4,809 research outputs found
Emergence of Leadership in Communication
We study a neuro-inspired model that mimics a discussion (or information
dissemination) process in a network of agents. During their interaction, agents
redistribute activity and network weights, resulting in emergence of leader(s).
The model is able to reproduce the basic scenarios of leadership known in
nature and society: laissez-faire (irregular activity, weak leadership, sizable
inter-follower interaction, autonomous sub-leaders); participative or
democratic (strong leadership, but with feedback from followers); and
autocratic (no feedback, one-way influence). Several pertinent aspects of these
scenarios are found as well---e.g., hidden leadership (a hidden clique of
agents driving the official autocratic leader), and successive leadership (two
leaders influence followers by turns). We study how these scenarios emerge from
inter-agent dynamics and how they depend on behavior rules of agents---in
particular, on their inertia against state changes.Comment: 17 pages, 11 figure
Field-control, phase-transitions, and life's emergence
Instances of critical-like characteristics in living systems at each
organizational level as well as the spontaneous emergence of computation
(Langton), indicate the relevance of self-organized criticality (SOC). But
extrapolating complex bio-systems to life's origins, brings up a paradox: how
could simple organics--lacking the 'soft matter' response properties of today's
bio-molecules--have dissipated energy from primordial reactions in a controlled
manner for their 'ordering'? Nevertheless, a causal link of life's macroscopic
irreversible dynamics to the microscopic reversible laws of statistical
mechanics is indicated via the 'functional-takeover' of a soft magnetic
scaffold by organics (c.f. Cairns-Smith's 'crystal-scaffold'). A
field-controlled structure offers a mechanism for bootstrapping--bottom-up
assembly with top-down control: its super-paramagnetic components obey
reversible dynamics, but its dissipation of H-field energy for aggregation
breaks time-reversal symmetry. The responsive adjustments of the controlled
(host) mineral system to environmental changes would bring about mutual
coupling between random organic sets supported by it; here the generation of
long-range correlations within organic (guest) networks could include SOC-like
mechanisms. And, such cooperative adjustments enable the selection of the
functional configuration by altering the inorganic network's capacity to assist
a spontaneous process. A non-equilibrium dynamics could now drive the
kinetically-oriented system towards a series of phase-transitions with
appropriate organic replacements 'taking-over' its functions.Comment: 54 pages, pdf fil
A stochastic model of catalytic reaction networks in protocells
Protocells are supposed to have played a key role in the self-organizing
processes leading to the emergence of life. Existing models either (i) describe
protocell architecture and dynamics, given the existence of sets of
collectively self-replicating molecules for granted, or (ii) describe the
emergence of the aforementioned sets from an ensemble of random molecules in a
simple experimental setting (e.g. a closed system or a steady-state flow
reactor) that does not properly describe a protocell. In this paper we present
a model that goes beyond these limitations by describing the dynamics of sets
of replicating molecules within a lipid vesicle. We adopt the simplest possible
protocell architecture, by considering a semi-permeable membrane that selects
the molecular types that are allowed to enter or exit the protocell and by
assuming that the reactions take place in the aqueous phase in the internal
compartment. As a first approximation, we ignore the protocell growth and
division dynamics. The behavior of catalytic reaction networks is then
simulated by means of a stochastic model that accounts for the creation and the
extinction of species and reactions. While this is not yet an exhaustive
protocell model, it already provides clues regarding some processes that are
relevant for understanding the conditions that can enable a population of
protocells to undergo evolution and selection.Comment: 20 pages, 5 figure
Colorectal Cancer Through Simulation and Experiment
Colorectal cancer has continued to generate a huge amount of research interest over several decades, forming a canonical example of tumourigenesis since its use in Fearon and Vogelstein’s linear model of genetic mutation. Over time, the field has witnessed a transition from solely experimental work to the inclusion of mathematical biology and computer-based modelling. The fusion of these disciplines has the potential to provide valuable insights into oncologic processes, but also presents the challenge of uniting many diverse perspectives. Furthermore, the cancer cell phenotype defined by the ‘Hallmarks of Cancer’ has been extended in recent times and provides an excellent basis for future research. We present a timely summary of the literature relating to colorectal cancer, addressing the traditional experimental findings, summarising the key mathematical and computational approaches, and emphasising the role of the Hallmarks in current and future developments. We conclude with a discussion of interdisciplinary work, outlining areas of experimental interest which would benefit from the insight that mathematical and computational modelling can provide
Chaotic exploration and learning of locomotion behaviours
We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage
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