Computational mechanisms underlying thalamic regulation of prefrontal signal-to-noise ratio in decision making

Abstract

AbstractThe mediodorsal (MD) thalamus is a critical partner for the prefrontal cortex (PFC) in cognitive flexibility. Animal experiments have shown that the MD enhances prefrontal signal-to-noise ratio (SNR) in decision making under uncertainty. However, the computational mechanisms of this cognitive process remain unclear. Here we use performance-optimized computational models to dissect these mechanisms. We find that the inclusion of an MD-like feedforward module increases robustness to sensory noise and enhances working memory maintenance in the recurrent PFC network performing a context-dependent decision-making task. Incorporating genetically identified thalamocortical pathways that regulate signal amplification and noise reduction further improves performance and replicates key neurophysiological findings of neuronal tuning. Our model reveals a key computational mechanism of context-invariant, cell-type specific regulation of sensory uncertainty in a task-phase specific manner. Additionally, it makes experimentally testable predictions that connect disrupted thalamocortical connectivity with classical theories of prefrontal excitation-inhibition (E/I) imbalance and dysfunctional inhibitory cell types.