Informational Neuro-Connections of the Brain with the Body Supporting the Informational Model of Consciousness

Introduction: The objective of this investigation is to analyse the informational circuits of the brain connections with the body from neurologic and neuroscience point of view, on the basis of the concepts of information promoted by the Informational Model of Consciousness. Analysis: Distinguishing between the virtual and matter-related information promoted by the Informational Model of Consciousness, the main specific features of consciousness are analyzed from the informational perspective, showing that the informational architecture of consciousness consists in seven groups of specific activities, defined as cognitive centres, each of them with specific distinct tasks, but correlated each other: centre of acquisition and storing of information (memory), centre of decision and command (decision), centre of the emotional states (emotions), centre of the body maintenance (power and health), centre of the genetic elaboration/transmission (reproduction) and the info-genetic generator, inherited from the parents (predispositions, talents and skills). A special centre, dedicated to the connectivity with some extra-power properties of the mind is also introduced, assuring an intimate supra-sensitive detection of the world to explain the associated phenomena of the near-death experiences. Result: The activity of all these centres should be supported neurologically by the brain neuro-connectivity to the body and to external and internal info-signals. On the basis of the informational analysis, the neuro-connections of the brain regions associated to the main characteristics of the cognitive centres are highlighted, showing the anatomic and neuro-functional relation between the distinct components of the brain and the specific operating body regions. These connections describe in terms of information the brain-body neuro-activities as informational specific circuits, composed by the info-operational subsystems managed by the brain, and sensors, transducer and execution elements. Conclusion: The components and connections mind-body stipulated by the Informational Model of Consciousness are supported by the neurologic/neuroscience evidenc

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201

Reinforcement Learning for UAV Attitude Control

Autopilot systems are typically composed of an "inner loop" providing stability and control, while an "outer loop" is responsible for mission-level objectives, e.g. way-point navigation. Autopilot systems for UAVs are predominately implemented using Proportional, Integral Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However more sophisticated control is required to operate in unpredictable, and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL) which has had success in other applications such as robotics. However previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with the state-of-the-art RL algorithms, Deep Deterministic Gradient Policy (DDGP), Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). To investigate these unknowns we first developed an open-source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then use our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.Comment: 13 pages, 9 figure

Information Based Hierarchical Brain Organization/Evolution from the Perspective of the Informational Model of Consciousness

Introduction: This article discusses the brain hierarchical organization/evolution as a consequence of the information-induced brain development, from the perspective of the Informational Model of Consciousness. Analysis: In the frame of the Informational Model of Consciousness, a detailed info-neural analysis ispresented, concerning the specific properties/functions of the informational system of the human body composed by the Center of Acquisition and Storing of Information, Center of Decision and Command, Info-Emotional Center, Maintenance Informational System, Genetic Transmission System, Info Genetic Generator and Info- Connection center, in relation with the neuro-connected brain areas, with a special attention to the Info-Connection and its specific properties. Besides a meticulous analysis of the info-connections/neuro-functions of these centers, a special attention was paid to limbic/cingulate cortex activities. Defined as a trust/confidence center, additional features are highlighted in correlation with the activity of the anterior cingulate cortex, consisting in the intervention/moderation of amygdala emotional signals, conflicting opposite YES/NO data and error elimination in the favor of the organism adaptation/survival, the intervention in the certainty/uncertainty balance to select a suitable pro-life information (antientropic effect), in moderation of pain and in the stimulation of the empathic inter-human relations/communication. Representing the correspondence between the informational subsystems and the brain area map, itis shown that the up/down integration of information by epigenetic mechanisms and the down/ up evolution are correlated. Results: The analysis of the functions of the anterior cingulate opens new gates of investigations concerning the involved intimate mechanisms at the level of cell microstructure, specifically on the compatibility with quantum assisted processes admitted by the Informational Model of Consciousness and the quantum-based models The discussion on the information integration/codification by epigenetic mechanisms shows that this process starts from the superior levels of brain conscious info-processing areas and progressively advances to the automatic/autonomic inferior levels ofthe informational system, under insistent/repetitive cues/stress conditions, pointing out an hierarchical functional/anatomical structure of the brain organization. Additional arguments are discussed, indicating thatthe down/up progressive scale representation is a suggestive illustration of the brain evolution, induced/assisted/determined by information, accelerated at humans by the antientropic functions of the Info-Connection center. Conclusions: The hierarchical organization of the brain is a consequence of the integration process of information, defining its development accordingly to the adaptation requirements for survival during successive evolution stages of the organism, information playing a determinant/key role

Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.<br /

Evaluation of the activity of the immune system and age-related tissue markers in Turquoise killifish \ud (Nothobranchius furzeri, Jubb 1971) \ud and their role in cell ageing\ud

Currently the Turquoise Killifish is considered the best animal model suitable for aging research. \ud This annual fish, from south east Africa, shows an exceptionally adaptive behaviour to dry periods: indeed, due to this extreme environmental characteristics, the life cycle of Nothobranchius furzeri is very fast, with an average lifespan of just about 8-9 weeks, making this species (more similar to highly developed vertebrates than nematodes or fruit flies) highly practical for aging studies. \ud The present study has evaluated the activity of the immune system as well as the expression of AGE-RAGE system, cell-damage related proteins (Bcl2, p53), mitosis activity marker (PCNA), and pro-apoptosis activity by T.U.N.E.L. method on the liver of four lifespan-specific strains of Turquoise Killifish (Nothobranchius furzeri, Jubb 1971), correlating the results with aging processes and tumor incidence. Some groups underwent caloric restriction in order to module their expected lifespan.\ud The results demonstrated an increase of age-related lesions along with the age in all the strains tested, due to a decrease of cellular-turn-over. This aspect was also influenced by the strain of the fish: longest lifespan strains showed later the similar lesions than short lifespan strains. Moreover caloric restriction groups showed lower incidence and severity of hepatic degeneration than control groups. Furthermore, there was a linear correspondence between the age of the model and its expected lifespan with the incidence and severity of neoplasm. The same relationship could be found in the expression of cell-damage related proteins (p53, Bcl2), age-related markers (AGE-RAGE system) and pro-apoptosis activity, as well as in the development of neoplasms. These results demonstrated the high feasibility of this fish as an excellent model to study the effects of aging processes and cancer genesis.\u

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 187

This supplement to Aerospace Medicine and Biology lists 247 reports, articles and other documents announced during November 1978 in Scientific and Technical Aerospace Reports (STAR) or in International Aerospace Abstracts (IAA). In its subject coverage, Aerospace Medicine and Biology concentrates on the biological, physiological, psychological, and environmental effects to which man is subjected during and following simulated or actual flight in the earth's atmosphere or in interplanetary space. References describing similar effects of biological organisms of lower order are also included. Emphasis is placed on applied research, but reference to fundamental studies and theoretical principles related to experimental development also qualify for inclusion. Each entry in the bibliography consists of a bibliographic citation accompanied in most cases by an abstract

Optimal Control of Unknown Nonlinear System From Inputoutput Data

Optimal control designers usually require a plant model to design a controller. The problem is the controller\u27s performance heavily depends on the accuracy of the plant model. However, in many situations, it is very time-consuming to implement the system identification procedure and an accurate structure of a plant model is very difficult to obtain. On the other hand, neuro-fuzzy models with product inference engine, singleton fuzzifier, center average defuzzifier, and Gaussian membership functions can be easily trained by many well-established learning algorithms based on given input-output data pairs. Therefore, this kind of model is used in the current optimal controller design. Two approaches of designing optimal controllers of unknown nonlinear systems based on neuro-fuzzy models are presented in the thesis. The first approach first utilizes neuro-fuzzy models to approximate the unknown nonlinear systems, and then the feasible-direction algorithm is used to achieve the numerical solution of the Euler-Lagrange equations of the formulated optimal control problem. This algorithm uses the steepest descent to find the search direction and then apply a one-dimensional search routine to find the best step length. Finally several nonlinear optimal control problems are simulated and the results show that the performance of the proposed approach is quite similar to that of optimal control to the system represented by an explicit mathematical model. However, due to the limitation of the feasible-direction algorithm, this method cannot be applied to highly nonlinear and dimensional plants. Therefore, another approach that can overcome these drawbacks is proposed. This method utilizes Takagi-Sugeno (TS) fuzzy models to design the optimal controller. TS fuzzy models are first derived from the direct linearization of the neuro-fuzzy models, which is close to the local linearization of the nonlinear dynamic systems. The operating points are chosen so that the TS fuzzy model is a good approximation of the neuro-fuzzy model. Based on the TS fuzzy model, the optimal control is implemented for a nonlinear two-link flexible robot and a rigid asymmetric spacecraft, thus providing the possibility of implementing the well-established optimal control method on unknown nonlinear dynamic systems