Although statistical methods were not used to predetermine sample sizes, our sample sizes were similar to those used in previous studies13 (link),14 (link). All well-isolated neurons were included in the neuronal recordings to prevent sampling bias. Blinding was not performed for investigators involved in data collection and analysis. No data were excluded unless otherwise stated. All statistical procedures were assessed by two-tailed tests and carried out using MATLAB Statistics and Machine Learning Toolbox, Signal Processing Toolbox, Parallel Computing Toolbox, Control System Toolbox, and Multivariate Granger Causality Toolbox (version 2018b and 2020b; MathWorks Inc.).
Parallel computing toolbox
Manufactured by MathWorks
Sourced in United States
The Parallel Computing Toolbox is a software product that enables the use of parallel computing techniques in MATLAB. It provides a set of functions and tools for distributing computations across multiple processors, cores, or machines, allowing users to leverage the power of parallel processing to solve complex, computationally intensive problems more efficiently.
Automatically generated - may contain errors
Lab products found in correlation
Control system toolbox, by MathWorks (1 mentions)
Signal processing toolbox, by MathWorks (1 mentions)
Matlab statistics and machine learning toolbox, by MathWorks (1 mentions)
Geforce gtx 1650, by NVIDIA (1 mentions)
Neural network toolbox, by MathWorks (1 mentions)
Matlab, by MathWorks (1 mentions)
Optimization toolbox, by MathWorks (1 mentions)
Statistics and machine learning toolbox, by MathWorks (1 mentions)
Econometrics toolbox, by MathWorks (1 mentions)
3 protocols using parallel computing toolbox
1
Neuronal Recordings with Comprehensive Analysis
2
GRNN ANN Modeling on Windows 10
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The GRNN model was implemented in the MATLAB® software package, Version R2016a 64-bit (win64), with the inclusion of the Neural Network Toolbox and Parallel Computing Toolbox (MathWorks, Natick, MA, USA). These toolboxes offer all the necessary resources for efficient work with ANNs and parallel computing. The MATLAB® software package was installed on a personal computer running Windows 10; Intel® CoreTM i5-12450H, CPU@ 2.4 GHz, 16 GB RAM, 64-bit; SSD 250 GB; GPU: NVIDIA GeForce GTX 1650, 6 GB.
3
Analyzing Cognitive Correlates of Moral Judgments
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All data and analysis code as well as experimental materials are available at https: //osf.io/x83pk (Clarmann von Clarenau et al., 2022) (link). The data were analyzed using Matlab version R2020b (The MathWorks, 2020a), with the Statistics and Machine Learning Toolbox (The MathWorks, 2020e), the Econometrics Toolbox (The MathWorks, 2020b), the Parallel Computing Toolbox (The MathWorks, 2020c), the Optimization Toolbox (The MathWorks, 2020d), and the BayesFactor Toolbox (Krekelberg, 2022) (link). The study design and analyses were not preregistered. The study was approved by the ethics committee of the Max Planck Institute for Human Development.
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