7 research outputs found
Memristive excitable cellular automata
The memristor is a device whose resistance changes depending on the polarity
and magnitude of a voltage applied to the device's terminals. We design a
minimalistic model of a regular network of memristors using
structurally-dynamic cellular automata. Each cell gets info about states of its
closest neighbours via incoming links. A link can be one 'conductive' or
'non-conductive' states. States of every link are updated depending on states
of cells the link connects. Every cell of a memristive automaton takes three
states: resting, excited (analog of positive polarity) and refractory (analog
of negative polarity). A cell updates its state depending on states of its
closest neighbours which are connected to the cell via 'conductive' links. We
study behaviour of memristive automata in response to point-wise and spatially
extended perturbations, structure of localised excitations coupled with
topological defects, interfacial mobile excitations and growth of information
pathways.Comment: Accepted to Int J Bifurcation and Chaos (2011
Randomly Evolving Idiotypic Networks: Structural Properties and Architecture
We consider a minimalistic dynamic model of the idiotypic network of
B-lymphocytes. A network node represents a population of B-lymphocytes of the
same specificity (idiotype), which is encoded by a bitstring. The links of the
network connect nodes with complementary and nearly complementary bitstrings,
allowing for a few mismatches. A node is occupied if a lymphocyte clone of the
corresponding idiotype exists, otherwise it is empty. There is a continuous
influx of new B-lymphocytes of random idiotype from the bone marrow.
B-lymphocytes are stimulated by cross-linking their receptors with
complementary structures. If there are too many complementary structures,
steric hindrance prevents cross-linking. Stimulated cells proliferate and
secrete antibodies of the same idiotype as their receptors, unstimulated
lymphocytes die.
Depending on few parameters, the autonomous system evolves randomly towards
patterns of highly organized architecture, where the nodes can be classified
into groups according to their statistical properties. We observe and describe
analytically the building principles of these patterns, which allow to
calculate number and size of the node groups and the number of links between
them. The architecture of all patterns observed so far in simulations can be
explained this way. A tool for real-time pattern identification is proposed.Comment: 19 pages, 15 figures, 4 table
Randomly Evolving Idiotypic Networks: Modular Mean Field Theory
We develop a modular mean field theory for a minimalistic model of the
idiotypic network. The model comprises the random influx of new idiotypes and a
deterministic selection. It describes the evolution of the idiotypic network
towards complex modular architectures, the building principles of which are
known. The nodes of the network can be classified into groups of nodes, the
modules, which share statistical properties. Each node experiences only the
mean influence of the groups to which it is linked. Given the size of the
groups and linking between them the statistical properties such as mean
occupation, mean life time, and mean number of occupied neighbors are
calculated for a variety of patterns and compared with simulations. For a
pattern which consists of pairs of occupied nodes correlations are taken into
account.Comment: 14 pages, 8 figures, 4 table
Red Queen Coevolution on Fitness Landscapes
Species do not merely evolve, they also coevolve with other organisms.
Coevolution is a major force driving interacting species to continuously evolve
ex- ploring their fitness landscapes. Coevolution involves the coupling of
species fit- ness landscapes, linking species genetic changes with their
inter-specific ecological interactions. Here we first introduce the Red Queen
hypothesis of evolution com- menting on some theoretical aspects and empirical
evidences. As an introduction to the fitness landscape concept, we review key
issues on evolution on simple and rugged fitness landscapes. Then we present
key modeling examples of coevolution on different fitness landscapes at
different scales, from RNA viruses to complex ecosystems and macroevolution.Comment: 40 pages, 12 figures. To appear in "Recent Advances in the Theory and
Application of Fitness Landscapes" (H. Richter and A. Engelbrecht, eds.).
Springer Series in Emergence, Complexity, and Computation, 201
ProtestLab: a computational laboratory for studying street protests
We present an Agent-Based model called ProtestLab for the simulation of street protests, with multiple types of agents (protesters, police and âmediaâ) and scenario features (attraction points, obstacles and entrances/exits). In ProtestLab agents can have multiple âpersonalitiesâ (implemented via agent subtypes), goals and possible states, including violent confrontation. The model includes quantitative measures of emergent crowd patterns, protest intensity, police effectiveness and potential ânews impactâ, which can be used to compare simulation outputs with estimates from videos of real protests for parametrization and validation. ProtestLab was applied to a scenario of policemen defending a government building from protesters (typical of anti-austerity protests in front of the Parliament in Lisbon, Portugal) and reproduced many features observed in real events, such as clustering of âactiveâ and âviolentâ protesters, formation of moving confrontation lines, occasional fights and arrests, âmediaâ agents wiggling around âhot spotsâ and policemen with defensive or offensive behaviour.info:eu-repo/semantics/acceptedVersio