In our ongoing quest to understand how the brain enables flexible cognition, the mediodorsal (MD) thalamus and its dialogue with the prefrontal cortex (PFC) have emerged as central players. Following a series of modeling papers from our lab—including Wei-Long Zheng’s recent Nature Communications work on thalamocortical inference—we now have another exciting advance to share. A new study led by Sage Chen’s lab at NYU and published in Nature Communications proposes a computational model of MD-PFC interactions, offering fresh insights into how these circuits support adaptive decision-making.
This collaborative work is driven by a burning question we have: Why is the brain wired this way? Why does the thalamus, nestled deep in the forebrain and reciprocally connected to cortex, play such a critical role in cognition? Our empirical work over the past decade has dissected thalamocortical dynamics in behaving animals, and our computational work including critical collaborations have helped us formalize these findings into testable frameworks. Sage’s new paper is a natural extension of this synergy—and with empirical support from our lab (spearheaded by postdoc Arghya Mukherjee), it opens new doors for exploration.
Key Advances in the New Model
- The MD Thalamus as a Dynamic Router
The study presents the MD thalamus not just as a passive relay, but as an active switchboard that flexibly gates information to the PFC based on task demands. This aligns with our lab’s empirical observations that thalamic neurons selectively amplify sensory inputs or internal signals depending on behavioral context. - Task-Dependent Cortical Prioritization
The model captures how the MD thalamus biases PFC representations—for example, emphasizing sensory cues during perceptual decisions versus memory traces during recall. This mirrors findings from our 2018 (Rikhye, Gilra & Halassa) and 2022 (Hummos et al.) models, where thalamic input helped partition PFC activity to avoid interference across tasks. - Bridging Theory and Experiment
Crucially, the model’s predictions were tested with in vivo data from our lab, reinforcing its biological plausibility. This back-and-forth between modeling and physiology is a hallmark of our approach, exemplified in Wei-Long Zheng’s 2024 study, where a thalamocortical RNN outperformed standard models in rapid inference tasks.
How This Drives Our Empirical Work Forward
- New Experiments to Test Gating Mechanisms
The model proposes specific thalamocortical connectivity rules for information routing. We’re now designing experiments to probe these mechanisms using optogenetics, electrophysiology, and imaging—asking how MD neurons dynamically recruit PFC microcircuits during task switching. - Linking to Schizophrenia-Relevant Dysfunction
Disrupted thalamocortical gating is implicated in schizophrenia. By refining Sage’s model with disease-relevant perturbations (e.g., thalamic silencing), we aim to pinpoint how maladaptive routing contributes to cognitive inflexibility. - The Next Generation of NeuroAI Models
Just as Wei-Long’s hybrid RNN incorporated biological constraints (e.g., thalamic reticular inhibition), future iterations of Sage’s model could integrate our latest empirical data—creating a virtuous cycle between theory and experiment.
The Bigger Picture: A Decade of Thalamocortical ModelingCognitive flexibility
This paper is the latest in a line of collaborative efforts to formalize MD-PFC interactions:
- Rikhye, Gilra & Halassa (2018): Showed thalamus mitigates “catastrophic forgetting” in PFC.
- Hummos et al. (2022): Derived a cortico-thalamic learning rule that compresses task context.
- Zheng et al. (2024): Demonstrated thalamus enables rapid inference and multi-task performance.
- Zhang, X. et al. (2025): extends this to hierarchical reasoning and handling multiple forms of uncertainty.
Together, these studies underscore the thalamus’s role as a locus of cognitive flexibility—a theme Sage’s work now extends with elegant mechanistic detail.
Looking Ahead
As NeuroAI gains momentum (evidenced by the 2024 Physics Nobel for foundational neural network work), our lab remains committed to grounding computational advances in biological reality. Sage’s model not only validates our empirical findings but also charts a course for future work—one where theory and experiment co-evolve to unravel the thalamus’s secrets.
For those who missed it, revisit our blog on Wei-Long Zheng’s paper here, and stay tuned as we put these models to the test!
Reference:
Zhang, X., Mukherjee, A., Halassa, M. M., & Chen, Z. S. (2025). Mediodorsal thalamus regulates task uncertainty to enable cognitive flexibility. Nature communications, 16(1), 2640. https://doi.org/10.1038/s41467-025-58011-1