26 research outputs found
Dynamical Encoding by Networks of Competing Neuron Groups: Winnerless Competition
Following studies of olfactory processing in insects and fish, we investigate neural networks whose dynamics in phase space is represented by orbits near the heteroclinic connections between saddle regions (fixed points or limit cycles). These networks encode input information as trajectories along the heteroclinic connections. If there are N neurons in the network, the capacity is approximately e(N-1)!, i.e., much larger than that of most traditional network structures. We show that a small winnerless competition network composed of FitzHugh-Nagumo spiking neurons efficiently transforms input information into a spatiotemporal output
Chaotic Free-Space Laser Communication over Turbulent Channel
The dynamics of errors caused by atmospheric turbulence in a
self-synchronizing chaos based communication system that stably transmits
information over a 5 km free-space laser link is studied experimentally.
Binary information is transmitted using a chaotic sequence of short-term pulses
as carrier. The information signal slightly shifts the chaotic time position of
each pulse depending on the information bit. We report the results of an
experimental analysis of the atmospheric turbulence in the channel and the
impact of turbulence on the Bit-Error-Rate (BER) performance of this chaos
based communication system.Comment: 4 pages, 5 figure
Application of microwave photonics in fiber optical sensors
Microwave photonics is a new scientific and technical area of research, which was formed as a result of intensive development of such fields as fiber, integrated and nonlinear optics, laser physics, optoelectronics and microelectronics. A positive trend in the field of microwave photonic devices development has appeared in recent decades. The trend is related to the fact that these devices can operate in ultra-high and super-high frequencies and microwave ranges, and have parameters, which are unattainable by conventional electronic devices. Technical characteristics of microwave
photonic measuring systems are comparable with those of traditional fiber-optic sensors. This technology can be used both for creation of new measuring devices and improvement of existing other types of measuring systems. This paper presents an analytical review of microwave photonics application technologies in fiber-optic measuring instruments. The general design concept for microwave photonic fiber-optic measuring devices is considered in the first part of the review paper. Microwave photonic filters are presented, which are the key elements of microwave photonic fiber-optic
measuring devices. Their design technologies are described with indication of the features, advantages and disadvantages. Methods for creation of microwave photonic finite impulse response filters with positive and negative coefficients are considered. The following sections are devoted directly to the analysis of microwave photonic fiber-optic measuring devices and contain classification of such devices according to their principle of operation. The classification of spectral and interferometric microwave photonic fiber-optic measuring devices with indication of their distinctive features is proposed. Experimental data of the most common sensors is presented and analyzed; the main characteristics and areas of their practical application are presented for each of them. New approaches and methods are considered for creation
of microwave photonic measuring systems and improvement of tactical and technical characteristics of existing devices. Comparison between microwave photonic fiber-optic measuring devices and traditional fiber-optic measuring systems is performed. According to comparison results, conclusions can be drawn about applicability of microwave photonic fiber-optic measuring devices and advantages of their use compared to other fiber-optic sensors.Π Π°Π΄ΠΈΠΎΡΠΎΡΠΎΠ½ΠΈΠΊΠ° ΡΠ²Π»ΡΠ΅ΡΡΡ Π½ΠΎΠ²ΡΠΌ Π½Π°ΡΡΠ½ΠΎ-ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΠΌ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΌ, ΠΊΠΎΡΠΎΡΠΎΠ΅ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π»ΠΎΡΡ Π² ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΠΈΠ½ΡΠ΅Π½-ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ°ΠΊΠΈΡ
ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ, ΠΊΠ°ΠΊ Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½Π°Ρ, ΠΈΠ½ΡΠ΅Π³ΡΠ°Π»ΡΠ½Π°Ρ ΠΈ Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½Π°Ρ ΠΎΠΏΡΠΈΠΊΠ°, Π»Π°Π·Π΅ΡΠ½Π°Ρ ΡΠΈΠ·ΠΈΠΊΠ°, ΠΎΠΏΡΠΎ- ΠΈ ΠΌΠΈΠΊΡΠΎΡΠ»Π΅ΠΊΡΡΠΎΠ½ΠΈΠΊΠ°. Π ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠ΅ Π΄Π΅ΡΡΡΠΈΠ»Π΅ΡΠΈΡ Π½Π°Π±Π»ΡΠ΄Π°Π΅ΡΡΡ ΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½Π°Ρ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ° ΠΏΠΎ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΠ°Π΄ΠΈΠΎΡΠΎΡΠΎΠ½Π½ΡΡ
ΡΡΡΡΠΎΠΉΡΡΠ², ΡΡΠ° ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΡ ΡΠ²ΡΠ·Π°Π½Π° Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΡ ΡΠΎΠ·Π΄Π°Π²Π°ΡΡ ΡΡΡΡΠΎΠΉΡΡΠ²Π° ΡΠ»ΡΡΡΠ°Π²ΡΡΠΎΠΊΠΈΡ
ΠΈ ΡΠ²Π΅ΡΡ
Π²ΡΡΠΎΠΊΠΈΡ
ΡΠ°ΡΡΠΎΡ Ρ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ, Π½Π΅Π΄ΠΎΡΡΠΈΠΆΠΈΠΌΡΠΌΠΈ ΠΎΠ±ΡΡΠ½ΡΠΌΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΡΠΌΠΈ ΡΡΡΡΠΎΠΉΡΡΠ²Π°ΠΌΠΈ. Π₯Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΡΠ°Π΄ΠΈΠΎΡΠΎΡΠΎΠ½Π½ΡΡ
ΠΈΠ·ΠΌΠ΅ΡΠΈ-ΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΡΠΎΠΏΠΎΡΡΠ°Π²ΠΈΠΌΡ Ρ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌΠΈ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΡ
Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π°ΡΡΠΈΠΊΠΎΠ², Π΄Π°Π½Π½Π°Ρ ΡΠ΅Ρ
Π½ΠΎ-Π»ΠΎΠ³ΠΈΡ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° ΠΊΠ°ΠΊ Π΄Π»Ρ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ Π½ΠΎΠ²ΡΡ
ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΈΠ±ΠΎΡΠΎΠ², ΡΠ°ΠΊ ΠΈ Π΄Π»Ρ ΡΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΡΠΆΠ΅ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ Π΄ΡΡΠ³ΠΈΡ
ΡΠΈΠΏΠΎΠ². Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΎΠ±Π·ΠΎΡ ΡΠΏΠΎΡΠΎΠ±ΠΎΠ²
ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ°Π΄ΠΈΠΎΡΠΎΡΠΎΠ½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π² Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΈΠ±ΠΎΡΠ°Ρ
. Π ΠΏΠ΅ΡΠ²ΠΎΠΉ ΡΠ°ΡΡΠΈ ΠΎΠ±Π·ΠΎΡΠ½ΠΎΠΉ ΡΡΠ°ΡΡΠΈ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ ΠΎΠ±ΡΠΈΠΉ ΠΏΡΠΈΠ½ΡΠΈΠΏ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΡΠ°Π΄ΠΈΠΎΡΠΎΡΠΎΠ½Π½ΡΡ
Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΈΠ±ΠΎΡΠΎΠ². ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΊΠ»ΡΡΠ΅Π²ΡΠ΅ ΡΠ»Π΅ΠΌΠ΅Π½ΡΡ ΠΏΠΎΠ΄ΠΎΠ±Π½ΠΎΠ³ΠΎ ΡΠΎΠ΄Π° ΡΠΈΡΡΠ΅ΠΌ β ΡΠ°Π΄ΠΈΠΎΡΠΎΡΠΎΠ½Π½ΡΠ΅ ΡΠΈΠ»ΡΡΡΡ. ΠΠΏΠΈΡΠ°Π½Ρ ΡΠ΅Ρ
-Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈΡ
ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ Ρ ΡΠΊΠ°Π·Π°Π½ΠΈΠ΅ΠΌ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ, ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ² ΠΈ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΊΠΎΠ². Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΡΠΏΠΎΡΠΎΠ±Ρ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΠ°Π΄ΠΈΠΎΡΠΎΡΠΎΠ½Π½ΡΡ
ΡΠΈΠ»ΡΡΡΠΎΠ² Ρ ΠΊΠΎΠ½Π΅ΡΠ½ΠΎΠΉ ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΠΎΠΉ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΎΠΉ Ρ ΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½ΡΠΌΠΈ ΠΈ ΠΎΡΡΠΈΡΠ°ΡΠ΅Π»ΡΠ½ΡΠΌΠΈ ΠΊΠΎΡΡ-ΡΠΈΡΠΈΠ΅Π½ΡΠ°ΠΌΠΈ. ΠΠΎΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ ΡΠ°Π·Π΄Π΅Π»Ρ ΠΏΠΎΡΠ²ΡΡΠ΅Π½Ρ Π½Π΅ΠΏΠΎΡΡΠ΅Π΄ΡΡΠ²Π΅Π½Π½ΠΎ Π°Π½Π°Π»ΠΈΠ·Ρ ΡΠ°Π΄ΠΈΠΎΡΠΎΡΠΎΠ½Π½ΡΡ
Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-ΠΎΠΏΡΠΈΡΠ΅-ΡΠΊΠΈΡ
ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΈΠ±ΠΎΡΠΎΠ² ΠΈ ΡΠΎΠ΄Π΅ΡΠΆΠ°Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΠ°ΠΊΠΈΡ
ΡΡΡΡΠΎΠΉΡΡΠ² ΠΏΠΎ ΠΈΡ
ΠΏΡΠΈΠ½ΡΠΈΠΏΡ ΡΠ°Π±ΠΎΡΡ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΈ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΡΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°Π΄ΠΈΠΎΡΠΎΡΠΎΠ½Π½ΡΡ
Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΈΠ±ΠΎΡΠΎΠ² Ρ ΡΠΊΠ°Π·Π°Π½ΠΈΠ΅ΠΌ ΠΈΡ
ΠΎΡΠ»ΠΈΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ². ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΈ ΠΏΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅, ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΠΈ ΠΎΠ±Π»Π°ΡΡΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΡ
Π΄Π°ΡΡΠΈ-ΠΊΠΎΠ². Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ Π½ΠΎΠ²ΡΠ΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΏΠΎ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΠ°Π΄ΠΈΠΎΡΠΎΡΠΎΠ½Π½ΡΡ
ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΈ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ ΡΠ°ΠΊΡΠΈΠΊΠΎ-ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
ΠΏΡΠΈΠ±ΠΎΡΠΎΠ². ΠΡΠΈΠ²Π΅Π΄Π΅Π½ΠΎ ΡΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠ°Π΄ΠΈΠΎ-
ΡΠΎΡΠΎΠ½Π½ΡΡ
Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΈΠ±ΠΎΡΠΎΠ² ΠΈ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΡ
Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π°ΡΡΠΈΠΊΠΎΠ², ΠΏΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΠΌΠΎΠΆΠ½ΠΎ ΡΠ΄Π΅Π»Π°ΡΡ Π²ΡΠ²ΠΎΠ΄ ΠΎ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΠΎΡΡΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠΏΠ° ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΈΠ±ΠΎΡΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π°Ρ
ΠΈΡ
ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Π΄ΡΡΠ³ΠΈΠΌΠΈ Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ Π΄Π°ΡΡΠΈΠΊΠ°ΠΌΠΈ
Dynamical coding of sensory information with competitive networks
Based on experiments with the locust olfactory system, we demonstrate that model sensory neural networks with lateral inhibition can generate stimulus specific identity-temporal patterns in the form of stimulus-dependent switching among small and dynamically changing neural ensembles (each ensemble being a group of synchronized projection neurons). Networks produce this switching mode of dynamical activity when lateral inhibitory connections are strongly non-symmetric. Such coding uses 'winner-less competitive' (WLC) dynamics. In contrast to the well known winner-take-all competitive (WTA) networks and Hopfield nets, winner-less competition represents sensory information dynamically. Such dynamics are reproducible, robust against intrinsic noise and sensitive to changes in the sensory input. We demonstrate the validity of sensory coding with WLC networks using two different formulations of the dynamics, namely the average and spiking dynamics of projection neurons (PN)
Odor encoding as an active, dynamical process: experiments, computation, and theory
We examine early olfactory processing in the vertebrate and insect olfactory systems, using a computational perspective. What transformations occur between the first and second olfactory processing stages? What are the causes and consequences of these transformations? To answer these questions, we focus on the functions of olfactory circuit structure and on the role of time in odor-evoked integrative processes. We argue that early olfactory relays are active and dynamical networks, whose actions change the format of odor-related information in very specific ways, so as to refine stimulus identification. Finally, we introduce a new theoretical framework (βwinnerless competitionβ) for the interpretation of these data
Multi-User Communication Using Chaotic Frequency Modulation
In this paper we consider the use of the chaotic frequency modulation (CFM) in multi-user communications. In this scheme the base station transmits the reference signal with chaotically varying frequency. All users synchronize their chaotic oscillators to this signal and use it to generate their own information-carrying CFM signal. Using numerical simulations and experiments with electronic circuits we evaluate the BER performance of CFM