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temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex

JOURNAL OF NEUROPHYSIOLOGY 2007年第6期97卷 4235-4257页

作者： Churchland, Mark M. Shenoy, Krishna V. Stanford Univ Dept Elect Engn Neurosci Program Stanford CA USA

The relationship between neural activity in motor cortex and movement is highly debated. Although many studies have examined the spatial tuning (e.g., for direction) of cortical responses, less attention has been paid to the temporal properties of individual neuron responses. We developed a novel task, employing two instructed speeds, that allows meaningful averaging of neural responses across reaches with nearly identical velocity profiles. Doing so preserves fine temporal structure and reveals considerable complexity and heterogeneity of response patterns in primary motor and premotor cortex. Tuning for direction was prominent, but the preferred direction was frequently inconstant with respect to time, instructed-speed, and/or reach distance. Response patterns were often temporally complex and multiphasic, and varied with direction and instructed speed in idiosyncratic ways. A wide variety of patterns was observed, and it was not uncommon for a neuron to exhibit a pattern shared by no other neuron in our dataset. Response patterns of individual neurons rarely, if ever, matched those of individual muscles. Indeed, the set of recorded responses spanned a much higher dimensional space than would be expected for a model in which neural responses relate to a moderate number of factors-dynamic, kinematic, or otherwise. Complex responses may provide a basis-set representing many parameters. Alternately, it may be necessary to discard the notion that responses exist to "represent" movement parameters. It has been argued that complex and heterogeneous responses are expected of a recurrent network that produces temporally patterned outputs, and the present results would seem to support this view.

关键词： .Heterogeneity temporally nearly tuning neural activity averaging Speeds Single-Neuron neural responses preferred direction instructed reaches response patterns Activity in Premotor temporal complexity Churchland MM

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temporal complexity of EEG encodes human alertness

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PHYSIOLOGICAL MEASUREMENT 2022年第9期43卷 095002-095002页

作者： Hadra, Mohammad Omidvarnia, Amir Mesbah, Mostefa Sultan Qaboos Univ Ctr Preparatory Studies POB 33 PC 123 Muscat Oman Res Ctr Julich Inst Neurosci & Med Brain & Behav INM7 Appl Machine Learning Grp Julich Germany Sultan Qaboos Univ Coll Engn Dept Elect & Comp Engn POB 33 PC 123 Muscat Oman Heinrich Heine Univ Dusseldorf Med Fac Inst Syst Neurosci Dusseldorf Germany

Objective. Automatic human alertness monitoring has recently become an important research topic with important applications in many areas such as the detection of drivers' fatigue, monitoring of monotonous tasks that require a high level of alertness such as traffic control and nuclear power plant monitoring, and sleep staging. In this study, we propose that balanced dynamics of Electroencephalography (EEG) (so called EEG temporal complexity) is a potentially useful feature for identifying human alertness states. Recently, a new signal entropy measure, called range entropy (RangeEn), was proposed to overcome some limitations of two of the most widely used entropy measures, namely approximate entropy (ApEn) and Sample Entropy (SampEn), and showed its relevance for the study of time domain EEG complexity. In this paper, we investigated whether the RangeEn holds discriminating information associated with human alertness states, namely awake, drowsy, and sleep and compare its performance against those of SampEn and ApEn. Approach. We used EEG data from 60 healthy subjects of both sexes and different ages acquired during whole night sleeps. Using a 30 s sliding window, we computed the three entropy measures of EEG and performed statistical analyses to evaluate the ability of these entropy measures to discriminate among the different human alertness states. Main results. Although the three entropy measures contained useful information about human alertness, RangeEn showed a higher discriminative capability compared to ApEn and SampEn especially when using EEG within the beta frequency band. Significance. Our findings highlight the EEG temporal complexity evolution through the human alertness states. This relationship can potentially be exploited for the development of automatic human alertness monitoring systems and diagnostic tools for different neurological and sleep disorders, including insomnia.

关键词： EEG human alertness human vigilance approximate entropy sample entropy range entropy temporal complexity

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Sustained Effect of Auditory Entrainment With Coordinated Movement Varies With temporal complexity of Sequential Tapping

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PERCEPTUAL AND MOTOR SKILLS 2023年第3期130卷 1013-1039页

作者： Wen, Xiao-Qian Zhang, Jun Ren, Jie Shanghai Univ Sport Sch Psychol Shanghai Peoples R China Heilongjiang Shooting Cycling & Archery Sports Man Harbin Peoples R China Shanghai Univ Sport Sch Kinesiol Shanghai Peoples R China Shandong Sport Univ Sch Sport Commun & Informat Technol Jinan Peoples R China Shanghai Univ Sport China Table Tennis Coll Shanghai Peoples R China Shanghai Univ Sport China Table Tennis Coll 399 Changhai Rd Shanghai 200438 Peoples R China

While the ability to coordinate movements temporally with rhythmic auditory stimuli is universal, previous investigators showed that accurate rhythm reproduction depends on temporal complexity. To date, the effect of multiple pitches on the timing of rhythmic movements has been assumed. Exploring a possible sustained entrainment effect of auditory stimuli on sequential movement might further elucidate the role of temporal complexity and its interaction with multiple pitch engagement. Thus, we investigated the sustained effect of auditory entrainment and the interaction between temporal complexity and pitch on predefined sequential tapping with tapping sequences predefined before a synchronization-timekeeping task. temporal complexity was manipulated by increasing the number of non-integer ratios in temporal rhythm. The rhythm sequences were presented with either multiple pitches or a single pitch. We found a reduction in mean asynchronies and ratio error in three rhythms with non-integer ratios, while inter-response interval error was reduced in the integer rhythm and the rhythm with one repetitive integer ratio and one non-integer ratio. Ratio error remanence was less in rhythms with two non-integer ratios. We found no significant difference between the two pitch types. There was a sustained entrainment effect of sequential tapping that varied with differing temporal complexity, and pitch information was not essential for auditory entrainment. These findings provide support for possible interventions aimed at motor learning.

关键词： auditory entrainment sequential movements non-integer ratio temporal complexity motion temporal processing time perception synchrony

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Function of EEG temporal complexity Analysis in Neural Activities Measurement

Function of EEG Temporal Complexity Analysis in Neural Activ...

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6th International Symposium on Neural Networks

作者： Li, Xiuquan Deng, Zhidong Zhang, Jianwei Tsinghua Univ Dept Comp Sci Tsinghua Natl Lab Informat Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Univ Hamburg Dept Informat TAMS D-22527 Hamburg Germany

ISBN: (纸本)9783642015069

We investigate the correlation between temporal complexity of EEG signal and the underlining neural activities. Fractal geometry has been proved useful in quantifying complexities of dynamical signals. temporal fractal dimension of EEG signals provides a new neurophysiological measure. In order to better understand what the complexity measure reveals about the underling brain process, a further exploration oil the neuronal generators of fractal geometry characteristics of EEG is conducted in this study. Our investigation suggests that the temporal fractal measure of EEG signals can be related to the activity diversity of neuronal population activities. The complexity measure also gives an indication oil the change in synchronization state under certain mental conditions. These assumptions are supported by experimental evidence from the Visual cortex and sensori-motor cortex. This work helps give an interpretation of the obtained results of the temporal complexity analysis on EEG signals and may be useful in further investigating the covert steps of brain information processing.

关键词： Electroencephalography(EEG) Event-related potentials(ERPs) temporal complexity Fractal analysis Event-related desynchronization (ERD)

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Unobtrusive Maintenance: temporal complexity, Latent Category Control and the Stalled Emergence of the Cleantech Sector

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JOURNAL OF MANAGEMENT STUDIES 2018年第7期55卷 1242-1277页

作者： Zietsma, Charlene Ruebottom, Trish Shantz, Angelique Slade Penn State Univ University Pk PA 16802 USA Brock Univ St Catharines ON Canada Univ Alberta Edmonton AB Canada

Disruptive innovation changes the basis of competition within an industry and poses substantial threats for market incumbents. While researchers have focused on whether incumbents can successfully adapt, we know little about how potentially disruptive innovation may be avoided. Studying clean technology in Canada, we examine incumbent resistance when potentially disruptive technologies are seen as socially beneficial. We identify actions taken by incumbents and other socio-technical regime actors to respond to the issue while simultaneously enacting legitimate stabilizing mechanisms within the regime's institutional infrastructure. Specifically, temporal and resource-based actions led to temporal complexity for disruptive cleantech entrepreneurs, and evaluation structuring work led to latent control of the cleantech category, privileging incumbents and resulting in unobtrusive maintenance. Our findings contribute to the disruptive innovation and institutional theory literatures by showing how disruption may be stalled by the enactment of legitimate elements of the institutional infrastructure rather than direct institutional defence.

关键词： categories disruptive innovation institutional maintenance temporal complexity

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temporal complexity of fMRI is reproducible and correlates with higher order cognition

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NEUROIMAGE 2021年 230卷 117760-117760页

作者： Omidvarnia, Amir Zalesky, Andrew Mansour, Sina L. Van de Ville, Dimitri Jackson, Graeme D. Pedersen, Mangor Ecole Polytech Fed Lausanne Ctr Biomed Imaging Inst Bioengn Ctr Neuroprosthet Lausanne Switzerland Univ Geneva Dept Radiol & Med Informat Geneva Switzerland Univ Melbourne Melbourne Neuropsychiat Ctr Dept Psychiat Melbourne Vic Australia Univ Melbourne Dept Biomed Engn Melbourne Vic Australia Melbourne Brain Ctr Florey Inst Neurosci & Mental Hlth Melbourne Vic Australia Univ Melbourne Florey Dept Neurosci & Mental Hlth Melbourne Vic Australia Austin Hlth Dept Neurol Melbourne Vic Australia Auckland Univ Technol Dept Psychol & Neurosci Auckland New Zealand

It has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that rsfMRI data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 987 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-min rsfMRI recordings and parcellated into 379 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and subcortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multiscale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014). Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the subcortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time T-R) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter.. is equal to 0.5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a non-random correlation was observed betwee

关键词： temporal complexity Multiscale entropy Resting state network Functional MRI Human connectome project Fluid intelligence Reproducibility

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temporal complexity measure of reaction time series: Operational versus event time

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BRAIN AND BEHAVIOR 2023年第7期13卷 e3069页

作者： Mahmoodi, Korosh Kerick, Scott E. Grigolini, Paolo Franaszczuk, Piotr J. West, Bruce J. US Combat Capabil Command Army Res Lab Aberdeen MD USA Univ North Texas Ctr Nonlinear Sci Denton TX USA Johns Hopkins Univ Dept Neurol Sch Med Baltimore MD USA North Carolina State Univ Off Res & Innovat Raleigh NC USA US Combat Capabil Command Army Res Lab Adelphi MD 21005 USA

IntroductionDetrended fluctuation analysis (DFA) is a well-established method to evaluate scaling indices of time series, which categorize the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time Y(n) time series, where n is the trial number. MethodsHerein we propose treating each reaction time as a duration time that changes the representation from operational (trial number) time n to event (temporal) time t, or X(t). The DFA algorithm was then applied to the X(t) time series to evaluate scaling indices. The dataset analyzed is based on a Go-NoGo shooting task that was performed by 30 participants under low and high time-stress conditions in each of six repeated sessions over a 3-week period. ResultsThis new perspective leads to quantitatively better results in (1) differentiating scaling indices between low versus high time-stress conditions and (2) predicting task performance outcomes. ConclusionWe show that by changing from operational time to event time, the DFA allows discrimination of time-stress conditions and predicts performance outcomes.

关键词： detrended fluctuation analysis reaction time series temporal complexity time-stress

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Predictability of short-term passengers' origin and destination demands in urban rail transit

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TRANSPORTATION 2023年第6期50卷 2375-2401页

作者： Yang, Fang Shuai, Chunyan Qian, Qian Wang, Wencong He, Mingwei He, Min Lee, Jaeyoung Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650504 Yunnan Peoples R China Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA

Accurate prediction of short-term passengers' origin and destination (OD) demands is key to efficient operation and management of urban rail transit (URT), especially in the case of congestion or an incident. However, short-term OD demand forecasting is more challenging than passenger flow forecasting, due to its uncertainty and high dimensions. So far, most OD prediction models capture the spatio-temporal dependencies of OD flow by means of training models on historical data, but what characteristics and laws influence the performance of OD prediction are still unknown. In this paper, we propose temporal Pearson correlation coefficients and approximate entropy, as well as spatial correlations, as indicators to reflect the inherent time-space correlations and complexity of the OD flow. Then, by analyzing automatic fare collection data of the Beijing and Shanghai URT system, this paper deeply discusses the relationships between the spatio-temporal correlations and complexity of the OD flow and the predictive performances of different models with regard to different intervals. Finally, this paper proposes the predictable problem of travel demands and points out that the spatial correlations of the OD matrix are more important than the temporal correlations and complexity in the short-term prediction of travel demands. In particular, the number of principal components of the OD flow can be a key indicator to measure the forecasting performance of a model. A reasonable interval is very important for short-term OD forecasting, and in the Beijing URT system, 30 min is a preferable choice for workdays and 50 min for weekends. All these findings are beneficial to guide users to build a suitable model or improve the existing model to obtain better prediction performances.

关键词： Predictability of travel demand Spatio-temporal correlation temporal complexity

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Content-adaptive bitstream-layer model for coding distortion assessment of H.264/AVC networked video

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2014年第5期25卷 1199-1208页

作者： Su, Honglei Yang, Fuzheng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China

Bitstream-layer models are designed to use the information extracted from both packet headers and payload for real-time and non-intrusive quality monitoring of networked video. This paper proposes a content-adaptive bitstream-layer (CABL) model for coding distortion assessment of H.264/AVC networked video. Firstly, the fundamental relationship between perceived coding distortion and quantization parameter (QP) is established. Then, considering the fact that the perceived coding distortion of a networked video significantly relies on both the spatial and temporal characteristics of video content, spatial and temporal complexities are incorporated in the proposed model. Assuming that the residuals before Discrete Cosine Transform (DCT) keep to the Laplace distribution, the scale parameters of the Laplace distribution are estimated utilizing QP and quantized coefficients on the basis of the Parseval theorem firstly. Then the spatial complexity is evaluated using QP and the scale parameters. Meanwhile, the temporal complexity is obtained using the weighted motion vectors (MV) considering the variations in temporal masking extent for high motion regions and low motion regions, respectively. Both the two characteristics of video content are extracted from the compressed bitstream without resorting to a complete decoding. Using content related information, the proposed model is able to adapt to different video contents. Experimental results show that the overall performance of CABL model significantly outperforms that of the P.1202.1 model and other coding distortion assessment models in terms of widely used performance criteria, including the Pearson Correlation Coefficient (PCC), the Spearman Rank Order Correlation Coefficient (SROCC), the Root-Mean-Squared Error (RMSE) and the Outlier Ratio (OR). (c) 2014 Elsevier Inc. All rights reserved.

关键词： No reference Video quality assessment Bitstream-layer model Coding distortion H.264/AVC Networked video Spatial complexity temporal complexity

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DeepVCA: Deep Video complexity Analyzer

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第9期34卷 8836-8847页

作者： Amirpour, Hadi Schoeffmann, Klaus Ghanbari, Mohammad Timmerer, Christian Alpen Adria Univ Christian Doppler Lab ATHENA A-9020 Klagenfurt Austria Alpen Adria Univ Inst Informat Technol A-9020 Klagenfurt Austria Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

Video streaming and its applications are growing rapidly, making video optimization a primary target for content providers looking to enhance their services. Enhancing the quality of videos requires the adjustment of different encoding parameters such as bitrate, resolution, and frame rate. To avoid brute force approaches for predicting optimal encoding parameters, video complexity features are typically extracted and utilized. To predict optimal encoding parameters effectively, content providers traditionally use unsupervised feature extraction methods, such as ITU-T's Spatial Information (SI) and temporal Information (TI) to represent the spatial and temporal complexity of video sequences. Recently, Video complexity Analyzer (VCA) was introduced to extract DCT-based features to represent the complexity of a video sequence (or parts thereof). These unsupervised features, however, cannot accurately predict video encoding parameters. To address this issue, this paper introduces a novel supervised feature extraction method named DeepVCA, which extracts the spatial and temporal complexity of video sequences using deep neural networks. In this approach, the encoding bits required to encode each frame in intra-mode and inter-mode are used as labels for spatial and temporal complexity, respectively. Initially, we benchmark various deep neural network structures to predict spatial complexity. We then leverage the similarity of features used to predict the spatial complexity of the current frame and its previous frame to rapidly predict temporal complexity. This approach is particularly useful as the temporal complexity may depend not only on the differences between two consecutive frames but also on their spatial complexity. Our proposed approach demonstrates significant improvement over unsupervised methods, especially for temporal complexity. As an example application, we verify the effectiveness of these features in predicting the encoding bitrate and encoding time of v

关键词： Video complexity video streaming spatial complexity temporal complexity VCA DNN

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