A computational and geometric approach to phase resetting curves and surfaces

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Sensitivity analysis of oscillator models in the space of phase-response curves: Oscillators as open systems

Oscillator models are central to the study of system properties such as entrainment or synchronization. Due to their nonlinear nature, few system-theoretic tools exist to analyze those models. The paper develops a sensitivity analysis for phase-response curves, a fundamental one-dimensional phase reduction of oscillator models. The proposed theoretical and numerical analysis tools are illustrated on several system-theoretic questions and models arising in the biology of cellular rhythms

A geometric approach to phase response curves and its numerical computation through the parameterization method

The final publication is available at link.springer.comThe phase response curve (PRC) is a tool used in neuroscience that measures the phase shift experienced by an oscillator due to a perturbation applied at different phases of the limit cycle. In this paper, we present a new approach to PRCs based on the parameterization method. The underlying idea relies on the construction of a periodic system whose corresponding stroboscopic map has an invariant curve. We demonstrate the relationship between the internal dynamics of this invariant curve and the PRC, which yields a method to numerically compute the PRCs. Moreover, we link the existence properties of this invariant curve as the amplitude of the perturbation is increased with changes in the PRC waveform and with the geometry of isochrons. The invariant curve and its dynamics will be computed by means of the parameterization method consisting of solving an invariance equation. We show that the method to compute the PRC can be extended beyond the breakdown of the curve by means of introducing a modified invariance equation. The method also computes the amplitude response functions (ARCs) which provide information on the displacement away from the oscillator due to the effects of the perturbation. Finally, we apply the method to several classical models in neuroscience to illustrate how the results herein extend the framework of computation and interpretation of the PRC and ARC for perturbations of large amplitude and not necessarily pulsatile.Peer ReviewedPostprint (author's final draft

Dynamics of phase locking in neuronal networks in the presence of synaptic plasticity

The behavior generated by neuronal networks depends on the phase relationships of its individual neurons. Observed phases result from the combined effects of individual cells and synaptic connections whose properties change dynamically. The properties of individual cells and synapses can often be characterized by driving the cell or synapse with inputs that arrive at different phases or frequencies, thus producing a feed-forward description of these properties. In this study, a recurrent network of two oscillatory neurons that are coupled with reciprocal synapses is considered. Feed-forward descriptions of the phase response curves of the neurons and the short-term synaptic plasticity properties are used to define Poincar´e maps for the activity of the network. The fixed points of these maps correspond to the phase locked modes of the network. These maps allow analysis of the dependence of the resulting network activity on the properties of network components. Using a combination of analysis and simulations, how various parameters of the model affect the existence and stability of phase-locked solutions is shown. It is also shown that synaptic plasticity provides flexibility and supports phase maintenance in networks. Conditions are found on the synaptic plasticity profiles and the phase response curves of the neurons for the network to be able to maintain a constant firing period, while varying the relative activity phase of the neurons or vice versa. Synaptic plasticity is shown to yield bistable phase locking modes. These results are geometrically demonstrated using a generalization to cobwebbing for two dimensional maps. Type I neurons modeled with Morris-Lecar and Quadratic Integrate-and-Fire are used to estimate the predictive power of the analytical results; however, the results hold in general. The properties of the Negative-Leak model are also studied; a recent conductance-based model which is obtained by replacing a regenerative inward current with a negative-slope-conductance linear current. The map methods are extended to analyze networking properties of Negative-Leak neurons by including burst response curves. Finally, geometric singular perturbation techniques are applied to analyze how a hyperpolarization-activated inward current contributes to the generation of oscillations in this model. This work introduces a general method to determine how changes in the phase response curves or synaptic dynamics affect phase locking in a recurrent network which can be generalized to study larger networks

The cyanobacterial circadian clock follows midday in vivo and in vitro

Circadian rhythms are biological oscillations that schedule daily changes in physiology. Outside the laboratory, circadian clocks do not generally free-run but are driven by daily cues whose timing varies with the seasons. The principles that determine how circadian clocks align to these external cycles are not well understood. Here, we report experimental platforms for driving the cyanobacterial circadian clock both in vivo and in vitro. We find that the phase of the circadian rhythm follows a simple scaling law in light-dark cycles, tracking midday across conditions with variable day length. The core biochemical oscillator comprised of the Kai proteins behaves similarly when driven by metabolic pulses in vitro, indicating that such dynamics are intrinsic to these proteins. We develop a general mathematical framework based on instantaneous transformation of the clock cycle by external cues, which successfully predicts clock behavior under many cycling environments