Parkinson's disease patient-specific neuronal networks carrying the LRRK2 G2019S mutation unveil early functional alterations that predate neurodegeneration.

A deeper understanding of early disease mechanisms occurring in Parkinson's disease (PD) is needed to reveal restorative targets. Here we report that human induced pluripotent stem cell (iPSC)-derived dopaminergic neurons (DAn) obtained from healthy individuals or patients harboring LRRK2 PD-causing mutation can create highly complex networks with evident signs of functional maturation over time. Compared to control neuronal networks, LRRK2 PD patients' networks displayed an elevated bursting behavior, in the absence of neurodegeneration. By combining functional calcium imaging, biophysical modeling, and DAn-lineage tracing, we found a decrease in DAn neurite density that triggered overall functional alterations in PD neuronal networks. Our data implicate early dysfunction as a prime focus that may contribute to the initiation of downstream degenerative pathways preceding DAn loss in PD, highlighting a potential window of opportunity for pre-symptomatic assessment of chronic degenerative diseases.

Antithetic population response to antibiotics in a polybacterial community.

Much is known about the effects of antibiotics on isolated bacterial species, but their influence on polybacterial communities is less understood. Here, we study the joint response of a mixed community of nonresistant Bacillus subtilis and Escherichia coli bacteria to moderate concentrations of the ß-lactam antibiotic ampicillin. We show that when the two organisms coexist, their population response to the antibiotic is opposite to that in isolation: Whereas in monoculture B. subtilis is tolerant and E. coli is sensitive to ampicillin, in coculture it is E. coli who can proliferate in the presence of the antibiotic, while B. subtilis cannot. This antithetic behavior is predicted by a mathematical model constrained only by the responses of the two species in isolation. Our results thus show that the collective response of mixed bacterial ecosystems to antibiotics can run counter to what single-species potency studies tell us about their efficacy.

Detection of generalized synchronization using echo state networks.

Generalized synchronization between coupled dynamical systems is a phenomenon of relevance in applications that range from secure communications to physiological modelling. Here, we test the capabilities of reservoir computing and, in particular, echo state networks for the detection of generalized synchronization. A nonlinear dynamical system consisting of two coupled Rössler chaotic attractors is used to generate temporal series consisting of time-locked generalized synchronized sequences interleaved with unsynchronized ones. Correctly tuned, echo state networks are able to efficiently discriminate between unsynchronized and synchronized sequences even in the presence of relatively high levels of noise. Compared to other state-of-the-art techniques of synchronization detection, the online capabilities of the proposed Echo State Network based methodology make it a promising choice for real-time applications aiming to monitor dynamical synchronization changes in continuous signals.

Consistency of heterogeneous synchronization patterns in complex weighted networks.

Synchronization within the dynamical nodes of a complex network is usually considered homogeneous through all the nodes. Here we show, in contrast, that subsets of interacting oscillators may synchronize in different ways within a single network. This diversity of synchronization patterns is promoted by increasing the heterogeneous distribution of coupling weights and/or asymmetries in small networks. We also analyze consistency, defined as the persistence of coexistent synchronization patterns regardless of the initial conditions. Our results show that complex weighted networks display richer consistency than regular networks, suggesting why certain functional network topologies are often constructed when experimental data are analyzed.

Emergent bimodal firing patterns implement different encoding strategies during gamma-band oscillations.

Upon sensory stimulation, primary cortical areas readily engage in narrow-band rhythmic activity between 30 and 90 Hz, the so-called gamma oscillations. Here we show that, when embedded in a balanced network, type-I excitable neurons entrained to the collective rhythm show a discontinuity in their firing-rates between a slow and a fast spiking mode. This jump in the spiking frequencies is characteristic to type II neurons, but is not present in the frequency-current curve (f-I curve) of isolated type I neurons. Therefore, this rate bimodality arises as an emerging network property in type I population models. We have studied the mechanisms underlying the generation of these two firing modes, in order to reproduce the spiking activity of in vivo cortical recordings, which is known to be highly irregular and sparse. We have also analyzed the relation between afferent inputs and the single unit activity, and between the latter and the local field potential (LFP) phase, in order to establish how the collective dynamics modulates the spiking activity of the individual neurons. Our results reveal that the inhibitory-excitatory balance allows two encoding mechanisms, for input rate variations and LFP phase, to coexist within the network.

Integration of cellular signals in chattering environments.

Cells are constantly exposed to fluctuating environmental conditions. External signals are sensed, processed and integrated by cellular signal transduction networks, which translate input signals into specific cellular responses by means of biochemical reactions. These networks have a complex nature, and we are still far from having a complete characterization of the process through which they integrate information, specially given the noisy environment in which that information is embedded. Guided by the many instances of constructive influences of noise that have been reported in the physical sciences in the last decades, here we explore how multiple signals are integrated in an eukaryotic cell in the presence of background noise, or chatter. To that end, we use a Boolean model of a typical human signal transduction network. Despite its complexity, we find that the network is able to display simple patterns of signal integration. Furthermore, our computational analysis shows that these integration patterns depend on the levels of fluctuating background activity carried by other cell inputs. Taken together, our results indicate that signal integration is sensitive to environmental fluctuations, and that this background noise effectively determines the information integration capabilities of the cell.

Zero-lag synchronization and bubbling in delay-coupled lasers.

We show experimentally that two semiconductor lasers mutually coupled via a passive relay fiber loop exhibit chaos synchronization at zero lag, and study how this synchronized regime is lost as the lasers' pump currents are increased. We characterize the synchronization properties of the system with high temporal resolution in two different chaotic regimes, namely, low-frequency fluctuations and coherence collapse, identifying significant differences between them. In particular, a marked decrease in synchronization quality develops as the lasers enter the coherence collapse regime. Our high-resolution measurements allow us to establish that synchronization loss is associated with bubbling events, the frequency of which increases with increasing pump current.

Dual-lag synchronization between coupled chaotic lasers due to path-delay interference.

We study experimentally the synchronization dynamics of two semiconductor lasers coupled unidirectionally via two different delayed paths. The emitter laser operates in a chaotic regime characterized by low-frequency fluctuations due to optical feedback and induces a synchronized dynamical activity in the receiver laser, which operates in the continuous-wave regime when uncoupled. Different delays in the two coupling paths lead to the coexistence of two time lags in the synchronized dynamics of the oscillators. This dual-lag synchronization degrades the average synchronization quality of the system of coupled lasers and hinders the transmission of information between them. Numerical simulation results agree with the experimental observations, and allow us to explore this phenomenon in a wide parameter range, and quantify the degree of signal transmission degradation caused by this chaotic path-delay interference.

Phase-response approach to firing-rate selectivity in neurons with subthreshold oscillations.

Subthreshold oscillations provide neurons with a filtering mechanism that allows their membrane potential to respond selectively to oscillatory currents depending on their frequency. On the other hand, the phase of such oscillations is known to affect the precise timing at which action potentials can be elicited by input spikes. Here we study the combined effect of these two properties by examining the response of a model neuron to periodic spike trains of defined frequency, in the presence of subthreshold oscillatory activity. Numerical results show a marked resonance with the input firing rate, irrespective of the initial relative phase between the input spike train and the intrinsic subthreshold oscillation. This behavior can be understood in terms of a delayed phase transition curve, from which an iterative map can be built that describes the evolution of the phase response to the periodic succession of input spike perturbations. Depending on the input period, the map exhibits stationary, periodic, or chaotic dynamics that predict in a quantitative way the response of the neuron to the spike train. Propagation of the spike train through a chain of neurons is also examined, and the resonant behavior is seen to be enhanced upon propagation.

Relaxation dynamics and frequency response of a noisy cell signaling network.

We investigate the dynamics of cell signaling using an experimentally based Boolean model of the human fibroblast signal transduction network. We determine via systematic numerical simulations the relaxation dynamics of the network in response to a constant set of inputs, both in the absence and in the presence of environmental fluctuations. We then study the network's response to periodically modulated signals, uncovering different types of behaviors for different pairs of driven input and output nodes. The phenomena observed include low-pass, high-pass, and band-pass filtering of the input modulations, among other nontrivial responses, at frequencies around the relaxation frequency of the network. The results reveal that the dynamic response to the external modulation of biologically realistic signaling networks is versatile and robust to noise.

Cooperative differentiation through clustering in multicellular populations.

The coordinated development of multicellular organisms is driven by intercellular communication. Differentiation into diverse cell types is usually associated with the existence of distinct attractors of gene regulatory networks, but how these attractors emerge from cell-cell coupling is still an open question. In order to understand and characterize the mechanisms through which coexisting attractors arise in multicellular systems, here we systematically investigate the dynamical behavior of a population of synthetic genetic oscillators coupled by chemical means. Using bifurcation analysis and numerical simulations, we identify various attractors and attempt to deduce from these findings a way to predict the organized collective behavior of growing populations. Our results show that dynamical clustering is a generic property of multicellular systems. We argue that such clustering might provide a basis for functional differentiation and variability in biological systems.

Stochastic suppression of gene expression oscillators under intercell coupling.

This paper examines the dynamics of an ensemble of hysteresis-based genetic relaxation oscillators, focusing on the influence of noise and cell-to-cell coupling on the appearance of new dynamical regimes. In particular, we show that control of the coupling strength and noise can effectively change the dynamics of the system leading to behaviors such as clustering, synchronous and asynchronous oscillations, and suppression. Moreover, under certain conditions an optimal amount of noise can lead to increased order in the system. The results obtained are correlated with relevant biological processes that occur in living organisms.

Self-sustained spatiotemporal oscillations induced by membrane-bulk coupling.

We propose a novel mechanism leading to spatiotemporal oscillations in extended systems that does not rely on local bulk instabilities. Instead, oscillations arise from the interaction of two subsystems of different spatial dimensionality. Specifically, we show that coupling a passive diffusive bulk of dimension d with an excitable membrane of dimension d-1 produces a self-sustained oscillatory behavior. An analytical explanation of the phenomenon is provided for d=1. Moreover, in-phase and antiphase synchronization of oscillations are found numerically in one and two dimensions. This novel dynamic instability could be used by biological systems such as cells, where the dynamics on the cellular membrane is necessarily different from that of the cytoplasmic bulk.

Phantom reflexes: muscle contractions at a frequency not physically present in the input stimuli.

In the motor system, the periodic stimulation of one Ia-afferent input produces reflex muscle contractions at the input frequency. However, we observed that when two Ia monosynaptic reflex-afferent inputs are involved the periodic muscle contractions may occur at a frequency physically not present in the afferent inputs even when these inputs are sub-threshold. How can the muscles respond with such phantom reflex contractions at a frequency physically absent in the sub-threshold Ia-afferent input stimuli? Here we provide an explanation for this phenomenon in the cat spinal cord, that we termed "ghost motor response". We recorded monosynaptic reflexes in the L7 ventral root, intracellular potentials in the motoneurons, and the associated muscular contractions elicited by stimulation of the lateral and medial gastrocnemius nerves. By stimulating with periodic pulses of sub-threshold intensities and distinct frequencies of 2 and 3 Hz the lateral and medial gastrocnemius nerves, respectively, we observed monosynaptic responses and phantom reflex muscle contractions occurring at the fundamental frequency (1 Hz), which was absent in the input stimuli. Thus we observed a reflex ghost motor response at a frequency not physically present in the inputs. We additionally studied the inharmonic case for sub-threshold stimuli and observed muscular contractions occurring at much lower frequencies, which were also conspicuously absent in the inputs. This is the first experimental evidence of a phantom reflex response in the nervous system. The observed behavior was modeled by numerical simulations of a pool of neurons subjected to two different input pulses.

Coupling-mediated ghost resonance in mutually injected lasers.

We experimentally and numerically study the phenomenon of ghost resonance in coupled nonlinear systems. Two mutually injected semiconductor lasers are externally perturbed in their pump currents by two respective periodic signals of different frequencies f(1) and f(2). For small amplitudes of the external modulations, the two laser intensities display synchronized optical pulses, in the form of dropout events occurring at irregular times. By adjusting the amplitude and frequencies of the driving signals, the system exhibits a ghost resonance in the dropout appearance at a frequency f(r) not present in the distributed inputs.

Delay-induced resonances in an optical system with feedback.

We study the influence of the delay time in the response of a delayed feedback system to external periodic driving. The nonlinear system we consider is a semiconductor laser with optical feedback operating in the low-frequency fluctuation regime. We numerically examine the consequences of varying the external cavity length of the system when a weak modulation is introduced through the laser's pump current. The harmonic modulation is seen to lead to a partial periodic entrainment of power dropouts, and the distribution of time intervals between the dropouts exhibits resonances with certain delay times. In other words, the response of the system to the external modulation is enhanced for particular values of the external cavity length. The same effect can be observed in the presence of noise, indicating that stochastic resonance can be enhanced or degraded depending on the feedback time.

Noise-induced excitability in oscillatory media.

A noise-induced phase transition to excitability is reported in oscillatory media with FitzHugh-Nagumo dynamics. This transition takes place via a noise-induced stabilization of a deterministically unstable fixed point of the local dynamics, while the overall phase-space structure of the system is maintained. Spatial coupling is required to prevent oscillations through suppression of fluctuations (via clustering in the case of local coupling). Thus, the joint action of coupling and noise leads to a different type of phase transition and results in a stabilization of the system. The resulting regime is shown to display characteristic traits of excitable media, such as stochastic resonance and wave propagation. This effect thus allows the transmission of signals through an otherwise globally oscillating medium.

Cavity solitons in two-level lasers with dense amplifying medium.

Local-field effects are known to induce bistability in dense optical media. We examine theoretically whether this property is preserved in broad-area cavities, and show that bistability between the homogeneous lasing and nonlasing states of the system persists provided a Fourier filtering technique is used to prevent off-axis emission. The resulting bistability gives rise to spatial light localization in the form of cavity solitons, which exhibit a particularly large degree of plasticity as a function of the characteristics of the addressing beam. This is the simplest laser able to sustain cavity solitons.

Symmetry breaking and high-frequency periodic oscillations in mutually coupled laser diodes.

We investigate the dynamical behavior of two laser diodes coupled through mutual injection of their optical fields when placed face to face with a small separation between them. We report symmetry breaking in periodic solutions at low coupling rates. In addition, we demonstrate that at higher coupling rates both lasers exhibit very fast periodic oscillations. The system is of practical interest, since it constitutes a tunable all-optical source of microwave oscillations.

Intrinsic noise-induced phase transitions: beyond the noise interpretation.

We discuss intrinsic noise effects in stochastic multiplicative-noise partial differential equations, which are qualitatively independent of the noise interpretation (Itô vs Stratonovich), in particular in the context of noise-induced ordering phase transitions. We study a model which, contrary to all cases known so far, exhibits such ordering transitions when the noise is interpreted not only according to Stratonovich, but also to Itô. The main feature of this model is the absence of a linear instability at the transition point. The dynamical properties of the resulting noise-induced growth processes are studied and compared in the two interpretations and with a reference Ginzburg-Landau-type model. A detailed discussion of a different numerical algorithm valid for both interpretations is also presented.