| Literature DB >> 28827741 |
Bryan Kelleher1,2, Bogusław Tykalewicz3,4, David Goulding3,4, Nikita Fedorov5, Ilya Dubinkin5, Thomas Erneux6, Evgeny A Viktorov5,6.
Abstract
Neurons communicate by brief bursts of spikes separated by silent phases and information may be encoded into the burst duration or through the structure of the interspike intervals. Inspired by the importance of bursting activities in neuronal computation, we have investigated the bursting oscillations of an optically injected quantum dot laser. We find experimentally that the laser periodically switches between two distinct operating states with distinct optical frequencies exhibiting either fast oscillatory or nearly steady state evolutions (two-color bursting oscillations). The conditions for their emergence and their control are analyzed by systematic simulations of the laser rate equations. By projecting the bursting solution onto the bifurcation diagram of a fast subsystem, we show how a specific hysteresis phenomenon explains the transitions between active and silent phases. Since size-controlled bursts can contain more information content than single spikes our results open the way to new forms of neuron inspired optical communication.Entities:
Year: 2017 PMID: 28827741 PMCID: PMC5566208 DOI: 10.1038/s41598-017-08751-y
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Figure 1(a,b) Show experimental times traces. The upper panel (a) shows an example of the dropout and recovery phenomenon. The lower panel (b) shows a zoom of (a).
Figure 2Experimental times traces showing four zooms of one of the dropouts of Fig. 1. The time axes show the positions of the zooms. (a) Shows the initial dropout and relaxation around the focus. (b–d) Show the evolution of the oscillations both in amplitude and shape until the eventual return to the high GS intensity and low ES intensity state.
Figure 3Experimental false colour histogram of the frequency evolution. The inset shows the individual frequency evolutions from consecutive bursts. By temporally shifting each of these so that they lie on top of each other one can build a two-dimensional histogram by creating bins in both the frequency and the temporal position. The resulting histogram is narrow and has a well-defined centre in each bin confirming the deterministic nature of the bursting trajectory.
Figure 4Numerical times traces. The upper panel (a) shows an example of the dropout and recovery phenomenon with the evolution of the detuning overlaid. The lower panel (b) shows a zoom of (a).
Figure 5Left: The bursting cycle (black) in the phase plane (I , Δ) is shown together with the bifurcation diagram I = I (Δ) of the fast subsystem (1)–(5) (red). Line (1) corresponds to the branch of steady states where I ≠ 0 and I = 0. Line (2) represents the branch of steady states where I ≠ 0 and I ≠ 0. It emerges at the bifurcation point BP. Stable and unstable branches are shown by full and broken lines, respectively. The points LP1 and H mark a limit point of steady states and a Hopf bifurcation point, respectively. Right: The bursting cycle is shown together with the branches of steady and periodic solutions. The branch of periodic solutions represented by the maximum intensity is emerging from H and snakes with various stability changes until it reaches the unstable steady state branch at a homoclinic bifurcation point (HOM). The figure shows that the bursting oscillations follows a stable branch of periodic solutions until it reaches a limit-point of limit-cycles (LP2). I is normalized by its maximal value I = 325.46. The fixed parameters are: , , C = 13.5, ε = 7, α = 3, β = 2.4, g = 0.55, J = 55, η = 0.04, Δ0 = 0.7, γ = 7 × 10−6, and c = 0.0055.