Noise, transient dynamics, and the generation of realistic interspike interval variation in square-wave burster neurons.

First return maps of interspike intervals for biological neurons that generate repetitive bursts of impulses can display stereotyped structures (neuronal signatures). Such structures have been linked to the possibility of multicoding and multifunctionality in neural networks that produce and control rhythmical motor patterns. In some cases, isolating the neurons from their synaptic network reveals irregular, complex signatures that have been regarded as evidence of intrinsic, chaotic behavior. We show that incorporation of dynamical noise into minimal neuron models of square-wave bursting (either conductance-based or abstract) produces signatures akin to those observed in biological examples, without the need for fine tuning of parameters or ad hoc constructions for inducing chaotic activity. The form of the stochastic term is not strongly constrained and can approximate several possible sources of noise, e.g., random channel gating or synaptic bombardment. The cornerstone of this signature generation mechanism is the rich, transient, but deterministic dynamics inherent in the square-wave (saddle-node and homoclinic) mode of neuronal bursting. We show that noise causes the dynamics to populate a complex transient scaffolding or skeleton in state space, even for models that (without added noise) generate only periodic activity (whether in bursting or tonic spiking mode).

Synchronization of two bubble trains in a viscous fluid: experiment and numerical simulation.

We investigate the interactions of two trains of bubbles, ejected by nozzles immersed in a viscous fluid, due only to the solution's circulation. The air fluxes (Q(1),Q(2)) are controlled independently, and we constructed parameter spaces of the periodicity of the attractors. We have observed complex behavior and many modes of phase synchronization that depend on these airflows as well as on the height (H) of the solution above the tops of the nozzles. Such synchronizations are shown in details in the parameter space (Q(1),Q(2)) and also in the (Q(1),H) space. We also observed that the coupling strength between the two trains of bubbles increases when the solution height increases. The experimental results were reasonably explained by numerical simulations of a model combining a simple bubble growth model for each bubble train and a coupling term between them, which was assumed symmetrical and proportional to the growth velocities.

Period adding cascades: experiment and modeling in air bubbling.

Period adding cascades have been observed experimentally/numerically in the dynamics of neurons and pancreatic cells, lasers, electric circuits, chemical reactions, oceanic internal waves, and also in air bubbling. We show that the period adding cascades appearing in bubbling from a nozzle submerged in a viscous liquid can be reproduced by a simple model, based on some hydrodynamical principles, dealing with the time evolution of two variables, bubble position and pressure of the air chamber, through a system of differential equations with a rule of detachment based on force balance. The model further reduces to an iterating one-dimensional map giving the pressures at the detachments, where time between bubbles come out as an observable of the dynamics. The model has not only good agreement with experimental data, but is also able to predict the influence of the main parameters involved, like the length of the hose connecting the air supplier with the needle, the needle radius and the needle length.

Bistability in bubble formation.

We obtain experimental data on time intervals of a bubble train generated from a nozzle with the air flow rate as the control parameter. Varying the length of the hose that connects the proportionating solenoid valve to the nozzle, we generate bifurcation diagrams showing period-adding cascades, among other dynamical phenomena. Then we construct a two-parameter family of one-dimensional maps whose bifurcation diagrams qualitatively match the experimental ones. The model indicates the existence of parameters where two attractors coexist, a phenomenon called bistability, and the same behavior is fully confirmed in the experiment.