Mixed selectivity morphs population codes in prefrontal cortex

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The prefrontal cortex maintains working memory information in the presence of distracting stimuli. It has long been thought that sustained activity in individual neurons or groups of neurons was responsible for maintaining information in the form of a persistent, stable code. Here we show that, upon the presentation of a distractor, information in the lateral prefrontal cortex was reorganized into a different pattern of activity to create a morphed stable code without losing information. In contrast, the code in the frontal eye fields persisted across different delay periods but exhibited substantial instability and information loss after the presentation of a distractor. We found that neurons with mixed-selective responses were necessary and sufficient for the morphing of code and that these neurons were more abundant in the lateral prefrontal cortex than the frontal eye fields. This suggests that mixed selectivity provides populations with code-morphing capability, a property that may underlie cognitive flexibility.

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  1. 1.

    Fuster, J. M. & Alexander, G. E. Neuron activity related to short-term memory. Science 173, 652–654 (1971).

  2. 2.

    Funahashi, S. Functions of delay-period activity in the prefrontal cortex and mnemonic scotomas revisited. Front. Syst. Neurosci 9, 2 (2015).

  3. 3.

    Suzuki, M. & Gottlieb, J. Distinct neural mechanisms of distractor suppression in the frontal and parietal lobe. Nat. Neurosci. 16, 98–104 (2013).

  4. 4.

    Stamm, J. S. Electrical stimulation of monkeys’ prefrontal cortex during delayed-response performance. J. Comp. Physiol. Psychol. 67, 535–546 (1969).

  5. 5.

    Wegener, S. P., Johnston, K. & Everling, S. Microstimulation of monkey dorsolateral prefrontal cortex impairs antisaccade performance. Exp. Brain Res. 190, 463–473 (2008).

  6. 6.

    Cohen, J. D. et al. Activation of the prefrontal cortex in a nonspatial working memory task with functional MRI. Hum. Brain Mapp. 1, 293–304 (1994).

  7. 7.

    Everling, S., Tinsley, C. J., Gaffan, D. & Duncan, J. Filtering of neural signals by focused attention in the monkey prefrontal cortex. Nat. Neurosci. 5, 671–676 (2002).

  8. 8.

    Sakai, K., Rowe, J. B. & Passingham, R. E. Active maintenance in prefrontal area 46 creates distractor-resistant memory. Nat. Neurosci. 5, 479–484 (2002).

  9. 9.

    Romo, R., Brody, C. D., Hernández, A. & Lemus, L. Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399, 470–473 (1999).

  10. 10.

    Miller, E. K., Erickson, C. A. & Desimone, R. Neural mechanisms of visual working memory in prefrontal cortex of the macaque. J. Neurosci. 16, 5154–5167 (1996).

  11. 11.

    Stokes, M. G. et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78, 364–375 (2013).

  12. 12.

    Asaad, W. F., Rainer, G. & Miller, E. K. Neural activity in the primate prefrontal cortex during associative learning. Neuron 21, 1399–1407 (1998).

  13. 13.

    Mansouri, F. A., Matsumoto, K. & Tanaka, K. Prefrontal cell activities related to monkeys’ success and failure in adapting to rule changes in a Wisconsin Card Sorting Test analog. J. Neurosci. 26, 2745–2756 (2006).

  14. 14.

    Hussar, C. R. & Pasternak, T. Flexibility of sensory representations in prefrontal cortex depends on cell type. Neuron 64, 730–743 (2009).

  15. 15.

    Warden, M. R. & Miller, E. K. Task-dependent changes in short-term memory in the prefrontal cortex. J. Neurosci. 30, 15801–15810 (2010).

  16. 16.

    Noudoost, B. & Moore, T. The role of neuromodulators in selective attention. Trends Cogn. Sci. 15, 585–591 (2011).

  17. 17.

    Liebe, S., Hoerzer, G. M., Logothetis, N. K. & Rainer, G. Theta coupling between V4 and prefrontal cortex predicts visual short-term memory performance. Nat. Neurosci. 15, 456–462 (2012). S1–S2.

  18. 18.

    Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).

  19. 19.

    Sarma, A., Masse, N. Y., Wang, X.-J. & Freedman, D. J. Task-specific versus generalized mnemonic representations in parietal and prefrontal cortices. Nat. Neurosci. 19, 143–149 (2016).

  20. 20.

    Fusi, S., Miller, E. K. & Rigotti, M. Why neurons mix: high dimensionality for higher cognition. Curr. Opin. Neurobiol. 37, 66–74 (2016).

  21. 21.

    Jacob, S. N. & Nieder, A. Complementary roles for primate frontal and parietal cortex in guarding working memory from distractor stimuli. Neuron 83, 226–237 (2014).

  22. 22.

    Murray, J. D. et al. Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex. Proc. Natl. Acad. Sci. USA 114, 394–399 (2017).

  23. 23.

    Enel, P., Procyk, E., Quilodran, R. & Dominey, P. F. Reservoir computing properties of neural dynamics in prefrontal cortex. PLOS Comput. Biol. 12, e1004967 (2016).

  24. 24.

    Miller, E. K. & Fusi, S. Limber neurons for a nimble mind. Neuron 78, 211–213 (2013).

  25. 25.

    Sreenivasan, K. K., Curtis, C. E. & D’Esposito, M. Revisiting the role of persistent neural activity during working memory. Trends Cogn. Sci. 18, 82–89 (2014).

  26. 26.

    Stokes, M. G. ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework. Trends Cogn. Sci. 19, 394–405 (2015).

  27. 27.

    Duncan, J. An adaptive coding model of neural function in prefrontal cortex. Nat. Rev. Neurosci. 2, 820–829 (2001).

  28. 28.

    Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).

  29. 29.

    Meyers, E. M., Freedman, D. J., Kreiman, G., Miller, E. K. & Poggio, T. Dynamic population coding of category information in inferior temporal and prefrontal cortex. J. Neurophysiol. 100, 1407–1419 (2008).

  30. 30.

    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).

  31. 31.

    Qi, X.-L. et al. Comparison of neural activity related to working memory in primate dorsolateral prefrontal and posterior parietal cortex. Front. Syst. Neurosci 4, 12 (2010).

  32. 32.

    Salazar, R. F., Dotson, N. M., Bressler, S. L. & Gray, C. M. Content-specific fronto-parietal synchronization during visual working memory. Science 338, 1097–1100 (2012).

  33. 33.

    Qi, X. L., Elworthy, A. C., Lambert, B. C. & Constantinidis, C. Representation of remembered stimuli and task information in the monkey dorsolateral prefrontal and posterior parietal cortex. J. Neurophysiol. 113, 44–57 (2015).

  34. 34.

    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).

  35. 35.

    Herbst, J. A., Gammeter, S., Ferrero, D. & Hahnloser, R. H. Spike sorting with hidden Markov models. J. Neurosci. Methods 174, 126–134 (2008).

  36. 36.

    Brainard, D. H. The psychophysics toolbox. Spat. Vis 10, 433–436 (1997).

  37. 37.

    Bruce, C. J., Goldberg, M. E., Bushnell, M. C. & Stanton, G. B. Primate frontal eye fields. II. Physiological and anatomical correlates of electrically evoked eye movements. J. Neurophysiol 54, 714–734 (1985).

  38. 38.

    Buschman, T. J., Siegel, M., Roy, J. E. & Miller, E. K. Neural substrates of cognitive capacity limitations. Proc. Natl. Acad. Sci. USA 108, 11252–11255 (2011).

  39. 39.

    Ojala, M. & Garriga, G. C. Permutation tests for studying classifier performance. J. Mach. Learn. Res. 11, 1833–1863 (2010).

  40. 40.

    Hedges, L. V. Distribution theory for Glass’s estimator of effect size and related estimators. J. Educ. Behav. Stat. 6, 107–128 (1981).

  41. 41.

    Fisher, R. A. The use of multiple measurements in taxonomic problems. Ann. Hum. Genet 7, 179–188 (1936).

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We thank A. Tan for comments and suggestions on an earlier version of this manuscript. We thank C. Lim, M.N. Lynn, L. Chan, K. Chng, and E.M. Peña for help with animal training, surgery, and care. This work was supported by startup grants from the Ministry of Education Tier 1 Academic Research Fund and SINAPSE to C.L., a grant from the NUS-NUHS Memory Networks Program to S.-C.Y., and a grant from the Ministry of Education Tier 2 Academic Research Fund to C.L. and S.-C.Y. (MOE2016-T2-2-117).

Author information

Author notes

    • Aishwarya Parthasarathy

    Present address: Institute of Molecular and Cell Biology, A*STAR, Singapore, Singapore

  1. Camilo Libedinsky and Shih-Cheng Yen contributed equally to this work.


  1. NUS Graduate School of Integrative Science and Engineering, National University of Singapore (NUS), Singapore, Singapore

    • Aishwarya Parthasarathy
    •  & Shih-Cheng Yen
  2. Department of Electrical and Computer Engineering, NUS, Singapore, Singapore

    • Roger Herikstad
    • , Jit Hon Bong
    •  & Shih-Cheng Yen
  3. Department of Psychology, NUS, Singapore, Singapore

    • Felipe Salvador Medina
    •  & Camilo Libedinsky
  4. Singapore Institute for Neurotechnology, NUS, Singapore, Singapore

    • Camilo Libedinsky
  5. Institute of Molecular and Cell Biology, A*STAR, Singapore, Singapore

    • Camilo Libedinsky


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C.L., A.P., R.H., J.H.B., and S.-C.Y. designed the experiments. A.P., R.H., and J.H.B. collected behavioral and electrophysiological data. F.S.M. and A.P. performed the microstimulation verification experiments. A.P. and R.H. analyzed behavioral and electrophysiological data. S.-C.Y. and C.L. guided the data analysis. All authors discussed the results, and A.P., C.L., and S.-C.Y. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Camilo Libedinsky or Shih-Cheng Yen.

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