The Dynamic Role of Breathing and Cellular Membrane Potentials in the Experience of Consciousness

Understanding the mechanics of consciousness remains one of the most important challenges in modern cognitive science. One key step toward understanding consciousness is to associate unconscious physiological processes with subjective experiences of sensory, motor, and emotional contents. This article explores the role of various cellular membrane potential differences and how they give rise to the dynamic infrastructure of conscious experience. This article explains that consciousness is a body-wide, biological process not limited to individual organs because the mind and body are unified as one entity; therefore, no single location of consciousness can be pinpointed. Consciousness exists throughout the entire body, and unified consciousness is experienced and maintained through dynamic repolarization during inhalation and expiration. Extant knowledge is reviewed to provide insight into how differences in cellular membrane potential play a vital role in the triggering of neural and non-neural oscillations. The role of dynamic cellular membrane potentials in the activity of the central nervous system, peripheral nervous system, cardiorespiratory system, and various other tissues (such as muscles and sensory organs) in the physiology of consciousness is also explored. Inspiration and expiration are accompanied by oscillating membrane potentials throughout all cells and play a vital role in subconscious human perception of feelings and states of mind. In addition, the role of the brainstem, hypothalamus, and complete nervous system (central, peripheral, and autonomic)within the mind-body space combine to allow consciousness to emerge and to come alive. This concept departs from the notion that the brain is the only organ that gives rise to consciousness

Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network