Determining the Optimal Model, Reaction Time Delay, and Preprocessing Technique for the Classification of Visual Stimuli from Mouse Visual Cortex Activity
DOI:
https://doi.org/10.58445/rars.193Keywords:
machine learning, computational neuroscience, visual cortex, preprocessing, reaction time offset, visual stimulus, model, allen institute, classifier, mouse model, scikit, solvers, kernels, logistic regression, multilayer perceptron, mlp, svm, support vector machineAbstract
Improving classification accuracies of visual stimuli from neural data is important in developing future models, including those for transfer learning, which will allow further research in the areas of visual process and vision loss. In this paper, several models and solvers/kernels, time delays, and preprocessing techniques are tested. This study finds several parameters that can be used to maximize prediction accuracy, ultimately producing an average accuracy of 81% when evaluated on the test set.
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