Statistics of visual responses in primate inferotemporal cortex to object stimuli

2011 ◽  
Vol 106 (3) ◽  
pp. 1097-1117 ◽  
Author(s):  
Sidney R. Lehky ◽  
Roozbeh Kiani ◽  
Hossein Esteky ◽  
Keiji Tanaka
Keyword(s):  
Single Neuron ◽  
Heavy Tails ◽  
Probability Distributions ◽  
Large Data ◽  
Visual Responses ◽  
Inferotemporal Cortex ◽  
Critical Feature ◽  
Data Set ◽  
Population Responses ◽  
Light Tails

We have characterized selectivity and sparseness in anterior inferotemporal cortex, using a large data set. Responses were collected from 674 monkey inferotemporal cells, each stimulated by 806 object photographs. This 806 × 674 matrix was examined in two ways: columnwise, looking at responses of a single neuron to all images (single-neuron selectivity), and rowwise, looking at the responses of all neurons caused by a single image (population sparseness). Selectivity and sparseness were measured as kurtosis of probability distributions. Population sparseness exceeded single-neuron selectivity, with specific values dependent on the size of the data sample. This difference was principally caused by inclusion, within the population, of neurons with a variety of dynamic ranges (standard deviations of responses over all images). Statistics of large responses were examined by quantifying how quickly the upper tail of the probability distribution decreased (tail heaviness). This analysis demonstrated that population responses had heavier tails than single-neuron responses, consistent with the difference between sparseness and selectivity measurements. Population responses with spontaneous activity subtracted had the heaviest tails, following a power law. The very light tails of single-neuron responses indicate that the critical feature for each neuron is simple enough to have a high probability of occurring within a limited stimulus set. Heavy tails of population responses indicate that there are a large number of different critical features to which different neurons are tuned. These results are inconsistent with some structural models of object recognition that posit that objects are decomposed into a small number of standard features.

Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex

1994 ◽  
Vol 71 (3) ◽  
pp. 856-867 ◽  
Author(s):  
E. Kobatake ◽  
K. Tanaka
Keyword(s):  
Anterior Part ◽  
Posterior Part ◽  
Visual Pathway ◽  
Complex Object ◽  
Visual Responses ◽  
Inferotemporal Cortex ◽  
Critical Feature ◽  
Ventral Visual Pathway ◽  
Object Features ◽  
Simple Features

1. To infer relative roles of cortical areas at different stages of the ventral visual pathway, we quantitatively examined visual responses of cells in V2, V4, the posterior part of the inferotemporal cortex (posterior IT), and the anterior part of the inferotemporal cortex (anterior IT), using anesthetized macaque monkeys. 2. The critical feature for the activation was first determined for each recorded cell by using a reduction method. We started from images of three-dimensional complex objects and simplified the image of effective stimuli step by step by eliminating a part of the features present in the image. The simplest feature that maximally activated the cell was determined as the critical feature. The response to the critical feature was then compared with responses of the same cell to a routine set of 32 simple stimuli, which included white and black bars of four different orientations and squares or spots of four different colors. 3. Cells that responded maximally to particular complex object features were found in posterior IT and V4 as well as in anterior IT. The cells in posterior IT and V4 were, however, different from the cells in anterior IT in that many of them responded to some extent to some simple features, that the size of the receptive field was small, and that they intermingled in single penetrations with cells that responded maximally to some simple features. The complex critical features in posterior IT and V4 varied; they consisted of complex shapes, combinations of a shape and texture, and combinations of a shape and color. 4. We suggest that local neuronal networks in V4 and posterior IT play an essential role in the formation of selective responses to complex object features.

scholarly journals Single-particle statistics in the southern Gulf of Mexico

2018 ◽  
Vol 57 (2) ◽  
Author(s):  
Luis Zavala Sansón ◽  
Julio Sheinbaum ◽  
Paula Pérez-Brunius
Keyword(s):  
Gulf Of Mexico ◽  
Single Particle ◽  
Probability Distributions ◽  
Large Data ◽  
Data Set ◽  
Gaussian Shape ◽  
Southern Gulf Of Mexico ◽  
Surface Drifters ◽  
Non Gaussian ◽  
Particle Statistics

We present single-particle statistics of a large data set of surface drifters released in the southern Gulf of Mexico. The study is focused on the probability density functions of velocities, Lagrangian scales and absolute dispersion estimates. The probability distributions display a non-Gaussian shape with extended tails. It is shown that the long tails are mainly associated with the Campeche gyre (an intense cyclonic circulation frequently formed in the region) and, to a lesser extent, with a flow along the western margin of the basin. The results are discussed in light of previous studies on ocean dispersion that have reported Gaussian and non-Gaussian distributions in other regions.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals The Complete DNA Sequence of the Mitochondrial Genome of a “Living Fossil,” the Coelacanth (Latimeria chalumnae)

Genetics ◽  
1997 ◽  
Vol 146 (3) ◽  
pp. 995-1010 ◽  
Author(s):  
Rafael Zardoya ◽  
Axel Meyer
Keyword(s):  
Mitochondrial Genome ◽  
Tandem Repeats ◽  
Phylogenetic Analyses ◽  
Large Data ◽  
Molecular Data ◽  
Phylogenetic Position ◽  
Data Set ◽  
Living Fossil ◽  
Latimeria Chalumnae ◽  
Relationship Of

The complete nucleotide sequence of the 16,407-bp mitochondrial genome of the coelacanth (Latimeria chalumnae) was determined. The coelacanth mitochondrial genome order is identical to the consensus vertebrate gene order which is also found in all ray-finned fishes, the lungfish, and most tetrapods. Base composition and codon usage also conform to typical vertebrate patterns. The entire mitochondrial genome was PCR-amplified with 24 sets of primers that are expected to amplify homologous regions in other related vertebrate species. Analyses of the control region of the coelacanth mitochondrial genome revealed the existence of four 22-bp tandem repeats close to its 3′ end. The phylogenetic analyses of a large data set combining genes coding for rRNAs, tRNA, and proteins (16,140 characters) confirmed the phylogenetic position of the coelacanth as a lobe-finned fish; it is more closely related to tetrapods than to ray-finned fishes. However, different phylogenetic methods applied to this largest available molecular data set were unable to resolve unambiguously the relationship of the coelacanth to the two other groups of extant lobe-finned fishes, the lungfishes and the tetrapods. Maximum parsimony favored a lungfish/coelacanth or a lungfish/tetrapod sistergroup relationship depending on which transversion:transition weighting is assumed. Neighbor-joining and maximum likelihood supported a lungfish/tetrapod sistergroup relationship.

Climatology of nutrient distributions in the South China Sea based on a large data set derived from a new algorithm

2021 ◽  
pp. 102586
Author(s):  
Chuanjun Du ◽  
Ruoying He ◽  
Zhiyu Liu ◽  
Tao Huang ◽  
Lifang Wang ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Large Data ◽  
The South China Sea ◽  
The South ◽  
Data Set ◽  
China Sea ◽  
Large Data Set

scholarly journals Spike detection: Inter-reader agreement and a statistical Turing test on a large data set

2017 ◽  
Vol 128 (1) ◽  
pp. 243-250 ◽  
Author(s):  
Mark L. Scheuer ◽  
Anto Bagic ◽  
Scott B. Wilson
Keyword(s):  
Large Data ◽  
Turing Test ◽  
Spike Detection ◽  
Data Set ◽  
Large Data Set

scholarly journals Horizontal interactions in local personal income taxes

2021 ◽  
Author(s):  
Johan Lundberg
Keyword(s):  
Population Size ◽  
Political Representation ◽  
Large Data ◽  
Personal Income ◽  
Set Covering ◽  
Tax Rates ◽  
Data Set ◽  
Relative Population ◽  
Tax Rate ◽  
Local Council

AbstractTheories of inter-jurisdictional tax and yardstick competition assume that the tax decisions of one jurisdiction will influence the tax decisions of other jurisdictions. This paper empirically addresses the issue of horizontal dependence in local personal income tax rates across jurisdictions. Based on a large data set covering Swedish municipalities over a period of 14 years, we test for interactions across municipalities that share a common border, across municipalities within a distance of 100 km of each other, and across municipalities with similar political representation in the local council. We also test the hypothesis that the tax rate of relatively larger municipalities has a greater influence on their neighbors' tax rate compared to the influence of their smaller neighbors. Our results suggest that when lagged tax rates are controlled for, the horizontal correlation across municipalities that share a common border or are within a distance of 100 km from each other becomes insignificant. This result is of importance as it suggests that lagged tax rates should be included or at least tested for when testing for horizontal interactions or mimicking in local tax rates. However, our results support the hypothesis of horizontal interactions across municipalities that share a common border when the influence of neighboring municipalities is also weighted by their relative population size, i.e. relatively larger neighbors tend to have a greater impact on their neighbor's tax rates than their relatively smaller neighbors. This is of importance as it suggests that distance or proximity matters, although only in combination with the relative population size. We also find some evidence of horizontal dependence across municipalities with similar political preferences.

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