Stabilization through self-coupling in networks of small-world and scale-free topology

Antifragile control systems in neuronal processing: a sensorimotor perspective

The stability-robustness-resilience-adaptiveness continuum in neuronal processing follows a hierarchical structure that explains interactions and information processing among the different time scales. Interestingly, using "canonical" neuronal computational circuits, such as Homeostatic Activity Regulation, Winner-Take-All, and Hebbian Temporal Correlation Learning, one can extend the behavior spectrum towards antifragility. Cast already in both probability theory and dynamical systems, antifragility can explain and define the interesting interplay among neural circuits, found, for instance, in sensorimotor control in the face of uncertainty and volatility. This perspective proposes a new framework to analyze and describe closed-loop neuronal processing using principles of antifragility, targeting sensorimotor control. Our objective is two-fold. First, we introduce antifragile control as a conceptual framework to quantify closed-loop neuronal network behaviors that gain from uncertainty and volatility. Second, we introduce neuronal network design principles, opening the path to neuromorphic implementations and transfer to technical systems.

On the neural networks of self and other bias and their role in emergent social interactions

Extensive research has documented the brain networks that play an integral role in bias, or the alteration and filtration of information processing in a manner that fundamentally favors an individual. The roots of bias, whether self- or other-oriented, are a complex constellation of neural and psychological processes that start at the most fundamental levels of sensory processing. From the millisecond information is received in the brain it is filtered at various levels and through various brain networks in relation to extant intrinsic activity to provide individuals with a perception of reality that complements and satisfies the conscious perceptions they have for themselves and the cultures in which they were reared. The products of these interactions, in turn, are dynamically altered by the introduction of others, be they friends or strangers who are similar or different in socially meaningful ways. While much is known about the various ways that basic biases alter specific aspects of neural function to support various forms of bias, the breadth and scope of the phenomenon remains entirely unclear. The purpose of this review is to examine the brain networks that shape (i.e., bias) the self-concept and how interactions with similar (ingroup) compared to dissimilar (outgroup) others alter these network (and subsequent interpersonal) interactions in fundamental ways. Throughout, focus is placed on an emerging understanding of the brain as a complex system, which suggests that many of these network interactions likely occur on a non-linear scale that blurs the lines between network hierarchies.

Sampling unknown large networks restricted by low sampling rates

Graph sampling plays an important role in data mining for large networks. Specifically, larger networks often correspond to lower sampling rates. Under the situation, traditional traversal-based samplings for large networks usually have an excessive preference for densely-connected network core nodes. Aim at this issue, this paper proposes a sampling method for unknown networks at low sampling rates, called SLSR, which first adopts a random node sampling to evaluate a degree threshold, utilized to distinguish the core from periphery, and the average degree in unknown networks, and then runs a double-layer sampling strategy on the core and periphery. SLSR is simple that results in a high time efficiency, but experiments verify that the proposed method can accurately preserve many critical structures of unknown large scale-free networks with low sampling rates and low variances.

Homeostasis and information processing: The key frames for the thermodynamics of biological systems

Life is a natural phenomenon ineluctably subject to the laws and principles of physics. In this framework, thermodynamics has a crucial role, since living beings are structured on a molecular and cellular basis that can only be maintained with extensive energy consumption. This imposes that living beings are necessarily open systems. But the survival of each type of organism depends on the relative stability of certain essential variables, even in the presence of the disturbances to which they are subjected. The stability of these variables is relative in the sense that they have a narrow range of variation. This stability of the essential variables is a consequence of refined control mechanisms developed in the course of evolution, that lead to the condition called homeostasis. This homeostasis requires that control mechanisms process the various types of information related to the internal structure of the organism and its environment. Consequently, a biological system, through information processing aimed at guiding the mechanisms that maintain its homeostasis, manages the conditions imposed by the principles of thermodynamics, obtaining the most efficient use of energy possible and keeping entropic degradation controlled. In this article, we discuss the close links between thermodynamics, homeostasis and the information processing necessary to maintain homeostasis.