scholarly journals A Neural Ensemble Correlation Code for Sound Category Identification

2018 ◽  
Author(s):  
Mina Sadeghi ◽  
Xiu Zhai ◽  
Ian H. Stevenson ◽  
Monty A. Escabí
Keyword(s):  
Human Performance ◽  
Temporal Correlation ◽  
Auditory Midbrain ◽  
Neural Recordings ◽  
Temporal Correlations ◽  
Neural Ensemble ◽  
Task Classification ◽  
Performance Trends ◽  
Sound Category ◽  
Correlation Statistics

ABSTRACTHumans and other animals effortlessly identify sounds and categorize them into behaviorally relevant categories. Yet, the acoustic features and neural transformations that enable the formation of perceptual categories are largely unknown. Here we demonstrate that correlation statistics between frequency-organized cochlear sound channels are reflected in the neural ensemble activity of the auditory midbrain and that such activity, in turn, can contribute to discrimination of perceptual categories. Using multi-channel neural recordings in the auditory midbrain of unanesthetized rabbits, we first demonstrate that neuron ensemble correlations are highly structured in both time and frequency and can be decoded to distinguish sounds. Next, we develop a probabilistic framework for measuring the nonstationary spectro-temporal correlation statistics between frequency organized channels in an auditory model. In a 13-category sound identification task, classification accuracy is consistently high (>80%), improving with sound duration and plateauing at ~ 1-3 seconds, mirroring human performance trends. Nonstationary short-term correlation statistics are more informative about the sound category than the time-average correlation statistics (84% vs. 73% accuracy). When tested independently, the spectral and temporal correlations between the model outputs achieved a similar level of performance and appear to contribute equally. These results outline a plausible neural code in which correlation statistics between neuron ensembles of different frequencies can be read-out to identify and distinguish acoustic categories.

scholarly journals Distinct neural ensemble response statistics are associated with recognition and discrimination of natural sound textures

2020 ◽  
Vol 117 (49) ◽  
pp. 31482-31493
Author(s):  
Xiu Zhai ◽  
Fatemeh Khatami ◽  
Mina Sadeghi ◽  
Fengrong He ◽  
Heather L. Read ◽  
...  
Keyword(s):  
Recognition Task ◽  
High Order ◽  
Summary Statistics ◽  
Natural Sounds ◽  
Auditory Midbrain ◽  
Sound Structure ◽  
Neural Ensemble ◽  
Average Activity ◽  
Correlation Statistics ◽  
Sound Spectrum

The perception of sound textures, a class of natural sounds defined by statistical sound structure such as fire, wind, and rain, has been proposed to arise through the integration of time-averaged summary statistics. Where and how the auditory system might encode these summary statistics to create internal representations of these stationary sounds, however, is unknown. Here, using natural textures and synthetic variants with reduced statistics, we show that summary statistics modulate the correlations between frequency organized neuron ensembles in the awake rabbit inferior colliculus (IC). These neural ensemble correlation statistics capture high-order sound structure and allow for accurate neural decoding in a single trial recognition task with evidence accumulation times approaching 1 s. In contrast, the average activity across the neural ensemble (neural spectrum) provides a fast (tens of milliseconds) and salient signal that contributes primarily to texture discrimination. Intriguingly, perceptual studies in human listeners reveal analogous trends: the sound spectrum is integrated quickly and serves as a salient discrimination cue while high-order sound statistics are integrated slowly and contribute substantially more toward recognition. The findings suggest statistical sound cues such as the sound spectrum and correlation structure are represented by distinct response statistics in auditory midbrain ensembles, and that these neural response statistics may have dissociable roles and time scales for the recognition and discrimination of natural sounds.

scholarly journals Probing Asymmetry in Spatial-temporal Correlations in Quantum Causal Inference

2021 ◽  
Author(s):  
Yu Meng ◽  
Zheng-Hao Liu ◽  
Zhikuan Zhao ◽  
Peng Yin ◽  
Yi-Tao Wang ◽  
...  
Keyword(s):  
Causal Inference ◽  
Quantum Physics ◽  
Temporal Correlation ◽  
Quantum Correlations ◽  
Quantum Channels ◽  
Temporal Correlations ◽  
Full Symmetry ◽  
Correlation Statistics ◽  
Temporal Symmetry ◽  
Insight Into

Abstract Quantum correlations in space-time encapsulate the most defining aspects of quantum physics. The dual of the spatial and temporal perspectives are bind with a one-to-one correspondence between bipartite quantum states and quantum channels. Consequently, causal relations between quantum events can sometimes be inferred solely from correlation statistics, apparently contradicting the classical \textit{credo}, `correlation does not imply causation'[1-6]. However, since the spatial-temporal duality does not imply a full symmetry of measurement statistics between the two domains[7], the extent to which correlation alone identifies quantum causality ponders inquiry vital for both fundamental and practical interests. Here, demonstrating a unified geometrical representation of spatial-temporal quantum correlation, we show that certain non-unital channels create temporal correlation without spatial analogue and break the spatial-temporal symmetry. By implementing such channels in a photonic architecture, we observe this asymmetry and classify quantum correlations using a distance criterion, thus bringing empirical insight into causal inference in quantum mechanics.

Expertise-Based Performance Measures in a Virtual Training Environment

2009 ◽  
Vol 18 (6) ◽  
pp. 449-467 ◽  
Author(s):  
Joel C Huegel ◽  
Ozkan Celik ◽  
Ali Israr ◽  
Marcia K O'Malley
Keyword(s):  
Performance Measures ◽  
Human Performance ◽  
Training Environment ◽  
Virtual Training ◽  
Frequency Capture ◽  
Performance Trends ◽  
Key Skills ◽  
Quantitative Performance ◽  
Manual Skills ◽  
Future Work

This paper introduces and validates quantitative performance measures for a rhythmic target-hitting task. These performance measures are derived from a detailed analysis of human performance during a month-long training experiment where participants learned to operate a 2-DOF haptic interface in a virtual environment to execute a manual control task. The motivation for the analysis presented in this paper is to determine measures of participant performance that capture the key skills of the task. This analysis of performance indicates that two quantitative measures—trajectory error and input frequency—capture the key skills of the target-hitting task, as the results show a strong correlation between the performance measures and the task objective of maximizing target hits. The performance trends were further explored by grouping the participants based on expertise and examining trends during training in terms of these measures. In future work, these measures will be used as inputs to a haptic guidance scheme that adjusts its control gains based on a real-time assessment of human performance of the task. Such guidance schemes will be incorporated into virtual training environments for humans to develop manual skills for domains such as surgery, physical therapy, and sports.

scholarly journals An Evaluation of Precipitation Forecasts from Operational Models and Reanalyses Including Precipitation Variations Associated with MJO Activity

2010 ◽  
Vol 138 (12) ◽  
pp. 4542-4560 ◽  
Author(s):  
John E. Janowiak ◽  
Peter Bauer ◽  
Wanqiu Wang ◽  
Phillip A. Arkin ◽  
Jon Gottschalck
Keyword(s):  
Prediction Models ◽  
Coupled Model ◽  
Warm Season ◽  
Temporal Correlation ◽  
Operational Model ◽  
Temporal Correlations ◽  
Operational Models ◽  
Environmental Prediction ◽  
The Tropics ◽  
Model Precipitation

Abstract In this paper, the results of an examination of precipitation forecasts for 1–30-day leads from global models run at the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) during November 2007–February 2008 are presented. The performance of the model precipitation forecasts are examined in global and regional contexts, and results of a case study of precipitation variations that are associated with a moderate to strong Madden–Julian oscillation (MJO) event are presented. The precipitation forecasts from the ECMWF and NCEP operational prediction models have nearly identical temporal correlation with observed precipitation at forecast leads from 2 to 9 days over the Northern Hemisphere during the cool season, despite the higher resolution of the ECMWF operational model, while the ECMWF operational model forecasts are slightly better in the tropics and the Southern Hemisphere during the warm season. The ECMWF Re-Analysis Interim (ERA-Interim) precipitation forecasts perform only slightly worse than the NCEP operational model, while NCEP’s Climate Forecast System low-resolution coupled model forecasts perform the worst among the four models. In terms of bias, the ECMWF operational model performs the best among the four model forecasts that were examined, particularly with respect to the ITCZ regions in both the Atlantic and Pacific. Local temporal correlations that were computed on daily precipitation totals for day-2 forecasts against observations indicate that the operational models at ECMWF and NCEP perform the best during the 4-month study period, and that all of the models have low to insignificant correlations over land and over much of the tropics. They perform the best in subtropical and extratropical oceanic regions. Also presented are results that show that striking improvements have been made over the past two decades in the ability of the models to represent precipitation variations that are associated with MJO. The model precipitation forecasts exhibit the ability to characterize the evolution of precipitation variations during a moderate–strong period of MJO conditions for forecast leads as long as 10 days.

scholarly journals Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction

2019 ◽  
Vol 9 (4) ◽  
pp. 615 ◽  
Author(s):  
Panbiao Liu ◽  
Yong Zhang ◽  
Dehui Kong ◽  
Baocai Yin
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Network ◽  
Temporal Correlation ◽  
Traffic Flow Prediction ◽  
Temporal Correlations ◽  
Building Models ◽  
Spatio Temporal ◽  
Specific Scenario

Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods.

scholarly journals Temporal correlations among functionally specialized striatal neural ensembles in reward-conditioned mice

2016 ◽  
Vol 115 (3) ◽  
pp. 1521-1532 ◽  
Author(s):  
Konstantin I. Bakhurin ◽  
Victor Mac ◽  
Peyman Golshani ◽  
Sotiris C. Masmanidis
Keyword(s):  
Large Scale ◽  
Network Dynamics ◽  
Projection Neurons ◽  
Striatal Neurons ◽  
Neural Recordings ◽  
Neural Ensembles ◽  
Temporal Correlations ◽  
Cross Correlation Analysis ◽  
Wide Range ◽  
Specific Outcome

As the major input to the basal ganglia, the striatum is innervated by a wide range of other areas. Overlapping input from these regions is speculated to influence temporal correlations among striatal ensembles. However, the network dynamics among behaviorally related neural populations in the striatum has not been extensively studied. We used large-scale neural recordings to monitor activity from striatal ensembles in mice undergoing Pavlovian reward conditioning. A subpopulation of putative medium spiny projection neurons (MSNs) was found to discriminate between cues that predicted the delivery of a reward and cues that predicted no specific outcome. These cells were preferentially located in lateral subregions of the striatum. Discriminating MSNs were more spontaneously active and more correlated than their nondiscriminating counterparts. Furthermore, discriminating fast spiking interneurons (FSIs) represented a highly prevalent group in the recordings, which formed a strongly correlated network with discriminating MSNs. Spike time cross-correlation analysis showed the existence of synchronized activity among FSIs and feedforward inhibitory modulation of MSN spiking by FSIs. These findings suggest that populations of functionally specialized (cue-discriminating) striatal neurons have distinct network dynamics that sets them apart from nondiscriminating cells, potentially to facilitate accurate behavioral responding during associative reward learning.

ESTIMATION OF CONVECTION SPEED THROUGH PIV MEASUREMENTS IN TURBULENT PLANAR MIXING LAYER

2009 ◽  
Vol 23 (03) ◽  
pp. 353-356
Author(s):  
CHIUAN-TING LI ◽  
KEH-CHIN CHANG ◽  
MUH-RONG WANG
Keyword(s):  
Particle Image ◽  
Mixing Layer ◽  
Temporal Correlation ◽  
Dominant Frequency ◽  
Piv Measurements ◽  
Temporal Correlations ◽  
Taylor’S Hypothesis ◽  
Image Velocimetry ◽  
Spatio Temporal ◽  
Convection Speed

The spatio-temporal correlations in a turbulent planar mixing layer are acquired using the particle image velocimetry. Estimation of convection speed is recommended to be made with the spatio-temporal correlations of fluctuating vorticity. The spatial correlation can be deduced from the temporal correlation through the use of the Taylor's hypothesis when applied to the region without apparent dominant frequency.

scholarly journals SCIAMACHY WFM-DOAS XCO<sub>2</sub>: comparison with CarbonTracker XCO<sub>2</sub> focusing on aerosols and thin clouds

2012 ◽  
Vol 5 (2) ◽  
pp. 2887-2931 ◽  
Author(s):  
J. Heymann ◽  
O. Schneising ◽  
M. Reuter ◽  
M. Buchwitz ◽  
V. V. Rozanov ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Temporal Correlation ◽  
Anthropogenic Activity ◽  
Data Sets ◽  
Spatial Correlations ◽  
Data Set ◽  
Cloud Data ◽  
Temporal Correlations ◽  
Retrieval Errors ◽  
The Difference

Abstract. Carbon dioxide (CO2) is the most important greenhouse gas whose atmospheric loading has been significantly increased by anthropogenic activity leading to global warming. Accurate measurements and models are needed in order to reliably predict our future climate. This, however, has challenging requirements. Errors in measurements and models need to be identified and minimised. In this context, we present a comparison between satellite-derived column-averaged dry air mole fractions of CO2, denoted XCO2, retrieved from SCIAMACHY/ENVISAT using the WFM-DOAS algorithm, and output from NOAA's global CO2 modelling and assimilation system CarbonTracker. We investigate to what extent differences between these two data sets are influenced by systematic retrieval errors due to aerosols and unaccounted clouds. We analyse seven years of SCIAMACHY WFM-DOAS version 2.1 retrievals (WFMDv2.1) using the latest version of CarbonTracker (version 2010). We investigate to what extent the difference between SCIAMACHY and CarbonTracker XCO2 are temporally and spatially correlated with global aerosol and cloud data sets. For this purpose, we use a global aerosol data set generated within the European GEMS project, which is based on assimilated MODIS satellite data. For clouds, we use a data set derived from CALIOP/CALIPSO. We find significant correlations of the SCIAMACHY minus CarbonTracker XCO2 difference with thin clouds over the Southern Hemisphere. The maximum temporal correlation we find for Darwin, Australia (r2 = 54%). Large temporal correlations with thin clouds are also observed over other regions of the Southern Hemisphere (e.g. 43% for South America and 31% for South Africa). Over the Northern Hemisphere the temporal correlations are typically much lower. An exception is India, where large temporal correlations with clouds and aerosols have also been found. For all other regions the temporal correlations with aerosol are typically low. For the spatial correlations the picture is less clear. They are typically low for both aerosols and clouds, but dependent on region and season, they may exceed 30% (the maximum value of 46% has been found for Darwin during September to November). Overall we find that the presence of thin clouds can potentially explain a significant fraction of the difference between SCIAMACHY WFMDv2.1 XCO2 and CarbonTracker over the Southern Hemisphere. Aerosols appear to be less of a problem. Our study indicates that the quality of the satellite derived XCO2 will significantly benefit from a reduction of scattering related retrieval errors at least for the Southern Hemisphere.

scholarly journals Dynamics of spontaneous alpha activity correlate with language ability in young children

2018 ◽  
Author(s):  
Elaine Y. L. Kwok ◽  
Janis Oram Cardy ◽  
Brian L. Allman ◽  
Prudence Allen ◽  
Björn Herrmann
Keyword(s):  
Oral Language ◽  
Time Course ◽  
Temporal Correlation ◽  
Language Ability ◽  
Brain Maturation ◽  
Alpha Power ◽  
Alpha Oscillations ◽  
Alpha Frequency ◽  
Temporal Correlations ◽  
Eeg Recordings

AbstractEarly childhood is a period of tremendous growth in both language ability and brain maturation. To understand the dynamic interplay between neural activity and spoken language development, we used resting-state EEG recordings to explore the relation between alpha oscillations (7–10 Hz) and oral language ability in 4- to 6-year-old children with typical development (N=41). Three properties of alpha oscillations were investigated: a) alpha power using spectral analysis, b) flexibility of the alpha frequency quantified via the oscillation’s moment-to-moment fluctuations, and c) scaling behavior of the alpha oscillator investigated via the long-range temporal correlation in the alpha-amplitude time course. All three properties of the alpha oscillator correlated with children’s oral language abilities. Higher language scores were correlated with lower alpha power, greater flexibility of the alpha frequency, and longer temporal correlations in the alpha-amplitude time course. Our findings demonstrate a cognitive role of several properties of the alpha oscillator that has largely been overlooked in the literature. Graphical Abstract

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