scholarly journals Ageing transitions in a network of Rulkov neurons

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Dhrubajyoti Biswas ◽  
Sayan Gupta
Keyword(s):  
Coupling Strength ◽  
Order Parameter ◽  
Additive Noise ◽  
Mean Field ◽  
Network Connectivity ◽  
Stochastic Networks ◽  
Field Coupling ◽  
Neuronal Interactions ◽  
Standard Order ◽  
Free Network

AbstractThe phenomenon of ageing transitions (AT) in a Erdős–Rényi network of coupled Rulkov neurons is studied with respect to parameters modelling network connectivity, coupling strength and the fractional ratio of inactive neurons in the network. A general mean field coupling is proposed to model the neuronal interactions. A standard order parameter is defined for quantifying the network dynamics. Investigations are undertaken for both the noise free network as well as stochastic networks, where the interneuronal coupling strength is assumed to be superimposed with additive noise. The existence of both smooth and explosive AT are observed in the parameter space for both the noise free and the stochastic networks. The effects of noise on AT are investigated and are found to play a constructive role in mitigating the effects of inactive neurons and reducing the parameter regime in which explosive AT is observed.

Controlling phase synchrony in the mean field coupled Hindmarsh–Rose neurons

2021 ◽  
Author(s):  
T. Remi ◽  
P. A. Subha ◽  
K. Usha
Keyword(s):  
Pulse Width ◽  
Order Parameter ◽  
Phase Synchronization ◽  
Mean Field ◽  
External Input ◽  
Phase Synchrony ◽  
Field Coupling ◽  
Temporal Domain ◽  
Coefficient Of Variability ◽  
Neural Disorders

The phase synchronization in a network of mean field coupled Hindmarsh–Rose neurons and the control of phase synchrony by an external input has been analyzed in this work. The analysis of interspike interval, with varying coupling strength, reveals the dynamical change induced in each neuron in the network. The bursting phase lines depict that mean field coupling induces phase synchrony in excitatory mode and desynchrony in inhibitory mode. The coefficient of variability, in spatial and temporal domain, signifies the deviations in firing times of neurons, in a collective manner. The Kuramoto order parameter quantifies the intermittent and complete phase synchrony, induced by excitatory mean field coupling. The capability of external input, in the form of spikes, to control the intermittent and complete phase synchrony has been analyzed. The coefficient of variability and Kuramoto order parameter has been studied by varying the amplitude, pulse width and frequency of the input. The studies have shown that high-frequency spike input, with optimum amplitude and pulse width, has high desynchronizing ability, which is substantiated by the parameter space analysis. The control of synchrony in the network of neurons may find application in rectifying neural disorders.

Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons

2010 ◽  
Vol 22 (5) ◽  
pp. 1272-1311 ◽  
Author(s):  
Lars Büsing ◽  
Benjamin Schrauwen ◽  
Robert Legenstein
Keyword(s):  
Analog Circuits ◽  
Memory Function ◽  
Mean Field ◽  
Network Connectivity ◽  
Network Models ◽  
Reservoir Computing ◽  
Computational Performance ◽  
Kernel Quality ◽  
The One ◽  
Long Timescales

Reservoir computing (RC) systems are powerful models for online computations on input sequences. They consist of a memoryless readout neuron that is trained on top of a randomly connected recurrent neural network. RC systems are commonly used in two flavors: with analog or binary (spiking) neurons in the recurrent circuits. Previous work indicated a fundamental difference in the behavior of these two implementations of the RC idea. The performance of an RC system built from binary neurons seems to depend strongly on the network connectivity structure. In networks of analog neurons, such clear dependency has not been observed. In this letter, we address this apparent dichotomy by investigating the influence of the network connectivity (parameterized by the neuron in-degree) on a family of network models that interpolates between analog and binary networks. Our analyses are based on a novel estimation of the Lyapunov exponent of the network dynamics with the help of branching process theory, rank measures that estimate the kernel quality and generalization capabilities of recurrent networks, and a novel mean field predictor for computational performance. These analyses reveal that the phase transition between ordered and chaotic network behavior of binary circuits qualitatively differs from the one in analog circuits, leading to differences in the integration of information over short and long timescales. This explains the decreased computational performance observed in binary circuits that are densely connected. The mean field predictor is also used to bound the memory function of recurrent circuits of binary neurons.

EVOLUTION OF ATOM-FIELD PROBABILITY IN A COUPLED CAVITY SYSTEM

2013 ◽  
Vol 22 (03) ◽  
pp. 1350029
Author(s):  
K. V. PRIYESH ◽  
RAMESH BABU THAYYULLATHIL
Keyword(s):  
Coupling Strength ◽  
Probability Amplitude ◽  
Field Coupling ◽  
Level Atom ◽  
Cavity System ◽  
Coupling Strengths ◽  
Field Excitation ◽  
Excitation Probability ◽  
Hopping Strength ◽  
Coupled Cavity

In this paper we have investigated the dynamics of two cavities each with a two-level atom, coupled together with photon hopping. The coupled cavity system is studied in single excitation subspace and the evolution of the atom (field) states probabilities are obtained analytically. The probability amplitude of states executes oscillations with different modes and amplitudes, determined by the coupling strengths. The evolution is examined in detail for different atom field coupling strength, g and field–field hopping strength, A. It is noticed that the exact atomic probability amplitude transfer occurs when g ≪ A with minimal field excitation probability and the period of probability transfer is calculated. In the limit g ≫ A there exists periodic exchange of probability between atom and field inside each cavity and also between cavity 1 and cavity 2. Periodicity of each exchange in this limit also obtained.

scholarly journals Coupled order-parameter system on a scale-free network

2009 ◽  
Vol 80 (1) ◽  
Author(s):  
V. Palchykov ◽  
C. von Ferber ◽  
R. Folk ◽  
Yu. Holovatch
Keyword(s):  
Order Parameter ◽  
Scale Free Network ◽  
Parameter System ◽  
Scale Free ◽  
Free Network

scholarly journals DISORDER INDUCED BCS-BEC CROSSOVER

2012 ◽  
Vol 11 ◽  
pp. 120-126 ◽  
Author(s):  
AYAN KHAN
Keyword(s):  
Order Parameter ◽  
Chemical Potential ◽  
Random Potential ◽  
Mean Field ◽  
Superconducting Order Parameter ◽  
Interesting Issue ◽  
Coupled Equations ◽  
Weak Regime ◽  
The Mean ◽  
Superconducting Fluctuations

Of late, the study of BCS-BEC crossover in the presence of weak random impurity is an interesting issue. In this proceedings we study the effect of this disorder which is included through the Nozières and Smith-Rink theory of superconducting fluctuations. In the weak regime, the random potential leaves an effect on the superconducting order parameter but it spares the chemical potential. Here we present the exact behavior of the mean field quantities as a function of the disorder by self-consistently solving the coupled equations.

CRITICAL DIMENSIONS OF SYSTEMS WITH JOINT MULTICRITICAL AND LIFSHITZ-POINT-LIKE BEHAVIOR

2011 ◽  
Vol 25 (22) ◽  
pp. 1839-1845 ◽  
Author(s):  
ARTEM V. BABICH ◽  
LESYA N. KITCENKO ◽  
VYACHESLAV F. KLEPIKOV
Keyword(s):  
Field Theory ◽  
Critical Phenomena ◽  
Critical Points ◽  
Order Parameter ◽  
Mean Field Theory ◽  
Mean Field ◽  
Critical Dimensions ◽  
Lifshitz Point ◽  
The Mean ◽  
Derivatives Of

In this article, we consider a model that allows one to describe critical phenomena in systems with higher powers and derivatives of order parameter. The systems considered have critical points with joint multicritical and Lifshitz-point-like properties. We assess the lower and upper critical dimensions of these systems. These calculation enable us to find the fluctuation region where the mean field theory description does not work.

BURSTING TYPES AND STABLE DOMAINS OF RULKOV NEURON NETWORK WITH MEAN FIELD COUPLING

2013 ◽  
Vol 23 (12) ◽  
pp. 1330041 ◽  
Author(s):  
HONGJUN CAO ◽  
YANGUO WU
Keyword(s):  
Parameter Space ◽  
Single Neuron ◽  
Complex Dynamics ◽  
Mean Field ◽  
Flip Bifurcation ◽  
Neuron Network ◽  
Square Wave ◽  
Field Coupling ◽  
The Mean ◽  
Stable Domains

Based on the detailed bifurcation analysis and the master stability function, bursting types and stable domains of the parameter space of the Rulkov map-based neuron network coupled by the mean field are taken into account. One of our main findings is that besides the square-wave bursting, there at least exist two kinds of triangle burstings after the mean field coupling, which can be determined by the crisis bifurcation, the flip bifurcation, and the saddle-node bifurcation. Under certain coupling conditions, there exists two kinds of striking transitions from the square-wave bursting (the spiking) to the triangle bursting (the square-wave bursting). Stable domains of fixed points, periodic solutions, quasiperiodic solutions and their corresponding firing regimes in the parameter space are presented in a rigorous mathematical way. In particular, as a function of the intrinsic control parameters of each single neuron and the external coupling strength, a stable coefficient of the Neimark–Sacker bifurcation is derived in a parameter plane. These results show that there exist complex dynamics and rich firing regimes in such a simple but thought-provoking neuron network.

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