Lateral geniculate neurons in behaving primates. II. Encoding of visual information in the temporal shape of the response

1991 ◽  
Vol 66 (3) ◽  
pp. 794-808 ◽  
Author(s):  
J. W. McClurkin ◽  
T. J. Gawne ◽  
L. M. Optican ◽  
B. J. Richmond
Keyword(s):  
Principal Components ◽  
Temporal Distribution ◽  
Principal Component ◽  
The Other ◽  
Spike Count ◽  
Stimulus Luminance ◽  
Temporal Code ◽  
Amount Of Information ◽  
Lateral Geniculate ◽  
The Individual

1. We used the Karhunen-Loeve (K-L) transform to quantify the temporal distribution of spikes in the responses of lateral geniculate (LGN) neurons. The basis functions of the K-L transform are a set of waveforms called principal components, which are extracted from the data set. The coefficients of the principal components are uncorrelated with each other and can be used to quantify individual responses. The shapes of each of the first three principal components were very similar across neurons. 2. The coefficient of the first principal component was highly correlated with the spike count, but the other coefficients were not. Thus the coefficient of the first principal component reflects the strength of the response, whereas the coefficients of the other principal components reflect aspects of the temporal distribution of spikes in the response that are uncorrelated with the strength of the response. Statistical analysis revealed that the coefficients of up to 10 principal components were driven by the stimuli. Therefore stimuli govern the temporal distribution as well as the number of spikes in the response. 3. Through the application of information theory, we were able to compare the amount of stimulus-related information carried by LGN neurons when two codes were assumed: first, a univariate code based on response strength alone; and second, a multivariate temporal code based on the coefficients of the first three principal components. We found that LGN neurons were able to transmit an average of 1.5 times as much information using the three-component temporal code as they could using the strength code. 4. The stimulus set we used allowed us to calculate the amount of information each neuron could transmit about stimulus luminance, pattern, and contrast. All neurons transmitted the greatest amount of information about stimulus luminance, but they also transmitted significant amounts of information about stimulus pattern. This pattern information was not a reflection of the luminance or contrast of the pixel centered on the receptive field. 5. In addition to measuring the average amount of information each neuron transmitted about all stimuli, we also measured the amount of information each neuron transmitted about the individual stimuli with both the univariate spike count code and the multivariate temporal code. We then compared the amount of information transmitted per stimulus with the magnitudes of the responses to the individual stimuli. We found that the magnitudes of both the univariate and the multivariate responses to individual stimuli were poorly correlated with the information transmitted about the individual stimuli.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Feed efficiency of lactating Holstein cows is less reproducible when changing dietary starch and fibre concentrations than within diet over subsequent lactation stages

2021 ◽  
Author(s):  
Amélie Fischer ◽  
Philippe Gasnier ◽  
Philippe Faverdin
Keyword(s):  
Principal Components ◽  
Feed Efficiency ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Holstein Cows ◽  
Diet Change ◽  
Total Variability ◽  
The One ◽  
The Individual ◽  
Starch Diet

ABSTRACTBackgroundImproving feed efficiency has become a common target for dairy farmers to meet the requirement of producing more milk with fewer resources. To improve feed efficiency, a prerequisite is to ensure that the cows identified as most or least efficient will remain as such, independently of diet composition. Therefore, the current research analysed the ability of lactating dairy cows to maintain their feed efficiency while changing the energy density of the diet by changing its concentration in starch and fibre. A total of 60 lactating Holstein cows, including 33 primiparous cows, were first fed a high starch diet (diet E+P+), then switched over to a low starch diet (diet E−P−). Near infra-red (NIR) spectroscopy was performed on each individual feed ingredient, diet and individual refusals to check for sorting behaviour. A principal component analysis (PCA) was performed to analyse if the variability in NIR spectra of the refusals was explained by the differences in feed efficiency.ResultsThe error of reproducibility of feed efficiency across diets was 2.95 MJ/d. This error was significantly larger than the errors of repeatability estimated within diet over two subsequent lactation stages, which were 2.01 MJ/d within diet E−P− and 2.40 MJ/d within diet E+P+. The coefficient of correlation of concordance (CCC) was 0.64 between feed efficiency estimated within diet E+P+ and feed efficiency estimated within diet E−P−. This CCC was smaller than the one observed for feed efficiency estimated within diet between two subsequent lactation stages (CCC = 0.72 within diet E+P+ and 0.85 within diet E−P−). The first two principal components of the PCA explained 90% of the total variability of the NIR spectra of the individual refusals. Feed efficiency was poorly correlated to those principal components, which suggests that feed sorting behaviour did not explain differences in feed efficiency.ConclusionsFeed efficiency was significantly less reproducible across diets than repeatable within the same diet over subsequent lactation stages, but cow’s ranking for feed efficiency was not significantly affected by diet change. The differences in sorting behaviour between cows were not associated to feed efficiency differences in this trial neither with the E+P+ diet nor with the E−P− diet. Those results have to be confirmed with cows fed with more extreme diets (for example roughage only) to ensure that the least and most efficient cows will not change.

On Cliff's Formula, the Kaiser-Guttman Rule, and the Number of Factors

1992 ◽  
Vol 74 (2) ◽  
pp. 595-598 ◽  
Author(s):  
Henry F. Kaiser
Keyword(s):  
Measurement Error ◽  
Internal Consistency ◽  
Principal Components ◽  
Principal Component ◽  
Internal Consistency Reliability ◽  
The Other ◽  
Factor Analytic ◽  
Number Of Factors ◽  
Physical Attributes ◽  
The Right

Cliff (1988) has presented a formula for the reliability of a principal component which is different from my long-known formula (Kaiser, 1957, 1991) for coefficient alpha of a principal component. Cliff claims that his approach is “correct” and mine “is the result of a misapplication of the formula for internal consistency reliability” Actually, both developments are correct but are based on different premises: Cliff considers measurement error within—but not between—attributes, while I consider measurement error between—but not within—attributes. The application of my formula to the knotty problem of the “number of factors”—the Kaiser-Guttman Rule—appears often to give the “right” result, when “right” means agreement with the subjective judgment of factor-analytic grandmasters. But when it fails it is approximately equally likely to overfactor as to underfactor. Cliff's formula, on the other hand, when used to establish the number of factors, almost invariably overfactors and, in the limit, as the within-attribute reliabilities all approach one (as with, say, physical attributes), nonsensically will declare all principal components perfectly reliable no matter how small their associated eigenvalues, yielding an absurd answer if used to establish the number of factors.

scholarly journals Analiza dejavnikov ribolovne učinkovitosti v metierjih Severnega Atlantika

2021 ◽  
Author(s):  
Janja Jerebic ◽  
Špela Kajzer ◽  
Anja Goričan ◽  
Drago Bokal
Keyword(s):  
North Atlantic ◽  
Principal Components ◽  
Principal Component ◽  
Fishing Effort ◽  
Human Consumption ◽  
High Complexity ◽  
Fishing Fleets ◽  
The Individual ◽  
Location Type ◽  
Variance Explained

The management of fishing fleets is an important factor in the sustainable exploitation of marine organisms for human consumption. Therefore, regulatory services monitor catches and limit them based on data. In this paper, we analyze North Atlantic Fishing Organization (NAFO) data on North Atlantic catches to direct the effectiveness of fishing stakeholders. Data on fishing time (month and year), equipment, location, type of catch, and, for us, the most interesting, data on the fishing effort are given, and their quality is analyzed. In the last part, The Principal Component Analysis for individual activities, among which fishing stakeholders can decide, is performed on a selected data sample. The complexity of the connections between the set of observed activities is explained by new uncorrelated variables - principal components - that are important for achieving the expected fishing catch. We find that the proportions of variance explained by the individual principal components are low, which indicates the high complexity of the topic discussed.

Primate striate and prestriate cortical neurons during discrimination. I. simultaneous temporal encoding of information about color and pattern

1996 ◽  
Vol 75 (1) ◽  
pp. 481-495 ◽  
Author(s):  
J. W. McClurkin ◽  
L. M. Optican
Keyword(s):  
Receptive Field ◽  
Cortical Neurons ◽  
Discrimination Task ◽  
Control Experiment ◽  
The Other ◽  
Spike Count ◽  
Wave Form ◽  
Amount Of Information ◽  
Stimulus Parameters ◽  
Visual Interactions

1. We recorded the responses of neurons in cortical areas V1, V2, and V4 to a set of 36 colored patterns while monkeys discriminated among the stimuli on the basis of their color or their pattern. In the discrimination task a colored square or a black and white pattern was presented foveally as a cue stimulus. The monkey was required to choose, by making a saccade, which of three peripheral targets had the same property as the cue. One of the peripheral targets was centered on the receptive field of the neuron, and the other two were positioned at equally distant points around the circumference of an imaginary circle centered on the cue and passing through the receptive field. 2. An examination of the responses to the stimuli showed that there was a complex interaction between the effects of color and of pattern on the neuronal responses. Because of these interactions, we tested sensitivity to color and pattern by sorting the responses to all stimuli according to the color or pattern of the stimulus. We found that the number of spikes in the responses was affected by only one or the other of the stimulus parameters, but that the temporal distribution of spikes was affected by both stimulus parameters. We quantified the relative sensitivities of each neuron to color and pattern by dividing the amount of information the neuron transmitted about color by the amount of information the neuron transmitted about pattern. The distributions of information ratios assuming a spike count code were broad, indicating that many neurons were sensitive to only one stimulus parameter or the other. In contrast, the distributions of information ratios assuming a wave-form code were narrow and centered near 1.0, indicating nearly equal sensitivities to both stimulus parameters. 3. In our initial experiments, it appeared that the color or pattern used as the cue for the discrimination task affected the responses of many neurons to stimuli on the receptive field. To determine whether the cue effect was due to simple visual interactions or to the cognitive requirements of the discrimination task, we performed a control experiment in which the cue was turned on 80 ms after the peripheral stimuli. For many of the neurons in the control experiment, an effect related to the cue appeared in the response before the cue had been turned on. Thus the effect we observed must have been due to visual interactions with the distractor targets, even though these were outside the neuron's classically defined receptive field. 4. We compared the rate at which color and pattern information developed in the response over time assuming either a spike count or a waveform code. The spike count code gained more of its information in the first 20ms of the response than did the waveform code, but thereafter the information carried by the spike count code developed more slowly and reached a lower asymptote than did the information carried by the waveform code. 5. The waveform codes carried nearly equal amounts of information about color and pattern, but the messages about these two parameters did not develop at the same rate in all areas. The messages about color and pattern developed at the same rate in area V1, but messages about color developed more slowly than did the messages about pattern in areas V2 and V4. 6. These results offer a neurophysiological basis for both the psychological separateness of color and pattern, and the binding of color and pattern into a unified percept. We propose that the separateness of color and form arises not by virtue of their being encoded by different populations of neurons, but by virtue of their being encoded by separable waveform codes in the responses of single neurons. We propose that the binding of color and form occurs by virtue of their codes being multiplexed on the same neurons.

Chemical Structure-Odor Correlation in Series of Synthetic Methylene Interrupted n-Nonadien-1-ols

1996 ◽  
Vol 51 (11-12) ◽  
pp. 841-848 ◽  
Author(s):  
Yasunobu Sakoda ◽  
Kenji Matsui ◽  
Yoshihiko Akakabe ◽  
Jun Suzuki ◽  
Akikazu Hatanaka ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Double Bond ◽  
Principal Components ◽  
Chemical Structure ◽  
Principal Component ◽  
Component Analysis ◽  
The Other ◽  
Analysis Method ◽  
In Series ◽  
Natural Characteristic

Abstract Chemical structure-odor correlations in the isomers of n-C9-methylene interrupted dienols were explored using synthetic nine isomers of these alcohols. The synthetic dienols were purified by recrystallization or column chromatography of their 3,5-dinitrobenzoate de­ rivatives. Chemical structure-odor correlations in all the isomers of the purified n-nonadien-1-ols were analyzed by treating the data obtained statistically with the principal component analysis method (Sakoda et al., 1995; Cramer et al., 1988) in comparison with those of n-nonen-1-ols. The odor profiles of the n-nonadien-1-ols were attributable largely to the geometries of the isomers, compared with n-nonen -1-ols (Sakoda et al., 1995). With the principal component analysis, the odor profiles of the series of the dienols were successfully integrated into the first and the second principal components. The first component (PC 1) consisted of combined characteristics of fruity, fresh, sweet, herbal and oily-fatty, and the second component (PC 2) leaf or grassy and vegetable-like. Of the methylene interrupted dienol isomers, (2E ,6Z)-and (3Z,6Z)-nonadien-1-ols which are natural products and have (6Z) in the same, deviated markedly from the other isomers as seen in (6Z)-nonen -1-ol of n-nonen-1-ols. That suggests that the double bond of (ω3Z) was an important factor for natural characteristic odor.

Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. III. Information theoretic analysis

1987 ◽  
Vol 57 (1) ◽  
pp. 162-178 ◽  
Author(s):  
L. M. Optican ◽  
B. J. Richmond
Keyword(s):  
Spike Train ◽  
Principal Components ◽  
Temporal Cortex ◽  
Statistical Properties ◽  
Single Units ◽  
Spike Count ◽  
Inferior Temporal Cortex ◽  
Two Dimensional ◽  
Amount Of Information ◽  
Coding Scheme

Ablation and single-unit studies in primates have shown that inferior temporal (IT) cortex is important for pattern discrimination. The first paper in this series suggested that single units in IT cortex of alert monkeys respond to a set of two-dimensional patterns with complex temporal modulation of their spike trains. The second paper quantified the waveform of the modulated responses of IT neurons with principal components and demonstrated that the coefficients of two to four of the principal components were stimulus dependent. Although the coefficients of the principal components are uncorrelated, it is possible that they are not statistically independent. That is, several coefficients could be determined by the same feature of the stimulus, and thus could be conveying the same information. The final part of this study examined this issue by comparing the amount of information about the stimulus that can be conveyed by two codes: a temporal waveform code derived from the coefficients of the first three principal components and a mean rate code derived from the spike count. We considered the neuron to be an information channel conveying messages about stimulus parameters. Previous applications of information theory to neurophysiology have dealt either with the theoretical capacity of neuronal channels or the temporal distribution of information within the spike train. This previous work usually used a general binary code to represent the spike train of a neuron's response. Such a general approach yields no indication of the nature of the neuron's intrinsic coding scheme because it depends only on the timing of spikes in the response. In particular, it is independent of any statistical properties of the responses. Our approach uses the principal components of the response waveform to derive a code for representing information about the stimuli. We regard this code as an indication of the neuron's intrinsic coding scheme, because it is based on the statistical properties of the neuronal responses. We measured how much information about the stimulus was present in the neuron's responses. This transmitted information was calculated for codes based on either the spike count or on the first three principal components of the response waveform. The information transmitted by each of the first three principal components was largely independent of that transmitted by the others. It was found that the average amount of information transmitted by the principal components was about twice as large as that transmitted by the spike count.(ABSTRACT TRUNCATED AT 400 WORDS)

Effects of display curvature and hand length on smartphone usability

2017 ◽  
Vol 61 (1) ◽  
pp. 1054-1057 ◽  
Author(s):  
Jihhyeon Yi ◽  
Sungryul Park ◽  
Juah Im ◽  
Seonyeong Jeon ◽  
Gyouhyung Kyung
Keyword(s):  
Principal Components ◽  
Principal Component Regression ◽  
Principal Component ◽  
The Other ◽  
Side Edge ◽  
Hand Size ◽  
Size Groups ◽  
The Mean ◽  
Hand Length ◽  
Video Calling

The purpose of this study was to examine the effects of display curvature and hand length on smartphone usability, which was assessed in terms of grip comfort, immersive feeling, typing performance, and overall satisfaction. A total of 20 younger individuals with the mean (SD) age of 20.8 (2.4) yrs were divided into three hand-size groups (small: 8, medium: 6, large: 6). Two smartphones of the same size were used – one with a flat display and the other with a side-edge curved display. Three tasks (watching video, calling, and texting) were used to evaluate smartphone usability. The smartphones were used in a landscape mode for the first task, and in a portrait mode for the other two. The flat display smartphone provided higher grip comfort during calling (p = 0.008) and texting (p = 0.006) and higher overall satisfaction (p = 0.0002) than the curved display smartphone. The principal component regression (adjusted R2 = 0.49) of overall satisfaction on three principal components comprised of the remaining measures showed that the first principal component on grip comfort was more important than the other two on watching experience and texting performance. It is thus necessary to carefully consider the effect of display curvature on grip comfort when applying curved displays to hand-held devices such as smartphones.

Air pollution and health risk assessment in Northeastern China: A case study of Jilin Province

2021 ◽  
pp. 1420326X2097927
Author(s):  
Li Bai ◽  
Chunhui Li ◽  
Chuck Wah Yu ◽  
Zijian He
Keyword(s):  
Air Pollution ◽  
Principal Components ◽  
Risk Model ◽  
Temporal Distribution ◽  
Principal Component ◽  
Northeastern China ◽  
Jilin Province ◽  
Atmospheric Environment ◽  
Annual Data ◽  
Model Calculations

In recent years, the pollution of the air environment has been increasing, which has seriously endangered human health. The large amount of coal burning and automobile exhaust emissions in winter have caused serious regional pollution, which has greatly increased the prevalence of respiratory disease of people. The purpose of this study is to use Jilin Province as an example to map out the environmental pollution status in Northeastern China, and to provide suggestions for the improvement of the atmospheric environment. Using the annual data of China's atmospheric monitoring, the atmospheric environment in Jilin was comprehensively analysed using ArcGIS, principal component analysis (PCA) and non-carcinogenic risk model calculations. The results showed that the temporal distribution of pollutants was winter > spring > autumn > summer, and the spatial distribution is decreasing from northwest to southeast. The PCA showed that the first principal components were PM2.5 and CO, and the second principal components were PM10 and O3. The main sources of air pollution were coal-fired (62.365%) and automobile emissions (19.153%). The non-carcinogenic risks of pollutants are all within the acceptable range (1 × 10−6), but as age increases, the risk gradually decreases, and the risk value of male is higher than that of female.

scholarly journals VARIACIONES NICTEMERALES EN LA COMUNIDAD DE ZOOPLANCTON DE LA BAHIA DE SANTA MARTA, MAR CARIBE COLOMBIANO

2016 ◽  
Vol 22 ◽  
Author(s):  
Alvaro Ramiro BernaI V. ◽  
Sven Zea
Keyword(s):  
Principal Components ◽  
Close Association ◽  
Incident Light ◽  
Sampling Site ◽  
Principal Component ◽  
Environmental Parameters ◽  
The Other ◽  
Surface Zone ◽  
The Mean ◽  
Components Analysis

Day-night and between-day variation in surficial zooplankton composition and its relationship to environmental parameters were analyzed in the Santa Marta Bay, Colombian Caribbean, by Principal Components Analysis. Sampling was carried out every four hours in three different days between August and October 1989. The greatest variation in abundance (first principal component) was due to an increase over the mean in most groups at nightfall and during night hours. This variation was inversely and significantly correlated with incident light intensity, and was interpreted as consequence of the vertical migration out of the surface zone. On the other hand, the second and third principal components showed differences amog days for groups and within groups; those that existed in the first sampling day with respect to the other two were highlighted by the second component,, and were negatively correlated with temperature and positively correlated with disolved nitrates. These results were interpreted as a consequence of movements of water masses with different physical-chemical characteristics and zooplankton composition over the sampling site. However, a case of close association between these parameters and the changes in migrating movements could not be ruled out.

Lateral geniculate neurons in behaving primates. III. Response predictions of a channel model with multiple spatial-to-temporal filters

1991 ◽  
Vol 66 (3) ◽  
pp. 809-823 ◽  
Author(s):  
T. J. Gawne ◽  
J. W. McClurkin ◽  
B. J. Richmond ◽  
L. M. Optican
Keyword(s):  
Principal Components ◽  
Channel Model ◽  
Receptive Fields ◽  
Principal Component ◽  
Spatial Filter ◽  
Large Set ◽  
Spatial Filters ◽  
Input Nonlinearity ◽  
Black And White ◽  
Lateral Geniculate

1. For the experiments reported in these papers, we recorded the responses of lateral geniculate (LGN) neurons to a large set of two-dimensional, black and white patterns based on Walsh functions and to a set of test stimuli. In the first two papers we reported that these neurons encode stimulus-related information in both the strength and the shape of the response waveforms and that there are more than two independent components in the response. These results cannot be explained by existing models. This paper provides a model of LGN neurons that not only accounts for the foregoing observations, but also yields predictions confirmed by direct tests. 2. The model represents a neuron as a set of three parallel channels. The input to each channel is an array of pixel luminances. Each channel consists of an input nonlinearity cascaded into a linear spatial-to-temporal filter. The output of each channel is a basic waveform, a principal component. The response of the neuron is the sum of the outputs of the three channels. 3. The model accounted for much of the variance in the coefficients of the first three principal components of the neuronal responses to the set of Walsh stimuli. Using parameters derived from the responses of neurons to the Walsh stimuli only, the model also predicted the responses to "center-surround" annuli of different contrasts and mean luminances, as well as to superpositions of pairs of Walsh patterns. The model made statistically significant predictions of the coefficients of two of the principal components of these responses. 4. After the parameters of the model had been fit to reproduce the responses of neurons to the Walsh stimuli, we found that the input nonlinearity of the model was compressed at both the high and low luminance levels. This compression produced response saturation that closely resembled the response saturation of neurons reported in the first paper in this series. Although not absolutely smooth, the spatial filter for the first channel had a dominant excitatory or inhibitory center and an antagonistic surround. Thus this spatial filter accounted for both the center and the surround structures of previous models of LGN receptive fields. There was greater variety in the structures of the spatial filters for the second and third channels, but none had a center-surround organization. Many of the spatial filters for these higher channels contained oriented ridges or valleys. Other spatial filters were dominated by a bipolar pair of pixels. 5. The model of LGN neurons that we present in this paper represents an extension over previous models in four ways. First, the model is capable of explaining the responses of neurons to a wider range of luminances than previous models. Second, the model is capable of explaining the shapes of the response waveforms as well as their magnitudes. Third, the concept of a single receptive field is extended to a series of spatial-to-temporal filters. Fourth, the model suggests that LGN neurons provide a description of both the brightness and the form of a stimulus in their response waveforms.

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