Decoding decision-making behavior from sparse neural spiking activity
Fig 7
Analysis of attention-filtered neurons for decision prediction.
A,B. Comparative validation of different neuronal selection methods using (A) SVM_RBF and (B) BiLSTM models, with error bars representing standard deviations, showing CA-BiLSTM’s superior performance. C. Comparison of mouse reaction times and stimulus intensities between trials correctly predicted by our model versus incorrectly predicted trials, revealing that trials our model predicted correctly exhibited slightly longer mouse reaction times and were associated with higher stimulus intensities. The bottom right panel shows normalized counts (divided by the maximum count across all stimulus intensities) to enable visual comparison. D. Average attention scores across brain regions for all mice, highlighting the importance of DG, LP, and PPC regions in decision-making processes.