1,361 research outputs found
Experiments with a Malkus-Lorenz water wheel: Chaos and Synchronization
We describe a simple experimental implementation of the Malkus-Lorenz water
wheel. We demonstrate that both chaotic and periodic behavior is found as wheel
parameters are changed in agreement with predictions from the Lorenz model. We
furthermore show that when the measured angular velocity of our water wheel is
used as an input signal to a computer model implementing the Lorenz equations,
high quality chaos synchronization of the model and the water wheel is
achieved. This indicates that the Lorenz equations provide a good description
of the water wheel dynamics.Comment: 12 pages, 7 figures. The following article has been accepted by the
American Journal of Physics. After it is published, it will be found at
http://scitation.aip.org/ajp
Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
Cortical synapse organization supports a range of dynamic states on multiple
spatial and temporal scales, from synchronous slow wave activity (SWA),
characteristic of deep sleep or anesthesia, to fluctuating, asynchronous
activity during wakefulness (AW). Such dynamic diversity poses a challenge for
producing efficient large-scale simulations that embody realistic metaphors of
short- and long-range synaptic connectivity. In fact, during SWA and AW
different spatial extents of the cortical tissue are active in a given timespan
and at different firing rates, which implies a wide variety of loads of local
computation and communication. A balanced evaluation of simulation performance
and robustness should therefore include tests of a variety of cortical dynamic
states. Here, we demonstrate performance scaling of our proprietary Distributed
and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and
AW for bidimensional grids of neural populations, which reflects the modular
organization of the cortex. We explored networks up to 192x192 modules, each
composed of 1250 integrate-and-fire neurons with spike-frequency adaptation,
and exponentially decaying inter-modular synaptic connectivity with varying
spatial decay constant. For the largest networks the total number of synapses
was over 70 billion. The execution platform included up to 64 dual-socket
nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz
clock rates. Network initialization time, memory usage, and execution time
showed good scaling performances from 1 to 1024 processes, implemented using
the standard Message Passing Interface (MPI) protocol. We achieved simulation
speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both
cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table
Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
Cortical synapse organization supports a range of dynamic states on multiple
spatial and temporal scales, from synchronous slow wave activity (SWA),
characteristic of deep sleep or anesthesia, to fluctuating, asynchronous
activity during wakefulness (AW). Such dynamic diversity poses a challenge for
producing efficient large-scale simulations that embody realistic metaphors of
short- and long-range synaptic connectivity. In fact, during SWA and AW
different spatial extents of the cortical tissue are active in a given timespan
and at different firing rates, which implies a wide variety of loads of local
computation and communication. A balanced evaluation of simulation performance
and robustness should therefore include tests of a variety of cortical dynamic
states. Here, we demonstrate performance scaling of our proprietary Distributed
and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and
AW for bidimensional grids of neural populations, which reflects the modular
organization of the cortex. We explored networks up to 192x192 modules, each
composed of 1250 integrate-and-fire neurons with spike-frequency adaptation,
and exponentially decaying inter-modular synaptic connectivity with varying
spatial decay constant. For the largest networks the total number of synapses
was over 70 billion. The execution platform included up to 64 dual-socket
nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz
clock rates. Network initialization time, memory usage, and execution time
showed good scaling performances from 1 to 1024 processes, implemented using
the standard Message Passing Interface (MPI) protocol. We achieved simulation
speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both
cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective
On metrics of density and power efficiency, neuromorphic technologies have
the potential to surpass mainstream computing technologies in tasks where
real-time functionality, adaptability, and autonomy are essential. While
algorithmic advances in neuromorphic computing are proceeding successfully, the
potential of memristors to improve neuromorphic computing have not yet born
fruit, primarily because they are often used as a drop-in replacement to
conventional memory. However, interdisciplinary approaches anchored in machine
learning theory suggest that multifactor plasticity rules matching neural and
synaptic dynamics to the device capabilities can take better advantage of
memristor dynamics and its stochasticity. Furthermore, such plasticity rules
generally show much higher performance than that of classical Spike Time
Dependent Plasticity (STDP) rules. This chapter reviews the recent development
in learning with spiking neural network models and their possible
implementation with memristor-based hardware
Predictability: a way to characterize Complexity
Different aspects of the predictability problem in dynamical systems are
reviewed. The deep relation among Lyapunov exponents, Kolmogorov-Sinai entropy,
Shannon entropy and algorithmic complexity is discussed. In particular, we
emphasize how a characterization of the unpredictability of a system gives a
measure of its complexity. Adopting this point of view, we review some
developments in the characterization of the predictability of systems showing
different kind of complexity: from low-dimensional systems to high-dimensional
ones with spatio-temporal chaos and to fully developed turbulence. A special
attention is devoted to finite-time and finite-resolution effects on
predictability, which can be accounted with suitable generalization of the
standard indicators. The problems involved in systems with intrinsic randomness
is discussed, with emphasis on the important problems of distinguishing chaos
from noise and of modeling the system. The characterization of irregular
behavior in systems with discrete phase space is also considered.Comment: 142 Latex pgs. 41 included eps figures, submitted to Physics Reports.
Related information at this http://axtnt2.phys.uniroma1.i
Practical implementation of nonlinear time series methods: The TISEAN package
Nonlinear time series analysis is becoming a more and more reliable tool for
the study of complicated dynamics from measurements. The concept of
low-dimensional chaos has proven to be fruitful in the understanding of many
complex phenomena despite the fact that very few natural systems have actually
been found to be low dimensional deterministic in the sense of the theory. In
order to evaluate the long term usefulness of the nonlinear time series
approach as inspired by chaos theory, it will be important that the
corresponding methods become more widely accessible. This paper, while not a
proper review on nonlinear time series analysis, tries to make a contribution
to this process by describing the actual implementation of the algorithms, and
their proper usage. Most of the methods require the choice of certain
parameters for each specific time series application. We will try to give
guidance in this respect. The scope and selection of topics in this article, as
well as the implementational choices that have been made, correspond to the
contents of the software package TISEAN which is publicly available from
http://www.mpipks-dresden.mpg.de/~tisean . In fact, this paper can be seen as
an extended manual for the TISEAN programs. It fills the gap between the
technical documentation and the existing literature, providing the necessary
entry points for a more thorough study of the theoretical background.Comment: 27 pages, 21 figures, downloadable software at
http://www.mpipks-dresden.mpg.de/~tisea
Applications of dynamical systems in ecology
This thesis consists of five original pieces of work contained in chapters 2, 4, 6, 7 and 8. These cover four topics within the subject area of theoretical ecology: epidemiology, chaos in ecology, evolution and spatially extended ecological systems.
Chapter 2 puts forward a new mechanism for producing chaos in ecology. We show that near extinctions in the SEIR model stabilise a chaotic repeller. This mechanism works for a wide-range of parameter values and so resolves the debate about which dynamic regime is associated with realistic values. It also highlights the problem of treating fluctuations as being either deterministically or stochastically produced.
Chapter 4 describes a new technique for identifying chaos based on measuring the divergence of trajectories over a range of spatial scales. It correctly identifies noise scales and chaos in model systems and is also applied to some real ecological data sets.
In chapters 4 and 5 we set evolutionary game theory in a nonlinear dynamical framework. We introduce a powerful new tool, the selective pressure, for analysing ecological models and identifying evolutionary stable states. It allows analysis of systems where complex attractors exist. We also study the evolution of phenotypic distributions and provide a new mechanism for evolutionary discontinuities.
In chapter 6 we look at an individually-based spatially extended system. This model is spatially heterogeneous and stochastic. However we show that the dynamics on a certain scale are deterministic and low-dimensional. We show how to identify the most efficient spatial scale at which to monitor the system
Front propagation into unstable states
This paper is an introductory review of the problem of front propagation into
unstable states. Our presentation is centered around the concept of the
asymptotic linear spreading velocity v*, the asymptotic rate with which
initially localized perturbations spread into an unstable state according to
the linear dynamical equations obtained by linearizing the fully nonlinear
equations about the unstable state. This allows us to give a precise definition
of pulled fronts, nonlinear fronts whose asymptotic propagation speed equals
v*, and pushed fronts, nonlinear fronts whose asymptotic speed v^dagger is
larger than v*. In addition, this approach allows us to clarify many aspects of
the front selection problem, the question whether for a given dynamical
equation the front is pulled or pushed. It also is the basis for the universal
expressions for the power law rate of approach of the transient velocity v(t)
of a pulled front as it converges toward its asymptotic value v*. Almost half
of the paper is devoted to reviewing many experimental and theoretical examples
of front propagation into unstable states from this unified perspective. The
paper also includes short sections on the derivation of the universal power law
relaxation behavior of v(t), on the absence of a moving boundary approximation
for pulled fronts, on the relation between so-called global modes and front
propagation, and on stochastic fronts.Comment: final version with some added references; a single pdf file of the
published version is available at http://www.lorentz.leidenuniv.nl/~saarloo
Topological Effects of Synaptic Time Dependent Plasticity
We show that the local Spike Timing-Dependent Plasticity (STDP) rule has the
effect of regulating the trans-synaptic weights of loops of any length within a
simulated network of neurons. We show that depending on STDP's polarity,
functional loops are formed or eliminated in networks driven to normal spiking
conditions by random, partially correlated inputs, where functional loops
comprise weights that exceed a non-zero threshold. We further prove that STDP
is a form of loop-regulating plasticity for the case of a linear network
comprising random weights drawn from certain distributions. Thus a notable
local synaptic learning rule makes a specific prediction about synapses in the
brain in which standard STDP is present: that under normal spiking conditions,
they should participate in predominantly feed-forward connections at all
scales. Our model implies that any deviations from this prediction would
require a substantial modification to the hypothesized role for standard STDP.
Given its widespread occurrence in the brain, we predict that STDP could also
regulate long range synaptic loops among individual neurons across all brain
scales, up to, and including, the scale of global brain network topology.Comment: 26 pages, 5 figure
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