normrnd generator Search Results


normrnd function  (MathWorks Inc)
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MathWorks Inc normrnd function
Normrnd Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab function: normrnd  (MathWorks Inc)
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MathWorks Inc matlab function: normrnd
Matlab Function: Normrnd, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab function: normrnd - by Bioz Stars, 2026-04
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normrnd generator  (MathWorks Inc)
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MathWorks Inc normrnd generator
Normrnd Generator, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/normrnd generator/product/MathWorks Inc
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normrnd generator - by Bioz Stars, 2026-04
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matlab function normrnd  (MathWorks Inc)
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MathWorks Inc matlab function normrnd
( A ) Reward contingencies used to achieve different behavioural states in awake mice. Top: mice were trained to discriminate between odours with ethyl butyrate (EB) vs. odours without EB. Middle: naïve mice received water reward 3 s after the onset of odour, on randomly selected trials. Bottom: naïve mice received water every trial, 15 s before odour onset. ( B ) Sniff patterns associated with trained and behaving mice (black), naïve and engaged mice (orange), and naïve and disengaged mice (light blue). Speed of inhalation during odour presentations shown. ( C ) Comparison of support vector machine (SVM) performance using data from behaving mice (black) vs. naïve engaged mice (left panel, orange), and vs. naïve disengaged mice (right panel, light blue). SVM was trained to discriminate responses to S+ vs. S- odours using randomly selected 80% of trials and tested on the remaining 20% of the trials. ( D ) Decoder performance for the results in ( C ) plotted using approximate z-values obtained from <t>MATLAB</t> implementation of Wilcoxon rank-sum test. ( E ) As in ( C ), but SVM was trained using responses to single odours, and tested on mixture responses. ( F ) As in ( D ) but for the results in ( E ). n = 13 fields of view, six mice for behaving, 17 fields of view, six mice for naïve, engaged, and 14 sessions, four mice for naïve, disengaged mice. See for linearity of summation.
Matlab Function Normrnd, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/matlab function normrnd/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab function normrnd - by Bioz Stars, 2026-04
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normrnd.m  (MathWorks Inc)
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MathWorks Inc normrnd.m
( A ) Reward contingencies used to achieve different behavioural states in awake mice. Top: mice were trained to discriminate between odours with ethyl butyrate (EB) vs. odours without EB. Middle: naïve mice received water reward 3 s after the onset of odour, on randomly selected trials. Bottom: naïve mice received water every trial, 15 s before odour onset. ( B ) Sniff patterns associated with trained and behaving mice (black), naïve and engaged mice (orange), and naïve and disengaged mice (light blue). Speed of inhalation during odour presentations shown. ( C ) Comparison of support vector machine (SVM) performance using data from behaving mice (black) vs. naïve engaged mice (left panel, orange), and vs. naïve disengaged mice (right panel, light blue). SVM was trained to discriminate responses to S+ vs. S- odours using randomly selected 80% of trials and tested on the remaining 20% of the trials. ( D ) Decoder performance for the results in ( C ) plotted using approximate z-values obtained from <t>MATLAB</t> implementation of Wilcoxon rank-sum test. ( E ) As in ( C ), but SVM was trained using responses to single odours, and tested on mixture responses. ( F ) As in ( D ) but for the results in ( E ). n = 13 fields of view, six mice for behaving, 17 fields of view, six mice for naïve, engaged, and 14 sessions, four mice for naïve, disengaged mice. See for linearity of summation.
Normrnd.M, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/normrnd.m/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
normrnd.m - by Bioz Stars, 2026-04
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random number generator normrnd  (MathWorks Inc)
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MathWorks Inc random number generator normrnd
( A ) Reward contingencies used to achieve different behavioural states in awake mice. Top: mice were trained to discriminate between odours with ethyl butyrate (EB) vs. odours without EB. Middle: naïve mice received water reward 3 s after the onset of odour, on randomly selected trials. Bottom: naïve mice received water every trial, 15 s before odour onset. ( B ) Sniff patterns associated with trained and behaving mice (black), naïve and engaged mice (orange), and naïve and disengaged mice (light blue). Speed of inhalation during odour presentations shown. ( C ) Comparison of support vector machine (SVM) performance using data from behaving mice (black) vs. naïve engaged mice (left panel, orange), and vs. naïve disengaged mice (right panel, light blue). SVM was trained to discriminate responses to S+ vs. S- odours using randomly selected 80% of trials and tested on the remaining 20% of the trials. ( D ) Decoder performance for the results in ( C ) plotted using approximate z-values obtained from <t>MATLAB</t> implementation of Wilcoxon rank-sum test. ( E ) As in ( C ), but SVM was trained using responses to single odours, and tested on mixture responses. ( F ) As in ( D ) but for the results in ( E ). n = 13 fields of view, six mice for behaving, 17 fields of view, six mice for naïve, engaged, and 14 sessions, four mice for naïve, disengaged mice. See for linearity of summation.
Random Number Generator Normrnd, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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normrnd routine  (MathWorks Inc)
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MathWorks Inc normrnd routine
( A ) Reward contingencies used to achieve different behavioural states in awake mice. Top: mice were trained to discriminate between odours with ethyl butyrate (EB) vs. odours without EB. Middle: naïve mice received water reward 3 s after the onset of odour, on randomly selected trials. Bottom: naïve mice received water every trial, 15 s before odour onset. ( B ) Sniff patterns associated with trained and behaving mice (black), naïve and engaged mice (orange), and naïve and disengaged mice (light blue). Speed of inhalation during odour presentations shown. ( C ) Comparison of support vector machine (SVM) performance using data from behaving mice (black) vs. naïve engaged mice (left panel, orange), and vs. naïve disengaged mice (right panel, light blue). SVM was trained to discriminate responses to S+ vs. S- odours using randomly selected 80% of trials and tested on the remaining 20% of the trials. ( D ) Decoder performance for the results in ( C ) plotted using approximate z-values obtained from <t>MATLAB</t> implementation of Wilcoxon rank-sum test. ( E ) As in ( C ), but SVM was trained using responses to single odours, and tested on mixture responses. ( F ) As in ( D ) but for the results in ( E ). n = 13 fields of view, six mice for behaving, 17 fields of view, six mice for naïve, engaged, and 14 sessions, four mice for naïve, disengaged mice. See for linearity of summation.
Normrnd Routine, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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normal random number generator  (MathWorks Inc)
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MathWorks Inc normal random number generator
( A ) Reward contingencies used to achieve different behavioural states in awake mice. Top: mice were trained to discriminate between odours with ethyl butyrate (EB) vs. odours without EB. Middle: naïve mice received water reward 3 s after the onset of odour, on randomly selected trials. Bottom: naïve mice received water every trial, 15 s before odour onset. ( B ) Sniff patterns associated with trained and behaving mice (black), naïve and engaged mice (orange), and naïve and disengaged mice (light blue). Speed of inhalation during odour presentations shown. ( C ) Comparison of support vector machine (SVM) performance using data from behaving mice (black) vs. naïve engaged mice (left panel, orange), and vs. naïve disengaged mice (right panel, light blue). SVM was trained to discriminate responses to S+ vs. S- odours using randomly selected 80% of trials and tested on the remaining 20% of the trials. ( D ) Decoder performance for the results in ( C ) plotted using approximate z-values obtained from <t>MATLAB</t> implementation of Wilcoxon rank-sum test. ( E ) As in ( C ), but SVM was trained using responses to single odours, and tested on mixture responses. ( F ) As in ( D ) but for the results in ( E ). n = 13 fields of view, six mice for behaving, 17 fields of view, six mice for naïve, engaged, and 14 sessions, four mice for naïve, disengaged mice. See for linearity of summation.
Normal Random Number Generator, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/normal random number generator/product/MathWorks Inc
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normal random number generator - by Bioz Stars, 2026-04
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random number generation function normrnd(⋅)  (MathWorks Inc)
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MathWorks Inc random number generation function normrnd(⋅)
( A ) Reward contingencies used to achieve different behavioural states in awake mice. Top: mice were trained to discriminate between odours with ethyl butyrate (EB) vs. odours without EB. Middle: naïve mice received water reward 3 s after the onset of odour, on randomly selected trials. Bottom: naïve mice received water every trial, 15 s before odour onset. ( B ) Sniff patterns associated with trained and behaving mice (black), naïve and engaged mice (orange), and naïve and disengaged mice (light blue). Speed of inhalation during odour presentations shown. ( C ) Comparison of support vector machine (SVM) performance using data from behaving mice (black) vs. naïve engaged mice (left panel, orange), and vs. naïve disengaged mice (right panel, light blue). SVM was trained to discriminate responses to S+ vs. S- odours using randomly selected 80% of trials and tested on the remaining 20% of the trials. ( D ) Decoder performance for the results in ( C ) plotted using approximate z-values obtained from <t>MATLAB</t> implementation of Wilcoxon rank-sum test. ( E ) As in ( C ), but SVM was trained using responses to single odours, and tested on mixture responses. ( F ) As in ( D ) but for the results in ( E ). n = 13 fields of view, six mice for behaving, 17 fields of view, six mice for naïve, engaged, and 14 sessions, four mice for naïve, disengaged mice. See for linearity of summation.
Random Number Generation Function Normrnd(⋅), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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random number generation function normrnd(⋅) - by Bioz Stars, 2026-04
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Image Search Results


( A ) Reward contingencies used to achieve different behavioural states in awake mice. Top: mice were trained to discriminate between odours with ethyl butyrate (EB) vs. odours without EB. Middle: naïve mice received water reward 3 s after the onset of odour, on randomly selected trials. Bottom: naïve mice received water every trial, 15 s before odour onset. ( B ) Sniff patterns associated with trained and behaving mice (black), naïve and engaged mice (orange), and naïve and disengaged mice (light blue). Speed of inhalation during odour presentations shown. ( C ) Comparison of support vector machine (SVM) performance using data from behaving mice (black) vs. naïve engaged mice (left panel, orange), and vs. naïve disengaged mice (right panel, light blue). SVM was trained to discriminate responses to S+ vs. S- odours using randomly selected 80% of trials and tested on the remaining 20% of the trials. ( D ) Decoder performance for the results in ( C ) plotted using approximate z-values obtained from MATLAB implementation of Wilcoxon rank-sum test. ( E ) As in ( C ), but SVM was trained using responses to single odours, and tested on mixture responses. ( F ) As in ( D ) but for the results in ( E ). n = 13 fields of view, six mice for behaving, 17 fields of view, six mice for naïve, engaged, and 14 sessions, four mice for naïve, disengaged mice. See for linearity of summation.

Journal: eLife

Article Title: State-dependent representations of mixtures by the olfactory bulb

doi: 10.7554/eLife.76882

Figure Lengend Snippet: ( A ) Reward contingencies used to achieve different behavioural states in awake mice. Top: mice were trained to discriminate between odours with ethyl butyrate (EB) vs. odours without EB. Middle: naïve mice received water reward 3 s after the onset of odour, on randomly selected trials. Bottom: naïve mice received water every trial, 15 s before odour onset. ( B ) Sniff patterns associated with trained and behaving mice (black), naïve and engaged mice (orange), and naïve and disengaged mice (light blue). Speed of inhalation during odour presentations shown. ( C ) Comparison of support vector machine (SVM) performance using data from behaving mice (black) vs. naïve engaged mice (left panel, orange), and vs. naïve disengaged mice (right panel, light blue). SVM was trained to discriminate responses to S+ vs. S- odours using randomly selected 80% of trials and tested on the remaining 20% of the trials. ( D ) Decoder performance for the results in ( C ) plotted using approximate z-values obtained from MATLAB implementation of Wilcoxon rank-sum test. ( E ) As in ( C ), but SVM was trained using responses to single odours, and tested on mixture responses. ( F ) As in ( D ) but for the results in ( E ). n = 13 fields of view, six mice for behaving, 17 fields of view, six mice for naïve, engaged, and 14 sessions, four mice for naïve, disengaged mice. See for linearity of summation.

Article Snippet: For testing the effect of trial-by-trial variability, Gaussian noise was generated using the MATLAB function normrnd with the mean set to 0, and added to responses from each trial, before the fractional deviation was calculated.

Techniques: Comparison, Plasmid Preparation