Fig. 6: The model MD supports continual learning in the PFC.
From: Rapid context inference in a thalamocortical model using recurrent neural networks

A The MD effects on the PFC neurons, enhancing current-context PFC activities and inhibiting other-context PFC activities. B The model performance (mean square error, MSE) of the PFC-MD model and the PFC only model during training in the attention-guided behavioral task. C The model performance of the third block when the learned context was switched back. In comparison with the PFC only model, the PFC-MD model had low prediction errors after context switch, alleviating catastrophic forgetting. D The experimental data were collected when mice performed similar three-block switching tasks. The left and middle boxplots show the effect of bilateral MD suppression on behavioral performance. The right boxplot shows the comparison of performance on the consecutive sessions. ***P < 0.001, Bonferroni-corrected rank-sum test. Figure 6D adapted from Fig. 5F of Rikhye, R.V., Gilra, A. & Halassa, M.M. Thalamic regulation of switching between cortical representations enables cognitive flexibility (https://doi.org/10.1038/s41593-018-0269-z). E The model performance of the PFC-MD model and the PFC model. MD suppression significantly degraded the performance when the model switched back to the previous context. ***P < 0.001, Bonferroni-corrected rank-sum test. F The boxplots of the changes in connection weights from the current-context and the other-context PFC neurons to the output neurons during Context 1 and Context 2 presentations. **P < 0.005, ***P < 0.001; statistical test with analysis of variance (ANOVA). Adding an MD component protected synaptic weights in neurons that were not currently context-relevant, which reduces interference in model parameters across different temporal contexts. G The mean performance of the PFC-MD model learning two cognitive tasks in Neurogym sequentially. Orange and green colors represent Task 1 and Task 2, respectively. Shaded areas denote the standard deviations. H We compared the PFC-MD model with two other continual learning methods: EWC and SI, and the PFC only model (left: task 1 performance, right: task 2 performance). The PFC-MD model obtained the best model performance among these methods. I The mean model performance of the PFC-MD model with more cognitive task learned sequentially. The PFC-MD model could flexibly switch between different tasks without forgetting.