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.

  • Article
  • Published:

Persistently active neurons in human medial frontal and medial temporal lobe support working memory

An Erratum to this article was published on 26 July 2017

This article has been updated

Abstract

Persistent neural activity is a putative mechanism for the maintenance of working memories. Persistent activity relies on the activity of a distributed network of areas, but the differential contribution of each area remains unclear. We recorded single neurons in the human medial frontal cortex and medial temporal lobe while subjects held up to three items in memory. We found persistently active neurons in both areas. Persistent activity of hippocampal and amygdala neurons was stimulus-specific, formed stable attractors and was predictive of memory content. Medial frontal cortex persistent activity, on the other hand, was modulated by memory load and task set but was not stimulus-specific. Trial-by-trial variability in persistent activity in both areas was related to memory strength, because it predicted the speed and accuracy by which stimuli were remembered. This work reveals, in humans, direct evidence for a distributed network of persistently active neurons supporting working memory maintenance.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Task, recording locations and behavioral results.
Figure 2: Stimulus-selective concept cells.
Figure 3: Concept cells are persistently active during WM maintenance.
Figure 4: Population analysis of MTL concept cells.
Figure 5: Persistent activity of maintenance cells in MFC.
Figure 6: Probe neurons reflect WM-retrieval related evoked activity in MFC.
Figure 7: Population decoding from all recorded neurons during the maintenance period.
Figure 8: Persistent activity during maintenance forms attractors.

Similar content being viewed by others

Change history

  • 04 May 2017

    In the version of this article initially published, the pink and gray symbols were switched in the key to Figure 7d and the plot of the outlier data points was mis-scaled relative to the axes. In Figure 6b, the horizontal axis was numbered 0 through 1 instead of −1 through 0. The errors have been corrected in the HTML and PDF versions of the article.

References

  1. Baddeley, A. Working memory: theories, models, and controversies. Annu. Rev. Psychol. 63, 1–29 (2012).

    Article  PubMed  Google Scholar 

  2. Eriksson, J., Vogel, E.K., Lansner, A., Bergström, F. & Nyberg, L. Neurocognitive architecture of working memory. Neuron 88, 33–46 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  4. 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).

    Article  CAS  PubMed  Google Scholar 

  5. 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).

    Article  CAS  PubMed  Google Scholar 

  6. Rainer, G., Asaad, W.F. & Miller, E.K. Selective representation of relevant information by neurons in the primate prefrontal cortex. Nature 393, 577–579 (1998).

    Article  CAS  PubMed  Google Scholar 

  7. 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).

    Article  CAS  PubMed  Google Scholar 

  8. Fuster, J.M. & Jervey, J.P. Inferotemporal neurons distinguish and retain behaviorally relevant features of visual stimuli. Science 212, 952–955 (1981).

    Article  CAS  PubMed  Google Scholar 

  9. Miller, E.K., Li, L. & Desimone, R. Activity of neurons in anterior inferior temporal cortex during a short-term memory task. J. Neurosci. 13, 1460–1478 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  11. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fuster, J.M. Cortex and memory: emergence of a new paradigm. J. Cogn. Neurosci. 21, 2047–2072 (2009).

    Article  PubMed  Google Scholar 

  14. Yarkoni, T., Poldrack, R.A., Nichols, T.E., Van Essen, D.C. & Wager, T.D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8, 665–670 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Dosenbach, N.U.F. et al. A core system for the implementation of task sets. Neuron 50, 799–812 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Squire, L.R., Stark, C.E.L. & Clark, R.E. The medial temporal lobe. Annu. Rev. Neurosci. 27, 279–306 (2004).

    Article  CAS  PubMed  Google Scholar 

  17. Jeneson, A. & Squire, L.R. Working memory, long-term memory, and medial temporal lobe function. Learn. Mem. 19, 15–25 (2011).

    Article  PubMed  Google Scholar 

  18. Axmacher, N. et al. Sustained neural activity patterns during working memory in the human medial temporal lobe. J. Neurosci. 27, 7807–7816 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ojemann, G.A., Creutzfeldt, O., Lettich, E. & Haglund, M.M. Neuronal activity in human lateral temporal cortex related to short-term verbal memory, naming and reading. Brain 111, 1383–1403 (1988).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  21. Hopfield, J.J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79, 2554–2558 (1982).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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).

    Article  CAS  PubMed  Google Scholar 

  23. Rutishauser, U. & Douglas, R.J. State-dependent computation using coupled recurrent networks. Neural Comput. 21, 478–509 (2009).

    Article  PubMed  Google Scholar 

  24. Quiroga, R.Q., Reddy, L., Kreiman, G., Koch, C. & Fried, I. Invariant visual representation by single neurons in the human brain. Nature 435, 1102–1107 (2005).

    Article  CAS  PubMed  Google Scholar 

  25. Mormann, F. et al. A category-specific response to animals in the right human amygdala. Nat. Neurosci. 14, 1247–1249 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sternberg, S. In defence of high-speed memory scanning. Q. J. Exp. Psychol. (Hove) 69, 2020–2075 (2016).

    Article  Google Scholar 

  27. 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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Rutishauser, U. et al. Representation of retrieval confidence by single neurons in the human medial temporal lobe. Nat. Neurosci. 18, 1041–1050 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Murray, J. et al. Stable population coding for working memory coexists with heterogeneous neuraldynamics in prefrontal cortex. Proc. Natl. Acad. Sci. USA 114, 394–399 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kobak, D. et al. Demixed principal component analysis of neural population data. eLife 5, 1–37 (2016).

    Article  CAS  Google Scholar 

  32. Milnor, J. On the concept of attractor. Commun. Math. Phys. 99, 177–195 (1985).

    Article  Google Scholar 

  33. Mormann, F. et al. Latency and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe. J. Neurosci. 28, 8865–8872 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Quian Quiroga, R., Kraskov, A., Koch, C. & Fried, I. Explicit encoding of multimodal percepts by single neurons in the human brain. Curr. Biol. 19, 1308–1313 (2009).

    Article  CAS  PubMed  Google Scholar 

  35. Waydo, S., Kraskov, A., Quian Quiroga, R., Fried, I. & Koch, C. Sparse representation in the human medial temporal lobe. J. Neurosci. 26, 10232–10234 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Gelbard-Sagiv, H., Mukamel, R., Harel, M., Malach, R. & Fried, I. Internally generated reactivation of single neurons in human hippocampus during free recall. Science 322, 96–101 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kreiman, G., Koch, C. & Fried, I. Imagery neurons in the human brain. Nature 408, 357–361 (2000).

    Article  CAS  PubMed  Google Scholar 

  38. Macoveanu, J., Klingberg, T. & Tegnér, J. A biophysical model of multiple-item working memory: a computational and neuroimaging study. Neuroscience 141, 1611–1618 (2006).

    Article  CAS  PubMed  Google Scholar 

  39. 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).

    Article  CAS  PubMed  Google Scholar 

  40. Laing, C.R., Troy, W.C., Gutkin, B. & Ermentrout, G.B. Multiple bumps in a neuronal model of working memory. SIAM J. Appl. Math. 63, 62–97 (2002).

    Article  Google Scholar 

  41. Lundqvist, M. et al. Gamma and beta bursts underlie working memory. Neuron 90, 152–164 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  44. van Vugt, M.K., Schulze-Bonhage, A., Litt, B., Brandt, A. & Kahana, M.J. Hippocampal gamma oscillations increase with memory load. J. Neurosci. 30, 2694–2699 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Davis, M. & Whalen, P.J. The amygdala: vigilance and emotion. Mol. Psychiatry 6, 13–34 (2001).

    Article  CAS  PubMed  Google Scholar 

  46. Buchanan, T.W., Tranel, D. & Adolphs, R. in The Human Amygdala (eds. Whalen, P.J. & Phelps, E.A.) 289–317 (2009).

  47. Peinado-Manzano, M.A. The role of the amygdala and the hippocampus in working memory for spatial and non-spatial information. Behav. Brain Res. 38, 117–134 (1990).

    Article  CAS  PubMed  Google Scholar 

  48. Saez, A., Rigotti, M., Ostojic, S., Fusi, S. & Salzman, C.D. Abstract context representations in primate amygdala and prefrontal cortex. Neuron 87, 869–881 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Bush, G., Luu, P. & Posner, M.I. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn. Sci. 4, 215–222 (2000).

    Article  CAS  PubMed  Google Scholar 

  50. Gläscher, J. et al. Lesion mapping of cognitive control and value-based decision making in the prefrontal cortex. Proc. Natl. Acad. Sci. USA 109, 14681–14686 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  52. Ségonne, F. et al. A hybrid approach to the skull stripping problem in MRI. Neuroimage 22, 1060–1075 (2004).

    Article  PubMed  Google Scholar 

  53. Reuter, M., Rosas, H.D. & Fischl, B. Highly accurate inverse consistent registration: a robust approach. Neuroimage 53, 1181–1196 (2010).

    PubMed  Google Scholar 

  54. Tyszka, J.M. & Pauli, W.M. In vivo delineation of subdivisions of the human amygdaloid complex in a high-resolution group template. Hum. Brain Mapp. 37, 3979–3998 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Avants, B. et al. Multivariate analysis of structural and diffusion imaging in traumatic brain injury. Acad. Radiol. 15, 1360–1375 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Rutishauser, U., Ross, I.B., Mamelak, A.N. & Schuman, E.M. Human memory strength is predicted by theta-frequency phase-locking of single neurons. Nature 464, 903–907 (2010).

    Article  CAS  PubMed  Google Scholar 

  57. Pouzat, C., Mazor, O. & Laurent, G. Using noise signature to optimize spike-sorting and to assess neuronal classification quality. J. Neurosci. Methods 122, 43–57 (2002).

    Article  PubMed  Google Scholar 

  58. Harris, K.D., Henze, D.A., Csicsvari, J., Hirase, H. & Buzsáki, G. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol. 84, 401–414 (2000).

    Article  CAS  PubMed  Google Scholar 

  59. Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).

    Article  PubMed  Google Scholar 

  60. Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164, 177–190 (2007).

    Article  PubMed  Google Scholar 

  61. Meyers, E.M. The neural decoding toolbox. Front. Neuroinform. 7, 8 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Fan, R.-E., Chen, P.-H. & Lin, C.-J. Working set selection using second order information for training support vector machines. J. Mach. Learn. Res. 6, 1889–1918 (2005).

    Google Scholar 

Download references

Acknowledgements

We thank J. Minxa, R. Adolphs and J. Dubois for discussion and the staff and physicians of the Epilepsy Monitoring Unit at Cedars-Sinai Medical Center and the Huntington Memorial Hospital for invaluable assistance. This work was supported by the National Science Foundation (1554105 to U.R.), the National Institute of Mental Health (R01MH110831 to U.R.), the McKnight Endowment Fund for Neuroscience (to U.R.), a NARSAD Young Investigator grant from the Brain & Behavior Research Foundation (23502 to U.R.) and the Pfeiffer Foundation (to U.R.).

Author information

Authors and Affiliations

Authors

Contributions

J.K. and U.R. designed the experiments. J.K. and U.R. performed experiments. J.K., S.S. and U.R. performed analysis. A.N.M. and I.B.R. performed surgery. J.M.C. provided patient care. J.K. and U.R. wrote the paper. All of the authors discussed the results at all stages of the project.

Corresponding author

Correspondence to Ueli Rutishauser.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Tuning of concept cells in MTL during the screening task.

(a) Examples of responses of five neurons classified as concept cells for the 54 tested images. Error bars represent ±s.e. across the 6 presentations of each image. The image shown in each panel represents the picture with the highest response. (b) Cumulative distribution of the depth of selectivity (S) index for all concept cells identified in the screening task (N=88). The average DOS of 0.68 indicates a sparse response. Note that some of the example images shown are different from those shown to patients due to copyright issues.

Supplementary Figure 2 Spike-quality metrics for all identified putative single-cells (clusters).

(a) Histogram of proportion of inter-spike intervals (ISIs) that were shorter than 3ms. The large majority of clusters had less than 0.5% of such short ISIs. (b) Histogram of average firing rate. (c) Histogram of the singal-to-noise ratio (SNR) of the peak of the mean waveform. (d) SNR of the whole waveform of all units. (e) Histogram of coefficient-of- variation (CV2) values for every neuron. (f) Pairwise distance, estimated using the projection test, between clusters where more than one unit was found on one wire. (g) Isolation distance for all units for which this metric was defined (median = 30.9).

Supplementary Figure 3 Examples of neurons recorded in MTL and MFC.

(a) Example of a concept cell recorded in the amygdala. Upper panel shows the PSTH (binsize 200ms, stepsize 2ms). Shaded areas represent s.e. across trials. Middle panel marks periods of significance between preferred vs. not preferred (corrected for multiple comparisons using a cluster-size correction, see methods). Bottom panel shows raster with trials re-ordered according to condition for plotting purposes only. This unit shows both visually evoked selective activity (red) and sustained activity (blue) during maintenance. Note how during maintenance, concept cells have elevated activity only when their preferred stimulus was held in memory (blue vs. gray). Also note how the sustained activity (blue) was suppressed during encoding of the non-preferred image (i.e. encoding 3) when the preferred stimulus was already held in memory. (b) Second example of a concept cell recorded in the amygdala. Notation is same than in (a). (c) Neuron which modulated its persistent activity as a function of load. (d) Example of neuron which modulated its persistent activity as a function of response speed (correct trials only).

Supplementary Figure 4 Separability of neuronal classes and effects of epilepsy

(a) Overlap of three neuronal classes we identified. The number of cells which qualified as both concept and maintenance cells was not larger than that expected if the two groups were independent (n=13, 14% of all concept cells, Fisher’s exact test, P=0.201). Similarly, cells that qualified both as concept and probe cells were few and not more than expected from independence (n=7, 7% of all concept cells, Fisher’s exact test, P=0.223). The percentage of maintenance cells that was classified as probe cells was 17% (N=22, Fisher exact test marginally significant, P=0.044). Nevertheless, most of the maintenance neurons (N=104) where not classified as probe neurons. (b) We verified whether the groups we identified truly constitute separate categories. Alternatively, they might represent the tails of a continuous distribution. To test this, we computed correlations between the effect sizes attributed to the selection criteria for each cell type. We used the t-statistic that were used to identify the different neuron classes as the effect size (see methods). We found no significant correlation between the effect sizes for image identity and maintenance selectivity for concept cells, maintenance cells, or all recorded cells (r=0.13; P=0.26, r=0.03; P=0.97; r=0.059; p=0.13, respectively). The marginal distributions show the density of observed values for each cell type (color-code as indicated). (c) Similarly, we found no significant correlation between the effect sizes for image selectivity and probe selectivity for concept cells, maintenance cells, or all recorded cells (r=-0.03; p=0.779, r=-0.0259; p=0.794,r-0.042; p=0.28, respectively). Lastly, we found no significant correlation between the effect sizes for probe and maintenance selectivity for maintenance cells, probe cells, and all recorded cells (r=-0.0259 p=0.794, r=0.078; P=0.5,r-0.043 p=0.27, respectively). Together, this shows that the three cell groups we quantified are largely distinct and non-overlapping. (a) Boxplot represents quartiles (25%, 75%), line is median, whiskers show range up to 1.5 times the interquartile range, and dots above whiskers show outliers. (d) We determined whether the effects observed in single unit analysis differed as a function of whether the neurons were recorded in tissue that was later determined to be epileptic or not (see Supplementary table 1). We labeled a neuron as located in a putative “epileptic” part of the brain if it was located in the same area as an electrophysiologically identified focus (of which a patient might have several; all were labeled epileptic for patients with generalized epilepsy). There was no significant difference in PSI (permuted t-test t[76]=0.66 P=0.38) for concept cells. We also found no difference in the proportion of load selective neurons (epileptic tissue 25% vs. non-epileptic: 34%, chi2[1]=0.021; P=0.88), and no difference in the proportion of cell showing selectivity for RT during maintenance (epileptic tissue 7% vs. non-epileptic: 20%, chi2[1]=1.45; P=0.22).

Supplementary Figure 5 Details of the dimensionality reduction technique (dPCA) used for state-space analysis

The first five demixed principal components (dPCs) in the different phases of the task during load 1. Colors denote the five different images. Note how dPCs 1,2,3 and 5 separate the different images. In contrast, dPC 4 was only modulated as a function of time but not image identity. Consequently, we did not include dPC 4 in Fig. 7.

Supplementary Figure 6 Controls and supplementary results for state-space analysis and dimensionality reduction.

(a,b) Same analysis as shown in Fig. 8f, but using the first 8 (a) or 12 (b) dPCs for which the highest percentage was attributed to image identity. Similar to Fig. 8f, where we used the first 4 dPCS, the distance to the attractor (DA) was significantly smaller for correct compared to incorrect trials only when concept cells were part of the. (c) The distance to the attractor (DA) for correct vs. incorrect trials separately for each load. The DA remains significantly smaller for correct compared to incorrect trials for all loads. (d) Percent variance explained by the first 15 dPCs. Each bar also contains information about the proportion of variance of each dPC attributable to image identity (green) and other factors (gray). Components 1,2,3,5,6, 8,9,10,11,12,13,15 have the highest percent of variance attributable to picture identity. (e) The observed percent of explained variance attributed to image identity by the first 15 dPCs (red) was significantly larger than that for data where we randomly scrambled image identity before performing the dPCA (P = 0.002). This shows that the dPCA did not overfit. (f) Regularization of the dimensionality reduction technique (dPCA) used for state-space analysis. Shown is the cross-validation error as a function of the regularization parameter lambda. The black dot denotes the lambda value we used for all analysis.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 and Supplementary Table 1 (PDF 1272 kb)

Supplementary Methods Checklist (PDF 437 kb)

Dynamic version of attractor dynamics in state space during working memory encoding and maintenance.

Illustration of the mean trajectories in neuronal state space formed by the three demixed principal components (dPCs) associated with picture identity during encoding (thin line) and maintenance (thick line). The transition from thin to thick lines indicates the onset of the maintenance period. Points of time are indicated in the upper left corner. Colors mark different images (only 4 of the total 5 are shown for clarity). Compare to Fig. 8a. (MP4 34840 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kamiński, J., Sullivan, S., Chung, J. et al. Persistently active neurons in human medial frontal and medial temporal lobe support working memory. Nat Neurosci 20, 590–601 (2017). https://doi.org/10.1038/nn.4509

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4509

This article is cited by

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