Exploring the dynamics of intentional sensorimotor desynchronization using phasing performance in music.

Humans tend to synchronize spontaneously to rhythmic stimuli or with other humans, but they can also desynchronize intentionally in certain situations. In this study, we investigate the dynamics of intentional sensorimotor desynchronization using phasing performance in music as an experimental paradigm. Phasing is a compositional technique in modern music that requires musicians to desynchronize from each other in a controlled manner. A previous case study found systematic nonlinear trajectories in the phasing performance between two expert musicians, which were explained by coordination dynamics arising from the interaction between the intrinsic tendency of synchronization and the intention of desynchronization. A recent exploratory study further examined the dynamics of phasing performance using a simplified task of phasing against a metronome. Here we present a further analysis and modeling of the data from the exploratory study, focusing on the various types of phasing behavior found in non-expert participants. Participants were instructed to perform one phasing lap, and individual trials were classified as successful (1 lap), unsuccessful (> 1 laps), or incomplete (0 lap) based on the number of laps made. It was found that successful phasing required a gradual increment of relative phase and that different types of failure (unsuccessful vs. incomplete) were prevalent at slow vs. fast metronome tempi. The results are explained from a dynamical systems perspective, and a dynamical model of phasing performance is proposed which captures the interaction of intrinsic dynamics and intentional control in an adaptive-frequency oscillator coupled to a periodic external stimulus. It is shown that the model can replicate the multiple types of phasing behavior as well as the effect of tempo observed in the human experiment. This study provides further evidence that phasing performance is governed by the nonlinear dynamics of rhythmic coordination. It also demonstrates that the musical technique of phasing provides a unique experimental paradigm for investigating human rhythmic behavior.

Emergence and Prevalence of an African Swine Fever Virus Variant in Wild Boar Populations in South Korea from 2019 to 2022.

African swine fever (ASF), a viral disease caused by the African swine fever virus (ASFV), is associated with high mortality rates in domestic pigs and wild boars. ASF has been spreading since its discovery in wild boars in Korea in October 2019. Genomic analyses have provided insights into the genetic diversity of the ASFV isolated from various regions, enabling a better understanding of the virus origin and transmission patterns. We conducted a genome analysis to evaluate the diversity and mutations of ASFV spreading among wild boars in Korea during 2019-2022. We compared the genomes of ASFV strains isolated from Korean wild boars and publicly available ASFV genomes. Genomic analysis revealed several single-nucleotide polymorphisms within multigene families (MGFs) 360-1La and 360-4L in Korean ASFV. MGF 360-1La and 360-4L variations were not observed in other ASFV strains, including those of genotype II. Finally, we partially analyzed MGFs 360-1La and 360-4L in ASFV-positive samples between 2019 and 2022, confirming the geographical distribution of the variants. Our findings can help identify new genetic markers for epidemiological ASFV analysis and provide essential information for effective disease management.

Hebbian learning with elasticity explains how the spontaneous motor tempo affects music performance synchronization.

A musician's spontaneous rate of movement, called spontaneous motor tempo (SMT), can be measured while spontaneously playing a simple melody. Data shows that the SMT influences the musician's tempo and synchronization. In this study we present a model that captures these phenomena. We review the results from three previously-published studies: solo musical performance with a pacing metronome tempo that is different from the SMT, solo musical performance without a metronome at a tempo that is faster or slower than the SMT, and duet musical performance between musicians with matching or mismatching SMTs. These studies showed, respectively, that the asynchrony between the pacing metronome and the musician's tempo grew as a function of the difference between the metronome tempo and the musician's SMT, musicians drifted away from the initial tempo toward the SMT, and the absolute asynchronies were smaller if musicians had matching SMTs. We hypothesize that the SMT constantly acts as a pulling force affecting musical actions at a tempo different from a musician's SMT. To test our hypothesis, we developed a model consisting of a non-linear oscillator with Hebbian tempo learning and a pulling force to the model's spontaneous frequency. While the model's spontaneous frequency emulates the SMT, elastic Hebbian learning allows for frequency learning to match a stimulus' frequency. To test our hypothesis, we first fit model parameters to match the data in the first of the three studies and asked whether this same model would explain the data the remaining two studies without further tuning. Results showed that the model's dynamics allowed it to explain all three experiments with the same set of parameters. Our theory offers a dynamical-systems explanation of how an individual's SMT affects synchronization in realistic music performance settings, and the model also enables predictions about performance settings not yet tested.

Dynamic models for musical rhythm perception and coordination.

Rhythmicity permeates large parts of human experience. Humans generate various motor and brain rhythms spanning a range of frequencies. We also experience and synchronize to externally imposed rhythmicity, for example from music and song or from the 24-h light-dark cycles of the sun. In the context of music, humans have the ability to perceive, generate, and anticipate rhythmic structures, for example, "the beat." Experimental and behavioral studies offer clues about the biophysical and neural mechanisms that underlie our rhythmic abilities, and about different brain areas that are involved but many open questions remain. In this paper, we review several theoretical and computational approaches, each centered at different levels of description, that address specific aspects of musical rhythmic generation, perception, attention, perception-action coordination, and learning. We survey methods and results from applications of dynamical systems theory, neuro-mechanistic modeling, and Bayesian inference. Some frameworks rely on synchronization of intrinsic brain rhythms that span the relevant frequency range; some formulations involve real-time adaptation schemes for error-correction to align the phase and frequency of a dedicated circuit; others involve learning and dynamically adjusting expectations to make rhythm tracking predictions. Each of the approaches, while initially designed to answer specific questions, offers the possibility of being integrated into a larger framework that provides insights into our ability to perceive and generate rhythmic patterns.

Multidimensional recurrence quantification analysis of human-metronome phasing.

Perception-action coordination (also known as sensorimotor synchronization, SMS) is often studied by analyzing motor coordination with auditory rhythms. The current study assesses phasing-a compositional technique in which two people tap the same rhythm at varying phases by adjusting tempi-to explore how SMS is impacted by individual and situational factors. After practice trials, participants engaged in the experimental phasing task with a metronome at tempi ranging from 80-140 beats per minute (bpm). Multidimensional recurrence quantification analysis (MdRQA) was used to compare nonlinear dynamics of phasing performance. Varying coupling patterns emerged and were significantly predicted by tempo and linguistic experience. Participants who successfully phased replicated findings from an original case study, demonstrating stable tapping patterns near in-phase and antiphase, while those unsuccessful at phasing showed weaker attraction to in-phase and antiphase.

Neural Entrainment to Musical Pulse in Naturalistic Music Is Preserved in Aging: Implications for Music-Based Interventions.

Neural entrainment to musical rhythm is thought to underlie the perception and production of music. In aging populations, the strength of neural entrainment to rhythm has been found to be attenuated, particularly during attentive listening to auditory streams. However, previous studies on neural entrainment to rhythm and aging have often employed artificial auditory rhythms or limited pieces of recorded, naturalistic music, failing to account for the diversity of rhythmic structures found in natural music. As part of larger project assessing a novel music-based intervention for healthy aging, we investigated neural entrainment to musical rhythms in the electroencephalogram (EEG) while participants listened to self-selected musical recordings across a sample of younger and older adults. We specifically measured neural entrainment to the level of musical pulse-quantified here as the phase-locking value (PLV)-after normalizing the PLVs to each musical recording's detected pulse frequency. As predicted, we observed strong neural phase-locking to musical pulse, and to the sub-harmonic and harmonic levels of musical meter. Overall, PLVs were not significantly different between older and younger adults. This preserved neural entrainment to musical pulse and rhythm could support the design of music-based interventions that aim to modulate endogenous brain activity via self-selected music for healthy cognitive aging.

Deep Learning-Based NMPC for Local Motion Planning of Last-Mile Delivery Robot.

Feasible local motion planning for autonomous mobile robots in dynamic environments requires predicting how the scene evolves. Conventional navigation stakes rely on a local map to represent how a dynamic scene changes over time. However, these navigation stakes depend highly on the accuracy of the environmental map and the number of obstacles. This study uses semantic segmentation-based drivable area estimation as an alternative representation to assist with local motion planning. Notably, a realistic 3D simulator based on an Unreal Engine was created to generate a synthetic dataset under different weather conditions. A transfer learning technique was used to train the encoder-decoder model to segment free space from the occupied sidewalk environment. The local planner uses a nonlinear model predictive control (NMPC) scheme that inputs the estimated drivable space, the state of the robot, and a global plan to produce safe velocity commands that minimize the tracking cost and actuator effort while avoiding collisions with dynamic and static obstacles. The proposed approach achieves zero-shot transfer from a simulation to real-world environments that have never been experienced during training. Several intensive experiments were conducted and compared with the dynamic window approach (DWA) to demonstrate the effectiveness of our system in dynamic sidewalk environments.

A Dynamical, Radically Embodied, and Ecological Theory of Rhythm Development.

Musical rhythm abilities-the perception of and coordinated action to the rhythmic structure of music-undergo remarkable change over human development. In the current paper, we introduce a theoretical framework for modeling the development of musical rhythm. The framework, based on Neural Resonance Theory (NRT), explains rhythm development in terms of resonance and attunement, which are formalized using a general theory that includes non-linear resonance and Hebbian plasticity. First, we review the developmental literature on musical rhythm, highlighting several developmental processes related to rhythm perception and action. Next, we offer an exposition of Neural Resonance Theory and argue that elements of the theory are consistent with dynamical, radically embodied (i.e., non-representational) and ecological approaches to cognition and development. We then discuss how dynamical models, implemented as self-organizing networks of neural oscillations with Hebbian plasticity, predict key features of music development. We conclude by illustrating how the notions of dynamical embodiment, resonance, and attunement provide a conceptual language for characterizing musical rhythm development, and, when formalized in physiologically informed dynamical models, provide a theoretical framework for generating testable empirical predictions about musical rhythm development, such as the kinds of native and non-native rhythmic structures infants and children can learn, steady-state evoked potentials to native and non-native musical rhythms, and the effects of short-term (e.g., infant bouncing, infant music classes), long-term (e.g., perceptual narrowing to musical rhythm), and very-long term (e.g., music enculturation, musical training) learning on music perception-action.

Integrating music-based interventions with Gamma-frequency stimulation: Implications for healthy ageing.

In recent years, music-based interventions (MBIs) have risen in popularity as a non-invasive, sustainable form of care for treating dementia-related disorders, such as Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD). Despite their clinical potential, evidence regarding the efficacy of MBIs on patient outcomes is mixed. Recently, a line of related research has begun to investigate the clinical impact of non-invasive Gamma-frequency (e.g., 40 Hz) sensory stimulation on dementia. Current work, using non-human-animal models of AD, suggests that non-invasive Gamma-frequency stimulation can remediate multiple pathophysiologies of dementia at the molecular, cellular and neural-systems scales, and, importantly, improve cognitive functioning. These findings suggest that the efficacy of MBIs could, in theory, be enhanced by incorporating Gamma-frequency stimulation into current MBI protocols. In the current review, we propose a novel clinical framework for non-invasively treating dementia-related disorders that combines previous MBIs with current approaches employing Gamma-frequency sensory stimulation. We theorize that combining MBIs with Gamma-frequency stimulation could increase the therapeutic power of MBIs by simultaneously targeting multiple biomarkers of dementia, restoring neural activity that underlies learning and memory (e.g., Gamma-frequency neural activity, Theta-Gamma coupling), and actively engaging auditory and reward networks in the brain to promote behavioural change.

Intelligent Tire Sensor-Based Real-Time Road Surface Classification Using an Artificial Neural Network.

Vehicles today have many advanced driver assistance control systems that improve vehicle safety and comfort. With the development of more sophisticated vehicle electronic control and autonomous driving technology, the need and effort to estimate road surface conditions is increasing. In this paper, a real-time road surface classification algorithm, based on a deep neural network, is developed using a database collected through an intelligent tire sensor system with a three-axis accelerometer installed inside the tire. Two representative types of network, fully connected neural network (FCNN) and convolutional neural network (CNN), are learned with each of the three-axis acceleration sensor signals, and their performances were compared to obtain an optimal learning network result. The learning results show that the road surface type can be classified in real-time with sufficient accuracy when the longitudinal and vertical axis acceleration signals are trained with the CNN. In order to improve classification accuracy, a CNN with multiple input that can simultaneously learn 2-axis or 3-axis acceleration signals is suggested. In addition, by analyzing how the accuracy of the network is affected by number of classes and length of input data, which is related to delay of classification, the appropriate network can be selected according to the application. The proposed real-time road surface classification algorithm is expected to be utilized with various vehicle electronic control systems and makes a contribution to improving vehicle performance.

Bouncing the network: A dynamical systems model of auditory-vestibular interactions underlying infants' perception of musical rhythm.

Previous work suggests that auditory-vestibular interactions, which emerge during bodily movement to music, can influence the perception of musical rhythm. In a seminal study on the ontogeny of musical rhythm, Phillips-Silver and Trainor (2005) found that bouncing infants to an unaccented rhythm influenced infants' perceptual preferences for accented rhythms that matched the rate of bouncing. In the current study, we ask whether nascent, diffuse coupling between auditory and motor systems is sufficient to bootstrap short-term Hebbian plasticity in the auditory system and explain infants' preferences for accented rhythms thought to arise from auditory-vestibular interactions. First, we specify a nonlinear, dynamical system in which two oscillatory neural networks, representing developmentally nascent auditory and motor systems, interact through weak, non-specific coupling. The auditory network was equipped with short-term Hebbian plasticity, allowing the auditory network to tune its intrinsic resonant properties. Next, we simulate the effect of vestibular input (e.g., infant bouncing) on infants' perceptual preferences for accented rhythms. We found that simultaneous auditory-vestibular training shaped the model's response to musical rhythm, enhancing vestibular-related frequencies in auditory-network activity. Moreover, simultaneous auditory-vestibular training, relative to auditory- or vestibular-only training, facilitated short-term auditory plasticity in the model, producing stronger oscillator connections in the auditory network. Finally, when tested on a musical rhythm, models which received simultaneous auditory-vestibular training, but not models that received auditory- or vestibular-only training, resonated strongly at frequencies related to their "bouncing," a finding qualitatively similar to infants' preferences for accented rhythms that matched the rate of infant bouncing.

Multifrequency Hebbian plasticity in coupled neural oscillators.

We study multifrequency Hebbian plasticity by analyzing phenomenological models of weakly connected neural networks. We start with an analysis of a model for single-frequency networks previously shown to learn and memorize phase differences between component oscillators. We then study a model for gradient frequency neural networks (GrFNNs) which extends the single-frequency model by introducing frequency detuning and nonlinear coupling terms for multifrequency interactions. Our analysis focuses on models of two coupled oscillators and examines the dynamics of steady-state behaviors in multiple parameter regimes available to the models. We find that the model for two distinct frequencies shares essential dynamical properties with the single-frequency model and that Hebbian learning results in stronger connections for simple frequency ratios than for complex ratios. We then compare the analysis of the two-frequency model with numerical simulations of the GrFNN model and show that Hebbian plasticity in the latter is locally dominated by a nonlinear resonance captured by the two-frequency model.

A canonical oscillator model of cochlear dynamics.

Nonlinear responses to acoustic signals arise through active processes in the cochlea, which has an exquisite sensitivity and wide dynamic range that can be explained by critical nonlinear oscillations of outer hair cells. Here we ask how the interaction of critical nonlinearities with the basilar membrane and other organ of Corti components could determine tuning properties of the mammalian cochlea. We propose a canonical oscillator model that captures the dynamics of the interaction between the basilar membrane and organ of Corti, using a pair of coupled oscillators for each place along the cochlea. We analyze two models in which a linear oscillator, representing basilar membrane dynamics, is coupled to a nonlinear oscillator poised at a Hopf instability. The coupling in the first model is unidirectional, and that of the second is bidirectional. Parameters are determined by fitting 496 auditory-nerve (AN) tuning curves of macaque monkeys. We find that the unidirectionally and bidirectionally coupled models account equally well for threshold tuning. In addition, however, the bidirectionally coupled model exhibits low-amplitude, spontaneous oscillation in the absence of stimulation, predicting that phase locking will occur before a significant increase in firing frequency, in accordance with well known empirical observations. This leads us to a canonical oscillator cochlear model based on the fundamental principles of critical nonlinear oscillation and coupling dynamics. The model is more biologically realistic than widely used linear or nonlinear filter-based models, yet parsimoniously displays key features of nonlinear mechanistic models. It is efficient enough for computational studies of auditory perception and auditory physiology.

Mode locking in periodically forced gradient frequency neural networks.

We study mode locking in a canonical model of gradient frequency neural networks under periodic forcing. The canonical model is a generic mathematical model for a network of nonlinear oscillators tuned to a range of distinct frequencies. It is mathematically more tractable than biological neuron models and allows close analysis of mode-locking behaviors. Here we analyze individual modes of synchronization for a periodically forced canonical model and present a complete set of driven behaviors for all parameter regimes available in the model. Using a closed-form approximation, we show that the Arnold tongue (i.e., locking region) for k:m synchronization gets narrower as k and m increase. We find that numerical simulations of the canonical model closely follow the analysis of individual modes when forcing is weak, but they deviate at high forcing amplitudes for which oscillator dynamics are simultaneously influenced by multiple modes of synchronization.

A Dynamical Model of Pitch Memory Provides an Improved Basis for Implied Harmony Estimation.

Tonal melody can imply vertical harmony through a sequence of tones. Current methods for automatic chord estimation commonly use chroma-based features extracted from audio signals. However, the implied harmony of unaccompanied melodies can be difficult to estimate on the basis of chroma content in the presence of frequent nonchord tones. Here we present a novel approach to automatic chord estimation based on the human perception of pitch sequences. We use cohesion and inhibition between pitches in auditory short-term memory to differentiate chord tones and nonchord tones in tonal melodies. We model short-term pitch memory as a gradient frequency neural network, which is a biologically realistic model of auditory neural processing. The model is a dynamical system consisting of a network of tonotopically tuned nonlinear oscillators driven by audio signals. The oscillators interact with each other through nonlinear resonance and lateral inhibition, and the pattern of oscillatory traces emerging from the interactions is taken as a measure of pitch salience. We test the model with a collection of unaccompanied tonal melodies to evaluate it as a feature extractor for chord estimation. We show that chord tones are selectively enhanced in the response of the model, thereby increasing the accuracy of implied harmony estimation. We also find that, like other existing features for chord estimation, the performance of the model can be improved by using segmented input signals. We discuss possible ways to expand the present model into a full chord estimation system within the dynamical systems framework.

The Effect of Traumatic Experiences and Psychiatric Symptoms on the Life Satisfaction of North Korean Refugees.

BACKGROUND: Successful adaptation of refugees to a new society can be hindered by traumatic experiences and psychiatric symptoms. This study aims to examine the relationship between trauma, psychiatric symptoms and life satisfaction of North Korean refugees resettled in South Korea. METHODS: A total of 211 North Korean refugees living in South Korea completed a series of questionnaires on the history of their previous traumatic experiences, life satisfaction in South Korea, depression, anxiety, somatization and post-traumatic stress disorder (PTSD) symptoms. RESULTS: North Korean refugees who had experienced more traumatic events were less satisfied with their economic status in South Korea. Severe depression, anxiety, somatization or PTSD symptoms negatively correlated with their overall satisfaction in South Korea. In the stepwise regression model including all psychiatric symptoms and the number of traumatic experiences as dependent variables, only anxiety, but not trauma, predicted lower life satisfaction in South Korea. CONCLUSIONS: Traumatic experiences of North Korean refugees negatively affected the life satisfaction, especially the economic satisfaction, in South Korea. Since the negative effect of trauma was mainly mediated by psychiatric symptoms, the strategy of relieving psychiatric symptoms of traumatized refugees may help the adaptation of refugees.

Effects of various freezing containers for vitrification freezing on mouse oogenesis.

BACKGROUND: In the present study, various freezing containers were tested for mouse embryos of respective developmental stages; embryos were vitrified and then their survival rate and developmental rate were monitored. Mouse two cell, 8 cell, and blastula stage embryos underwent vitrification freezing-thawing and then their recovery rate, survival rate, development rate, and hatching rate were investigated. METHODS: EM-grid, OPS, and cryo-loop were utilized for vitrification freezing-thawing of mouse embryos. RESULTS: It was found that recovery rate and survival rate were higher in the group of cryo-loop compared to those of EM-grid (p < 0.05). Embryonic development rate, two cell embryos to blastocyst, as well as hatching rate were higher in the control group compared to the EM-grid group and OPS group (p < 0.05), yet no difference was noted between the control group and cryo-loop group. Development rate and hatching rate of eight cell morulae and blastocysts were all lower in the treatment groups than the control group whilst hatching rate of blastocysts was higher in the control group compared to the groups of EM-grid and OPS (p < 0.05); although the cryo-loop group was shown to be slightly higher than other groups, it was not statistically significant. CONCLUSIONS: In the study, we investigate effects of freezing containers on vitrified embryos of respective developmental stages; it was demonstrated that higher developmental rate was shown in more progressed (or developed) embryos with more blastomeres. There was however, no difference in embryonic development rate was shown amongst containers. Taken together, further additional studies are warranted with regards to 1) manipulation techniques of embryos for various vitrification freezing containers and 2) preventive measures against contamination via liquid nitrogen.

Note: Position/torque control of antagonistic robot joint with high-compliant twisted string actuators (TSAs).

A tendon-driven robot joint that has a low inertia compared with a conventional drive system is proposed. The robot joint displaces the drive system toward the robot base, and it is driven by twisted string actuators (TSAs), which are a substitute for the conventional heavy drive system. The design of the proposed robot joint is based on an antagonistic scheme that is actuated by two motors. The torques from the motors are transmitted to the robot joint through the TSAs. Based on the geometric analysis of TSAs, strategies for position and torque control are proposed for an antagonistic robot joint driven by TSAs. To verify the proposed control strategies, several control experiments are conducted using a developed prototype of a robot joint.

Signal Processing in Periodically Forced Gradient Frequency Neural Networks.

Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing.

Mode-locking neurodynamics predict human auditory brainstem responses to musical intervals.

The auditory nervous system is highly nonlinear. Some nonlinear responses arise through active processes in the cochlea, while others may arise in neural populations of the cochlear nucleus, inferior colliculus and higher auditory areas. In humans, auditory brainstem recordings reveal nonlinear population responses to combinations of pure tones, and to musical intervals composed of complex tones. Yet the biophysical origin of central auditory nonlinearities, their signal processing properties, and their relationship to auditory perception remain largely unknown. Both stimulus components and nonlinear resonances are well represented in auditory brainstem nuclei due to neural phase-locking. Recently mode-locking, a generalization of phase-locking that implies an intrinsically nonlinear processing of sound, has been observed in mammalian auditory brainstem nuclei. Here we show that a canonical model of mode-locked neural oscillation predicts the complex nonlinear population responses to musical intervals that have been observed in the human brainstem. The model makes predictions about auditory signal processing and perception that are different from traditional delay-based models, and may provide insight into the nature of auditory population responses. We anticipate that the application of dynamical systems analysis will provide the starting point for generic models of auditory population dynamics, and lead to a deeper understanding of nonlinear auditory signal processing possibly arising in excitatory-inhibitory networks of the central auditory nervous system. This approach has the potential to link neural dynamics with the perception of pitch, music, and speech, and lead to dynamical models of auditory system development.