34 research outputs found
Rapid convergence of time-averaged frequency in phase synchronized systems
Numerical and experimental evidence is presented to show that many phase
synchronized systems of non-identical chaotic oscillators, where the chaotic
state is reached through a period-doubling cascade, show rapid convergence of
the time-averaged frequency. The speed of convergence toward the natural
frequency scales as the inverse of the measurement period. The results also
suggest an explanation for why such chaotic oscillators can be phase
synchronized.Comment: 6 pages, 9 figure
Detectability of non-differentiable generalized synchrony
Generalized synchronization of chaos is a type of cooperative behavior in
directionally-coupled oscillators that is characterized by existence of stable
and persistent functional dependence of response trajectories from the chaotic
trajectory of driving oscillator. In many practical cases this function is
non-differentiable and has a very complex shape. The generalized synchrony in
such cases seems to be undetectable, and only the cases, in which a
differentiable synchronization function exists, are considered to make sense in
practice. We show that this viewpoint is not always correct and the
non-differentiable generalized synchrony can be revealed in many practical
cases. Conditions for detection of generalized synchrony are derived
analytically, and illustrated numerically with a simple example of
non-differentiable generalized synchronization.Comment: 8 pages, 8 figures, submitted to PR
Data driven optimal filtering for phase and frequency of noisy oscillations: application to vortex flowmetering
A new method for extracting the phase of oscillations from noisy time series
is proposed. To obtain the phase, the signal is filtered in such a way that the
filter output has minimal relative variation in the amplitude (MIRVA) over all
filters with complex-valued impulse response. The argument of the filter output
yields the phase. Implementation of the algorithm and interpretation of the
result are discussed. We argue that the phase obtained by the proposed method
has a low susceptibility to measurement noise and a low rate of artificial
phase slips. The method is applied for the detection and classification of mode
locking in vortex flowmeters. A novel measure for the strength of mode locking
is proposed.Comment: 12 pages, 10 figure
Democratization in a passive dendritic tree : an analytical investigation
One way to achieve amplification of distal synaptic inputs on a dendritic tree is to scale the amplitude and/or duration of the synaptic conductance with its distance from the soma. This is an example of what is often referred to as “dendritic democracy”. Although well studied experimentally, to date this phenomenon has not been thoroughly explored from a mathematical perspective. In this paper we adopt a passive model of a dendritic tree with distributed excitatory synaptic conductances and analyze a number of key measures of democracy. In particular, via moment methods we derive laws for the transport, from synapse to soma, of strength, characteristic time, and dispersion. These laws lead immediately to synaptic scalings that overcome attenuation with distance. We follow this with a Neumann approximation of Green’s representation that readily produces the synaptic scaling that democratizes the peak somatic voltage response. Results are obtained for both idealized geometries and for the more realistic geometry of a rat CA1 pyramidal cell. For each measure of democratization we produce and contrast the synaptic scaling associated with treating the synapse as either a conductance change or a current injection. We find that our respective scalings agree up to a critical distance from the soma and we reveal how this critical distance decreases with decreasing branch radius
Generalized Phase Synchronization in unidirectionally coupled chaotic oscillators
We investigate phase synchronization between two identical or detuned
response oscillators coupled to a slightly different drive oscillator. Our
result is that phase synchronization can occur between response oscillators
when they are driven by correlated (but not identical) inputs from the drive
oscillator. We call this phenomenon Generalized Phase Synchronization (GPS) and
clarify its characteristics using Lyapunov exponents and phase difference
plots.Comment: 4 pages, 5 figure
Branching dendrites with resonant membrane: a “sum-over-trips” approach
Dendrites form the major components of neurons. They are complex branching structures that receive and process thousands of synaptic inputs from other neurons. It is well known that dendritic morphology plays an important role in the function of dendrites. Another important contribution to the response characteristics of a single neuron comes from the intrinsic resonant properties of dendritic membrane. In this paper we combine the effects of dendritic branching and resonant membrane dynamics by generalising the “sum-over-trips” approach (Abbott et al. in Biol Cybernetics 66, 49–60 1991). To illustrate how this formalism can shed light on the role of architecture and resonances in determining neuronal output we consider dual recording and reconstruction data from a rat CA1 hippocampal pyramidal cell. Specifically we explore the way in which an Ih current contributes to a voltage overshoot at the soma
Improved community structure detection using a modified fine tuning strategy
The community structure of a complex network can be determined by finding the
partitioning of its nodes that maximizes modularity. Many of the proposed
algorithms for doing this work by recursively bisecting the network. We show
that this unduely constrains their results, leading to a bias in the size of
the communities they find and limiting their effectivness. To solve this
problem, we propose adding a step to the existing algorithms that does not
increase the order of their computational complexity. We show that, if this
step is combined with a commonly used method, the identified constraint and
resulting bias are removed, and its ability to find the optimal partitioning is
improved. The effectiveness of this combined algorithm is also demonstrated by
using it on real-world example networks. For a number of these examples, it
achieves the best results of any known algorithm.Comment: 6 pages, 3 figures, 1 tabl
Impact of network structure and cellular response on spike time correlations
Novel experimental techniques reveal the simultaneous activity of larger and
larger numbers of neurons. As a result there is increasing interest in the
structure of cooperative -- or correlated -- activity in neural populations,
and in the possible impact of such correlations on the neural code. A
fundamental theoretical challenge is to understand how the architecture of
network connectivity along with the dynamical properties of single cells shape
the magnitude and timescale of correlations. We provide a general approach to
this problem by extending prior techniques based on linear response theory. We
consider networks of general integrate-and-fire cells with arbitrary
architecture, and provide explicit expressions for the approximate
cross-correlation between constituent cells. These correlations depend strongly
on the operating point (input mean and variance) of the neurons, even when
connectivity is fixed. Moreover, the approximations admit an expansion in
powers of the matrices that describe the network architecture. This expansion
can be readily interpreted in terms of paths between different cells. We apply
our results to large excitatory-inhibitory networks, and demonstrate first how
precise balance --- or lack thereof --- between the strengths and timescales of
excitatory and inhibitory synapses is reflected in the overall correlation
structure of the network. We then derive explicit expressions for the average
correlation structure in randomly connected networks. These expressions help to
identify the important factors that shape coordinated neural activity in such
networks
Stochastic Delay Accelerates Signaling in Gene Networks
The creation of protein from DNA is a dynamic process consisting of numerous reactions, such as transcription, translation and protein folding. Each of these reactions is further comprised of hundreds or thousands of sub-steps that must be completed before a protein is fully mature. Consequently, the time it takes to create a single protein depends on the number of steps in the reaction chain and the nature of each step. One way to account for these reactions in models of gene regulatory networks is to incorporate dynamical delay. However, the stochastic nature of the reactions necessary to produce protein leads to a waiting time that is randomly distributed. Here, we use queueing theory to examine the effects of such distributed delay on the propagation of information through transcriptionally regulated genetic networks. In an analytically tractable model we find that increasing the randomness in protein production delay can increase signaling speed in transcriptional networks. The effect is confirmed in stochastic simulations, and we demonstrate its impact in several common transcriptional motifs. In particular, we show that in feedforward loops signaling time and magnitude are significantly affected by distributed delay. In addition, delay has previously been shown to cause stable oscillations in circuits with negative feedback. We show that the period and the amplitude of the oscillations monotonically decrease as the variability of the delay time increases
25th Annual Computational Neuroscience Meeting: CNS-2016
Abstracts of the 25th Annual Computational Neuroscience
Meeting: CNS-2016
Seogwipo City, Jeju-do, South Korea. 2–7 July 201