Decoding decision-making behavior from sparse neural spiking activity
Fig 6
Attention-based neuronal filtering enhances decision prediction performance.
Error bars represent standard deviations; asterisks indicate significance levels (*P < 0.05, **P < 0.01, ***P < 0.001, two-tailed t-test). A. Brain region distribution comparison between all neurons and attention-filtered neurons for the ibl_witten_29 mouse, demonstrating that decision-making processes are driven by a specific subset of neurons. B. SVM_RBF model performance across different selection thresholds comparing three conditions: CA-BiLSTM attention-filtered neurons, randomly selected neurons, and all neurons. Attention-filtered neurons consistently yield superior prediction accuracy across all threshold values. C. Comparative performance analysis at 0.1 selection threshold across individual mouse samples. showing that CA-BiLSTM-selected neurons outperform randomly selected neurons and full neuronal ensembles in most cases, indicating that excluding irrelevant neural signals improves decision prediction accuracy. Among them, error bars represent the standard deviation of five fold cross validation.