123 research outputs found
Correlated metallic two particle bound states in quasiperiodic chains
Single particle states in a chain with quasiperiodic potential show a
metal-insulator transition upon the change of the potential strength. We
consider two particles with local interaction in the single particle insulating
regime. The two particle states change from being localized to delocalized upon
an increase of the interaction strength to a nonperturbative finite value. At
even larger interaction strength the states become localized again. This
transition of two particle bound states into a correlated metal is due to a
resonant mixing of the noninteracting two particle eigenstates. In the
discovered correlated metal states two particles move coherently together
through the whole chain, therefore contributing to a finite conductivity.Comment: 4 pages, 4 figure
Explosive synchronization in multiplex neuron-glial networks
Explosive synchronization refers to an abrupt (first order) transition to
non-zero phase order parameter in oscillatory networks, underpinned by the
bistability of synchronous and asynchronous states. Growing evidence suggests
that this phenomenon might be no less general then the celebrated Kuramoto
scenario that belongs to the second order universality class. Importantly, the
recent examples demonstrate that explosive synchronization can occur for
certain network topologies and coupling types, like the global higher-order
coupling, without specific requirements on the individial oscillator dynamics
or dynamics-network correlations. Here we demonstrate a rich picture of
explosive synchronization and desynchronization transitions in multiplex
networks, where it is sufficient to have a single random sparsly connected
layer with higher-order coupling terms (and not necessarily in the
synchronization regime on its own), the other layer being a regular lattice
without own phase transitions at all. Moreover, explosive synchronization
emerges even when the random layer has only low-order pairwise coupling,
althoug the hysteresis interval becomes narrow and explosive desynchronization
is no longer observed. The relevance to the normal and pathological dynamics of
neural-glial networks is pointed out.Comment: 8 pages, 6 figure
Multi-input distributed classifiers for synthetic genetic circuits
For practical construction of complex synthetic genetic networks able to
perform elaborate functions it is important to have a pool of relatively simple
"bio-bricks" with different functionality which can be compounded together. To
complement engineering of very different existing synthetic genetic devices
such as switches, oscillators or logical gates, we propose and develop here a
design of synthetic multiple input distributed classifier with learning
ability. Proposed classifier will be able to separate multi-input data, which
are inseparable for single input classifiers. Additionally, the data classes
could potentially occupy the area of any shape in the space of inputs. We study
two approaches to classification, including hard and soft classification and
confirm the schemes of genetic networks by analytical and numerical results
Mammalian Brain As a Network of Networks
Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD
Distributed classifier based on genetically engineered bacterial cell cultures
We describe a conceptual design of a distributed classifier formed by a
population of genetically engineered microbial cells. The central idea is to
create a complex classifier from a population of weak or simple classifiers. We
create a master population of cells with randomized synthetic biosensor
circuits that have a broad range of sensitivities towards chemical signals of
interest that form the input vectors subject to classification. The randomized
sensitivities are achieved by constructing a library of synthetic gene circuits
with randomized control sequences (e.g. ribosome-binding sites) in the front
element. The training procedure consists in re-shaping of the master population
in such a way that it collectively responds to the "positive" patterns of input
signals by producing above-threshold output (e.g. fluorescent signal), and
below-threshold output in case of the "negative" patterns. The population
re-shaping is achieved by presenting sequential examples and pruning the
population using either graded selection/counterselection or by
fluorescence-activated cell sorting (FACS). We demonstrate the feasibility of
experimental implementation of such system computationally using a realistic
model of the synthetic sensing gene circuits.Comment: 31 pages, 9 figure
Quasi-stationary states of game-driven systems: a dynamical approach
Evolutionary game theory is a framework to formalize the evolution of
collectives ("populations") of competing agents that are playing a game and,
after every round, update their strategies to maximize individual payoffs.
There are two complementary approaches to modeling evolution of player
populations. The first addresses essentially finite populations by implementing
the apparatus of Markov chains. The second assumes that the populations are
infinite and operates with a system of mean-field deterministic differential
equations. By using a model of two antagonistic populations, which are playing
a game with stationary or periodically varying payoffs, we demonstrate that it
exhibits metastable dynamics that is reducible neither to an immediate
transition to a fixation (extinction of all but one strategy in a finite-size
population) nor to the mean-field picture. In the case of stationary payoffs,
this dynamics can be captured with a system of stochastic differential
equations and interpreted as a stochastic Hopf bifurcation. In the case of
varying payoffs, the metastable dynamics is much more complex than the dynamics
of the means
- …