1,148 research outputs found
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
COMPUTER SIMULATION AND COMPUTABILITY OF BIOLOGICAL SYSTEMS
The ability to simulate a biological organism by employing a computer is related to the
ability of the computer to calculate the behavior of such a dynamical system, or the "computability" of the system.* However, the two questions of computability and simulation are not equivalent. Since the question of computability can be given a precise answer in terms of recursive functions, automata theory and dynamical systems, it will be appropriate to consider it first. The more elusive question of adequate simulation of biological systems by a computer will be then addressed and a possible connection between the two answers given will be considered. A conjecture is formulated that suggests the possibility of employing an algebraic-topological, "quantum" computer (Baianu, 1971b)
for analogous and symbolic simulations of biological systems that may include chaotic processes that are not, in genral, either recursively or digitally computable. Depending on the biological network being modelled, such as the Human Genome/Cell Interactome or a trillion-cell Cognitive Neural Network system, the appropriate logical structure for such simulations might be either the Quantum MV-Logic (QMV) discussed in recent publications (Chiara, 2004, and references cited therein)or Lukasiewicz Logic Algebras that were shown to be isomorphic to MV-logic algebras (Georgescu et al, 2001)
Boolean Dynamics with Random Couplings
This paper reviews a class of generic dissipative dynamical systems called
N-K models. In these models, the dynamics of N elements, defined as Boolean
variables, develop step by step, clocked by a discrete time variable. Each of
the N Boolean elements at a given time is given a value which depends upon K
elements in the previous time step.
We review the work of many authors on the behavior of the models, looking
particularly at the structure and lengths of their cycles, the sizes of their
basins of attraction, and the flow of information through the systems. In the
limit of infinite N, there is a phase transition between a chaotic and an
ordered phase, with a critical phase in between.
We argue that the behavior of this system depends significantly on the
topology of the network connections. If the elements are placed upon a lattice
with dimension d, the system shows correlations related to the standard
percolation or directed percolation phase transition on such a lattice. On the
other hand, a very different behavior is seen in the Kauffman net in which all
spins are equally likely to be coupled to a given spin. In this situation,
coupling loops are mostly suppressed, and the behavior of the system is much
more like that of a mean field theory.
We also describe possible applications of the models to, for example, genetic
networks, cell differentiation, evolution, democracy in social systems and
neural networks.Comment: 69 pages, 16 figures, Submitted to Springer Applied Mathematical
Sciences Serie
The Kinetic Basis of Self-Organized Pattern Formation
In his seminal paper on morphogenesis (1952), Alan Turing demonstrated that
different spatio-temporal patterns can arise due to instability of the
homogeneous state in reaction-diffusion systems, but at least two species are
necessary to produce even the simplest stationary patterns. This paper is aimed
to propose a novel model of the analog (continuous state) kinetic automaton and
to show that stationary and dynamic patterns can arise in one-component
networks of kinetic automata. Possible applicability of kinetic networks to
modeling of real-world phenomena is also discussed.Comment: 8 pages, submitted to the 14th International Conference on the
Synthesis and Simulation of Living Systems (Alife 14) on 23.03.2014, accepted
09.05.201
Phase transition in a class of non-linear random networks
We discuss the complex dynamics of a non-linear random networks model, as a
function of the connectivity k between the elements of the network. We show
that this class of networks exhibit an order-chaos phase transition for a
critical connectivity k = 2. Also, we show that both, pairwise correlation and
complexity measures are maximized in dynamically critical networks. These
results are in good agreement with the previously reported studies on random
Boolean networks and random threshold networks, and show once again that
critical networks provide an optimal coordination of diverse behavior.Comment: 9 pages, 3 figures, revised versio
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity
Using a large database (~ 215 000 records) of relevant articles, we
empirically study the "complex systems" field and its claims to find universal
principles applying to systems in general. The study of references shared by
the papers allows us to obtain a global point of view on the structure of this
highly interdisciplinary field. We show that its overall coherence does not
arise from a universal theory but instead from computational techniques and
fruitful adaptations of the idea of self-organization to specific systems. We
also find that communication between different disciplines goes through
specific "trading zones", ie sub-communities that create an interface around
specific tools (a DNA microchip) or concepts (a network).Comment: Journal of the American Society for Information Science and
Technology (2012) 10.1002/asi.2264
Towards a Boolean network-based Computational Model for Cell Differentiation and its applications to Robotics
Living organisms are the ultimate product of a series of complex processes that take place within—and among—biological cells. Most of these processes, such as cell differentiation, are currently poorly understood. Cell differentiation is the process by which cells progressively specialise. Being a fundamental process within cells, its dysregulations have dramatic implications in biological organisms ranging from developmental issues to cancer formation.
The thesis objective is to contribute to the progress in the understanding of cell differentiation and explore the applications of its properties for designing artificial systems. The proposed approach, which relies on Boolean networks based modelling and on the theory of dynamical systems, aims at investigating the general mechanisms underlying cell differentiation. The results obtained contribute to taking a further step towards the formulation of a general theoretical framework—so far missing—for cellular differentiation.
We conducted an in-depth analysis of the impact of self-loops in random Boolean networks ensembles. We proposed a new model of differentiation driven by a simplified bio-inspired methylation mechanism in Boolean models of genetic regulatory networks. On the artificial side, by introducing the conceptual metaphor of the “attractor landscape” and related proofs of concept that support its potential, we paved the way for a new research direction in robotics called behavioural differentiation robotics: a branch of robotics dealing with the designing of robots capable of expressing different behaviours in a way similar to that of biological cells that undergo differentiation.
The implications of the results achieved may have beneficial effects on medical research. Indeed, the proposed approach can foster new questions, experiments and in turn, models that hopefully in the next future will take us to cure differentiation-related diseases such as cancer. Our work may also contribute to address questions concerning the evolution of complex behaviours and to help design robust and adaptive robots
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