93 research outputs found
Piecewise linear differential equations and integrate-and-fire neurons : insights from two-dimensional membrane models
We derive and study two-dimensional generalizations of integrate-and-fire models which can be found from a piecewise linear idealization of the FitzHugh-Nagumo or Morris-Lecar model. These models give rise to new properties not present in one-dimensional integrate-and-fire models. A detailed analytical study of the models is presented. In particular, (i) for the piecewise linear FitzHugh-Nagumo model, we determine analytically the bistability regime between stationary solutions and oscillations, that is, typical for class-II models. (ii) In the piecewise Morris-Lecar model, we find a noncanonical class-I transition from a stationary state to oscillations with logarithmic dependence similar to that found for leaky integrate-and-fire models. (iii) Furthermore, we establish a relation to the recently proposed resonate-and-fire model and show that a short input current pulse can trigger several spikes
Wave trains, self-oscillations and synchronization in discrete media
We study wave propagation in networks of coupled cells which can behave as
excitable or self-oscillatory media. For excitable media, an asymptotic
construction of wave trains is presented. This construction predicts their
shape and speed, as well as the critical coupling and the critical separation
of time scales for propagation failure. It describes stable wave train
generation by repeated firing at a boundary. In self-oscillatory media, wave
trains persist but synchronization phenomena arise. An equation describing the
evolution of the oscillator phases is derived.Comment: to appear in Physica D: Nonlinear Phenomen
Scaling law for the transient behavior of type-II neuron models
We study the transient regime of type-II biophysical neuron models and
determine the scaling behavior of relaxation times near but below the
repetitive firing critical current, . For both
the Hodgkin-Huxley and Morris-Lecar models we find that the critical exponent
is independent of the numerical integration time step and that both systems
belong to the same universality class, with . For appropriately
chosen parameters, the FitzHugh-Nagumo model presents the same generic
transient behavior, but the critical region is significantly smaller. We
propose an experiment that may reveal nontrivial critical exponents in the
squid axon.Comment: 6 pages, 9 figures, accepted for publication in Phys. Rev.
On the birth of limit cycles for non-smooth dynamical systems
The main objective of this work is to develop, via Brower degree theory and
regularization theory, a variation of the classical averaging method for
detecting limit cycles of certain piecewise continuous dynamical systems. In
fact, overall results are presented to ensure the existence of limit cycles of
such systems. These results may represent new insights in averaging, in
particular its relation with non smooth dynamical systems theory. An
application is presented in careful detail
Nonlinear stability of oscillatory wave fronts in chains of coupled oscillators
We present a stability theory for kink propagation in chains of coupled
oscillators and a new algorithm for the numerical study of kink dynamics. The
numerical solutions are computed using an equivalent integral equation instead
of a system of differential equations. This avoids uncertainty about the impact
of artificial boundary conditions and discretization in time. Stability results
also follow from the integral version. Stable kinks have a monotone leading
edge and move with a velocity larger than a critical value which depends on the
damping strength.Comment: 11 figure
Pulse propagation in discrete excitatory networks of integrate-and-fire neurons
We study the propagation of solitary waves in a discrete excitatory network of integrate-and-fire neurons. We show the existence and the stability of a fast wave and a family of slow waves. Fast waves are similar to those already described in continuum networks. Stable slow waves have not been previously reported in purely excitatory networks and their propagation is particular to the discrete nature of the network. The robustness of our results is studied in the presence of noise
A Markovian event-based framework for stochastic spiking neural networks
In spiking neural networks, the information is conveyed by the spike times,
that depend on the intrinsic dynamics of each neuron, the input they receive
and on the connections between neurons. In this article we study the Markovian
nature of the sequence of spike times in stochastic neural networks, and in
particular the ability to deduce from a spike train the next spike time, and
therefore produce a description of the network activity only based on the spike
times regardless of the membrane potential process.
To study this question in a rigorous manner, we introduce and study an
event-based description of networks of noisy integrate-and-fire neurons, i.e.
that is based on the computation of the spike times. We show that the firing
times of the neurons in the networks constitute a Markov chain, whose
transition probability is related to the probability distribution of the
interspike interval of the neurons in the network. In the cases where the
Markovian model can be developed, the transition probability is explicitly
derived in such classical cases of neural networks as the linear
integrate-and-fire neuron models with excitatory and inhibitory interactions,
for different types of synapses, possibly featuring noisy synaptic integration,
transmission delays and absolute and relative refractory period. This covers
most of the cases that have been investigated in the event-based description of
spiking deterministic neural networks
Modelling hair follicle growth dynamics as an excitable medium
The hair follicle system represents a tractable model for the study of stem cell behaviour in regenerative adult epithelial tissue. However, although there are numerous spatial scales of observation (molecular, cellular, follicle and multi follicle), it is not yet clear what mechanisms underpin the follicle growth cycle. In this study we seek to address this problem by describing how the growth dynamics of a large population of follicles can be treated as a classical excitable medium. Defining caricature interactions at the molecular scale and treating a single follicle as a functional unit, a minimal model is proposed in which the follicle growth cycle is an emergent phenomenon. Expressions are derived, in terms of parameters representing molecular regulation, for the time spent in the different functional phases of the cycle, a formalism that allows the model to be directly compared with a previous cellular automaton model and experimental measurements made at the single follicle scale. A multi follicle model is constructed and numerical simulations are used to demonstrate excellent qualitative agreement with a range of experimental observations. Notably, the excitable medium equations exhibit a wider family of solutions than the previous work and we demonstrate how parameter changes representing altered molecular regulation can explain perturbed patterns in Wnt over-expression and BMP down-regulation mouse models. Further experimental scenarios that could be used to test the fundamental premise of the model are suggested. The key conclusion from our work is that positive and negative regulatory interactions between activators and inhibitors can give rise to a range of experimentally observed phenomena at the follicle and multi follicle spatial scales and, as such, could represent a core mechanism underlying hair follicle growth
Attraction Basins as Gauges of Robustness against Boundary Conditions in Biological Complex Systems
One fundamental concept in the context of biological systems on which researches have flourished in the past decade is that of the apparent robustness of these systems, i.e., their ability to resist to perturbations or constraints induced by external or boundary elements such as electromagnetic fields acting on neural networks, micro-RNAs acting on genetic networks and even hormone flows acting both on neural and genetic networks. Recent studies have shown the importance of addressing the question of the environmental robustness of biological networks such as neural and genetic networks. In some cases, external regulatory elements can be given a relevant formal representation by assimilating them to or modeling them by boundary conditions. This article presents a generic mathematical approach to understand the influence of boundary elements on the dynamics of regulation networks, considering their attraction basins as gauges of their robustness. The application of this method on a real genetic regulation network will point out a mathematical explanation of a biological phenomenon which has only been observed experimentally until now, namely the necessity of the presence of gibberellin for the flower of the plant Arabidopsis thaliana to develop normally
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