Cyclic cellular automata (CCA) are models of excitable media. Started from random initial conditions, they produce several di#erent kinds of spatial structure, depending on their control parameters. We introduce new tools from information theory that let us calculate the dynamical information content of spatial random processes. This complexity measure allows us to quantitatively determine the rate of self-organization of these cellular automata, and establish the relationship between parameter values and self-organization in CCA. The method is very general and can easily be applied to other cellular automata or even digitized experimental data.

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Statistical complexity is a measure of complexity of discrete-time stationary stochastic processes, which has many applications. We investigate its more abstract properties as a non-linear functional on the space of processes and show its close relation to Knight’s prediction process. We prove lower semicontinuity, concavity, and a formula for the ergodic decomposition of statistical complexity. On the way, we show that the discrete version of the prediction process has a continuous Markov transition. We also prove that, given the past output of a partially deterministic hidden Markov model (HMM), the uncertainty of the internal state is constant over time and knowledge of the internal state gives no additional information on the future output. Using this fact, we show that the causal state distribution is

Resumen: CSSR es un algoritmo de aprendizaje de automatas para representar los patrones de un proceso a partir de datos sequenciales. Este artículo estudia la aplicabilidad del CSSR al reconocimiento de sintagmas nominales. Estudiaremos la habilidad del CSSR para capturar los patrones que hay detrás de esta tarea y en que condiciones el algoritmo los aprende mejor. También presentaremos un método para aplicar los modelos obtenidos para realizar tareas de anotación de sintagmas nominales. Dados todos los resultados, discutiremos la aplicabilidad del CSSR a tareas de PLN. Palabras clave: Tareas seqüenciales de PLN, aprendizage de automatas, detección de sintagmas nominales Abstract: CSSR algorithm learns automata representing the patterns of a process from sequential data. This paper studies the applicability of CSSR to some Noun Phrase detection. The ability of the algorithm to capture the patterns behind this tasks and the conditions under which it performs better are studied. Also, an approach to use the acquired models to annotate new sentences is pointed out and, at the sight of all results, the applicability of CSSR to NLP tasks is discussed.

In this study we reconstruct predator-prey relationships from biomass time series of a simulated system of interacting species. To overcome the shortcomings of a static food webs representation we introduce a new model which accounts for both population and interaction dynamics. It is a derived version of the light-cone model from special relativity theory. To identify the existence of predator–prey relationships in the system we quantify the notion of distance in a food web. We use known measures from information theory, namely mutual information and transfer entropy, and we introduce a new measure based on causal states of point and patch predictors. To evaluate our results we compare the distances measured with a minimum distance measure from the underlying food web, and examine the accuracy of the measures in inferring the existence of the actual predator–prey relationships. First results show that our new measure based on causal states of point and patch predictors together with the transfer entropy

The causal states of computational mechanics define the minimal sufficient (prescient) memory for a given stationary stochastic process. They induce the ε-machine which is a hidden Markov model (HMM) generating the process. The ε-machine is, however, not the minimal generative HMM and minimal internal state entropy of a generative HMM is a tighter upper bound for excess entropy than provided by statistical complexity. We propose a notion of prediction that does not require sufficiency. The corresponding models can be substantially

We advocate the use of an explicit time representation in syntactic pattern recognition because it can result in more succinct models and easier learning problems. We apply this approach to the real-world problem of learning models for the driving behavior of truck drivers. We discretize the values of onboard sensors into simple events. Instead of the common syntactic pattern recognition approach of sampling the signal values at a fixed rate, we model the time constraints using timed models. We learn these models using the RTI+ algorithm from grammatical inference, and show how to use computational mechanics and a form of semi-supervised classification to construct a real-time automaton classifier for driving behavior. Promising results are shown using this new approach. 1

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: Consciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition? Presentation of the hypothesis: This paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation – the availability of a very large number of conscious experiences; and integration – the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Φ value of a complex of elements. Φ

Abstract: We develop a geometric approach to complexity based on the principle that complexity requires interactions at different scales of description. Complex systems are more than the sum of their parts of any size, and not just more than the sum of their elements. Using information geometry, we therefore analyze the decomposition of a system in terms of an interaction hierarchy. In mathematical terms, we present a theory of complexity measures for finite random fields using the geometric framework of hierarchies of exponential families. Within our framework, previously proposed complexity measures find their natural place and gain a new interpretation.