Bio-Inspired Synchronization of Pulse-Coupled Oscillators and its Application to Wireless Sensor Networks

Precise synchronization among networked agents is responsible for phenomena as diverse as coral spawning and consistency in stock market transactions. The importance of synchronization in biological and engineering systems has triggered an avalanche of studies analyzing the emergence of a synchronized behavior within a network of, possibly heterogeneous, agents. In particular, synchronization of networks of coupled oscillators has received great attention since limit cycle oscillators are a natural abstraction for systems where periodicity is a distinctive property. Examples of such systems include circadian rhythms and alternate-current power generators. This work deals with synchronization of pulse-coupled limit cycle oscillators (PCOs). A reverse engineering approach is taken with the objective of obtaining an abstraction for PCO networks able to capture the key properties observed in the classical biological PCO model, to finally implement it in an en gineering system. To this end, we first reformulate the PCO model as a hybrid system, able to integrate in a smooth manner the continuous-time dynamics of the individual oscillators and the impulsive effect of the coupling. Using our new model, we analyze the existence and stability of synchronization in a variety of PCO network topologies, starting from the simplest all-to-all network where global synchronization is proven to exist, to end giving synchronization conditions in the general strongly connected network case. Inspired by the strong synchronization properties of PCO networks we design a PCO-inspired time synchronization protocol for wireless sensor networks that enjoys all the advantages of our optimized PCO setup. A pilot implementation is presented going from a simulation stage to a hardware implementation in Gumstix development boards and industrial acoustic sensors. To test the potential of the protocol in a real application, we implement the PCO-based time synchronization protocol in a distributed acoustic event detection system, where a sensor network combines local measurements over an infrastructure-free wireless network to find the source of an acoustic event. An evaluation by simulation is given to illustrate the advantages of using the pulse-coupled synchronization strategy.The contributions of this thesis range from the theoretical synchronization conditions for a variety of PCO networks to the design and implementation of a synchronization strategy for wireless sensor networks that seems to be the natural choice when using an infrastructure-free wireless network due to its simple formulation and natural scalability

Kick synchronization versus diffusive synchronization

The paper provides an introductory discussion about two fundamental models of oscillator synchronization: the (continuous-time) diffusive model, that dominates the mathematical literature on synchronization, and the (hybrid) kick model, that accounts for most popular examples of synchronization, but for which only few theoretical results exist. The paper stresses fundamental differences between the two models, such as the different contraction measures underlying the analysis, as well as important analogies that can be drawn in the limit of weak coupling.Peer reviewe

Neural dynamics of social behavior : An evolutionary and mechanistic perspective on communication, cooperation, and competition among situated agents

Social behavior can be found on almost every level of life, ranging from microorganisms to human societies. However, explaining the evolutionary emergence of cooperation, communication, or competition still challenges modern biology. The most common approaches to this problem are based on game-theoretic models. The problem is that these models often assume fixed and limited rules and actions that individual agents can choose from, which excludes the dynamical nature of the mechanisms that underlie the behavior of living systems. So far, there exists a lack of convincing modeling approaches to investigate the emergence of social behavior from a mechanistic and evolutionary perspective. Instead of studying animals, the methodology employed in this thesis combines several aspects from alternative approaches to study behavior in a rather novel way. Robotic models are considered as individual agents which are controlled by recurrent neural networks representing non-linear dynamical system. The topology and parameters of these networks are evolved following an open-ended evolution approach, that is, individuals are not evaluated on high-level goals or optimized for specific functions. Instead, agents compete for limited resources to enhance their chance of survival. Further, there is no restriction with respect to how individuals interact with their environment or with each other. As its main objective, this thesis aims at a complementary approach for studying not only the evolution, but also the mechanisms of basic forms of communication. For this purpose it can be shown that a robot does not necessarily have to be as complex as a human, not even as complex as a bacterium. The strength of this approach is that it deals with rather simple, yet complete and situated systems, facing similar real world problems as animals do, such as sensory noise or dynamically changing environments. The experimental part of this thesis is substantiated in a five-part examination. First, self-organized aggregation patterns are discussed. Second, the advantages of evolving decentralized control with respect to behavioral robustness and flexibility is demonstrated. Third, it is shown that only minimalistic local acoustic communication is required to coordinate the behavior of large groups. This is followed by investigations of the evolutionary emergence of communication. Finally, it is shown how already evolved communicative behavior changes during further evolution when a population is confronted with competition about limited environmental resources. All presented experiments entail thorough analysis of the dynamical mechanisms that underlie evolved communication systems, which has not been done so far in the context of cooperative behavior. This framework leads to a better understanding of the relation between intrinsic neurodynamics and observable agent-environment interactions. The results discussed here provide a new perspective on the evolution of cooperation because they deal with aspects largely neglected in traditional approaches, aspects such as embodiment, situatedness, and the dynamical nature of the mechanisms that underlie behavior. For the first time, it can be demonstrated how noise influences specific signaling strategies and that versatile dynamics of very small-scale neural networks embedded in sensory-motor feedback loops give rise to sophisticated forms of communication such as signal coordination, cooperative intraspecific communication, and, most intriguingly, aggressive interspecific signaling. Further, the results demonstrate the development of counteractive niche construction based on a modification of communication strategies which generates an evolutionary feedback resulting in an active reduction of selection pressure, which has not been shown so far. Thus, the novel findings presented here strongly support the complementary nature of robotic experiments to study the evolution and mechanisms of communication and cooperation.</p

Nonlinearity and stochasticity in biochemical networks

Recent advances in biology have revolutionized our understanding of living systems. For the first time, it is possible to study the behavior of individual cells. This has led to the discovery of many amazing phenomena. For example, cells have developed intelligent mechanisms for foraging, communicating, and responding to environmental changes. These diverse functions in cells are controlled through biochemical networks consisting of many different proteins and signaling molecules. These molecules interact and affect gene expression, which in turn affects protein production. This results in a complex mesh of feedback and feedforward interactions. These complex networks are generally highly nonlinear and stochastic, making them difficult to study quantitatively. Recent studies have shown that biochemical networks are also highly modular, meaning that different parts of the network do not interact strongly with each other. These modules tend to be conserved across species and serve specific biological functions. However, detect- ing modules and identifying their function tends to be a very difficult task. To overcome some of these complexities, I present an alternative modeling approach that builds quantitative models using coarse-grained biological processes. These coarse-grained models are often stochastic (probabilistic) and highly non-linear. In this thesis, I focus on modeling biochemical networks in two distinct biological systems: Dictyostelium discoideum and microRNAs. Chapters 2 and 3 focus on cellular communication in the social amoebae Dictyostelium discoideum. Using universality, I propose a stochastic nonlinear model that describes the behavior of individual cells and cellular populations. In chapter 4 I study the interaction between messenger RNAs and noncoding RNAs, using Langevin equations

Embodied Cognitive Science of Music. Modeling Experience and Behavior in Musical Contexts

Recently, the role of corporeal interaction has gained wide recognition within cognitive musicology. This thesis reviews evidence from different directions in music research supporting the importance of body-based processes for the understanding of music-related experience and behaviour. Stressing the synthetic focus of cognitive science, cognitive science of music is discussed as a modeling approach that takes these processes into account and may theoretically be embedded within the theory of dynamic systems. In particular, arguments are presented for the use of robotic devices as tools for the investigation of processes underlying human music-related capabilities (musical robotics)

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201