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Background: The temporal structure of macroscopic brain activity displays both oscillatory and scale-free dynamics. While the functional relevance of neural oscillations has been largely investigated, both the nature and the role of scale-free dynamics in brain processing have been disputed., New Method: Here, we offer a novel method to rigorously enrich the characterization of scale-free brain activity using a robust wavelet-based assessment of self-similarity and multifractality. For this, we analyzed human brain activity recorded with magnetoencephalography (MEG) while participants were at rest or performing a visual motion discrimination task., Results: First, we report consistent infraslow (from 0.1 to 1.5 Hz) scale-free dynamics (i.e., self-similarity and multifractality) in resting-state and task data. Second, we observed a fronto-occipital gradient of self-similarity reminiscent of the known hierarchy of temporal scales from sensory to higher-order cortices; the anatomical gradient was more pronounced in task than in rest. Third, we observed a significant increase of multifractality during task as compared to rest. Additionally, the decrease in self-similarity and the increase in multifractality from rest to task were negatively correlated in regions involved in the task, suggesting a shift from structured global temporal dynamics in resting-state to locally bursty and non Gaussian scale-free structures during task., Comparison With Existing Method(s): We showed that the wavelet leader based multifractal approach extends power spectrum estimation methods in the way of characterizing finely scale-free brain dynamics., Conclusions: Altogether, our approach provides novel fine-grained characterizations of scale-free dynamics in human brain activity., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The aim of the current study is to enhance our understanding of cognitive creativity, specifically divergent thinking, by employing an interdisciplinary methodological approach. By integrating methodology from computational linguistics and complex systems into creativity research, the current study aims to shed light on the relationship between divergent thinking and the temporal structure of semantic associations. In complex systems, temporal structures can be described on a continuum from random to flexible-stable and to persistent. Random structures are highly unpredictable, persistent structures are highly predictable, and flexible-stable structures are in-between, they are partly predictable from previous observations. Temporal structures of associations that are random (e.g., dog-graveyard-north pole) or persistent (e.g., dog-cat-rat) are hypothesized to be detrimental to divergent thinking. However, a flexible-stable structure (e.g., dog-police-drugs) is hypothesized to be related to enhanced divergent thinking (inverted-U). This notion was tested ( N = 59) in an association chain task, combined with a frequently used measure of divergent thinking (i.e., Alternative Uses Test). Latent Semantic Analysis from computational linguistics was used to quantify the associations, and methods from complex systems in form of Power Spectral Density analysis and detrended fluctuation analysis were used to estimate the temporal structure of those associations. Although the current study does not confirm that a flexible-stable (vs. random/persistent) temporal structure of associations is related to enhanced divergent thinking skills, it hopefully challenges fellow researchers to refine the recent methodological developments for assessing the (temporal) structure of associations. Moreover, the current cross-fertilization of methodological approaches may inspire creativity researchers to take advantage of other fields' ideas and methods. To derive a theoretically sound cognitive theory of creativity, it is important to integrate research ideas and empirical methods from a variety of disciplines.

Insects have acquired excellent sensory information processing abilities in the process of evolution. In addition, insects have developed communication schemes based on the temporal patterns of specific sensory signals. For instance, male moths approach a female by detecting the spatiotemporal pattern of a pheromone plume released by the female. Male crickets attract a conspecific female as a mating partner using calling songs with species-specific temporal patterns. The dance communication of honeybees relies on a unique temporal pattern of vibration caused by wingbeats during the dance. Underlying these behaviors, neural circuits involving inhibitory connections play a critical common role in processing the exact timing of the signals in the primary sensory centers of the brain. Here, we discuss common mechanisms for processing the temporal patterns of sensory signals in the insect brain.

Musical sounds, along with speech, are the most prominent sounds in our daily lives. They are highly dynamic, yet well structured in the temporal domain in a hierarchical manner. The temporal structures enhance the predictability of musical sounds. Western music provides an excellent example: while time intervals between musical notes are highly variable, underlying beats can be realized. The beat-level temporal structure provides a sense of regular pulses. Beats can be further organized into units, giving the percept of alternating strong and weak beats (i.e. metrical structure or meter). Examining neural processing at the meter level offers a unique opportunity to understand how the human brain extracts temporal patterns, predicts future stimuli and optimizes neural resources for processing. The present study addresses two important questions regarding meter processing, using the mismatch negativity (MMN) obtained with electroencephalography (EEG): 1) how tempo (fast vs. slow) and type of metrical structure (duple: two beats per unit vs. triple: three beats per unit) affect the neural processing of metrical structure in non-musically trained individuals, and 2) how early music training modulates the neural processing of metrical structure. Metrical structures were established by patterns of consecutive strong and weak tones (Standard) with occasional violations that disrupted and reset the structure (Deviant). Twenty non-musicians listened passively to these tones while their neural activities were recorded. MMN indexed the neural sensitivity to the meter violations. Results suggested that MMNs were larger for fast tempo and for triple meter conditions. Further, 20 musically trained individuals were tested using the same methods and the results were compared to the non-musicians. While tempo and meter type similarly influenced MMNs in both groups, musicians overall exhibited significantly reduced MMNs, compared to their non-musician counterparts. Further analyses indicated that the reduction was driven by responses to sounds that defined the structure (Standard), not by responses to Deviants. We argue that musicians maintain a more accurate and efficient mental model for metrical structures, which incorporates occasional disruptions using significantly fewer neural resources., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Quantifying how patterns of behavior relate across multiple levels of measurement typically requires long time series for reliable parameter estimation. We describe a novel analysis that estimates patterns of variability across multiple scales of analysis suitable for time series of short duration. The multiscale coefficient of variation (MSCV) measures the distance between local coefficient of variation estimates within particular time windows and the overall coefficient of variation across all time samples. We first describe the MSCV analysis and provide an example analytical protocol with corresponding MATLAB implementation and code. Next, we present a simulation study testing the new analysis using time series generated by ARFIMA models that span white noise, short-term and long-term correlations. The MSCV analysis was observed to be sensitive to specific parameters of ARFIMA models varying in the type of temporal structure and time series length. We then apply the MSCV analysis to short time series of speech phrases and musical themes to show commonalities in multiscale structure. The simulation and application studies provide evidence that the MSCV analysis can discriminate between time series varying in multiscale structure and length.

Purpose: Previous research has demonstrated that attorney question format relates to child witness' response productivity. However, little work has examined the relations between the extent to which attorneys provide temporal structure in their questions, and the effects of this structure on children's responding. The purpose of the present study was to address this gap in the literature in order to identify methods by which attorneys increase children's response productivity on the stand without risking objections from opposing counsel for "calling for narrative answers"., Methods: In the present study we coded criminal court transcripts involving child witnesses (5-18 years) for narrative structure in attorney questions and productivity in children's responses. Half of the transcripts resulted in convictions, half in acquittals, balanced across key variables: child age, allegation severity, the child's relationship to the perpetrator, and the number of allegations., Results: Prosecutors and defense attorneys varied substantially in their questioning tactics. Prosecutors used more temporal structure in their questions and varied their questioning by the age of the child. These variations had implications for children's response productivity., Conclusions: Results indicate that temporal structure is a novel and viable method for enhancing children's production of case-relevant details on the witness stand.

Human language is a recombinant system that achieves its productivity through the combination of a limited set of sounds. Research investigating the evolutionary origin of this generative capacity has generally focused on the capacity of non-human animals to combine different types of discrete sounds to encode new meaning, with less emphasis on meaning-differentiating mechanisms achieved through potentially simpler temporal modifications within a sequence of repeated sounds. Here we show that pied babblers (Turdoides bicolor) generate two functionally distinct vocalisations composed of the same sound type, which can only be distinguished by the number of repeated elements. Specifically, babblers produce extended 'purrs' composed of, on average, around 17 element repetitions when drawing young offspring to a food source and truncated 'clucks' composed of a fixed number of 2-3 elements when collectively mediating imminent changes in foraging site. We propose that meaning-differentiating temporal structuring might be a much more widespread combinatorial mechanism than currently recognised and is likely of particular value for species with limited vocal repertoires in order to increase their communicative output.

Temporal codes are theoretically powerful encoding schemes, but their precise form in the neocortex remains unknown in part because of the large number of possible codes and the difficulty in disambiguating informative spikes from statistical noise. A biologically plausible and computationally powerful temporal coding scheme is the Hebbian assembly phase sequence (APS), which predicts reliable propagation of spikes between functionally related assemblies of neurons. Here, we sought to measure the inherent capacity of neocortical networks to produce reliable sequences of spikes, as would be predicted by an APS code. To record microcircuit activity, the scale at which computation is implemented, we used two-photon calcium imaging to densely sample spontaneous activity in murine neocortical networks ex vivo. We show that the population spike histogram is sufficient to produce a spatiotemporal progression of activity across the population. To more comprehensively evaluate the capacity for sequential spiking that cannot be explained by the overall population spiking, we identify statistically significant spike sequences. We found a large repertoire of sequence spikes that collectively comprise the majority of spiking in the circuit. Sequences manifest probabilistically and share neuron membership, resulting in unique ensembles of interwoven sequences characterizing individual spatiotemporal progressions of activity. Distillation of population dynamics into its constituent sequences provides a way to capture trial-to-trial variability and may prove to be a powerful decoding substrate in vivo. Informed by these data, we suggest that the Hebbian APS be reformulated as interwoven sequences with flexible assembly membership due to shared overlapping neurons. NEW & NOTEWORTHY Neocortical computation occurs largely within microcircuits comprised of individual neurons and their connections within small volumes (<500 μm 3 ). We found evidence for a long-postulated temporal code, the Hebbian assembly phase sequence, by identifying repeated and co-occurring sequences of spikes. Variance in population activity across trials was explained in part by the ensemble of active sequences. The presence of interwoven sequences suggests that neuronal assembly structure can be variable and is determined by previous activity., (Copyright © 2017 the American Physiological Society.)

Neural systems are characterized by their complex dynamics, reflected on signals produced by neurons and neuronal ensembles. This complexity exhibits specific features in health, disease and in different states of consciousness, and can be considered a hallmark of certain neurologic and neuropsychiatric conditions. To measure complexity from neurophysiologic signals, a number of different nonlinear tools of analysis are available. However, not all of these tools are easy to implement, or able to handle clinical data, often obtained in less than ideal conditions in comparison to laboratory or simulated data. Recently, the temporal structure function emerged as a powerful tool for the analysis of complex properties of neuronal activity. The temporal structure function is efficient computationally and it can be robustly estimated from short signals. However, the application of this tool to neuronal data is relatively new, making the interpretation of results difficult. In this methods paper we describe a step by step algorithm for the calculation and characterization of the structure function. We apply this algorithm to oscillatory, random and complex toy signals, and test the effect of added noise. We show that: (1) the mean slope of the structure function is zero in the case of random signals; (2) oscillations are reflected on the shape of the structure function, but they don't modify the mean slope if complex correlations are absent; (3) nonlinear systems produce structure functions with nonzero slope up to a critical point, where the function turns into a plateau. Two characteristic numbers can be extracted to quantify the behavior of the structure function in the case of nonlinear systems: (1). the point where the plateau starts (the inflection point, where the slope change occurs), and (2). the height of the plateau. While the inflection point is related to the scale where correlations weaken, the height of the plateau is related to the noise present in the signal. To exemplify our method we calculate structure functions of neuronal recordings from the basal ganglia of parkinsonian and healthy rats, and draw guidelines for their interpretation in light of the results obtained from our toy signals.

This study was undertaken to investigate whether, in rat interactive activities, recurring sequences of behavioural events might be identified and how and to what extent each component of the pair is involved. To this aim, the multivariate temporal-pattern (t-pattern) analysis was applied to the social interactions of 9 pairs of male Wistar rats tested in open field. Interactive activities were classified into intra- and inter-subjects. Quantitative evaluations showed that intra-subject behavioural elements represented 62.37% and inter-subject ones 37.63% of the comprehensive behaviour. T-pattern analysis revealed the presence of 221 different t-patterns organized in four different categories: containing exclusively inter-subject elements; containing both inter- and intra-subject elements; consisting of rat 1 and rat 2 intra-subject elements and, finally, consisting of intra-subject elements carried out by one of the two subjects. Results show that the activity of two interacting Wistar rats is structured on the basis of several recurring temporal sequences. Moreover, social interactions appear to be expressed also by t-patterns where the behavioural elements are carried out by animals seemingly not interacting. A support of t-pattern analysis to studies on Autism Spectrum Disorders is proposed., (Copyright © 2017 Elsevier B.V. All rights reserved.)

How can we grasp the temporal structure of events? A few studies have indicated that representations of temporal structure are acquired when there is an intention to learn, but not when learning is incidental. Response-to-stimulus intervals, uncorrelated temporal structures, unpredictable ordinal information, and lack of metrical organization have been pointed out as key obstacles to incidental temporal learning, but the literature includes piecemeal demonstrations of learning under all these circumstances. We suggest that the unacknowledged effects of ordinal load may help reconcile these conflicting findings, ordinal load referring to the cost of identifying the sequence of events (e.g., tones, locations) where a temporal pattern is embedded. In a first experiment, we manipulated ordinal load into simple and complex levels. Participants learned ordinal-simple sequences, despite their uncorrelated temporal structure and lack of metrical organization. They did not learn ordinal-complex sequences, even though there were no response-to-stimulus intervals nor unpredictable ordinal information. In a second experiment, we probed learning of ordinal-complex sequences with strong metrical organization, and again there was no learning. We conclude that ordinal load is a key obstacle to incidental temporal learning. Further analyses showed that the effect of ordinal load is to mask the expression of temporal knowledge, rather than to prevent learning.

Background: Females are markedly underinvestigated in the biological and behavioral sciences due to the presumption that cyclic hormonal changes across the ovulatory cycle introduce excess variability to measures of interest in comparison to males. However, recent analyses indicate that male and female mice and rats exhibit comparable variability across numerous physiological and behavioral measures, even when the stage of the estrous cycle is not considered. Hormonal changes across the ovulatory cycle likely contribute cyclic, intra-individual variability in females, but the source(s) of male variability has, to our knowledge, not been investigated. It is unclear whether male variability, like that of females, is temporally structured and, therefore, quantifiable and predictable. Finally, whether males and females exhibit variability on similar time scales has not been explored., Methods: These questions were addressed by collecting chronic, high temporal resolution locomotor activity (LA) and core body temperature (CBT) data from male and female BALB/c mice., Results: Contrary to expectation, males are more variable than females over the course of the day (diel variability) and exhibit higher intra-individual daily range than females in both LA and CBT. Between mice of a given sex, variability is comparable for LA but the inter-individual daily range in CBT is greater for males. To identify potential rhythmic processes contributing to these sex differences, we employed wavelet transformations across a range of periodicities (1-39 h)., Conclusions: Although variability in circadian power is comparable between the sexes for both LA and CBT, infradian variability is greater in females and ultradian variability is greater in males. Thus, exclusion of female mice from studies because of estrous cycle variability may increase variance in investigations where only male measures are collected over a span of several hours and limit generalization of findings from males to females.

Temporal structure reveals the potential adaptive strategies employed during upper extremity movements. The authors compared the temporal structure of upper extremity joints under 3 different reaching conditions: preferred speed, fast speed, and reaching with rhythmic auditory cues in 10 individuals poststroke. They also investigated the temporal structure of these 3 reaching conditions in 8 healthy controls to aid in the interpretation of the observed patterns in the poststroke cohort. Approximate entropy (ApEn) was used to measure the temporal structure of the upper extremity joints. ApEn was similar between conditions in controls. After stroke, ApEn was significantly higher for shoulder, elbow, and wrist both at fast speed and with rhythmic cues compared with preferred speed. ApEn at index finger was significantly higher only with rhythmic cues compared with preferred speed. The authors propose that practice reaching at faster speed and with rhythmic cues as a component of rehabilitation interventions may enhance adaptability after stroke.

Recent studies of brain plasticity that pertain to time perception have shown that fast training of temporal discrimination in one modality, for example, the auditory modality, can improve performance of temporal discrimination in another modality, such as the visual modality. We here examined whether the perception of visual Ternus motion could be recalibrated through fast crossmodal statistical binding of temporal information and stimuli properties binding. We conducted two experiments, composed of three sessions each: pre-test, learning, and post-test. In both the pre-test and the post-test, participants classified the Ternus display as either "element motion" or "group motion." For the training session in Experiment 1, we constructed two types of temporal structures, in which two consecutively presented sound beeps were dominantly (80%) flanked by one leading visual Ternus frame and by one lagging visual Ternus frame (VAAV) or dominantly inserted by two Ternus visual frames (AVVA). Participants were required to respond which interval (auditory vs. visual) was longer. In Experiment 2, we presented only a single auditory-visual pair but with similar temporal configurations as in Experiment 1, and asked participants to perform an audio-visual temporal order judgment. The results of these two experiments support that statistical binding of temporal information and stimuli properties can quickly and selectively recalibrate the sensitivity of perceiving visual motion, according to the protocols of the specific bindings.

The performance of a lateralization task based on interaural time or level differences (ITDs or ILDs) often varies among listeners. This study examined the extent to which this inter-listener variation could be accounted for by the coding efficiency of the temporal-structure or level information below the stage of interaural interaction. Young listeners (20s to 30s) and early-elderly (60s) listeners with or without mild hearing loss were tested. The ITD, ILD, TIME, and LEVEL tasks were intended to measure sensitivities to ITDs, ILDs, the temporal structure of the stimulus encoded by the neural phase locking, and the stimulus level, respectively. The performances of the ITD and ILD tasks were not significantly different between the age groups, while the elderly listeners exhibited significantly poorer performance in the TIME task (and in the LEVEL with a high-frequency stimulus only) than the young listeners. Significant correlations were found between thresholds for the ILD and LEVEL tasks with low- and high-frequency stimuli and for the ITD and TIME tasks for the high-frequency stimulus, implying peripheral coding efficiency as a major factor determining lateralization performance. However, we failed to find a correlation between the ITD and TIME tasks for the low-frequency stimulus, despite a large range of threshold values in the TIME task. This implies that in a low frequency region, the peripheral coding efficiency of the stimulus temporal structure is a relatively minor factor in the ITD-based lateralization performance.

An abstract of the article "Rhythmic Structure of Hindi and English: New Insights From a Computational Analysis," by Tanusree Das and colleagues is presented.

This review paper deals with methodological and technical foundations of the Operational Architectonics framework of brain and mind functioning. This theory provides a framework for mapping and understanding important aspects of the brain mechanisms that constitute perception, cognition, and eventually consciousness. The methods utilized within Operational Architectonics framework allow analyzing with an incredible detail the operational behavior of local neuronal assemblies and their joint activity in the form of unified and metastable operational modules, which constitute the whole hierarchy of brain operations, operations of cognition and phenomenal consciousness.