What's That Sound? Auditory Area CLM Encodes Stimulus Surprise, Not Intensity or Intensity Changes

High-level sensory neurons encoding natural stimuli are not well described by linear models operating on the time-varying stimulus intensity. Here we show that firing rates of neurons in a secondary sensory forebrain area can be better modeled by linear functions of how surprising the stimulus is. We modeled auditory neurons in the caudal lateral mesopallium (CLM) of adult male zebra finches under urethane anesthesia with linear filters convolved not with stimulus intensity, but with stimulus surprise. Surprise was quantified as the logarithm of the probability of the stimulus given the local recent stimulus history and expectations based on conspecific song. Using our surprise method, the predictions of neural responses to conspecific song improved by 67% relative to those obtained using stimulus intensity. Similar prediction improvements cannot be replicated by assuming CLM performs derivative detection. The explanatory power of surprise increased from the midbrain through the primary forebrain and to CLM. When the stimulus presented was a random synthetic ripple noise, CLM neurons (but not neurons in lower auditory areas) were best described as if they were expecting conspecific song, finding the inconsistencies between birdsong and noise surprising. In summary, spikes in CLM neurons indicate stimulus surprise more than they indicate stimulus intensity features. The concept of stimulus surprise may be useful for modeling neural responses in other higher-order sensory areas whose functions have been poorly understood.

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Olfactory mucosal and neural responses in the frog

American Journal of Physiology-Legacy Content ◽

10.1152/ajplegacy.1962.203.2.353 ◽

1962 ◽

Vol 203 (2) ◽

pp. 353-358 ◽

Cited By ~ 22

Author(s):

Maxwell Mark Mozell

Keyword(s):

Stimulus Intensity ◽

Olfactory Epithelium ◽

Flow Rate ◽

Olfactory Nerve ◽

Neural Responses ◽

Criterion Measure ◽

Repeated Stimulation ◽

Two Measures ◽

Electrophysiological Technique

A comparatively recent electrophysiological technique for studying peripheral olfactory events is to record sustained negative potentials from the olfactory epithelium. This method is rapidly replacing the older technique of recording multifiber discharges from the olfactory nerve or bulb. Therefore, the extent to which the results from the two methods correlate with each other was studied by simultaneously recording from the nerve and from the mucosa under several conditions. Although most often parallel, some differences between the two measures were found. Their response maxima did not always temporally coincide. Their amplitudes did not always correlate. Certain stimuli reduced subsequent mucosal responses but not the neural. Repeated stimulation sometimes produced similar differences. Finally, the two responses were not linearly related as a function of stimulus intensity or flow rate. However, for reasons discussed, it is difficult to conclude that these discrepancies necessarily reflect unfavorably upon the reliability of the mucosal potential as the criterion measure of peripheral olfactory activity. Nevertheless, the mucosal potential should not be accepted unequivocally as such a criterion measure until it is more thoroughly understood.

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Cortical response to naturalistic stimuli is largely predictable with deep neural networks

Science Advances ◽

10.1126/sciadv.abe7547 ◽

2021 ◽

Vol 7 (22) ◽

pp. eabe7547

Author(s):

Meenakshi Khosla ◽

Gia H. Ngo ◽

Keith Jamison ◽

Amy Kuceyeski ◽

Mert R. Sabuncu

Keyword(s):

Brain Function ◽

Deep Neural Networks ◽

Neural Responses ◽

Dynamic Interactions ◽

Hierarchical Processing ◽

Human Connectome Project ◽

Neural Information ◽

Neural Information Processing ◽

Visual Interactions ◽

High Level

Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm. Using movie-watching data from the Human Connectome Project, we build group-level models of neural activity that incorporate several inductive biases about neural information processing, including hierarchical processing, temporal assimilation, and auditory-visual interactions. We demonstrate how incorporating these biases leads to remarkable prediction performance across large areas of the cortex, beyond the sensory-specific cortices into multisensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize to task-bound paradigms. Together, our findings underscore the potential of encoding models as powerful tools for studying brain function in ecologically valid conditions.

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Quantifying the signals contained in heterogeneous neural responses and determining their relationships with task performance

Journal of Neurophysiology ◽

10.1152/jn.00260.2014 ◽

2014 ◽

Vol 112 (6) ◽

pp. 1584-1598 ◽

Cited By ~ 15

Author(s):

Marino Pagan ◽

Nicole C. Rust

Keyword(s):

Task Performance ◽

Single Neuron ◽

Weighted Sums ◽

Neural Responses ◽

Signal Modulation ◽

Neural Data ◽

Match To Sample ◽

Neural Populations ◽

Different Types ◽

High Level

The responses of high-level neurons tend to be mixtures of many different types of signals. While this diversity is thought to allow for flexible neural processing, it presents a challenge for understanding how neural responses relate to task performance and to neural computation. To address these challenges, we have developed a new method to parse the responses of individual neurons into weighted sums of intuitive signal components. Our method computes the weights by projecting a neuron's responses onto a predefined orthonormal basis. Once determined, these weights can be combined into measures of signal modulation; however, in their raw form these signal modulation measures are biased by noise. Here we introduce and evaluate two methods for correcting this bias, and we report that an analytically derived approach produces performance that is robust and superior to a bootstrap procedure. Using neural data recorded from inferotemporal cortex and perirhinal cortex as monkeys performed a delayed-match-to-sample target search task, we demonstrate how the method can be used to quantify the amounts of task-relevant signals in heterogeneous neural populations. We also demonstrate how these intuitive quantifications of signal modulation can be related to single-neuron measures of task performance ( d′).

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Visual search for object categories is predicted by the representational architecture of high-level visual cortex

Journal of Neurophysiology ◽

10.1152/jn.00569.2016 ◽

2017 ◽

Vol 117 (1) ◽

pp. 388-402 ◽

Cited By ~ 14

Author(s):

Michael A. Cohen ◽

George A. Alvarez ◽

Ken Nakayama ◽

Talia Konkle

Keyword(s):

Visual Cortex ◽

Visual Search ◽

Visual System ◽

Visual Processing ◽

Search Task ◽

Visual Search Task ◽

Visual Object ◽

Neural Responses ◽

Object Categories ◽

High Level

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.

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Implications of Activity Classification Aggregation in Urban Freight Trip Generation Linear Models

Advances in Logistics, Operations, and Management Science - Handbook of Research on Urban and Humanitarian Logistics ◽

10.4018/978-1-5225-8160-4.ch002 ◽

2019 ◽

pp. 22-37 ◽

Cited By ~ 1

Author(s):

Ivan D. Sanchez-Diaz ◽

Jesus Gonzalez-Feliu

Keyword(s):

Data Collection ◽

Linear Models ◽

Statistical Significance ◽

Detailed Data ◽

Trip Generation ◽

Commercial Activity ◽

Urban Freight ◽

Levels Of Detail ◽

High Level

This chapter studies the implication of aggregating establishments by categories with different levels of detail for modeling FTG. To this effect, the chapter conducts an assessment of freight trip generation (FTG) patterns homogeneity inside activity-based grouping. The method implemented is econometric in nature, which allows the assessment of the statistical significance of variables representing commercial activity sectors and sub-sectors. The results show that for some sectors the traditional high-level aggregation includes sub-sectors with homogenous FTG patterns and thus produces appropriate models; in some other cases (e.g., retail, manufacturing), the sub-sectors have different FTG patterns and thus more detailed data is needed to calibrate accurate models. This research can be used to enhance the efficiency of data collection, as it identifies some sub-sectors that need larger efforts for data collection, and some other categories where FTG homogeneity allows for less detailed data collection without hampering the quality of the models.

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Study of Noise Removal Techniques for Digital Images

Biometrics ◽

10.4018/978-1-5225-0983-7.ch044 ◽

2017 ◽

pp. 1105-1144

Author(s):

Punyaban Patel ◽

Bibekananda Jena ◽

Bibhudatta Sahoo ◽

Pritam Patel ◽

Banshidhar Majhi

Keyword(s):

Image Processing ◽

Gaussian Noise ◽

Statistical Models ◽

Noise Removal ◽

Image Degradation ◽

Linear Filters ◽

Restoration Techniques ◽

Filtering Technique ◽

High Level ◽

Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

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Study of Noise Removal Techniques for Digital Images

Emerging Technologies in Intelligent Applications for Image and Video Processing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9685-3.ch003 ◽

2016 ◽

pp. 48-87

Author(s):

Punyaban Patel ◽

Bibekananda Jena ◽

Bibhudatta Sahoo ◽

Pritam Patel ◽

Banshidhar Majhi

Keyword(s):

Image Processing ◽

Gaussian Noise ◽

Statistical Models ◽

Noise Removal ◽

Image Degradation ◽

Linear Filters ◽

Restoration Techniques ◽

Filtering Technique ◽

High Level ◽

Camera Sensors

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Spatial-Temporal Pattern and Its Influencing Factors on Urban Tourism Competitiveness in City Agglomerations Across the Guanzhong Plain

Sustainability ◽

10.3390/su11236743 ◽

2019 ◽

Vol 11 (23) ◽

pp. 6743

Author(s):

Jia Wan ◽

Junping Yan ◽

Xiaomeng Wang ◽

Ziqiang Liu ◽

Hui Wang ◽

...

Keyword(s):

Cluster Analysis ◽

Influencing Factors ◽

Temporal Pattern ◽

Explanatory Power ◽

Urban Tourism ◽

Ecological Environment ◽

Single Factor ◽

Tourism Resources ◽

High Level ◽

Environment Condition

Strengthening research on urban tourism competitiveness is vital in evaluating the current situation and potential of urban tourism, maintaining the sustainable development of the tourism economy and assisting in the regional macro decision making. In this study, an index system evaluation of urban tourism competitiveness in city agglomerations across the Guanzhong Plain is established by collecting cross-section data from the years 2017 and 2010. The entropy value method is adopted to determine the index weight. Cluster analysis is performed and the spatial-temporal pattern and evolution laws of urban tourism competitiveness among city agglomerations in the Guanzhong Plain are analyzed and the geographic detector utilized to discuss the influencing factors. Results show that the spatial gradient difference of urban tourism competitiveness of agglomerations in the Guanzhong Plain is significant. In 2010, it presented the characteristic of ‘the high and middle levels having a zonal distribution from east to west, and the low level was distributed along the north and south wings’. In 2017, the characteristic of ‘polarization’ became highly prominent, that is, the scope of high-level and low-level cities expanded and the scope of medium-level cities decreased. Urban tourism competitiveness in city agglomerations across the Guanzhong Plain exhibited a trend of ‘strengthening in the east, weakening in the west’. The competitiveness of resources and management shifted aggressively and supporting factors competitiveness underwent a slight change. The urban tourism competitiveness of city agglomerations in the Guanzhong Plain is generally low, while the urban tourism competitiveness of Xi’an had an absolute advantage in city agglomerations of the Guanzhong Plain. According to the cluster analysis results, resources and management competitiveness, supporting factors competitiveness, demand conditions competitiveness, situational conditions competitiveness and urban tourism competitiveness of Xi’an in 2010 and 2017 were all at an extremely high level, which was relatively higher than the index values of other cities in the city agglomerations of the Guanzhong Plain. Tourism resources, service support capacity, infrastructure support capacity, tourism income scale, tourism reception scale and economic development power are the core influencing factors of urban tourism competitiveness among city agglomerations in the Guanzhong Plain. The single factor explanatory power of destination management indicates a downward trend while the single factor explanatory power of the ecological environment condition shows an upward trend. Tourism resources are the leading interactive factor of urban tourism competitiveness, and destination management and ecological environment condition are the most significant indicators for the collaborative effect.

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Estimators of Random Effects Variance Components in Meta-Analysis

Journal of Educational and Behavioral Statistics ◽

10.3102/10769986025001001 ◽

2000 ◽

Vol 25 (1) ◽

pp. 1-12 ◽

Cited By ~ 13

Author(s):

Lynn Friedman

Keyword(s):

Closed Form ◽

Random Effects ◽

Quadratic Forms ◽

Relative Efficiency ◽

Variance Component ◽

Linear Models ◽

Random Effect ◽

Linear Functions ◽

Effect Sizes ◽

Random Effects Model

In meta-analyses, groups of study effect sizes often do not fit the model of a single population with only sampling, or estimation, variance differentiating the estimates. If the effect sizes in a group of studies are not homogeneous, a random effects model should be calculated, and a variance component for the random effect estimated. This estimate can be made in several ways, but two closed form estimators are in common use. The comparative efficiency of the two is the focus of this report. We show here that these estimators vary in relative efficiency with the actual size of the random effects model variance component. The latter depends on the study effect sizes. The closed form estimators are linear functions of quadratic forms whose moments can be calculated according to a well-known theorem in linear models. We use this theorem to derive the variances of the estimators, and show that one of them is smaller when the random effects model variance is near zero; however, the variance of the other is smaller when the model variance is larger. This leads to conclusions about their relative efficiency.

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