Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The what, where and how of delay activity

Abstract

Working memory is characterized by neural activity that persists during the retention interval of delay tasks. Despite the ubiquity of this delay activity across tasks, species and experimental techniques, our understanding of this phenomenon remains incomplete. Although initially there was a narrow focus on sustained activation in a small number of brain regions, methodological and analytical advances have allowed researchers to uncover previously unobserved forms of delay activity various parts of the brain. In light of these new findings, this Review reconsiders what delay activity is, where in the brain it is found, what roles it serves and how it may be generated.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Schematic examples of different types of delay activity.
Fig. 2: Schematic depictions of the properties of delay activity in different brain regions.

References

  1. 1.

    D’Esposito, M. & Postle, B. R. The cognitive neuroscience of working memory. Annu. Rev. Psychol. 66, 115–142 (2015).

    PubMed  Google Scholar 

  2. 2.

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

    CAS  PubMed  Google Scholar 

  3. 3.

    Kubota, K. & Niki, H. Prefrontal cortical unit activity and delayed alternation performance in monkeys. J. Neurophysiol. 34, 337–347 (1971).

    CAS  PubMed  Google Scholar 

  4. 4.

    Rosenkilde, C. E., Bauer, R. H. & Fuster, J. M. Single cell activity in ventral prefrontal cortex of behaving monkeys. Brain Res. 209, 375–394 (1981).

    CAS  PubMed  Google Scholar 

  5. 5.

    Goldman-Rakic, P. S. Cellular basis of working memory. Neuron 14, 477–485 (1995).

    CAS  PubMed  Google Scholar 

  6. 6.

    Chelazzi, L., Miller, E. K., Duncan, J. & Desimone, R. A neural basis for visual search in inferior temporal cortex. Nature 363, 345–347 (1993).

    CAS  PubMed  Google Scholar 

  7. 7.

    Curtis, C. E. & Lee, D. Beyond working memory: the role of persistent activity in decision making. Trends Cogn. Sci. 14, 216–222 (2010).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Baeg, E. H. et al. Dynamics of population code for working memory in the prefrontal cortex. Neuron 40, 177–188 (2003).

    CAS  PubMed  Google Scholar 

  9. 9.

    Yang, S.-T., Shi, Y., Wang, Q., Peng, J.-Y. & Li, B.-M. Neuronal representation of working memory in the medial prefrontal cortex of rats. Mol. Brain 7, 61 (2014).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Funahashi, S., Bruce, C. J. & Goldman-Rakic, P. S. Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. J. Neurophysiol. 61, 331–349 (1989).

    CAS  PubMed  Google Scholar 

  11. 11.

    Gnadt, J. W. & Andersen, R. A. Memory related motor planning activity in posterior parietal cortex of macaque. Exp. Brain Res. 70, 216–220 (1988).

    CAS  PubMed  Google Scholar 

  12. 12.

    Chafee, M. V. & Goldman-Rakic, P. S. Matching patterns of activity in primate prefrontal area 8a and parietal area 7ip neurons during a spatial working memory task. J. Neurophysiol. 79, 2919–2940 (1998).

    CAS  PubMed  Google Scholar 

  13. 13.

    Fuster, J. M. & Jervey, J. P. Neuronal firing in the inferotemporal cortex of the monkey in a visual memory task. J. Neurosci. 2, 361–375 (1982).

    CAS  PubMed  Google Scholar 

  14. 14.

    Nakamura, K. & Kubota, K. Mnemonic firing of neurons in the monkey temporal pole during a visual recognition memory task. J. Neurophysiol. 74, 162–178 (1995).

    CAS  PubMed  Google Scholar 

  15. 15.

    Kaminski, J. et al. Persistently active neurons in human medial frontal and medial temporal lobe support working memory. Nat. Neurosci. 20, 590–601 (2017).

    Google Scholar 

  16. 16.

    Kornblith, S., Quian Quiroga, R., Koch, C., Fried, I. & Mormann, F. Persistent single-neuron activity during working memory in the human medial temporal lobe. Curr. Biol. 27, 1026–1032 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Wang, X. J. Synaptic reverberation underlying mnemonic persistent activity. Trends Neurosci. 24, 455–463 (2001).

    CAS  PubMed  Google Scholar 

  18. 18.

    Amit, D. J. & Brunel, N. Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb. Cortex 7, 237–252 (1997).

    CAS  PubMed  Google Scholar 

  19. 19.

    Compte, A., Brunel, N., Goldman-Rakic, P. S. & Wang, X. J. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 10, 910–923 (2000).

    CAS  PubMed  Google Scholar 

  20. 20.

    Zylberberg, J. & Strowbridge, B. W. Mechanisms of persistent activity in cortical circuits: possible neural substrates for working memory. Annu. Rev. Neurosci. 40, 603–627 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Yuste, R. From the neuron doctrine to neural networks. Nat. Rev. Neurosci. 16, 487–497 (2015).

    CAS  PubMed  Google Scholar 

  22. 22.

    Averbeck, B. B., Latham, P. E. & Pouget, A. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7, 358–366 (2006).

    CAS  PubMed  Google Scholar 

  23. 23.

    Sporns, O. Structure and function of complex brain networks. Dialogues Clin. Neurosci. 15, 247–262 (2013).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Qi, X.-L., Meyer, T., Stanford, T. R. & Constantinidis, C. Changes in prefrontal neuronal activity after learning to perform a spatial working memory task. Cereb. Cortex 21, 2722–2732 (2011).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Meyer, T., Qi, X.-L., Stanford, T. R. & Constantinidis, C. Stimulus selectivity in dorsal and ventral prefrontal cortex after training in working memory tasks. J. Neurosci. 31, 6266–6276 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Liu, D. et al. Medial prefrontal activity during delay period contributes to learning of a working memory task. Science 346, 458–463 (2014).

    CAS  PubMed  Google Scholar 

  27. 27.

    Vogel, E. K. & Machizawa, M. G. Neural activity predicts individual differences in visual working memory capacity. Nature 428, 748–751 (2004).

    CAS  PubMed  Google Scholar 

  28. 28.

    Voytek, B. & Knight, R. T. Prefrontal cortex and basal ganglia contributions to visual working memory. Proc. Natl Acad. Sci. USA 107, 18167–18172 (2010).

    CAS  PubMed  Google Scholar 

  29. 29.

    Reinhart, R. M. G. et al. Homologous mechanisms of visuospatial working memory maintenance in macaque and human: properties and sources. J. Neurosci. 32, 7711–7722 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Pipa, G. et al. Performance- and stimulus-dependent oscillations in monkey prefrontal cortex during short-term memory. Front. Integr. Neurosci. 3, 25 (2009).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Haller, M. et al. Persistent neuronal activity in human prefrontal cortex links perception and action. Nat. Hum. Behav. 2, 80–91 (2018).

    PubMed  Google Scholar 

  32. 32.

    Honkanen, R., Rouhinen, S., Wang, S. H., Palva, J. M. & Palva, S. Gamma oscillations underlie the maintenance of feature-specific information and the contents of visual working memory. Cereb. Cortex 25, 3788–3801 (2015).

    PubMed  Google Scholar 

  33. 33.

    Tallon-Baudry, C., Bertrand, O. & Fischer, C. Oscillatory synchrony between human extrastriate areas during visual short-term memory maintenance. J. Neurosci. 21, RC177 (2001).

    CAS  PubMed  Google Scholar 

  34. 34.

    Raghavachari, S. et al. Gating of human theta oscillations by a working memory task. J. Neurosci. 21, 3175–3183 (2001).

    CAS  PubMed  Google Scholar 

  35. 35.

    Miller, E. K., Lundqvist, M. & Bastos, A. M. Working memory 2.0. Neuron 100, 463–475 (2018).

    CAS  PubMed  Google Scholar 

  36. 36.

    Klimesch, W., Doppelmayr, M., Schwaiger, J., Auinger, P. & Winkler, T. ‘Paradoxical’ alpha synchronization in a memory task. Brain Res. Cogn. Brain Res. 7, 493–501 (1999).

    CAS  PubMed  Google Scholar 

  37. 37.

    Jensen, O., Gelfand, J., Kounios, J. & Lisman, J. E. Oscillations in the alpha band (9–12 Hz) increase with memory load during retention in a short-term memory task. Cereb. Cortex 12, 877–882 (2002).

    PubMed  Google Scholar 

  38. 38.

    Jokisch, D. & Jensen, O. Modulation of gamma and alpha activity during a working memory task engaging the dorsal or ventral stream. J. Neurosci. 27, 3244–3251 (2007).

    CAS  PubMed  Google Scholar 

  39. 39.

    van Ede, F., Jensen, O. & Maris, E. Supramodal theta, gamma, and sustained fields predict modality-specific modulations of alpha and beta oscillations during visual and tactile working memory. J. Cogn. Neurosci. 29, 1455–1472 (2017).

    PubMed  Google Scholar 

  40. 40.

    van Ede, F. Mnemonic and attentional roles for states of attenuated alpha oscillations in perceptual working memory: a review. Eur. J. Neurosci. 48, 2509–2515 (2018).

    PubMed  Google Scholar 

  41. 41.

    Lundqvist, M. et al. Gamma and beta bursts underlie working memory. Neuron 90, 152–164 (2016). By analysing single-trial LFP data, this paper demonstrates that WM delay activity is characterized by transient bursts of activity in the gamma and beta frequency ranges. Importantly, (gamma) LFP bursts are associated with spiking activity that encodes information about WM memoranda, whereas sustained LFP activity does not exhibit a relationship with information encoding.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Lundqvist, M., Herman, P., Warden, M. R., Brincat, S. L. & Miller, E. K. Gamma and beta bursts during working memory readout suggest roles in its volitional control. Nat. Commun. 9, 394 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Mitzdorf, U. Evoked potentials and current source densities in the cat visual cortex. Electroencephalogr. Clin. Neurophysiol. 61, S179 (1985).

    Google Scholar 

  44. 44.

    Baillet, S., Mosher, J. C. & Leahy, R. M. Electromagnetic brain mapping. IEEE Signal Process. Mag. 18, 14–30 (2001).

    Google Scholar 

  45. 45.

    Pesaran, B., Pezaris, J. S., Sahani, M., Mitra, P. P. & Andersen, R. A. Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat. Neurosci. 5, 805–811 (2002).

    CAS  PubMed  Google Scholar 

  46. 46.

    Backen, T., Treue, S. & Martinez-Trujillo, J. C. Encoding of spatial attention by primate prefrontal cortex neuronal ensembles. eNeuro. https://doi.org/10.1523/ENEURO.0372-16.2017 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    O’Keefe, J. & Recce, M. L. Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3, 317–330 (1993).

    PubMed  Google Scholar 

  48. 48.

    Jacobs, J., Kahana, M. J., Ekstrom, A. D. & Fried, I. Brain oscillations control timing of single-neuron activity in humans. J. Neurosci. 27, 3839–3844 (2007).

    CAS  PubMed  Google Scholar 

  49. 49.

    Rasch, M. J., Gretton, A., Murayama, Y., Maass, W. & Logothetis, N. K. Inferring spike trains from local field potentials. J. Neurophysiol. 99, 1461–1476 (2008).

    PubMed  Google Scholar 

  50. 50.

    Siegel, M., Warden, M. R. & Miller, E. K. Phase-dependent neuronal coding of objects in short-term memory. Proc. Natl Acad. Sci. USA 106, 21341–21346 (2009).

    CAS  PubMed  Google Scholar 

  51. 51.

    Buzsáki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13, 407–420 (2012).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Roux, F. & Uhlhaas, P. J. Working memory and neural oscillations: α-γ versus θ-γ codes for distinct WM information? Trends Cogn. Sci. 18, 16–25 (2014).

    PubMed  Google Scholar 

  53. 53.

    Lisman, J. E. & Jensen, O. The θ-γ neural code. Neuron 77, 1002–1016 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Fries, P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn. Sci. 9, 474–480 (2005).

    PubMed  Google Scholar 

  55. 55.

    Palva, J. M., Palva, S. & Kaila, K. Phase synchrony among neuronal oscillations in the human cortex. J. Neurosci. 25, 3962–3972 (2005).

    CAS  PubMed  Google Scholar 

  56. 56.

    Womelsdorf, T. et al. Modulation of neuronal interactions through neuronal synchronization. Science 316, 1609–1612 (2007).

    CAS  PubMed  Google Scholar 

  57. 57.

    Courtney, S. M., Ungerleider, L. G., Keil, K. & Haxby, J. V. Transient and sustained activity in a distributed neural system for human working memory. Nature 386, 608–611 (1997).

    CAS  PubMed  Google Scholar 

  58. 58.

    Zarahn, E., Aguirre, G. & D’Esposito, M. A trial-based experimental design for fMRI. Neuroimage 6, 122–138 (1997).

    CAS  PubMed  Google Scholar 

  59. 59.

    Jha, A. P. & McCarthy, G. The influence of memory load upon delay-interval activity in a working-memory task: an event-related functional MRI study. J. Cogn. Neurosci. 12 (Suppl. 2), 90–105 (2000).

    PubMed  Google Scholar 

  60. 60.

    Leung, H.-C., Gore, J. C. & Goldman-Rakic, P. S. Sustained mnemonic response in the human middle frontal gyrus during on-line storage of spatial memoranda. J. Cogn. Neurosci. 14, 659–671 (2002).

    PubMed  Google Scholar 

  61. 61.

    Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

    CAS  PubMed  Google Scholar 

  62. 62.

    Goense, J. B. M. & Logothetis, N. K. Neurophysiology of the BOLD fMRI signal in awake monkeys. Curr. Biol. 18, 631–640 (2008).

    CAS  PubMed  Google Scholar 

  63. 63.

    Murayama, Y. et al. Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA. Magn. Reson. Imaging 28, 1095–1103 (2010).

    PubMed  Google Scholar 

  64. 64.

    Winawer, J. et al. Asynchronous broadband signals are the principal source of the BOLD response in human visual cortex. Curr. Biol. 23, 1145–1153 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Takata, N. et al. Optogenetic astrocyte activation evokes BOLD fMRI response with oxygen consumption without neuronal activity modulation. Glia 66, 2013–2023 (2018).

    PubMed  Google Scholar 

  66. 66.

    Khursheed, F. et al. Frequency-specific electrocorticographic correlates of working memory delay period fMRI activity. Neuroimage 56, 1773–1782 (2011).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Serences, J. T. & Saproo, S. Computational advances towards linking BOLD and behavior. Neuropsychologia 50, 435–446 (2012).

    PubMed  Google Scholar 

  68. 68.

    Vo, V. A., Sprague, T. C. & Serences, J. T. Spatial tuning shifts increase the discriminability and fidelity of population codes in visual cortex. J. Neurosci. 37, 3386–3401 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Constantinidis, C. & Procyk, E. The primate working memory networks. Cogn. Affect. Behav. Neurosci. 4, 444–465 (2004).

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Gazzaley, A., Rissman, J. & D’Esposito, M. Functional connectivity during working memory maintenance. Cogn. Affect. Behav. Neurosci. 4, 580–599 (2004).

    PubMed  Google Scholar 

  71. 71.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Barak, O., Tsodyks, M. & Romo, R. Neuronal population coding of parametric working memory. J. Neurosci. 30, 9424–9430 (2010).

    CAS  PubMed  Google Scholar 

  73. 73.

    Serences, J. T., Ester, E. F., Vogel, E. K. & Awh, E. Stimulus-specific delay activity in human primary visual cortex. Psychol. Sci. 20, 207–214 (2009).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Harrison, S. A. & Tong, F. Decoding reveals the contents of visual working memory in early visual areas. Nature 458, 632–635 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Riggall, A. C. & Postle, B. R. The relationship between working memory storage and elevated activity as measured with functional magnetic resonance imaging. J. Neurosci. 32, 12990–12998 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Christophel, T. B., Hebart, M. N. & Haynes, J.-D. Decoding the contents of visual short-term memory from human visual and parietal cortex. J. Neurosci. 32, 12983–12989 (2012).

    CAS  PubMed  Google Scholar 

  77. 77.

    Sreenivasan, K. K., Vytlacil, J. & D’Esposito, M. Distributed and dynamic storage of working memory stimulus information in extrastriate cortex. J. Cogn. Neurosci. 26, 1141–1153 (2014).

    PubMed  PubMed Central  Google Scholar 

  78. 78.

    Foster, J. J., Sutterer, D. W., Serences, J. T., Vogel, E. K. & Awh, E. The topography of alpha-band activity tracks the content of spatial working memory. J. Neurophysiol. 115, 168–177 (2016).

    PubMed  Google Scholar 

  79. 79.

    Stokes, M. & Spaak, E. The importance of single-trial analyses in cognitive neuroscience. Trends Cogn. Sci. 20, 483–486 (2016).

    PubMed  Google Scholar 

  80. 80.

    Compte, A. et al. Temporally irregular mnemonic persistent activity in prefrontal neurons of monkeys during a delayed response task. J. Neurophysiol. 90, 3441–3454 (2003).

    PubMed  Google Scholar 

  81. 81.

    Brody, C. D., Hernández, A., Zainos, A. & Romo, R. Timing and neural encoding of somatosensory parametric working memory in macaque prefrontal cortex. Cereb. Cortex 13, 1196–1207 (2003).

    PubMed  Google Scholar 

  82. 82.

    Shafi, M. et al. Variability in neuronal activity in primate cortex during working memory tasks. Neuroscience 146, 1082–1108 (2007).

    CAS  PubMed  Google Scholar 

  83. 83.

    Durstewitz, D. & Seamans, J. K. Beyond bistability: biophysics and temporal dynamics of working memory. Neuroscience 139, 119–133 (2006).

    CAS  PubMed  Google Scholar 

  84. 84.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Watanabe, K. & Funahashi, S. Prefrontal delay-period activity reflects the decision process of a saccade direction during a free-choice ODR task. Cereb. Cortex 17 (Suppl. 1), i88–i100 (2007).

    PubMed  Google Scholar 

  86. 86.

    Quintana, J. & Fuster, J. M. Mnemonic and predictive functions of cortical neurons in a memory task. Neuroreport 3, 721–724 (1992).

    CAS  PubMed  Google Scholar 

  87. 87.

    Quintana, J. & Fuster, J. M. From perception to action: temporal integrative functions of prefrontal and parietal neurons. Cereb. Cortex 9, 213–221 (1999).

    CAS  PubMed  Google Scholar 

  88. 88.

    Kojima, S. & Goldman-Rakic, P. S. Delay-related activity of prefrontal neurons in rhesus monkeys performing delayed response. Brain Res. 248, 43–49 (1982).

    CAS  PubMed  Google Scholar 

  89. 89.

    Goldman-Rakic, P. S. Circuitry of primate prefrontal cortex and regulation of behavior by representational memory. Compr. Physiol. https://doi.org/10.1002/cphy.cp010509 (2011).

    Article  Google Scholar 

  90. 90.

    Howard, M. W. Memory as perception of the past: compressed time inMind and brain. Trends Cogn. Sci. 22, 124–136 (2018).

    PubMed  PubMed Central  Google Scholar 

  91. 91.

    Pastalkova, E., Itskov, V., Amarasingham, A. & Buzsáki, G. Internally generated cell assembly sequences in the rat hippocampus. Science 321, 1322–1327 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    MacDonald, C. J., Lepage, K. Q., Eden, U. T. & Eichenbaum, H. Hippocampal ‘time cells’ bridge the gap in memory for discontiguous events. Neuron 71, 737–749 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Batuev, A. S., Pirogov, A. A., Orlov, A. A. & Sheafer, V. I. Cortical mechanisms of goal-directed motor acts in the rhesus monkey. Acta Neurobiol. Exp. 40, 27–49 (1980).

    CAS  Google Scholar 

  94. 94.

    Meyers, E. M. Dynamic population coding and its relationship to working memory. J. Neurophysiol. 120, 2260–2268 (2018). This review provides an in-depth discussion of the potential benefits and costs of dynamic population coding of WM information, highlighting the ways in which information from dynamic codes may be interpreted by downstream brain regions.

    PubMed  Google Scholar 

  95. 95.

    Wang, J., Narain, D., Hosseini, E. A. & Jazayeri, M. Flexible timing by temporal scaling of cortical responses. Nat. Neurosci. 21, 102–110 (2018).

    CAS  PubMed  Google Scholar 

  96. 96.

    Tiganj, Z., Cromer, J. A., Roy, J. E., Miller, E. K. & Howard, M. W. Compressed timeline of recent experience in monkey lateral prefrontal cortex. J. Cogn. Neurosci. 30, 935–950 (2018).

    PubMed  Google Scholar 

  97. 97.

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

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    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). This study addresses the question of how WM information is stably encoded by dynamic population codes. The authors apply principal component analysis to the complex temporal dynamics exhibited by NHP lPFC neurons and identify a low-dimensional population code that is stable across the delay.

    CAS  PubMed  Google Scholar 

  99. 99.

    Druckmann, S. & Chklovskii, D. B. Neuronal circuits underlying persistent representations despite time varying activity. Curr. Biol. 22, 2095–2103 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Myers, N. E. et al. Testing sensory evidence against mnemonic templates. eLife 4, e09000 (2015).

    PubMed  PubMed Central  Google Scholar 

  101. 101.

    Constantinidis, C. et al. Persistent spiking activity underlies working memory. J. Neurosci. 38, 7020–7028 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Lundqvist, M., Herman, P. & Miller, E. K. Working memory: delay activity, yes! persistent activity? maybe not. J. Neurosci. 38, 7013–7019 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Lee, S.-H. & Baker, C. I. Multi-voxel decoding and the topography of maintained information during visual working memory. Front. Syst. Neurosci. 10, 2 (2016).

    PubMed  PubMed Central  Google Scholar 

  104. 104.

    Leavitt, M. L., Mendoza-Halliday, D. & Martinez-Trujillo, J. C. Sustained activity encoding working memories: not fully distributed. Trends Neurosci. 40, 328–346 (2017).

    CAS  PubMed  Google Scholar 

  105. 105.

    Curtis, C. E. & D’Esposito, M. Persistent activity in the prefrontal cortex during working memory. Trends Cogn. Sci. 7, 415–423 (2003).

    PubMed  Google Scholar 

  106. 106.

    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).

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    Lara, A. H. & Wallis, J. D. The role of prefrontal cortex in working memory: a mini review. Front. Syst. Neurosci. 9, 173 (2015).

    PubMed  PubMed Central  Google Scholar 

  108. 108.

    Riley, M. R. & Constantinidis, C. Role of prefrontal persistent activity in working memory. Front. Syst. Neurosci. 9, 181 (2015).

    PubMed  Google Scholar 

  109. 109.

    Constantinidis, C., Franowicz, M. N. & Goldman-Rakic, P. S. The sensory nature of mnemonic representation in the primate prefrontal cortex. Nat. Neurosci. 4, 311–316 (2001).

    CAS  PubMed  Google Scholar 

  110. 110.

    Sprague, T. C. & Serences, J. T. Attention modulates spatial priority maps in the human occipital, parietal and frontal cortices. Nat. Neurosci. 16, 1879–1887 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Curtis, C. E., Rao, V. Y. & D’Esposito, M. Maintenance of spatial and motor codes during oculomotor delayed response tasks. J. Neurosci. 24, 3944–3952 (2004).

    CAS  PubMed  Google Scholar 

  112. 112.

    Zaksas, D. & Pasternak, T. Directional signals in the prefrontal cortex and in area MT during a working memory for visual motion task. J. Neurosci. 26, 11726–11742 (2006).

    CAS  PubMed  Google Scholar 

  113. 113.

    Mendoza-Halliday, D., Torres, S. & Martinez-Trujillo, J. C. Sharp emergence of feature-selective sustained activity along the dorsal visual pathway. Nat. Neurosci. 17, 1255–1262 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

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

    CAS  PubMed  Google Scholar 

  115. 115.

    Lewis-Peacock, J. A. & Postle, B. R. Temporary activation of long-term memory supports working memory. J. Neurosci. 28, 8765–8771 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116.

    Rainer, G., Rao, S. C. & Miller, E. K. Prospective coding for objects in primate prefrontal cortex. J. Neurosci. 19, 5493–5505 (1999).

    CAS  PubMed  Google Scholar 

  117. 117.

    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).

    PubMed  PubMed Central  Google Scholar 

  118. 118.

    Wutz, A., Loonis, R., Roy, J. E., Donoghue, J. A. & Miller, E. K. Different levels of category abstraction by different dynamics in different prefrontal areas. Neuron 97, 716–726 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. 119.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120.

    Wallis, J. D., Anderson, K. C. & Miller, E. K. Single neurons in prefrontal cortex encode abstract rules. Nature 411, 953–956 (2001).

    CAS  PubMed  Google Scholar 

  121. 121.

    Leon, M. I. & Shadlen, M. N. Effect of expected reward magnitude on the response of neurons in the dorsolateral prefrontal cortex of the macaque. Neuron 24, 415–425 (1999).

    CAS  PubMed  Google Scholar 

  122. 122.

    Cavanagh, S. E., Towers, J. P., Wallis, J. D., Hunt, L. T. & Kennerley, S. W. Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex. Nat. Commun. 9, 3498 (2018).

    PubMed  PubMed Central  Google Scholar 

  123. 123.

    D’Esposito, M., Postle, B. R., Ballard, D. & Lease, J. Maintenance versus manipulation of information held in working memory: An event-related fMRI study. Brain and Cognition 41, 66–86 (1999).

    CAS  PubMed  Google Scholar 

  124. 124.

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

    CAS  PubMed  Google Scholar 

  125. 125.

    Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013). This paper presents evidence for nonlinear mixed selectivity in NHP lPFC neurons and shows that the high-dimensional representations that are enabled by nonlinear mixed selectivity are crucial for behaviour.

    CAS  PubMed  PubMed Central  Google Scholar 

  126. 126.

    Parthasarathy, A. et al. Mixed selectivity morphs population codes in prefrontal cortex. Nat. Neurosci. 20, 1770–1779 (2017).

    CAS  PubMed  Google Scholar 

  127. 127.

    Percheron, G., François, C. & Pouget, P. What makes a frontal area of primate brain the frontal eye field? Front. Integr. Neurosci. 9, 33 (2015).

    PubMed  PubMed Central  Google Scholar 

  128. 128.

    Schall, J. D. et al. in Evolution of Nervous Systems 2nd edn (ed. Kaas, J. H.) Vol. 4 249–275 (Academic Press, 2016).

  129. 129.

    Constantinidis, C. & Steinmetz, M. A. Neuronal activity in posterior parietal area 7a during the delay periods of a spatial memory task. J. Neurophysiol. 76, 1352–1355 (1996).

    CAS  PubMed  Google Scholar 

  130. 130.

    Todd, J. J. & Marois, R. Capacity limit of visual short-term memory in human posterior parietal cortex. Nature 428, 751–754 (2004).

    CAS  PubMed  Google Scholar 

  131. 131.

    Schluppeck, D., Curtis, C. E., Glimcher, P. W. & Heeger, D. J. Sustained activity in topographic areas of human posterior parietal cortex during memory-guided saccades. J. Neurosci. 26, 5098–5108 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. 132.

    Bettencourt, K. C. & Xu, Y. Decoding the content of visual short-term memory under distraction in occipital and parietal areas. Nat. Neurosci. 19, 150–157 (2016).

    CAS  PubMed  Google Scholar 

  133. 133.

    Meltzer, J. A. et al. Effects of working memory load on oscillatory power in human intracranial EEG. Cereb. Cortex 18, 1843–1855 (2008).

    PubMed  Google Scholar 

  134. 134.

    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).

    CAS  PubMed  Google Scholar 

  135. 135.

    Chein, J. M., Ravizza, S. M. & Fiez, J. A. Using neuroimaging to evaluate models of working memory and their implications for language processing. J. Neurolinguistics 16, 315–339 (2003).

    Google Scholar 

  136. 136.

    Berryhill, M. E., Chein, J. & Olson, I. R. At the intersection of attention and memory: the mechanistic role of the posterior parietal lobe in working memory. Neuropsychologia 49, 1306–1315 (2011).

    PubMed  PubMed Central  Google Scholar 

  137. 137.

    Corbetta, M., Miezin, F. M., Shulman, G. L. & Petersen, S. E. A. PET study of visuospatial attention. J. Neurosci. 13, 1202–1226 (1993).

    CAS  PubMed  Google Scholar 

  138. 138.

    Elston, G. N. Pyramidal cells of the frontal lobe: all the more spinous to think with. J. Neurosci. 20, RC95 (2000).

    CAS  PubMed  Google Scholar 

  139. 139.

    Katsuki, F. et al. Differences in intrinsic functional organization between dorsolateral prefrontal and posterior parietal cortex. Cereb. Cortex 24, 2334–2349 (2014).

    PubMed  Google Scholar 

  140. 140.

    Katsuki, F. & Constantinidis, C. Unique and shared roles of the posterior parietal and dorsolateral prefrontal cortex in cognitive functions. Front. Integr. Neurosci. 6, 17 (2012).

    PubMed  PubMed Central  Google Scholar 

  141. 141.

    Mackey, W. E. & Curtis, C. E. Distinct contributions by frontal and parietal cortices support working memory. Sci. Rep. 7, 6188 (2017).

    PubMed  PubMed Central  Google Scholar 

  142. 142.

    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).

    CAS  PubMed  Google Scholar 

  143. 143.

    Masse, N. Y., Hodnefield, J. M. & Freedman, D. J. Mnemonic encoding and cortical organization in parietal and prefrontal cortices. J. Neurosci. 37, 6098–6112 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. 144.

    Ranganath, C. & Blumenfeld, R. S. Doubts about double dissociations between short- and long-term memory. Trends Cogn. Sci. 9, 374–380 (2005).

    PubMed  Google Scholar 

  145. 145.

    Jeneson, A. & Squire, L. R. Working memory, long-term memory, and medial temporal lobe function. Learn. Mem. 19, 15–25 (2012).

    PubMed  PubMed Central  Google Scholar 

  146. 146.

    Rissman, J., Gazzaley, A. & D’Esposito, M. Dynamic adjustments in prefrontal, hippocampal, and inferior temporal interactions with increasing visual working memory load. Cereb. Cortex 18, 1618–1629 (2008).

    PubMed  Google Scholar 

  147. 147.

    Suzuki, W. A., Miller, E. K. & Desimone, R. Object and place memory in the macaque entorhinal cortex. J. Neurophysiol. 78, 1062–1081 (1997).

    CAS  PubMed  Google Scholar 

  148. 148.

    Alvarez, G. A. & Cavanagh, P. The capacity of visual short-term memory is set both by visual information load and by number of objects. Psychol. Sci. 15, 106–111 (2004).

    CAS  PubMed  Google Scholar 

  149. 149.

    Brady, T. F. & Alvarez, G. A. No evidence for a fixed object limit in working memory: Spatial ensemble representations inflate estimates of working memory capacity for complex objects. J. Exp. Psychol. Learn. Mem. Cogn. 41, 921–929 (2015).

    PubMed  Google Scholar 

  150. 150.

    Olson, I. R., Page, K., Moore, K. S., Chatterjee, A. & Verfaellie, M. Working memory for conjunctions relies on the medial temporal lobe. J. Neurosci. 26, 4596–4601 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  151. 151.

    Davachi, L. Item, context and relational episodic encoding in humans. Curr. Opin. Neurobiol. 16, 693–700 (2006).

    CAS  PubMed  Google Scholar 

  152. 152.

    Hasselmo, M. E. & Stern, C. E. Mechanisms underlying working memory for novel information. Trends Cogn. Sci. 10, 487–493 (2006).

    PubMed  PubMed Central  Google Scholar 

  153. 153.

    Ranganath, C. & D’Esposito, M. Medial temporal lobe activity associated with active maintenance of novel information. Neuron 31, 865–873 (2001).

    CAS  PubMed  Google Scholar 

  154. 154.

    Buzsáki, G. & Tingley, D. Space and time: the hippocampus as a sequence generator. Trends Cogn. Sci. 22, 853–869 (2018).

    PubMed  Google Scholar 

  155. 155.

    Chung, G. H., Han, Y. M. & Kim, C. S. Functional MRI of the supplementary motor area: comparison of motor and sensory tasks. J. Comput. Assisted Tomogr. 24, 521–525 (2000).

    CAS  Google Scholar 

  156. 156.

    Kaufman, M. T., Churchland, M. M., Ryu, S. I. & Shenoy, K. V. Cortical activity in the null space: permitting preparation without movement. Nat. Neurosci. 17, 440–448 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. 157.

    di Pellegrino, G. & Wise, S. P. Visuospatial versus visuomotor activity in the premotor and prefrontal cortex of a primate. J. Neurosci. 13, 1227–1243 (1993).

    PubMed  Google Scholar 

  158. 158.

    Ohbayashi, M., Ohki, K. & Miyashita, Y. Conversion of working memory to motor sequence in the monkey premotor cortex. Science 301, 233–236 (2003).

    CAS  PubMed  Google Scholar 

  159. 159.

    Wallis, J. D. & Miller, E. K. From rule to response: neuronal processes in the premotor and prefrontal cortex. J. Neurophysiol. 90, 1790–1806 (2003).

    PubMed  Google Scholar 

  160. 160.

    Petit, L., Courtney, S. M., Ungerleider, L. G. & Haxby, J. V. Sustained activity in the medial wall during working memory delays. J. Neurosci. 18, 9429–9437 (1998).

    CAS  PubMed  Google Scholar 

  161. 161.

    Buchsbaum, B. R. & D’Esposito, M. A sensorimotor view of verbal working memory. Cortex 112, 134–148 (2019).

    PubMed  Google Scholar 

  162. 162.

    Simon, S. R. et al. Spatial attention and memory versus motor preparation: premotor cortex involvement as revealed by fMRI. J. Neurophysiol. 88, 2047–2057 (2002).

    PubMed  Google Scholar 

  163. 163.

    Brovelli, A., Lachaux, J.-P., Kahane, P. & Boussaoud, D. High gamma frequency oscillatory activity dissociates attention from intention in the human premotor cortex. Neuroimage 28, 154–164 (2005).

    PubMed  Google Scholar 

  164. 164.

    Vergara, J., Rivera, N., Rossi-Pool, R. & Romo, R. A. Neural parametric code for storing information of more than one sensory modality in working memory. Neuron 89, 54–62 (2016).

    CAS  PubMed  Google Scholar 

  165. 165.

    Badre, D. & D’Esposito, M. Is the rostro-caudal axis of the frontal lobe hierarchical? Nat. Rev. Neurosci. 10, 659–669 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  166. 166.

    Miyashita, Y. & Chang, H. S. Neuronal correlate of pictorial short-term memory in the primate temporal cortex. Nature 331, 68–70 (1988).

    CAS  PubMed  Google Scholar 

  167. 167.

    Scott, B. H., Mishkin, M. & Yin, P. Neural correlates of auditory short-term memory in rostral superior temporal cortex. Curr. Biol. 24, 2767–2775 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  168. 168.

    Ranganath, C., DeGutis, J. & D’Esposito, M. Category-specific modulation of inferior temporal activity during working memory encoding and maintenance. Brain Res. Cogn. Brain Res. 20, 37–45 (2004).

    PubMed  Google Scholar 

  169. 169.

    Lepsien, J. & Nobre, A. C. Attentional modulation of object representations in working memory. Cereb. Cortex 17, 2072–2083 (2007).

    PubMed  Google Scholar 

  170. 170.

    Bonnefond, M. & Jensen, O. Alpha oscillations serve to protect working memory maintenance against anticipated distracters. Curr. Biol. 22, 1969–1974 (2012).

    CAS  PubMed  Google Scholar 

  171. 171.

    Huang, Y., Matysiak, A., Heil, P., König, R. & Brosch, M. Persistent neural activity in auditory cortex is related to auditory working memory in humans and nonhuman primates. eLife 5, e15441 (2016).

    PubMed  PubMed Central  Google Scholar 

  172. 172.

    Wang, L. et al. Persistent neuronal firing in primary somatosensory cortex in the absence of working memory of trial-specific features of the sample stimuli in a haptic working memory task. J. Cogn. Neurosci. 24, 664–676 (2012).

    CAS  PubMed  Google Scholar 

  173. 173.

    Super, H. A. Neural correlate of working memory in the monkey primary visual cortex. Science 293, 120–124 (2001).

    CAS  PubMed  Google Scholar 

  174. 174.

    Ester, E. F., Anderson, D. E., Serences, J. T. & Awh, E. A neural measure of precision in visual working memory. J. Cogn. Neurosci. 25, 754–761 (2013).

    PubMed  PubMed Central  Google Scholar 

  175. 175.

    Rahmati, M., Saber, G. T. & Curtis, C. E. Population dynamics of early visual cortex during working memory. J. Cogn. Neurosci. 30, 219–233 (2018). This study uses fMRI to examine the precision with which encoding models can reconstruct WM representations in human visual cortex. The authors use an innovative method to model and quantify fMRI delay activity in order to demonstrate a link between the precision of model reconstruction and activity in higher-order parietal and frontal regions.

    PubMed  Google Scholar 

  176. 176.

    Woloszyn, L. & Sheinberg, D. L. Neural dynamics in inferior temporal cortex during a visual working memory task. J. Neurosci. 29, 5494–5507 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  177. 177.

    Pasternak, T. & Greenlee, M. W. Working memory in primate sensory systems. Nat. Rev. Neurosci. 6, 97–107 (2005).

    CAS  PubMed  Google Scholar 

  178. 178.

    D’Esposito, M. From cognitive to neural models of working memory. Phil. Trans. R. Soc. B Biol. Sci. 362, 761–772 (2007).

    Google Scholar 

  179. 179.

    Xu, Y. Reevaluating the sensory account of visual working memory storage. Trends Cogn. Sci. 21, 794–815 (2017).

    PubMed  Google Scholar 

  180. 180.

    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).

    CAS  PubMed  Google Scholar 

  181. 181.

    Lorenc, E. S., Sreenivasan, K. K., Nee, D. E., Vandenbroucke, A. R. E. & D’Esposito, M. Flexible coding of visual working memory representations during distraction. J. Neurosci. 38, 5267–5276 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  182. 182.

    Scimeca, J. M., Kiyonaga, A. & D’Esposito, M. Reaffirming the sensory recruitment account of working memory. Trends Cogn. Sci. 22, 190–192 (2018).

    PubMed  Google Scholar 

  183. 183.

    Wolff, M. J., Jochim, J., Akyürek, E. G. & Stokes, M. G. Dynamic hidden states underlying working-memory-guided behavior. Nat. Neurosci. 20, 864–871 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  184. 184.

    O’Reilly, R. C. & Frank, M. J. Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Comput. 18, 283–328 (2006).

    PubMed  Google Scholar 

  185. 185.

    Chatham, C. H., Frank, M. J. & Badre, D. Corticostriatal output gating during selection from working memory. Neuron 81, 930–942 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  186. 186.

    Alexander, G. E. Selective neuronal discharge in monkey putamen reflects intended direction of planned limb movements. Exp. Brain Res. 67, 623–634 (1987).

    CAS  PubMed  Google Scholar 

  187. 187.

    Johnstone, S. & Rolls, E. T. Delay, discriminatory, and modality specific neurons in striatum and pallidum during short-term memory tasks. Brain Res. 522, 147–151 (1990).

    CAS  PubMed  Google Scholar 

  188. 188.

    Soltysik, S., Hull, C. D., Buchwald, N. A. & Fekete, T. Single unit activity in basal ganglia of monkeys during performance of a delayed response task. Electroencephalogr. Clin. Neurophysiol. 39, 65–78 (1975).

    CAS  PubMed  Google Scholar 

  189. 189.

    Hikosaka, O. & Sakamoto, M. Cell activity in monkey caudate nucleus preceding saccadic eye movements. Exp. Brain Res. 63, 659–662 (1986).

    CAS  PubMed  Google Scholar 

  190. 190.

    Postle, B. R. & D’Esposito, M. Dissociation of human caudate nucleus activity in spatial and nonspatial working memory: an event-related fMRI study. Brain Res. Cogn. Brain Res. 8, 107–115 (1999).

    CAS  PubMed  Google Scholar 

  191. 191.

    Postle, B. R. & D’Esposito, M. Spatial working memory activity of the caudate nucleus is sensitive to frame of reference. Cogn. Affect. Behav. Neurosci. 3, 133–144 (2003).

    PubMed  Google Scholar 

  192. 192.

    Chang, C., Crottaz-Herbette, S. & Menon, V. Temporal dynamics of basal ganglia response and connectivity during verbal working memory. Neuroimage 34, 1253–1269 (2007).

    PubMed  Google Scholar 

  193. 193.

    Harrington, D. L., Zimbelman, J. L., Hinton, S. C. & Rao, S. M. Neural modulation of temporal encoding, maintenance, and decision processes. Cereb. Cortex 20, 1274–1285 (2010).

    PubMed  Google Scholar 

  194. 194.

    Fuster, J. M. & Alexander, G. E. Firing changes in cells of the nucleus medialis dorsalis associated with delayed response behavior. Brain Res. 61, 79–91 (1973).

    CAS  PubMed  Google Scholar 

  195. 195.

    Kubota, K., Niki, H. & Goto, A. Thalamic unit activity and delayed alternation performance in the monkey. Acta Neurobiol. Exp. 32, 177–192 (1972).

    CAS  Google Scholar 

  196. 196.

    Watanabe, Y. & Funahashi, S. Neuronal activity throughout the primate mediodorsal nucleus of the thalamus during oculomotor delayed-responses. II. Activity encoding visual versus motor signal. J. Neurophysiol. 92, 1756–1769 (2004).

    PubMed  Google Scholar 

  197. 197.

    Watanabe, Y., Takeda, K. & Funahashi, S. Population vector analysis of primate mediodorsal thalamic activity during oculomotor delayed-response performance. Cereb. Cortex 19, 1313–1321 (2009).

    PubMed  Google Scholar 

  198. 198.

    Watanabe, Y. & Funahashi, S. Thalamic mediodorsal nucleus and working memory. Neurosci. Biobehav. Rev. 36, 134–142 (2012).

    PubMed  Google Scholar 

  199. 199.

    Funahashi, S. Thalamic mediodorsal nucleus and its participation in spatial working memory processes: comparison with the prefrontal cortex. Front. Syst. Neurosci. 7, 36 (2013).

    PubMed  PubMed Central  Google Scholar 

  200. 200.

    Klein, J. C. et al. Topography of connections between human prefrontal cortex and mediodorsal thalamus studied with diffusion tractography. Neuroimage 51, 555–564 (2010).

    PubMed  PubMed Central  Google Scholar 

  201. 201.

    McFarland, N. R. & Haber, S. N. Thalamic relay nuclei of the basal ganglia form both reciprocal and nonreciprocal cortical connections, linking multiple frontal cortical areas. J. Neurosci. 22, 8117–8132 (2002).

    CAS  PubMed  Google Scholar 

  202. 202.

    Schmitt, L. I. et al. Thalamic amplification of cortical connectivity sustains attentional control. Nature 545, 219–223 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  203. 203.

    Guo, Z. V. et al. Maintenance of persistent activity in a frontal thalamocortical loop. Nature 545, 181–186 (2017). Along with Schmitt et al. (2017), this study demonstrates that the thalamus has a key role in sustaining PFC delay spiking. Optogenetic suppression of thalamic delay activity was shown to eliminate sustained WM representations in PFC and to impair behaviour.

    CAS  PubMed  PubMed Central  Google Scholar 

  204. 204.

    Bolkan, S. S. et al. Thalamic projections sustain prefrontal activity during working memory maintenance. Nat. Neurosci. 20, 987–996 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  205. 205.

    Peräkylä, J. et al. Causal evidence from humans for the role of mediodorsal nucleus of the thalamus in working memory. J. Cogn. Neurosci. 29, 2090–2102 (2017).

    PubMed  Google Scholar 

  206. 206.

    Nakajima, M. & Halassa, M. M. Thalamic control of functional cortical connectivity. Curr. Opin. Neurobiol. 44, 127–131 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  207. 207.

    Pergola, G. et al. The regulatory role of the human mediodorsal thalamus. Trends Cogn. Sci. 22, 1011–1025 (2018).

    PubMed  PubMed Central  Google Scholar 

  208. 208.

    Halassa, M. M. & Kastner, S. Thalamic functions in distributed cognitive control. Nat. Neurosci. 20, 1669–1679 (2017).

    CAS  PubMed  Google Scholar 

  209. 209.

    Rikhye, R. V., Gilra, A. & Halassa, M. M. Thalamic regulation of switching between cortical representations enables cognitive flexibility. Nat. Neurosci. 21, 1753–1763 (2018).

    CAS  PubMed  Google Scholar 

  210. 210.

    Naselaris, T., Kay, K. N., Nishimoto, S. & Gallant, J. L. Encoding and decoding in fMRI. Neuroimage 56, 400–410 (2011).

    PubMed  Google Scholar 

  211. 211.

    Ester, E. F., Sprague, T. C. & Serences, J. T. Parietal and frontal cortex encode stimulus-specific mnemonic representations during visual working memory. Neuron 87, 893–905 (2015). This fMRI study uses encoding models to demonstrate stimulus-selective delay activity throughout the human dorsal visual hierarchy — most notably in frontal and parietal regions.

    CAS  PubMed  PubMed Central  Google Scholar 

  212. 212.

    Sprague, T. C., Ester, E. F. & Serences, J. T. Restoring latent visual working memory representations in human cortex. Neuron 91, 694–707 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  213. 213.

    Dotson, N. M., Hoffman, S. J., Goodell, B. & Gray, C. M. Feature-based visual short-term memory is widely distributed and hierarchically organized. Neuron 99, 215–226 (2018).

    CAS  PubMed  Google Scholar 

  214. 214.

    Gilad, A., Gallero-Salas, Y., Groos, D. & Helmchen, F. Behavioral strategy determines frontal or posterior location of short-term memory in neocortex. Neuron 99, 814–828 (2018). This paper represents an exciting attempt to reconcile evidence for WM storage-related delay activity in frontal and sensory regions. The key finding is that delay activity in these two regions reflects different behavioural strategies: prospective and action-oriented for frontal regions and retrospective and sensory-oriented for sensory regions.

    CAS  PubMed  Google Scholar 

  215. 215.

    Chaudhuri, R. & Fiete, I. Computational principles of memory. Nat. Neurosci. 19, 394–403 (2016).

    CAS  PubMed  Google Scholar 

  216. 216.

    Durstewitz, D., Seamans, J. K. & Sejnowski, T. J. Neurocomputational models of working memory. Nat. Neurosci. 3, 1184–1191 (2000).

    CAS  PubMed  Google Scholar 

  217. 217.

    Barak, O. & Tsodyks, M. Working models of working memory. Curr. Opin. Neurobiol. 25, 20–24 (2014).

    CAS  PubMed  Google Scholar 

  218. 218.

    Major, G. & Tank, D. Persistent neural activity: prevalence and mechanisms. Curr. Opin. Neurobiol. 14, 675–684 (2004).

    CAS  PubMed  Google Scholar 

  219. 219.

    Traub, R. D. & Jefferys, J. G. Are there unifying principles underlying the generation of epileptic afterdischarges in vitro? Prog. Brain Res. 102, 383–394 (1994).

    CAS  PubMed  Google Scholar 

  220. 220.

    Egorov, A. V., Hamam, B. N., Fransén, E., Hasselmo, M. E. & Alonso, A. A. Graded persistent activity in entorhinal cortex neurons. Nature 420, 173–178 (2002).

    CAS  PubMed  Google Scholar 

  221. 221.

    Navaroli, V. L., Zhao, Y., Boguszewski, P. & Brown, T. H. Muscarinic receptor activation enables persistent firing in pyramidal neurons from superficial layers of dorsal perirhinal cortex. Hippocampus 22, 1392–1404 (2012).

    CAS  PubMed  Google Scholar 

  222. 222.

    Guigon, E., Dorizzi, B., Burnod, Y. & Schultz, W. Neural correlates of learning in the prefrontal cortex of the monkey: a predictive model. Cereb. Cortex 5, 135–147 (1995).

    CAS  PubMed  Google Scholar 

  223. 223.

    Fransén, E., Tahvildari, B., Egorov, A. V., Hasselmo, M. E. & Alonso, A. A. Mechanism of graded persistent cellular activity of entorhinal cortex layer V neurons. Neuron 49, 735–746 (2006).

    PubMed  Google Scholar 

  224. 224.

    Russo, R. E. & Hounsgaard, J. Dynamics of intrinsic electrophysiological properties in spinal cord neurones. Prog. Biophys. Mol. Biol. 72, 329–365 (1999).

    CAS  PubMed  Google Scholar 

  225. 225.

    Diesmann, M., Gewaltig, M. O. & Aertsen, A. Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529–533 (1999).

    CAS  PubMed  Google Scholar 

  226. 226.

    Rajan, K., Harvey, C. D. & Tank, D. W. Recurrent network models of sequence generation and memory. Neuron 90, 128–142 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  227. 227.

    Goldman, M. S. Memory without feedback in a neural network. Neuron 61, 621–634 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  228. 228.

    Abeles, M. Corticonics: Neural Circuits of the Cerebral Cortex (Cambridge Univ. Press, 1991).

  229. 229.

    Brody, C. D., Romo, R. & Kepecs, A. Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic representations. Curr. Opin. Neurobiol. 13, 204–211 (2003).

    CAS  PubMed  Google Scholar 

  230. 230.

    Wimmer, K., Nykamp, D. Q., Constantinidis, C. & Compte, A. Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory. Nat. Neurosci. 17, 431–439 (2014). This single-unit study is noteworthy for testing and confirming a fundamental prediction that was generated by a continuous attractor model: that drift in population delay spiking should predict the direction and magnitude of behavioural errors. This work demonstrates how biophysical models can be used to inform empirical studies.

    CAS  PubMed  Google Scholar 

  231. 231.

    Itskov, V., Hansel, D. & Tsodyks, M. Short-term facilitation may stabilize parametric working memory trace. Front. Comput. Neurosci. 5, 40 (2011).

    PubMed  PubMed Central  Google Scholar 

  232. 232.

    Koulakov, A. A., Raghavachari, S., Kepecs, A. & Lisman, J. E. Model for a robust neural integrator. Nat. Neurosci. 5, 775–782 (2002).

    CAS  PubMed  Google Scholar 

  233. 233.

    Inagaki, H. K., Fontolan, L., Romani, S. & Svoboda, K. Discrete attractor dynamics underlies persistent activity in the frontal cortex. Nature 566, 212–217 (2019).

    CAS  PubMed  Google Scholar 

  234. 234.

    Hansel, D. & Mato, G. Short-term plasticity explains irregular persistent activity in working memory tasks. J. Neurosci. 33, 133–149 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  235. 235.

    Mongillo, G., Barak, O. & Tsodyks, M. Synaptic theory of working memory. Science 319, 1543–1546 (2008).

    CAS  PubMed  Google Scholar 

  236. 236.

    Sugase-Miyamoto, Y., Liu, Z., Wiener, M. C., Optican, L. M. & Richmond, B. J. Short-term memory trace in rapidly adapting synapses of inferior temporal cortex. PLOS Comput. Biol. 4, e1000073 (2008).

    PubMed  PubMed Central  Google Scholar 

  237. 237.

    Mi, Y., Katkov, M. & Tsodyks, M. Synaptic correlates of working memory capacity. Neuron 93, 323–330 (2017).

    CAS  PubMed  Google Scholar 

  238. 238.

    Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat. Neurosci. 9, 534–542 (2006).

    CAS  PubMed  Google Scholar 

  239. 239.

    Sandberg, A., Tegnér, J. & Lansner, A. A working memory model based on fast Hebbian learning. Network 14, 789–802 (2003).

    CAS  PubMed  Google Scholar 

  240. 240.

    Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces GluR1-dependent associative short-term potentiation: a potential mechanism for short-term memory. J. Cogn. Neurosci. 22, 2530–2540 (2010).

    PubMed  PubMed Central  Google Scholar 

  241. 241.

    Fiebig, F. & Lansner, A. A. Spiking working memory model based on hebbian short-term potentiation. J. Neurosci. 37, 83–96 (2017). This paper incorporates Hebbian STP into an attractor model in order to explain how synaptic and spiking delay mechanisms can be used to encode WM for multiple novel items.

    CAS  PubMed  PubMed Central  Google Scholar 

  242. 242.

    Sreenivasan, K. K., Katz, J. & Jha, A. P. Temporal characteristics of top-down modulations during working memory maintenance: an event-related potential study of the N170 component. J. Cogn. Neurosci. 19, 1836–1844 (2007).

    PubMed  Google Scholar 

  243. 243.

    Murray, J. D. et al. A hierarchy of intrinsic timescales across primate cortex. Nat. Neurosci. 17, 1661–1663 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  244. 244.

    Schneegans, S. & Bays, P. M. Restoration of fMRI decodability does not imply latent working memory states. J. Cogn. Neurosci. 29, 1977–1994 (2017).

    PubMed  PubMed Central  Google Scholar 

  245. 245.

    Camperi, M. & Wang, X. J. A model of visuospatial working memory in prefrontal cortex: recurrent network and cellular bistability. J. Comput. Neurosci. 5, 383–405 (1998).

    CAS  PubMed  Google Scholar 

  246. 246.

    Lundqvist, M., Herman, P. & Lansner, A. Theta and gamma power increases and alpha/beta power decreases with memory load in an attractor network model. J. Cogn. Neurosci. 23, 3008–3020 (2011). The model described in this paper combines elements of STP with an attractor network model to recapitulate empirical LFP findings, including the relationship between LFP amplitude and WM load. The key advance of this model is that its architecture results in WM storage-related LFP bursting — a prediction that was later confirmed empirically by Lundqvist et al. ( Neuron , 2016) and Lundqvist et al. ( Nat. Commun . , 2018).

    PubMed  Google Scholar 

  247. 247.

    Lisman, J. E., Fellous, J. M. & Wang, X. J. A role for NMDA-receptor channels in working memory. Nat. Neurosci. 1, 273–275 (1998).

    CAS  PubMed  Google Scholar 

  248. 248.

    Orhan, A. E. & Ma, W. J. A diverse range of factors affect the nature of neural representations underlying short-term memory. Nat. Neurosci. 22, 275–283 (2019).

    CAS  PubMed  Google Scholar 

  249. 249.

    Rose, N. S. et al. Reactivation of latent working memories with transcranial magnetic stimulation. Science 354, 1136–1139 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  250. 250.

    Myers, N. E., Stokes, M. G. & Nobre, A. C. Prioritizing information during working memory: beyond sustained internal attention. Trends Cogn. Sci. 21, 449–461 (2017).

    PubMed  Google Scholar 

  251. 251.

    Azevedo, F. A. C. et al. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 513, 532–541 (2009).

    PubMed  Google Scholar 

  252. 252.

    Ransom, B. R. Neuroglia 3rd edn (eds Kettenmann, H. & Ransom, B. R.) (Oxford Univ. Press, 2013).

  253. 253.

    Araque, A., Parpura, V., Sanzgiri, R. P. & Haydon, P. G. Tripartite synapses: glia, the unacknowledged partner. Trends Neurosci. 22, 208–215 (1999).

    CAS  PubMed  Google Scholar 

  254. 254.

    Santello, M., Toni, N. & Volterra, A. Astrocyte function from information processing to cognition and cognitive impairment. Nat. Neurosci. 22, 154–166 (2019).

    CAS  PubMed  Google Scholar 

  255. 255.

    Vardjan, N., Parpura, V. & Zorec, R. Loose excitation-secretion coupling in astrocytes. Glia 64, 655–667 (2016).

    PubMed  Google Scholar 

  256. 256.

    Halassa, M. M. et al. Astrocytic modulation of sleep homeostasis and cognitive consequences of sleep loss. Neuron 61, 213–219 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  257. 257.

    Haydon, P. G. Glia: listening and talking to the synapse. Nat. Rev. Neurosci. 2, 185–193 (2001).

    CAS  PubMed  Google Scholar 

  258. 258.

    Papouin, T., Dunphy, J., Tolman, M., Foley, J. C. & Haydon, P. G. Astrocytic control of synaptic function. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160154 (2017).

    Google Scholar 

  259. 259.

    Wang, X. et al. Astrocytic Ca2+ signaling evoked by sensory stimulation in vivo. Nat. Neurosci 9, 816–823 (2006).

    CAS  PubMed  Google Scholar 

  260. 260.

    Schummers, J., Yu, H. & Sur, M. Tuned responses of astrocytes and their influence on hemodynamic signals in the visual cortex. Science 320, 1638–1643 (2008).

    CAS  PubMed  Google Scholar 

  261. 261.

    Lee, H. S. et al. Astrocytes contribute to gamma oscillations and recognition memory. Proc. Natl Acad. Sci. USA 111, E3343–E3352 (2014).

    CAS  PubMed  Google Scholar 

  262. 262.

    Pittà, M. D., De Pittà, M., Ben-Jacob, E. & Berry, H. Astrocytic theory of working memory. BMC Neurosci. 15 (Suppl. 1), P206 (2014).

    PubMed Central  Google Scholar 

  263. 263.

    Wang, X. J. Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. J. Neurosci. 19, 9587–9603 (1999).

    CAS  PubMed  Google Scholar 

  264. 264.

    Aura, J. & Riekkinen, P. Jr. Blockade of NMDA receptors located at the dorsomedial prefrontal cortex impairs spatial working memory in rats. Neuroreport 10, 243–248 (1999).

    CAS  PubMed  Google Scholar 

  265. 265.

    Verma, A. & Moghaddam, B. NMDA receptor antagonists impair prefrontal cortex function as assessed via spatial delayed alternation performance in rats: modulation by dopamine. J. Neurosci. 16, 373–379 (1996).

    CAS  PubMed  Google Scholar 

  266. 266.

    Baron, S. P. & Wenger, G. R. Effects of drugs of abuse on response accuracy and bias under a delayed matching-to-sample procedure in squirrel monkeys. Behav. Pharmacol. 12, 247–256 (2001).

    CAS  PubMed  Google Scholar 

  267. 267.

    Krystal, J. H. et al. Subanesthetic effects of the noncompetitive NMDA antagonist, ketamine, in humans. Psychotomimetic, perceptual, cognitive, and neuroendocrine responses. Arch. Gen. Psychiatry 51, 199–214 (1994).

    CAS  PubMed  Google Scholar 

  268. 268.

    Ghoneim, M. M., Hinrichs, J. V., Mewaldt, S. P. & Petersen, R. C. Ketamine: behavioral effects of subanesthetic doses. J. Clin. Psychopharmacol. 5, 70–77 (1985).

    CAS  PubMed  Google Scholar 

  269. 269.

    Driesen, N. R. et al. The impact of NMDA receptor blockade on human working memory-related prefrontal function and connectivity. Neuropsychopharmacology 38, 2613–2622 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  270. 270.

    Dudkin, K. N., Kruchinin, V. K. & Chueva, I. V. Effect of NMDA on the activity of cortical glutaminergic structures in delayed visual differentiation in monkeys. Neurosci. Behav. Physiol. 27, 153–158 (1997).

    CAS  PubMed  Google Scholar 

  271. 271.

    Wang, M. et al. NMDA receptors subserve persistent neuronal firing during working memory in dorsolateral prefrontal cortex. Neuron 77, 736–749 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  272. 272.

    Wang, H., Stradtman, G. G. 3rd, Wang, X. J. & Gao, W. J. A specialized NMDA receptor function in layer 5 recurrent microcircuitry of the adult rat prefrontal cortex. Proc. Natl Acad. Sci. USA 105, 16791–16796 (2008).

    CAS  PubMed  Google Scholar 

  273. 273.

    McQuail, J. A. et al. NR2A-containing NMDARs in the prefrontal cortex are required for working memory and associated with age-related cognitive decline. J. Neurosci. 36, 12537–12548 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  274. 274.

    Buzsáki, G. & Wang, X.-J. Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35, 203–225 (2012).

    PubMed  PubMed Central  Google Scholar 

  275. 275.

    Wang, J. D1 dopamine receptors potentiate NMDA-mediated excitability increase in layer V prefrontal cortical pyramidal neurons. Cereb. Cortex 11, 452–462 (2001).

    CAS  PubMed  Google Scholar 

  276. 276.

    Williams, G. V. & Goldman-Rakic, P. S. Modulation of memory fields by dopamine D1 receptors in prefrontal cortex. Nature 376, 572–575 (1995).

    CAS  PubMed  Google Scholar 

  277. 277.

    Durstewitz, D., Seamans, J. K. & Sejnowski, T. J. Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex. J. Neurophysiol. 83, 1733–1750 (2000).

    CAS  PubMed  Google Scholar 

  278. 278.

    Arnsten, A. F., Cai, J. X., Murphy, B. L. & Goldman-Rakic, P. S. Dopamine D1 receptor mechanisms in the cognitive performance of young adult and aged monkeys. Psychopharmacology 116, 143–151 (1994).

    CAS  PubMed  Google Scholar 

  279. 279.

    Wang, M. et al. α2A-adrenoceptors strengthen working memory networks by inhibiting cAMP–HCN channel signaling in prefrontal cortex. Cell 129, 397–410 (2007).

    CAS  PubMed  Google Scholar 

  280. 280.

    Thuault, S. J. et al. Prefrontal cortex HCN1 channels enable intrinsic persistent neural firing and executive memory function. J. Neurosci. 33, 13583–13599 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  281. 281.

    Zhang, Z., Cordeiro Matos, S., Jego, S., Adamantidis, A. & Séguéla, P. Norepinephrine drives persistent activity in prefrontal cortex via synergistic α1 and α2 adrenoceptors. PLOS ONE 8, e66122 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  282. 282.

    Neymotin, S. A. et al. Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex. Neuroscience 316, 344–366 (2016).

    CAS  PubMed  Google Scholar 

  283. 283.

    Arnsten, A. F. T. Stress signalling pathways that impair prefrontal cortex structure and function. Nat. Rev. Neurosci. 10, 410–422 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  284. 284.

    Ollinger, J. M., Shulman, G. L. & Corbetta, M. Separating processes within a trial in event-related functional MRI I. The method. Neuroimage 13, 210–217 (2001).

    CAS  PubMed  Google Scholar 

  285. 285.

    Ruge, H., Goschke, T. & Braver, T. S. Separating event-related BOLD components within trials: the partial-trial design revisited. Neuroimage 47, 501–513 (2009).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank A. Kiyonaga, E. Lorenc and D. Bliss for their helpful comments on previous versions of this manuscript. This work was supported by US National Institutes of Health Grant MH63901 to M.D.

Reviewer information

Nature Reviews Neuroscience thanks E. K. Miller and the other anonymous reviewers for their contribution to the peer review of this work.

Author information

Affiliations

Authors

Contributions

Both authors researched data for the article and made substantial contributions to the discussion of content. K.K.S. wrote the article and K.K.S. and M.D. reviewed or edited the manuscript before submission.

Corresponding authors

Correspondence to Kartik K. Sreenivasan or Mark D’Esposito.

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.

Glossary

WM delay task

A task that temporally segregates working memory (WM) encoding, maintenance and response by introducing an unfilled memory delay between a memory stimulus and the contingent behavioural response.

Voxels

The volumetric units of functional MRI (fMRI) measurement. A 3D fMRI brain image contains ~100,000 voxels, each of which represents the activity of tens of thousands of neurons.

Population coding

A coding scheme wherein information is encoded in the combined activity of a population of neurons (or electrodes or voxels) as opposed to the activity of individual neurons (or electrodes or voxels).

Haemodynamic response

The temporal pattern of blood oxygen level-dependent signal observed by functional MRI in response to a brief impulse of neural activity. It takes ~20 s to return to baseline.

General linear model

(GLM). A model that describes the output of a system as a linear combination of predictors. GLMs are used to estimate blood oxygen level-dependent responses to features of an experimental task.

Impulse response function

The output of a dynamic system in response to a brief input.

Nonlinear mixed selectivity

A property that allows neurons to respond to combinations of stimulus or task features with nonlinear changes in firing rates.

WM load

The amount of information that is held in working memory (WM). WM load can be manipulated by varying the number or complexity of memory items.

WM capacity

The upper bound on the amount of information that an individual can store at once in working memory (WM).

Encoding models

Models that form a prediction of brain activity for a given set of experimental features (for example, specific memory items during a working memory delay task).

Attractor state

A stable state of the activity of a network of (usually recurrently connected) neurons that persists in the absence of input.

Short-term plasticity

(STP). Synaptic plasticity in response to brief (~1s) stimulation. Hebbian forms (involving presynaptic and postsynaptic changes) and non-Hebbian forms (involving only presynaptic changes) of STP have been proposed to underlie working memory.

Matched filter

A linear filter that can help detect the presence of a known stimulus in a noisy observed signal by correlating the known stimulus with the observed signal.

Time constants

Values that describe the time required for a neuron to return to a baseline state following an input.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sreenivasan, K.K., D’Esposito, M. The what, where and how of delay activity. Nat Rev Neurosci 20, 466–481 (2019). https://doi.org/10.1038/s41583-019-0176-7

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing