4,952 research outputs found
Visual Spike-based Convolution Processing with a Cellular Automata Architecture
this paper presents a first approach for
implementations which fuse the Address-Event-Representation
(AER) processing with the Cellular Automata using FPGA and
AER-tools. This new strategy applies spike-based convolution
filters inspired by Cellular Automata for AER vision
processing. Spike-based systems are neuro-inspired circuits
implementations traditionally used for sensory systems or
sensor signal processing. AER is a neuromorphic
communication protocol for transferring asynchronous events
between VLSI spike-based chips. These neuro-inspired
implementations allow developing complex, multilayer,
multichip neuromorphic systems and have been used to design
sensor chips, such as retinas and cochlea, processing chips, e.g.
filters, and learning chips. Furthermore, Cellular Automata is a
bio-inspired processing model for problem solving. This
approach divides the processing synchronous cells which
change their states at the same time in order to get the solution.Ministerio de Educación y Ciencia TEC2006-11730-C03-02Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Junta de Andalucía P06-TIC-0141
Self-repair ability of evolved self-assembling systems in cellular automata
Self-repairing systems are those that are able to reconfigure themselves following disruptions to bring them back into a defined normal state. In this paper we explore the self-repair ability of some cellular automata-like systems, which differ from classical cellular automata by the introduction of a local diffusion process inspired by chemical signalling processes in biological development. The update rules in these systems are evolved using genetic programming to self-assemble towards a target pattern. In particular, we demonstrate that once the update rules have been evolved for self-assembly, many of those update rules also provide a self-repair ability without any additional evolutionary process aimed specifically at self-repair
Identification of cellular automata based on incomplete observations with bounded time gaps
In this paper, the problem of identifying the cellular automata (CAs) is considered. We frame and solve this problem in the context of incomplete observations, i.e., prerecorded, incomplete configurations of the system at certain, and unknown time stamps. We consider 1-D, deterministic, two-state CAs only. An identification method based on a genetic algorithm with individuals of variable length is proposed. The experimental results show that the proposed method is highly effective. In addition, connections between the dynamical properties of CAs (Lyapunov exponents and behavioral classes) and the performance of the identification algorithm are established and analyzed
Complex Systems: A Survey
A complex system is a system composed of many interacting parts, often called
agents, which displays collective behavior that does not follow trivially from
the behaviors of the individual parts. Examples include condensed matter
systems, ecosystems, stock markets and economies, biological evolution, and
indeed the whole of human society. Substantial progress has been made in the
quantitative understanding of complex systems, particularly since the 1980s,
using a combination of basic theory, much of it derived from physics, and
computer simulation. The subject is a broad one, drawing on techniques and
ideas from a wide range of areas. Here I give a survey of the main themes and
methods of complex systems science and an annotated bibliography of resources,
ranging from classic papers to recent books and reviews.Comment: 10 page
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