Fig 1.
The CA-BiLSTM model accurately decodes mouse perceptual decision-making.
A, The collection of neural spike data. In the context of perceptual decision tasks in mice, we collected neural spike data pertaining to the interval between the observation of a grating and the subsequent rotation of a wheel. B, Predicting the decision behavior of mice (Left or right) based on neural spike data. C, A schematic showing the architecture of CA-BiLSTM for neural spike data. The CA-BiLSTM model consists of three components: a channel attention layer, a BiLSTM layer, and a Dense layer.
Fig 2.
Session filtering process.
Table 1.
Mouse information after pre-processing.
Fig 3.
Comparison of neural signal preprocessing methods and their impact on classification performance.
A, Comparison of neural signal preprocessing strategies: The upper panel illustrates the time-interval-based preprocessing method (quantifying spike counts within fixed time windows); the lower panel shows the equal-segment-based preprocessing method (dividing decision time uniformly across trials and calculating spike counts within each segment). Due to significant variations in decision times across trials, the equal-segment-based approach enables standardized dimensional representation of data from different trials, effectively minimizing interference from redundant information. B, Performance comparison of preprocessing methods: Scatter plot showing classification accuracy of the CA-BiLSTM model for individual mouse samples under different preprocessing approaches, with triangular markers indicating mean accuracy across all mice. Results demonstrate that the equal-segment-based preprocessing method yields significantly higher accuracy for the majority of samples, validating the effectiveness of the proposed preprocessing strategy.
Fig 4.
Performance comparison of different models for neural decision prediction.
A. Bar chart comparing average accuracy and AUC metrics across different models for 25 mouse samples with 7 segments. Among them, the error bars represent the standard deviation of the accuracy rates across different mice. B. Comparative analysis of accuracy and AUC across different segment numbers for various algorithms.
Fig 5.
Neuronal attention weight distribution in mouse decision-making.
Visualization of attention mechanism outputs from the CA-BiLSTM model for the ibl_witten_29 mouse across five-fold cross-validation. Top: Distribution of attention weights for all neurons, with values from -1 to 1 indicating each neuron’s contribution to the decision-making process. Middle: Heatmap showing trial-specific attention weights for each neuron across the test set. Bottom: Mean positive attention weights across all test trials. The consistent weight distributions across five-fold cross-validation demonstrate the stability and reliability of the identified neural correlates.
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.
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.
Fig 8.
Behavioral performance and neural decoding across probability conditions.
Analysis of reaction times, accuracy, and model performance for different stimulus probability conditions (P(Left) = 0.2, 0.5, 0.8) in a perceptual decision-making task. A. Mean reaction times for behaviorally correct (blue) and behaviorally incorrect (orange) trials across probability conditions. B. Mean reaction times for model-predicted correct and model-predicted incorrect trials by probability condition. Significant differences between trial types are marked with asterisks. C. Model prediction accuracy across probability conditions. D. Neural decoder performance (ROC AUC) for each probability condition. E. Reaction time histograms overlaid by model prediction outcome and probability condition, showing distributional differences between model-predicted correct and incorrect responses.