4,435 research outputs found
Nonlinear dynamics of full-range CNNs with time-varying delays and variable coefficients
In the article, the dynamical behaviours of the full-range cellular neural networks (FRCNNs) with variable coefficients and time-varying delays are considered. Firstly, the improved model of the FRCNNs is proposed, and the existence and uniqueness of the solution are studied by means of differential inclusions and set-valued analysis. Secondly, by using the Hardy inequality, the matrix analysis, and the Lyapunov functional method, we get some criteria for achieving the globally exponential stability (GES). Finally, some examples are provided to verify the correctness of the theoretical results
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On the spectra of certain integro-differential-delay problems with applications in neurodynamics
We investigate the spectrum of certain integro-differential-delay equations (IDDEs) which arise naturally within spatially distributed, nonlocal, pattern formation problems. Our approach is based on the reformulation of the relevant dispersion relations with the use of the Lambert function. As a particular application of this approach, we consider the case of the Amari delay neural field equation which describes the local activity of a population of neurons taking into consideration the finite propagation speed of the electric signal. We show that if the kernel appearing in this equation is symmetric around some point a= 0 or consists of a sum of such terms, then the relevant dispersion relation yields spectra with an infinite number of branches, as opposed to finite sets of eigenvalues considered in previous works. Also, in earlier works the focus has been on the most rightward part of the spectrum and the possibility of an instability driven pattern formation. Here, we numerically survey the structure of the entire spectra and argue that a detailed knowledge of this structure is important within neurodynamical applications. Indeed, the Amari IDDE acts as a filter with the ability to recognise and respond whenever it is excited in such a way so as to resonate with one of its rightward modes, thereby amplifying such inputs and dampening others. Finally, we discuss how these results can be generalised to the case of systems of IDDEs
Physiologically motivated multiplex Kuramoto model describes phase diagram of cortical activity
We derive a two-layer multiplex Kuramoto model from weakly coupled
Wilson-Cowan oscillators on a cortical network with inhibitory synaptic time
delays. Depending on the coupling strength and a phase shift parameter, related
to cerebral blood flow and GABA concentration, respectively, we numerically
identify three macroscopic phases: unsynchronized, synchronized, and chaotic
dynamics. These correspond to physiological background-, epileptic seizure-,
and resting-state cortical activity, respectively. We also observe frequency
suppression at the transition from resting-state to seizure activity.Comment: 8 pages, 3 figure
On Local Bifurcations in Neural Field Models with Transmission Delays
Neural field models with transmission delay may be cast as abstract delay
differential equations (DDE). The theory of dual semigroups (also called
sun-star calculus) provides a natural framework for the analysis of a broad
class of delay equations, among which DDE. In particular, it may be used
advantageously for the investigation of stability and bifurcation of steady
states. After introducing the neural field model in its basic functional
analytic setting and discussing its spectral properties, we elaborate
extensively an example and derive a characteristic equation. Under certain
conditions the associated equilibrium may destabilise in a Hopf bifurcation.
Furthermore, two Hopf curves may intersect in a double Hopf point in a
two-dimensional parameter space. We provide general formulas for the
corresponding critical normal form coefficients, evaluate these numerically and
interpret the results
Measuring information-transfer delays
In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener’s principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics
Algebraic robust control of a closed circuit heating-cooling system with a heat exchanger and internal loop delays
This study demonstrates the use of a simple algebraic controller design for a cooling-heating plant with a through-flow air-water heat exchanger that evinces long internal delays with respect to the robustness to plant model uncertainties and variable ambient temperature conditions during the season. The advantage of the proposed design method consists in that the delays are not approximated but fully considered. Moreover, the reduction of sensitivity to model parameters’ variations yields the better applicability regardless modeling errors or environmental fluctuations. The infinite-dimensional mathematical model of the plant has been obtained by using anisochronic modeling principles. The key tool for the design is the ring special of quasipolynomial meromorphic functions (RQM). The Two-Feedback-Controllers (TFC) rather than the simple negative control feedback loop is utilized, which enables to solve the reference tracking and disturbance rejection independently and more efficiently. The eventual controller is then tuned such that robust stability and robust performance requirements are fulfilled. The tuning procedure is supported by a performance optimization idea. Since the originally obtained controller is of the infinite-dimensional nature, a possible way how to substitute it by a simplified finite-dimensional one is proposed for engineering practice. The functionality of both the controllers is compared and verified by simulations as well as by real measurements which prove a very good performance. © 2016 Elsevier LtdEuropean Regional Development Fund under the project CEBIA-Tech Instrumentation [CZ.1.05/2.1.00/19.0376
Finite-time synchronization of Markovian neural networks with proportional delays and discontinuous activations
In this paper, finite-time synchronization of neural networks (NNs) with discontinuous activation functions (DAFs), Markovian switching, and proportional delays is studied in the framework of Filippov solution. Since proportional delay is unbounded and different from infinite-time distributed delay and classical finite-time analytical techniques are not applicable anymore, new 1-norm analytical techniques are developed. Controllers with and without the sign function are designed to overcome the effects of the uncertainties induced by Filippov solutions and further synchronize the considered NNs in a finite time. By designing new Lyapunov functionals and using M-matrix method, sufficient conditions are derived to guarantee that the considered NNs realize synchronization in a settling time without introducing any free parameters. It is shown that, though the proportional delay can be unbounded, complete synchronization can still be realized, and the settling time can be explicitly estimated. Moreover, it is discovered that controllers with sign function can reduce the control gains, while controllers without the sign function can overcome chattering phenomenon. Finally, numerical simulations are given to show the effectiveness of theoretical results
A unified view on weakly correlated recurrent networks
The diversity of neuron models used in contemporary theoretical neuroscience
to investigate specific properties of covariances raises the question how these
models relate to each other. In particular it is hard to distinguish between
generic properties and peculiarities due to the abstracted model. Here we
present a unified view on pairwise covariances in recurrent networks in the
irregular regime. We consider the binary neuron model, the leaky
integrate-and-fire model, and the Hawkes process. We show that linear
approximation maps each of these models to either of two classes of linear rate
models, including the Ornstein-Uhlenbeck process as a special case. The classes
differ in the location of additive noise in the rate dynamics, which is on the
output side for spiking models and on the input side for the binary model. Both
classes allow closed form solutions for the covariance. For output noise it
separates into an echo term and a term due to correlated input. The unified
framework enables us to transfer results between models. For example, we
generalize the binary model and the Hawkes process to the presence of
conduction delays and simplify derivations for established results. Our
approach is applicable to general network structures and suitable for
population averages. The derived averages are exact for fixed out-degree
network architectures and approximate for fixed in-degree. We demonstrate how
taking into account fluctuations in the linearization procedure increases the
accuracy of the effective theory and we explain the class dependent differences
between covariances in the time and the frequency domain. Finally we show that
the oscillatory instability emerging in networks of integrate-and-fire models
with delayed inhibitory feedback is a model-invariant feature: the same
structure of poles in the complex frequency plane determines the population
power spectra
Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks
Recent advances in electronics are enabling substantial processing to be
performed at each node (robots, sensors) of a networked system. Local
processing enables data compression and may mitigate measurement noise, but it
is still slower compared to a central computer (it entails a larger
computational delay). However, while nodes can process the data in parallel,
the centralized computational is sequential in nature. On the other hand, if a
node sends raw data to a central computer for processing, it incurs
communication delay. This leads to a fundamental communication-computation
trade-off, where each node has to decide on the optimal amount of preprocessing
in order to maximize the network performance. We consider a network in charge
of estimating the state of a dynamical system and provide three contributions.
First, we provide a rigorous problem formulation for optimal real-time
estimation in processing networks in the presence of delays. Second, we show
that, in the case of a homogeneous network (where all sensors have the same
computation) that monitors a continuous-time scalar linear system, the optimal
amount of local preprocessing maximizing the network estimation performance can
be computed analytically. Third, we consider the realistic case of a
heterogeneous network monitoring a discrete-time multi-variate linear system
and provide algorithms to decide on suitable preprocessing at each node, and to
select a sensor subset when computational constraints make using all sensors
suboptimal. Numerical simulations show that selecting the sensors is crucial.
Moreover, we show that if the nodes apply the preprocessing policy suggested by
our algorithms, they can largely improve the network estimation performance.Comment: 15 pages, 16 figures. Accepted journal versio
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