matlab functions r2010a Search Results


machine learning toolbox function sobolset  (MathWorks Inc)
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MathWorks Inc machine learning toolbox function sobolset
Machine Learning Toolbox Function Sobolset, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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lfp frequency bands  (MathWorks Inc)
98
MathWorks Inc lfp frequency bands
Contribution ratio ( % Contribution ) of MUA and <t>LFP</t> features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the <t>LFP</t> <t>frequency</t> bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.
Lfp Frequency Bands, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 98/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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standard matlab curve fitting routines  (MathWorks Inc)
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MathWorks Inc standard matlab curve fitting routines
Contribution ratio ( % Contribution ) of MUA and <t>LFP</t> features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the <t>LFP</t> <t>frequency</t> bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.
Standard Matlab Curve Fitting Routines, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab r r2012a image processing toolbox  (MathWorks Inc)
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MathWorks Inc matlab r r2012a image processing toolbox
Contribution ratio ( % Contribution ) of MUA and <t>LFP</t> features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the <t>LFP</t> <t>frequency</t> bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.
Matlab R R2012a Image Processing Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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compiler runtime mcr version 8 r2015a 32 bit  (MathWorks Inc)
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MathWorks Inc compiler runtime mcr version 8 r2015a 32 bit
Contribution ratio ( % Contribution ) of MUA and <t>LFP</t> features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the <t>LFP</t> <t>frequency</t> bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.
Compiler Runtime Mcr Version 8 R2015a 32 Bit, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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fmicon function  (MathWorks Inc)
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MathWorks Inc fmicon function
Contribution ratio ( % Contribution ) of MUA and <t>LFP</t> features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the <t>LFP</t> <t>frequency</t> bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.
Fmicon Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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r2010a  (MathWorks Inc)
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MathWorks Inc r2010a
Contribution ratio ( % Contribution ) of MUA and <t>LFP</t> features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the <t>LFP</t> <t>frequency</t> bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.
R2010a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab r2018a environment  (MathWorks Inc)
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MathWorks Inc matlab r2018a environment
Contribution ratio ( % Contribution ) of MUA and <t>LFP</t> features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the <t>LFP</t> <t>frequency</t> bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.
Matlab R2018a Environment, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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symbolic math toolbox r2013a  (MathWorks Inc)
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MathWorks Inc symbolic math toolbox r2013a
Contribution ratio ( % Contribution ) of MUA and <t>LFP</t> features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the <t>LFP</t> <t>frequency</t> bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.
Symbolic Math Toolbox R2013a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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s function toolbox  (MathWorks Inc)
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MathWorks Inc s function toolbox
Contribution ratio ( % Contribution ) of MUA and <t>LFP</t> features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the <t>LFP</t> <t>frequency</t> bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.
S Function Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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oscill8  (SourceForge net)
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SourceForge net oscill8

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non-ideal multiplier  (STMicroelectronics Pte)
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Image Search Results


Contribution ratio ( % Contribution ) of MUA and LFP features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the LFP frequency bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.

Journal: APL Bioengineering

Article Title: Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces

doi: 10.1063/5.0250296

Figure Lengend Snippet: Contribution ratio ( % Contribution ) of MUA and LFP features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the LFP frequency bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.

Article Snippet: Frequency-spectrum features were widely used for processing LFPs; therefore, LFP raw data were further down-sampled to a 1-kHz sampling rate and converted to power spectral density using a short-time Fourier transform with a Hanning window of 1 f m ms in length and time step of 33-ms, where f m is the minimum frequency of each LFP frequency bands (using the spectrogram function from the Signal Processing Toolbox, MATLAB R2019a, MathWorks).

Techniques: Labeling

Journal: iScience

Article Title: The hepatocyte growth factor/c-met pathway is a key determinant of the fibrotic kidney local microenvironment

doi: 10.1016/j.isci.2021.103112

Figure Lengend Snippet:

Article Snippet: Numerical bifurcation analysis was performed with OSCILL8 ( http://oscill8.sourceforge.net/ ), while the time-course simulation was performed with MATLAB (R2017a) function ode23s.

Techniques: Degradation Assay, Staining, Software, Immunohistochemical staining