Math ANN encoding/decoding data & code
<p>Here we provide analysis scripts for "Artificial neural network modelling of the neural population code underlying mathematical operations".</p> <p>The preprocessed fMRI data are provided elsewhere (https://doi.org/10.5281/zenodo.6605258).</p> <p>The analysis scripts require MATLAB (R2019b) and FreeSurfer.</p> <p>Ridge regression</p> <pre><code>ID = 'sub-01'; Method = 'Transformer'; Code = 6; %1-6: Average Q vectors (encoding layer 1 to layer 6) %7-12: Average K vectors %13-18: Average V vectors %19-24: Average R vectors %25-27: Q vector of each symbol, single operator problems %28: Q vector of the result value, single operator problems %29: Average Q vector, single operator problems %30-34: Q vector of each symbol, double operator problems %35: Q vector of the intermediate result value, double operator problems %36: Q vector of the final result value, double operator problems %37: Average Q vector, double operator problems Ridge_FormOnly(ID, Method, Code) FDRcorr_FormOnly(ID, Method, Code)</code></pre> <p>Ridge regression with non-math regressors:</p> <pre><code>Ridge_FormOnly_Reg(ID, Method, Code) FDRcorr_FormOnly_Reg(ID, Method, Code)</code></pre> <p>Representational similarity analysis </p> <pre><code>Method = 'Transformer'; %Method = 'ME', %Method = 'Word2Vec' Code = 6; %Code = 1, %Code = 1 RSMwithFeat(Method, Code)</code></pre> <p>Feature brain similarity analysis</p> <pre><code>FeatBrainSim_ModelCompare FeatBrainSim_TransformerCompare </code></pre> <p>Decoding analysis</p> <pre><code>Ridge_Decoding_FormOnly_New(ID, Method, Code) Ridge_Decoding_FormOnly_Single2Double(ID, Method, Code) Ridge_Decoding_FormOnly_Double2Single(ID, Method, Code) Ridge_Decoding_FormOnly_NonAdd2Add(ID, Method, Code) Ridge_Decoding_FormOnly_NonSub2Sub(ID, Method, Code) Ridge_Decoding_FormOnly_NonMul2Mul(ID, Method, Code) Ridge_Decoding_FormOnly_NonDiv2Div(ID, Method, Code)</code></pre> <p> </p> <p>If you have any questions, please send an email to nakai.tomoya [at] neuro.mimoza.jp. </p>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 20
- Reuse readiness
- 8
- Engagement
- 0