How Diet and Lifestyle Can Fine-Tune Gut Microbiomes for Healthy Aging.

Many physical, social, and psychological changes occur during aging that raise the risk of developing chronic diseases, frailty, and dependency. These changes adversely affect the gut microbiota, a phenomenon known as microbe-aging. Those microbiota alterations are, in turn, associated with the development of age-related diseases. The gut microbiota is highly responsive to lifestyle and dietary changes, displaying a flexibility that also provides anactionable tool by which healthy aging can be promoted. This review covers, firstly, the main lifestyle and socioeconomic factors that modify the gut microbiota composition and function during healthy or unhealthy aging and, secondly, the advances being made in defining and promoting healthy aging, including microbiome-informed artificial intelligence tools, personalized dietary patterns, and food probiotic systems.

A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron.

In this paper we present a unified framework for extreme learning machines and reservoir computing (echo state networks), which can be physically implemented using a single nonlinear neuron subject to delayed feedback. The reservoir is built within the delay-line, employing a number of "virtual" neurons. These virtual neurons receive random projections from the input layer containing the information to be processed. One key advantage of this approach is that it can be implemented efficiently in hardware. We show that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware.

Optoelectronic reservoir computing: tackling noise-induced performance degradation.

We present improved strategies to perform photonic information processing using an optoelectronic oscillator with delayed feedback. In particular, we study, via numerical simulations and experiments, the influence of a finite signal-to-noise ratio on the computing performance. We illustrate that the performance degradation induced by noise can be compensated for via multi-level pre-processing masks.

Anticipated synchronization and the predict-prevent control method in the FitzHugh-Nagumo model system.

We study the synchronization region of two unidirectionally coupled FitzHugh-Nagumo systems, in a master-slave configuration, under the influence of external forcing terms. In the particular region where the slave anticipates the dynamics of the master system, we observe that the synchronization is robust to the different types of forcings. We then use the predict-prevent control method to suppress unwanted pulses in the master system by using the information of the slave output. We find that this method is more efficient than the direct control method based on the master dynamics. Finally, we observe that a perfect matching between the parameters of the master and the slave is not necessary for the control to be efficient. Moreover, this parameter mismatch can, in some cases, improve the control.

Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing.

Many information processing challenges are difficult to solve with traditional Turing or von Neumann approaches. Implementing unconventional computational methods is therefore essential and optics provides promising opportunities. Here we experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback. We implement a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities. We particularly exploit the transient response of a complex dynamical system to an input data stream. We employ spoken digit recognition and time series prediction tasks as benchmarks, achieving competitive processing figures of merit.

Information processing using a single dynamical node as complex system.

Novel methods for information processing are highly desired in our information-driven society. Inspired by the brain's ability to process information, the recently introduced paradigm known as 'reservoir computing' shows that complex networks can efficiently perform computation. Here we introduce a novel architecture that reduces the usually required large number of elements to a single nonlinear node with delayed feedback. Through an electronic implementation, we experimentally and numerically demonstrate excellent performance in a speech recognition benchmark. Complementary numerical studies also show excellent performance for a time series prediction benchmark. These results prove that delay-dynamical systems, even in their simplest manifestation, can perform efficient information processing. This finding paves the way to feasible and resource-efficient technological implementations of reservoir computing.

Permutation-information-theory approach to unveil delay dynamics from time-series analysis.

In this paper an approach to identify delay phenomena from time series is developed. We show that it is possible to perform a reliable time delay identification by using quantifiers derived from information theory, more precisely, permutation entropy and permutation statistical complexity. These quantifiers show clear extrema when the embedding delay τ of the symbolic reconstruction matches the characteristic time delay τ(S) of the system. Numerical data originating from a time delay system based on the well-known Mackey-Glass equations operating in the chaotic regime were used as test beds. We show that our method is straightforward to apply and robust to additive observational and dynamical noise. Moreover, we find that the identification of the time delay is even more efficient in a noise environment. Our permutation approach is also able to recover the time delay in systems with low feedback rate or high nonlinearity.

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.

Coherent regimes of mutually coupled Chua's circuits.

We study the dynamical regimes that emerge from the strong coupling between two Chua's circuits with parameters mismatch. For the region around the perfect synchronous state we show how to combine parameter diversity and coupling in order to robustly and precisely target a desired regime. This target process allows us to obtain regimes that may lie outside parameter ranges accessible for any isolated circuit. The results are obtained by following a recently developed theoretical technique, the order parameter expansion, and are verified both by numerical simulations and on electronic circuits. The theoretical results indicate that the same predictable change in the collective dynamics can be obtained for large populations of strongly coupled circuits with parameter mismatches.

Synchronization regimes of optical-feedback-induced chaos in unidirectionally coupled semiconductor lasers.

We numerically study the synchronization of two unidirectionally coupled single-mode semiconductor lasers in a master-slave configuration. The master laser is an external-cavity laser that operates in a chaotic regime while for the slave laser we consider two configurations. In the first one, the slave laser is also an external-cavity laser, subjected to, its own optical feedback and the optical injection from the master laser. In the second one, the slave laser is subject only to the optical injection from the master laser. Depending on the operating conditions the synchronization between the two lasers, whenever it exists, can be either isochronous or anticipated. We perform a detailed study of the parameter regions in which these synchronization regimes occur and how small variations of parameter yield one or the other type of synchronization or an unsynchronized regime.

Effect of external noise correlation in optical coherence resonance.

Coherence resonance occurring in semiconductor lasers with optical feedback is studied via the Lang-Kobayashi model with external nonwhite noise in the pumping current. The temporal correlation and the amplitude of the noise have a highly relevant influence in the system, leading to an optimal coherent response for suitable values of both the noise amplitude and correlation time. This phenomenon is quantitatively characterized by means of several statistical measures.

Chaos synchronization and spontaneous symmetry-breaking in symmetrically delay-coupled semiconductor lasers.

We present experimental and numerical investigations of the dynamics of two device-identical, optically coupled semiconductor lasers exhibiting a delay in the coupling. Our results give evidence for subnanosecond coupling-induced synchronized chaotic dynamics in conjunction with a spontaneous symmetry-breaking: we find a well-defined time lag between the dynamics of the two lasers, and an asymmetric physical role of the subsystems. We demonstrate that the leading laser synchronizes its lagging counterpart, whereas the synchronized lagging laser drives the coupling-induced instabilities.

Numerical statistics of power dropouts based on the Lang-Kobayashi model.

The statistics of power dropouts in semiconductor lasers subjected to delayed optical feedback have been numerically investigated using the Lang-Kobayashi model. The data from the numerical simulations have then been used to calculate the probability distribution functions, mean values, and return maps of the time that elapses between dropouts. In addition, the transition from the "low frequency fluctuation" to the "coherence collapse" regime has also been investigated. The numerical simulations compare well with both experimental results, obtained from multilongitudinal mode lasers, and analytical results obtained from other theoretical models. Evidence of "excitability" within the Lang-Kobayashi model is also reported.

Statistical properties of low-frequency fluctuations during single-mode operation in distributed-feedback lasers: experiments and modeling.

Extensive experimental and numerical investigations of feedback-induced instabilities in single-mode distributed-feedback lasers are presented that confirm the basic assumptions of the Lang-Kobayashi model. We give experimental evidence of the occurrence of low-frequency fluctuation (LFF), alternation between LFF and stable emission, and coherence collapse during single-mode operation of the laser. We have obtained quantitative agreement between modeling and experiment in long-time statistical investigations of the time intervals between subsequent LFF dropouts. In particular, we show that even the dependence of the dynamics on the injection current, which results in a scaling law, is quantitatively identical in modeling and experiment.

Prevention of coherence collapse in diode lasers by dynamic targeting.

We numerically show that a laser that would suffer from coherence collapse if precautions were not taken can be made to operate with a small linewidth and a stable maximum output power by application of a new dynamic targeting technique.

Pulse statistics in single-mode semiconductor lasers modulated at gigahertz rates.

The statistics of switch-on time, maximum light intensity, and pulse width of single-mode lasers modulated at gigahertz rates are analyzed. Numerical results obtained from noise-driven rate equations are reported. Pulse statistics, and in particular timing jitter, are shown to be rather insensitive to the bias current at this high-speed modulation. In addition, pulse statistics become rather independent of the modulation period when biasing slightly below threshold.