213 research outputs found
Loss of coherence in dynamical networks: spatial chaos and chimera states
We discuss the breakdown of spatial coherence in networks of coupled
oscillators with nonlocal interaction. By systematically analyzing the
dependence of the spatio-temporal dynamics on the range and strength of
coupling, we uncover a dynamical bifurcation scenario for the
coherence-incoherence transition which starts with the appearance of narrow
layers of incoherence occupying eventually the whole space. Our findings for
coupled chaotic and periodic maps as well as for time-continuous R\"ossler
systems reveal that intermediate, partially coherent states represent
characteristic spatio-temporal patterns at the transition from coherence to
incoherence.Comment: 4 pages, 4 figure
ΠΠ΅ΠΊΠΎΡΠΎΡΡΠ΅ Π²ΠΎΠΏΡΠΎΡΡ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΡΠ»ΡΡΠ°ΠΉΠ½ΡΡ Π½Π΅ΠΊΠΎΡΡΠ΅Π»ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ ΠΏΠΎΠΌΠ΅Ρ ΠΈ ΡΠΈΠ³Π½Π°Π»Π° Π² ΡΠΈΡΡΠ΅ΠΌΠ΅ ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ
ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΠΎ Π΄Π»Ρ ΠΎΡΡΠ½ΠΊΠΈ Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ ΡΠΈΠ³Π½Π°Π»Ρ Π· ΠΏΠ΅ΡΠ΅ΡΠΊΠΎΠ΄Π°ΠΌΠΈ ΡΠΈΡΡΠ΅ΠΌΡ ΠΌΠΎΠΆΠ½Π° ΡΡΠ²ΠΈΡΠΈ Π² Π²ΠΈΠ³Π»ΡΠ΄Ρ Π΅ΠΊΠ²ΡΠ²Π°Π»Π΅Π½ΡΠ½ΠΎΡ ΡΡ
Π΅ΠΌΠΈ Π· Π΄ΠΆΠ΅ΡΠ΅Π»ΠΎΠΌ ΡΠΈΠ³Π½Π°Π»Ρ Ρ ΠΊΠ°ΡΠΊΠ°Π΄Π½ΠΎ Π²ΠΊΠ»ΡΡΠ΅Π½ΠΈΠΌΠΈ ΠΊΠΎΠ»Π°ΠΌΠΈ Π½Π°Π²Π°Π½ΡΠ°ΠΆΠ΅Π½Π½Ρ, ΠΊΠΎΠ½ΡΡΠ³ΡΡΠ°ΡΡΡ ΡΠΊΠΎΡ Π·Π°Π»Π΅ΠΆΠΈΡΡ Π²ΡΠ΄ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠΈΠ³Π½Π°Π»Ρ Ρ ΠΏΠ΅ΡΠ΅ΡΠΊΠΎΠ΄ ΡΠ° ΡΡ
Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ: Π°Π΄ΠΈΡΠΈΠ²Π½ΠΎΡ, ΠΌΡΠ»ΡΡΠΈΠΏΠ»ΡΠΊΠ°ΡΠΈΠ²Π½ΠΎΡ Π°Π±ΠΎ Π·ΠΌΡΡΠ°Π½ΠΎΡ. ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΡ ΠΌΠΎΠΆΠ½Π° Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°ΡΠΈ Π΄Π»Ρ Π°Π½Π°Π»ΡΠ·Ρ Π²ΠΏΠ»ΠΈΠ²Ρ Π²ΠΈΠΏΠ°Π΄ΠΊΠΎΠ²ΠΎΡ ΠΏΠΎΡ
ΠΈΠ±ΠΊΠΈ Π²ΠΈΠΌΡΡΡΠ²Π°Π½Π½Ρ.It is shown that for estimation of the
interaction of the signal with hindrance system possible to present in the manner of equivalent scheme with the source of the signal and cascade included chain of the load, which deskside depends on features of the signal and hindrances, as well as their interactions: additive, multiplicative or mixed. The methods possible to use for analysis of the influence to casual inaccuracy of the measurement are broughted.ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ
ΡΠΈΠ³Π½Π°Π»Π° Ρ ΠΏΠΎΠΌΠ΅Ρ
ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΌΠΎΠΆΠ½ΠΎ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΠΈΡΡ Π² Π²ΠΈΠ΄Π΅ ΡΠΊΠ²ΠΈΠ²Π°Π»Π΅Π½ΡΠ½ΠΎΠΉ ΡΡ
Π΅ΠΌΡ Ρ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ ΡΠΈΠ³Π½Π°Π»Π° ΠΈ ΠΊΠ°ΡΠΊΠ°Π΄Π½ΠΎ Π²ΠΊΠ»ΡΡΠ΅Π½Π½ΡΠΌΠΈ ΡΠ΅ΠΏΡΠΌΠΈ Π½Π°Π³ΡΡΠ·ΠΊΠΈ, ΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΡ ΠΊΠΎΡΠΎΡΠΎΠΉ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠΈΠ³Π½Π°Π»Π° ΠΈ ΠΏΠΎΠΌΠ΅Ρ
, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΡ
Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ: Π°Π΄Π΄ΠΈΡΠΈΠ²Π½ΠΎΠ³ΠΎ, ΠΌΡΠ»ΡΡΠΈΠΏΠ»ΠΈΠΊΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΈΠ»ΠΈ ΡΠΌΠ΅ΡΠ°Π½Π½ΠΎΠ³ΠΎ. ΠΡΠΈΠ²Π΅Π΄Π΅Π½Π½ΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΡ ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π΄Π»Ρ Π°Π½Π°Π»ΠΈΠ·Π° Π²Π»ΠΈΡΠ½ΠΈΡ ΡΠ»ΡΡΠ°ΠΉΠ½ΠΎΠΉ ΠΏΠΎΠ³ΡΠ΅ΡΠ½ΠΎΡΡΠΈ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ
Base system development results of virtual electric-machine laboratory
Electric-machine virtual laboratory base system is the set of virtual laboratories where fundamental experiments with all base types of electric machines (transformers, direct current electric machines and asynchronous alternative current machines) could be produced. Virtual laboratory is developed based on modified classical mathematical models of electric machines and technology of virtual realit
APPLICATION OF UKRAINIAN GRID INFRASTRUCTURE FOR INVESTIGATION OF NONLINEAR DYNAMICS IN LARGE NEURONAL NETWORKS
Β Β Β In present work the UkrainianΒ National Grid (UNG) infrastructure was appliedΒ for investigation of synchronization in large networksΒ of interacting neurons. This applicationΒ is important for solving of modern neuroscienceΒ problems related to mechanisms of nervous systemΒ activities (memory, cognition etc.) and nervousΒ pathologies (epilepsy, Parkinsonism, etc.). ModernΒ non-linear dynamics theories and applicationsΒ provides powerful basis for computer simulationsΒ of biological neuronal networks and investigationΒ of phenomena which mechanisms hardly could beΒ clarified by other approaches. Cubic millimeter ofΒ brain tissue contains about 105 neurons, so realisticΒ (Hodgkin-Huxley model) and phenomenologicalΒ (Kuramoto-Sakaguchi, FitzHugh-Nagumo, etc.Β models) simulations require consideration of largeΒ neurons numbers
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