The importance of mixed selectivity in complex cognitive tasks


Single-neuron activity in the prefrontal cortex (PFC) is tuned to mixtures of multiple task-related aspects. Such mixed selectivity is highly heterogeneous, seemingly disordered and therefore difficult to interpret. We analysed the neural activity recorded in monkeys during an object sequence memory task to identify a role of mixed selectivity in subserving the cognitive functions ascribed to the PFC. We show that mixed selectivity neurons encode distributed information about all task-relevant aspects. Each aspect can be decoded from the population of neurons even when single-cell selectivity to that aspect is eliminated. Moreover, mixed selectivity offers a significant computational advantage over specialized responses in terms of the repertoire of input–output functions implementable by readout neurons. This advantage originates from the highly diverse nonlinear selectivity to mixtures of task-relevant variables, a signature of high-dimensional neural representations. Crucially, this dimensionality is predictive of animal behaviour as it collapses in error trials. Our findings recommend a shift of focus for future studies from neurons that have easily interpretable response tuning to the widely observed, but rarely analysed, mixed selectivity neurons.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Low and high-dimensional neural representations, and mixed selectivity.
Figure 2: Behavioural task from ref. 3.
Figure 3: Mixed selectivity in recorded single-cell activity and population decoding.
Figure 4: The recorded neural representations are high-dimensional.
Figure 5: The dimensionality of the neural representations predicts animal behaviour.


  1. 1

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

  2. 2

    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)

  3. 3

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

  4. 4

    Buonomano, D. V. & Merzenich, M. M. Temporal information transformed into a spatial code by a neural network with realistic properties. Science 267, 1028–1030 (1995)

  5. 5

    Maass, W., Natschlager, T. & Markram, H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002)

  6. 6

    Jaeger, H. & Haas, H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304, 78–80 (2004)

  7. 7

    Buonomano, D. V. & Maass, W. State-dependent computations: spatiotemporal processing in cortical networks. Nature Rev. Neurosci. 10, 113–125 (2009)

  8. 8

    Sussillo, D. & Abbott, L. F. Generating coherent patterns of activity from chaotic neural networks. Neuron 63, 544–557 (2009)

  9. 9

    Rigotti, M., Ben Dayan Rubin, D. D., Wang, X.-J. & Fusi, S. Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses. Front. Comput. Neurosci. 4, 24 (2010)

  10. 10

    Pascanu, R. & Jaeger, H. A neurodynamical model for working memory. Neural Netw. 24, 199–207 (2011)

  11. 11

    Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995)

  12. 12

    Warden, M. R. & Miller, E. K. The representation of multiple objects in prefrontal neuronal delay activity. Cereb. Cortex 17 (Suppl. 1). i41–i50 (2007)

  13. 13

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

  14. 14

    Yuste, R. Dendritic spines and distributed circuits. Neuron 71, 772–781 (2011)

  15. 15

    Machens, C. K., Romo, R. & Brody, C. D. Functional, but not anatomical, separation of “what” and “when” in prefrontal cortex. J. Neurosci. 30, 350–360 (2010)

  16. 16

    DiCarlo, J. J., Zoccolan, D. & Rust, N. C. How does the brain solve visual object recognition? Neuron 73, 415–434 (2012)

  17. 17

    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)

  18. 18

    Klampfl, S., David, S. V., Yin, P., Shamma, S. A. & Maass, W. A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons. J. Neurophysiol. 108, 1366–1380 (2012)

  19. 19

    Churchland, M. M., Yu, B. M., Ryu, S. I., Santhanam, G. & Shenoy, K. V. Neural variability in premotor cortex provides a signature of motor preparation. J. Neurosci. 26, 3697–3712 (2006)

  20. 20

    Barak, O., Rigotti, M. & Fusi, S. The sparseness of mixed selectivity neurons controls the generalization–discrimination trade-off. J. Neurosci. 33, 3844–3856 (2013)

  21. 21

    Braitenberg, V. & Schüz, A. Cortex: Statistics and Geometry of Neuronal Connectivity 2nd edn (Springer, 1998)

  22. 22

    Sosulski, D. L., Bloom, M. L., Cutforth, T., Axel, R. & Datta, S. R. Distinct representations of olfactory information in different cortical centres. Nature 472, 213–216 (2011)

  23. 23

    Rosenblatt, F. Principles of Neurodynamics (Spartan Books, 1962)

Download references


We are grateful to L.F. Abbott for comments on the manuscript and for discussions. Work supported by the Gatsby Foundation, the Swartz Foundation and the Kavli Foundation. M.R. is supported by Swiss National Science Foundation grant PBSKP3-133357 and the Janggen-Poehn Foundation; N.D.D. is supported by the McKnight Foundation and the McDonnell Foundation; E.K.M. is supported by NIMH grant 5-R37-MH087027-04 and The Picower Foundation; M.R.W. from the Brain & Behavior Research Foundation and the NARSAD Young Investigator grant.

Author information




M.R.W. and E.K.M. performed the experiments and collected the data. M.R., O.B., X.-J.W. and S.F. developed the theoretical framework. M.R., O.B., N.D.D. and S.F. conceived the data analyses. M.R. performed the data analyses. M.R., O.B. and S.F. wrote the paper.

Corresponding author

Correspondence to Stefano Fusi.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains Supplementary Methods M1-M8, which includes Supplementary Figures M1-M8, Supplementary Sections S1-S20, which includes Supplementary Figures S1-S22 and additional references. (PDF 2918 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.