit: design of a neuromorphic circuit based on appropriately selected hybrid dynamics of synchronous sequential logic, asynchronous sequential logic, and numerical integration. Based on the proposed design method, a novel central nervous system model of C. elegans, and an olfactory bulb model are presented. It is then shown that the presented models can realize typical responses of a conventional central nervous system model of C. elegans, and the observation of chaos in the olfactory bulb. Furthermore, the presented models are implemented by a field programmable gate array and the presented model of C.elegans is used to control a prototype robot of C. elegans body. Then, experiments validate that the presented central nervous system model enables the body robot to reproduce typical chemotaxis behaviors of the conventional C. elegans model. In addition, comparisons show that the presented model consumes fewer circuit elements and lower power compared to various central nervous system models of C. elegans based on synchronous sequential logic, asynchronous sequential logic, and numerical integration

ERGODIC CELLULAR AUTOMATON NEURON MODEL FOR A VIRTUAL CLINICAL TRIAL OF NEURAL PROSTHESIS

A novel cellular automaton neuron model and its cellular differentiation method are presented. It is shown that the differentiation method enables the neuron model to reproduce typical nonlinear responses of a given neuron model. Then a virtual clinical trial of neural prosthesis is executed, i.e., a target neuron model in a network composed of biologically plausible differential equation neuron models is replaced with the presented neuron model that is differentiated to reproduce the target neuron model. The presented neuron model is implemented in a field programmable gate array and the virtual clinical trial is validated by experiments. The results show the presented neuron model is much more hardware-efficient compared to a simplified differential equation neuron model

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Computational Multiscale Methods

Computational Multiscale Methods play an important role in many modern computer simulations in material sciences with different time scales and different scales in space. Besides various computational challenges, the meeting brought together various applications from many disciplines and scientists from various scientific communities

Self-Organized Criticality as a Neurodynamical Correlate of Consciousness: A neurophysiological approach to measure states of consciousness based on EEG-complexity features

Background and Objectives This thesis was based on the hypothesis that the physics-derived theoretical framework of self-organized criticality can be applied to the neuronal dynamics of the human brain. From a consciousness science perspective, this is especially appealing as critical brain dynamics imply a vicinity a phase transition, which is associated with optimized information processing functions as well as the largest repertoire of configurations that a system explores throughout its temporal evolution. Hence, self-organised criticality could serve as a neurodynamical correlate for consciousness, which provides the possibility of deriving empirically testable neurophysiological indices suitable to characterise and quantify states of consciousness. The purpose of this work was to experimentally examine the feasibility of the self-organized criticality theory as a correlate for states of consciousness. Therefore, it was aimed at answering the following research questions based on the analysis of three 64 channel EEG datasets: (i) Can signatures of self-organized criticality be found on the level of the EEG in terms of scale-free distribution of neuronal avalanches and the presence of long-range temporal correlations (LRTC) in neuronal oscillations? (ii) Are criticality features suitable to differentiate state of consciousness in the spectrum of wakefulness? (iii) Can the neuronal dynamics be shifted towards the critical point of a phase transition associated with optimized information processing function by mind-body interventions? (iv) Can an explicit relationship to other nonlinear complexity features and power spectral density parameter be identified? (v) Do EEG-based criticality features reflect individual temperament traits? Material and Methods (1): Re-analysis: Thirty participants highly proficient in meditation (mean age 47 years, 11 females/19 males, meditation experience of at least 5 years practice or more than 1000 h of total meditation time) were measured with 64-channel EEG during one session consisting of a task-free baseline resting, a reading condition and three meditation conditions, namely thoughtless emptiness, presence monitoring and focused attention. (2): 64-channel EEG was recorded from 34 participants (mean age 36.0 ±13.4 years, 24 females/ 10 males) before, during and after a professional singing bowl massage. Further, psychometric data was assessed including absorption capacity defined as the individual’s capacity for engaging attentional resources in sensory and imaginative experiences measured by the Tellegen-Absorption Scale (TAS-D), subjective changes in in body sensation, emotional state, and mental state (CSP-14) as well as the phenomenology of consciousness (PCI-K). (3): Electrophysiological data (64 channels of EEG, EOG, ECG, skin conductance, and respiration) was recorded from 116 participants (mean age 40.0 ±13.4 years, 83 females/ 33 males) – in collaboration with the Institute of Psychology, Bundeswehr University Munich - during a task-free baseline resting state. The individual level of sensory processing sensitivity was assessed using the High Sensitive Person Scale (HSPS-G). The datasets were analysed applying analytical tools from self-organized criticality theory (detrended fluctuation analysis, neuronal avalanche analysis), nonlinear complexity algorithms (multiscale entropy, Higuchi’s fractal dimension) and power spectral density. In study 1 and 2, task conditions were contrasted, and effect sizes were compared using a paired two-tailed t-test calculated across participants, and features. T-values were corrected for multiple testing using false discovery rate. To calculate correlations between the EEG features, Spearman’s rank correlation was applied after determining that the distribution was not appropriate for parametric testing by the Shapiro-Wilk test. In addition, in study 1, a discrimination analysis was carried out to determine the classification performance of the EEG features. Here, partial least squares regression and receiver operating characteristics analysis was applied. To determine whether the EEG features reflect individual temperament traits, the individual level of absorption capacity (study 2) and sensory processing sensitivity (study 3) was correlated with the EEG features using Spearman’s rank correlation. Results Signatures of self-organized criticality in the form of scale-free distribution of neuronal avalanches and long-range temporal correlations (LRTCs) in the amplitude of neural oscillations were observed in three distinct EEG-datasets. EEG criticality as well as complexity features were suitable to characterise distinct states of consciousness. In study 1, compared to the task-free resting condition, all three meditative states revealed significantly reduced long-range temporal correlation with moderate effect sizes (presence monitoring: d= -0.49, p<.001; thoughtless emptiness: d= -0.37, p<.001; and focused attention: d= -0.28, p=.003). The critical exponent was suitable to differentiate between focused attention and presence monitoring (d= -0.32, p=.02). Further, in study 2, the criticality features significantly changed during the course of the experiment, whereby values indicated a shift towards the critical regime during the sound condition. Both analyses of the first and second dataset revealed that the critical exponent was significantly negatively correlated with the sample entropy, the scaling exponent resulting from the DFA denoting the amount of long-range temporal correlations as well as Higuchi’s fractal dimension in each condition, respectively. In addition, the critical scaling exponent was found to be significantly negatively correlated with the trait absorption (Spearman's ρ= -0.39, p= .007), whereas an association between critical dynamics and the level of sensory processing sensitivity could not be verified (study 3). Conclusion The findings of this thesis suggest that neuronal dynamics are governed by the phenomena of self-organized criticality. EEG-based criticality features were shown to be sensitive to detect experimentally induced alterations in the state of consciousness. Further, an explicit relationship with nonlinear measures determining the degree of neuronal complexity was identified. Thus, self-organized criticality seems feasible as a neurodynamical correlate for consciousness with the potential to quantify and characterize states of consciousness. Its agreement with the current most influencing theories in the field of consciousness research is discussed

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

Paradoxes of interactivity: perspectives for media theory, human-computer interaction, and artistic investigations

Current findings from anthropology, genetics, prehistory, cognitive and neuroscience indicate that human nature is grounded in a co-evolution of tool use, symbolic communication, social interaction and cultural transmission. Digital information technology has recently entered as a new tool in this co-evolution, and will probably have the strongest impact on shaping the human mind in the near future. A common effort from the humanities, the sciences, art and technology is necessary to understand this ongoing co- evolutionary process. Interactivity is a key for understanding the new relationships formed by humans with social robots as well as interactive environments and wearables underlying this process. Of special importance for understanding interactivity are human-computer and human-robot interaction, as well as media theory and New Media Art. "Paradoxes of Interactivity" brings together reflections on "interactivity" from different theoretical perspectives, the interplay of science and art, and recent technological developments for artistic applications, especially in the realm of sound

Paradoxes of Interactivity

Current findings from anthropology, genetics, prehistory, cognitive and neuroscience indicate that human nature is grounded in a co-evolution of tool use, symbolic communication, social interaction and cultural transmission. Digital information technology has recently entered as a new tool in this co-evolution, and will probably have the strongest impact on shaping the human mind in the near future. A common effort from the humanities, the sciences, art and technology is necessary to understand this ongoing co- evolutionary process. Interactivity is a key for understanding the new relationships formed by humans with social robots as well as interactive environments and wearables underlying this process. Of special importance for understanding interactivity are human-computer and human-robot interaction, as well as media theory and New Media Art. »Paradoxes of Interactivity« brings together reflections on »interactivity« from different theoretical perspectives, the interplay of science and art, and recent technological developments for artistic applications, especially in the realm of sound