Narrow Results

The adaptive dynamics is known as a new mathematics to treat with a complex phenomena, for example, chaos, quantum algorithm and psychological phenomena. In this paper, we briefly review the notion of the adaptive dynamics, and explain the definition of the generalized Turing machine (GTM) and recognition process represented by the Fock space. Moreover, we show that there exists the quantum channel which is described by the GKSL master equation to achieve the Chaos Amplifier used in [M. Ohya and I. V. Volovich, J. Opt. B5(6) (2003) 639., M. Ohya and I. V. Volovich, Rep. Math. Phys.52(1) (2003) 25.]

The paper starts out with a discussion of the difference between mythology and feasible concepts in robotics. Based on a novel brain model and an appropriate formalism, a distinction is made between auto-reflection and hetero-reflection of the robot and self-reflection of its constructor. Whereas conscious robots are able to auto-reflect their mechanical behavior and hetero-reflect the behavior with regard to the environment, the capability of self-reflection must remain within the constructor of the robot. This limitation of the construction of conscious robots is mainly brain-theoretically and philosophically founded. Finally, it is proposed that in addition to a second nature, human technology may succeed in creating a third nature embodied as a society of robots.

Technology of brain–computer interface (BCI) provides a new way of communication and control without language or physical action. Brain signal tracking and positioning is the basis of BCI research, while brain modeling affects the treatment analysis of (EEG) and functional magnetic resonance imaging (fMRI) directly. This paper proposes human ellipsoid brain modeling method. Then, we use non-parametric spectral estimation method of time–frequency analysis to deal with simulation and real EEG of epilepsy patients, which utilizes both the high spatial and the high time resolution to improve the doctor’s diagnostic efficiency.

Brain-inspired models for conscious robots should refer to the cellular double structure of the brain, consisting of the neuronal system and the glial system, embodying two ontological realms. Therefore, a purely neurobiological approach to machine consciousness is biased by an ontological fault in exclusively referring to the neuronal system. The brain model for self-observing agents outlined in this paper focuses on the glial-neuronal synaptic units (tripartite synapses). Whereas the neuronal component of the synapse embodies objective subjectivity processing sensory information, the glial component (astrocyte) embodies subjective subjectivity generating subjective behavior (intentions, consciousness) in its interactions with the neuronal part of the synapse. The elementary principle of the implementation of self-observing agents is this: a brain is capable of self-observation, if the concept of intention to observe something and the concept of the observed are located in different places. Based on a formalism of qualitative information processing, the architecture of self-observation is described in increasing complexity, building networks. It is suggested that if a robot brain is equipped with a network of modules for self-observation, the robot may generate subjective perspectives of self-observation indicating self-consciousness.

A prerequisite for a synthetic description of the learning and memory (LM), neural bases is a model of the brain. The model we have adopted is based on functional anatomy and consists of three interacting systems. The first, R for representation, is made of the neurons which code sensory information or motor programs with the highest precision. The second, A for activation, comprises the neurons which are most directly involved in arousal and motivation. The third system, S for supervision, controls goal-directed behaviors. The best illustration of its functions is the “voluntary act” in humans: it involves a representation of a goal and of the appropriate strategies, the evaluation of the results and the correction of errors.

The memory system is not an anatomically separate entity but a set of interactions between R, A and S. In these interactions, the three systems have different though complementary functions. R is mainly involved in encoding and storage. S plays a role in encoding and retrieval through the control of attention and cognitive strategies. Structures of A, which modulate R and S activities, are involved in all stages of memory processes. Different types of LM set into play different types of interactions between R, A and S.

Within this general scheme, we considered data from different levels of organization, from the whole brain to the molecule, through intermediates such as small networks (for example, the cortical column). Finally, an attempt is made at defining the perspectives for future research. Among its main objectives are the integration of LM bases in the neurobiology of the whole behavior, the genetic and developmental factors, new therapies for improving memory in aged and demented people, the design of formalisms able to represent large-scale neural networks.