Abstract
Interactions between the prefrontal cortex (PFC) and mediodorsal thalamus are critical for cognitive flexibility, yet the underlying computations are unknown. To investigate frontothalamic substrates of cognitive flexibility, we developed a behavioral task in which mice switched between different sets of learned cues that guided attention toward either visual or auditory targets. We found that PFC responses reflected both the individual cues and their meaning as task rules, indicating a hierarchical cue-to-rule transformation. Conversely, mediodorsal thalamus responses reflected the statistical regularity of cue presentation and were required for switching between such experimentally specified cueing contexts. A subset of these thalamic responses sustained context-relevant PFC representations, while another suppressed the context-irrelevant ones. Through modeling and experimental validation, we find that thalamic-mediated suppression may not only reduce PFC representational interference but could also preserve unused cortical traces for future use. Overall, our study provides a computational foundation for thalamic engagement in cognitive flexibility.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All data are available from the corresponding author upon reasonable request.
References
Richter, F. R. & Yeung, N. Memory and cognitive control in task switching. Psychol. Sci. 23, 1256–1263 (2012).
Hanks, T. D. & Summerfield, C. Perceptual decision making in rodents, monkeys, and humans. Neuron 93, 15–31 (2017).
Stokes, M. G. et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78, 364–375 (2013).
Dias, R., Robbins, T. W. & Roberts, A. C. Dissociation in prefrontal cortex of affective and attentional shifts. Nature 380, 69–72 (1996).
Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).
Inagaki, H. K., Inagaki, M., Romani, S. & Svoboda, K. Low-dimensional and monotonic preparatory activity in mouse anterior lateral motor cortex. J. Neurosci. 38, 4163–4185 (2018).
Noonan, M. P., Crittenden, B. M., Jensen, O. & Stokes, M. G. Selective inhibition of distracting input. Behav. Brain Res. 355, 36–47 (2018).
Weinberger, D. R. & Berman, K. F. Prefrontal function in schizophrenia: confounds and controversies. Phil. Trans. R. Soc. Lond. B 351, 1495–1503 (1996).
Woodward, N. D., Karbasforoushan, H. & Heckers, S. Thalamocortical dysconnectivity in schizophrenia. Am. J. Psychiatry 169, 1092–1099 (2012).
Kirkpatrick, J. et al. Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. USA 114, 3521–3526 (2017).
Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017).
Sakai, K. & Passingham, R. E. Prefrontal interactions reflect future task operations. Nat. Neurosci. 6, 75–81 (2003).
Miller, E. K. & Buschman, T. J. Cortical circuits for the control of attention. Curr. Opin. Neurobiol. 23, 216–222 (2013).
Buschman, T. J. & Miller, E. K. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315, 1860–1862 (2007).
Buschman, T. J. & Miller, E. K. Goal-direction and top-down control. Phil. Trans. R. Soc. Lond. B 369, 20130471 (2014).
Spaak, E., Watanabe, K., Funahashi, S. & Stokes, M. G. Stable and dynamic coding for working memory in primate prefrontal cortex. J. Neurosci. 37, 6503–6516 (2017).
Schmitt, L. I. et al. Thalamic amplification of cortical connectivity sustains attentional control. Nature 545, 219–223 (2017).
Bolkan, S. S. et al. Thalamic projections sustain prefrontal activity during working memory maintenance. Nat. Neurosci. 20, 987–996 (2017).
Parnaudeau, S. et al. Mediodorsal thalamus hypofunction impairs flexible goal-directed behavior. Biol. Psychiatry 77, 445–453 (2015).
Rikhye, R. V., Wimmer, R. D. & Halassa, M. M. Toward an integrative theory of thalamic function. Annu. Rev. Neurosci. 41, 163–183 (2018).
Mitchell, A. S. & Chakraborty, S. What does the mediodorsal thalamus do? Front. Syst. Neurosci. 7, 37 (2013).
Marton, T., Seifikar, H., Luongo, F.J., Lee, A.T. & Sohal, V.S. Roles of prefrontal cortex and mediodorsal thalamus in task engagement and behavioral flexibility. J. Neurosci. 1728-17 (2018).
Wimmer, R. D. et al. Thalamic control of sensory selection in divided attention. Nature 526, 705–709 (2015).
Braver, T. S., Reynolds, J. R. & Donaldson, D. I. Neural mechanisms of transient and sustained cognitive control during task switching. Neuron 39, 713–726 (2003).
Shipp, S. The brain circuitry of attention. Trends Cogn. Sci. 8, 223–230 (2004).
Bruno, R. M. & Simons, D. J. Feedforward mechanisms of excitatory and inhibitory cortical receptive fields. J. Neurosci. 22, 10966–10975 (2002).
Diester, I. & Nieder, A. Complementary contributions of prefrontal neuron classes in abstract numerical categorization. J. Neurosci. 28, 7737–7747 (2008).
Quirk, M. C., Sosulski, D. L., Feierstein, C. E., Uchida, N. & Mainen, Z. F. A defined network of fast-spiking interneurons in orbitofrontal cortex: responses to behavioral contingencies and ketamine administration. Front. Syst. Neurosci. 3, 13 (2009).
Wallis, J. D., Anderson, K. C. & Miller, E. K. Single neurons in prefrontal cortex encode abstract rules. Nature 411, 953–956 (2001).
Miller, E. K., Freedman, D. J. & Wallis, J. D. The prefrontal cortex: categories, concepts and cognition. Phil. Trans. R. Soc. Lond. B 357, 1123–1136 (2002).
Yates, J. L., Park, I. M., Katz, L. N., Pillow, J. W. & Huk, A. C. Functional dissection of signal and noise in MT and LIP during decision-making. Nat. Neurosci. 20, 1285–1292 (2017).
Park, I. M., Meister, M. L. R., Huk, A. C. & Pillow, J. W. Encoding and decoding in parietal cortex during sensorimotor decision-making. Nat. Neurosci. 17, 1395–1403 (2014).
Parnaudeau, S., Bolkan, S. S. & Kellendonk, C. The mediodorsal thalamus: an essential partner of the prefrontal cortex for cognition. Biol. Psychiatry 83, 648–656 (2018).
Ferguson, B. R. & Gao, W.-J. Thalamic control of cognition and social behavior via regulation of gamma-aminobutyric acidergic signaling and excitation/inhibition balance in the medial prefrontal cortex. Biol. Psychiatry 83, 657–669 (2018).
Delevich, K., Tucciarone, J., Huang, Z. J. & Li, B. The mediodorsal thalamus drives feedforward inhibition in the anterior cingulate cortex via parvalbumin interneurons. J. Neurosci. 35, 5743–5753 (2015).
Kim, H. R., Hong, S. Z. & Fiorillo, C. D. T-type calcium channels cause bursts of spikes in motor but not sensory thalamic neurons during mimicry of natural patterns of synaptic input. Front. Cell. Neurosci. 9, 428 (2015).
Masse, N. Y., Grant, G. D. & Freedman, D. J. Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization.” Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1803839115 (2018).
Enel, P., Procyk, E., Quilodran, R. & Dominey, P. F. Reservoir computing properties of neural dynamics in prefrontal cortex. PLoS Comput. Biol. 12, e1004967 (2016).
Maass, W., Natschläger, T. & Markram, H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002).
Haykin, S. Neural Networks and Learning Machines. (Pearson, London, UK, 2008).
Minsky, M. & Papert, S.A. Perceptrons: an Introduction to Computational Geometry. (MIT Press, Boston, MA, USA, 2017).
Movshon, J. A., Thompson, I. D. & Tolhurst, D. J. Spatial summation in the receptive fields of simple cells in the cat’s striate cortex. J. Physiol. (Lond.) 283, 53–77 (1978).
Muhammad, R., Wallis, J. D. & Miller, E. K. A comparison of abstract rules in the prefrontal cortex, premotor cortex, inferior temporal cortex, and striatum. J. Cogn. Neurosci. 18, 974–989 (2006).
Guillery, R. W. & Sherman, S. M. Thalamic relay functions and their role in corticocortical communication: generalizations from the visual system. Neuron 33, 163–175 (2002).
Yang, G. R., Murray, J. D. & Wang, X.-J. A dendritic disinhibitory circuit mechanism for pathway-specific gating. Nat. Commun. 7, 12815 (2016).
Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016).
Groh, A. et al. Convergence of cortical and sensory driver inputs on single thalamocortical cells. Cereb. Cortex 24, 3167–3179 (2014).
Jaramillo, J., Mejias, J.F. & Wang, X.-J. Engagement of pulvino-cortical feedforward and feedback pathways in cognitive computations. Preprint at bioRxiv https://doi.org/10.1101/322560 (2018).
Imamizu, H. et al. Explicit contextual information selectively contributes to predictive switching of internal models. Exp. Brain Res. 181, 395–408 (2007).
Liang, L. et al. Scalable, lightweight, integrated and quick-to-assemble (SLIQ) hyperdrives for functional circuit dissection. Front. Neural Circuits 11, 8 (2017).
Berndt, A. et al. Structural foundations of optogenetics: determinants of channelrhodopsin ion selectivity. Proc. Natl. Acad. Sci. USA 113, 822–829 (2016).
Gradinaru, V., Thompson, K. R. & Deisseroth, K. eNpHR: a Natronomonas halorhodopsin enhanced for optogenetic applications. Brain Cell Biol. 36, 129–139 (2008).
Akrami, A., Kopec, C. D., Diamond, M. E. & Brody, C. D. Posterior parietal cortex represents sensory history and mediates its effects on behaviour. Nature 554, 368–372 (2018).
Chung, J. E. et al. A fully automated approach to spike sorting. Neuron 95, 1381–1394.e6 (2017).
Bayati, H., Davoudi, H. & Fatemizadeh, E. A heuristic method for finding the optimal number of clusters with application in medical data. IEEE Eng. Med. Biol. Soc. Annu. Conf. 2008, 4684–4687 (2008).
Meyers, E. M. The neural decoding toolbox. Front. Neuroinform. 7, 8 (2013).
Pillow, J. W. et al. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454, 995–999 (2008).
Pillow, J. W., Paninski, L., Uzzell, V. J., Simoncelli, E. P. & Chichilnisky, E. J. Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J. Neurosci. 25, 11003–11013 (2005).
Yu, B. M. et al. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J. Neurophysiol. 102, 614–635 (2009).
Acknowledgements
We thank R.D. Wimmer for help with experiments and members of the Halassa Lab for technical assistance and discussions. We also thank W. Gerstner, M. Fee, E. Miller, and M. Wilson for helpful discussions, and we thank J.W. Pillow and D. Zlotowski for advice on the GLM. This work was supported by grants from the National Institutes of Health and from the Brain and Behavior, Klingenstein, Pew, and Simons Foundations, as well as the Human Frontiers Science Program to M.M.H. and the German Federal Ministry of Education and Research to A.G. through a Bernstein Award to R. Memmesheimer.
Author information
Authors and Affiliations
Contributions
R.V.R. conceived and performed experiments, analyzed and interpreted data, and wrote the paper. A.G. developed, simulated, and analyzed the thalamocortical computational model. M.M.H. conceived and supervised experiments, analyzed and interpreted the data, and wrote the paper. M.M.H. also acquired funding.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary Figures 1–19
Supplementary Figures 1–19
Rights and permissions
About this article
Cite this article
Rikhye, R.V., Gilra, A. & Halassa, M.M. Thalamic regulation of switching between cortical representations enables cognitive flexibility. Nat Neurosci 21, 1753–1763 (2018). https://doi.org/10.1038/s41593-018-0269-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41593-018-0269-z
This article is cited by
-
Lifespan development of thalamic nuclei and characterizing thalamic nuclei abnormalities in schizophrenia using normative modeling
Neuropsychopharmacology (2024)
-
Long-range inhibition synchronizes and updates prefrontal task activity
Nature (2023)
-
How deep is the brain? The shallow brain hypothesis
Nature Reviews Neuroscience (2023)
-
The impact of the human thalamus on brain-wide information processing
Nature Reviews Neuroscience (2023)
-
Aberrant cortico-thalamo-cerebellar network interactions and their association with impaired cognitive functioning in patients with schizophrenia
Schizophrenia (2023)