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Computational principles of memory

Abstract

The ability to store and later use information is essential for a variety of adaptive behaviors, including integration, learning, generalization, prediction and inference. In this Review, we survey theoretical principles that can allow the brain to construct persistent states for memory. We identify requirements that a memory system must satisfy and analyze existing models and hypothesized biological substrates in light of these requirements. We also highlight open questions, theoretical puzzles and problems shared with computer science and information theory.

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Figure 1: Stable states from positive feedback.
Figure 2: Circuit mechanisms for persistent states.
Figure 3: Long-term maintenance of synapse size.

Katie Vicari/Nature Publishing Group

Figure 4: Robustness of persistent activity architectures.
Figure 5: The tradeoff between capacity and robustness.
Figure 6: Complexity cost of storing a continuous variable in a set of well-separated discrete attractors.

References

  1. 1

    Pavlov, I.P. Conditioned Reflexes (Oxford University Press, London, 1927).

  2. 2

    Carew, T.J., Walters, E.T. & Kandel, E.R. Classical conditioning in a simple withdrawal reflex in Aplysia californica. J. Neurosci. 1, 1426–1437 (1981).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  3. 3

    Rescorla, R.A. & Wagner, A.R. A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. in Classical Conditioning: Current Research and Theory (Appleton-Century-Crofts, 1972).

  4. 4

    Mackintosh, N.J. The Psychology of Animal Learning (Academic Press, Oxford, 1974).

  5. 5

    Vidyasagar, M. A Theory of Learning and Generalization (Springer-Verlag, New York, 2002).

  6. 6

    Schultz, W., Dayan, P. & Montague, P.R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7

    Bialek, W., Nemenman, I. & Tishby, N. Predictability, complexity and learning. Neural Comput. 13, 2409–2463 (2001).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  8. 8

    Robinson, D.A. Integrating with neurons. Annu. Rev. Neurosci. 12, 33–45 (1989).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  9. 9

    Seung, H.S. How the brain keeps the eyes still. Proc. Natl. Acad. Sci. USA 93, 13339–13344 (1996).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10

    Zhang, K. Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory. J. Neurosci. 16, 2112–2126 (1996).

    CAS  Article  Google Scholar 

  11. 11

    Kamil, A.C. & Roitblat, H.L. The ecology of foraging behavior: implications for animal learning and memory. Annu. Rev. Psychol. 36, 141–169 (1985).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  12. 12

    Gold, J.I. & Shadlen, M.N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).

    CAS  Article  Google Scholar 

  13. 13

    James, W. The Principles of Psychology Vol. 1 (Holt, New York, 1890).

  14. 14

    Scoville, W.B. & Milner, B. Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20, 11–21 (1957).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15

    Tetzlaff, C., Kolodziejski, C., Markelic, I. & Wörgötter, F. Time scales of memory, learning, and plasticity. Biol. Cybern. 106, 715–726 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  16. 16

    Bailey, C.H., Kandel, E.R. & Harris, K.M. Structural components of synaptic plasticity and memory consolidation. Cold Spring Harb. Perspect. Biol. 7, a021758 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. 17

    Koch, C., Rapp, M. & Segev, I. A brief history of time (constants). Cereb. Cortex 6, 93–101 (1996).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18

    Destexhe, A., Mainen, Z.F. & Sejnowski, T.J. Kinetic models of synaptic transmission. Methods Neuronal Model. 2, 1–25 (1998).

    Google Scholar 

  19. 19

    Zucker, R.S. & Regehr, W.G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002).

    CAS  Article  Google Scholar 

  20. 20

    Cambridge, S.B. et al. Systems-wide proteomic analysis in mammalian cells reveals conserved, functional protein turnover. J. Proteome Res. 10, 5275–5284 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  21. 21

    Koch, C. Biophysics of Computation: Information Processing in Single Neurons (Oxford University Press, New York, New York, 1999).

  22. 22

    Softky, W.R. & Koch, C. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334–350 (1993).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23

    Shadlen, M.N. & Newsome, W.T. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 18, 3870–3896 (1998).

    CAS  Article  Google Scholar 

  24. 24

    Hanus, C. & Schuman, E.M. Proteostasis in complex dendrites. Nat. Rev. Neurosci. 14, 638–648 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  25. 25

    Shannon, C. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948).

    Article  Google Scholar 

  26. 26

    MacKay, D. Information Theory, Inference and Learning Algorithms (Cambridge University Press, Cambridge, 2004).

  27. 27

    Sreenivasan, S. & Fiete, I. Grid cells generate an analog error-correcting code for singularly precise neural computation. Nat. Neurosci. 14, 1330–1337 (2011).

    CAS  Article  Google Scholar 

  28. 28

    Pereira, J. & Wang, X.-J. A tradeoff between accuracy and flexibility in a working memory circuit endowed with slow feedback mechanisms. Cereb. Cortex 25, 3586–3601 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  29. 29

    McCloskey, M. & Cohen, N.J. Catastrophic interference in connectionist networks: the sequential learning problem. Psychol. Learn. Motiv. 24, 109–165 (1989).

    Article  Google Scholar 

  30. 30

    Fusi, S., Drew, P.J. & Abbott, L.F. Cascade models of synaptically stored memories. Neuron 45, 599–611 (2005).

    CAS  Article  Google Scholar 

  31. 31

    Lahiri, S. & Ganguli, S. A memory frontier for complex synapses. Adv. Neural Inf. Process. Syst. 26, 1034–1042 (2013).

    Google Scholar 

  32. 32

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33

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

    CAS  Article  Google Scholar 

  34. 34

    Ranganath, C. & D'Esposito, M. Directing the mind's eye: prefrontal, inferior and medial temporal mechanisms for visual working memory. Curr. Opin. Neurobiol. 15, 175–182 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  35. 35

    Bliss, T.V. & Lomo, T. Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. (Lond.) 232, 331–356 (1973).

    CAS  Article  Google Scholar 

  36. 36

    Cajal, S.R.Y. The Croonian Lecture: La fine structure des centres nerveux. Proc. R. Soc. Lond. 55, 444–468 (1894).

    Article  Google Scholar 

  37. 37

    Konorski, J. Conditioned Reflexes and Neuron Organization (Cambridge University Press, Cambridge, 1948).

  38. 38

    Hebb, D.O. The Organization of Behavior: a Neuropsychological Theory (New York: Wiley & Sons, 1949).

  39. 39

    Takeuchi, T., Duszkiewicz, A.J. & Morris, R.G. The synaptic plasticity and memory hypothesis: encoding, storage and persistence. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20130288 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. 40

    Kandel, E.R., Dudai, Y. & Mayford, M.R. The molecular and systems biology of memory. Cell 157, 163–186 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  41. 41

    Lorente de Nó, R. Vestibulo-ocular reflex arc. Arch. Neurol. Psychiatry 30, 245–291 (1933).

    Article  Google Scholar 

  42. 42

    Eccles, W.H. & Jordan, F.W. Improvements in ionic relays. British patent number: GB 148582 (1918).

  43. 43

    von Neumann, J. First Draft of a Report on the EDVAC (University of Pennsylvania, Philadelphia, 1945).

  44. 44

    Jacob, B., Ng, S. & Wang, D. Memory Systems: Cache, Dram, Disk (Morgan Kaufmann, 2010).

  45. 45

    Lisman, J.E. A mechanism for memory storage insensitive to molecular turnover: a bistable autophosphorylating kinase. Proc. Natl. Acad. Sci. USA 82, 3055–3057 (1985).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  46. 46

    Bierman, A. Studies on the effects of structure on the behavior of enzyme systems. Bull. Math. Biophys. 16, 203–257 (1954).

    Article  Google Scholar 

  47. 47

    Wilson, H.R. & Cowan, J.D. Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12, 1–24 (1972).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48

    Hopfield, J.J. & Tank, D.W. Computing with neural circuits: a model. Science 233, 625–633 (1986).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  49. 49

    Little, W. The existence of persistent states in the brain. Math. Biosci. 19, 101–120 (1974).

    Article  Google Scholar 

  50. 50

    Ben-Yishai, R., Bar-Or, R.L. & Sompolinsky, H. Theory of orientation tuning in visual cortex. Proc. Natl. Acad. Sci. USA 92, 3844–3848 (1995).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51

    Burak, Y. & Fiete, I.R. Accurate path integration in continuous attractor network models of grid cells. PLoS Comput. Biol. 5, e1000291 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. 52

    Renart, A., Song, P. & Wang, X.-J. Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks. Neuron 38, 473–485 (2003).

    CAS  Article  Google Scholar 

  53. 53

    Widloski, J. & Fiete, I.R. A model of grid cell development through spatial exploration and spike time-dependent plasticity. Neuron 83, 481–495 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  54. 54

    Goldman, M.S., Levine, J.H., Major, G., Tank, D.W. & Seung, H.S. Robust persistent neural activity in a model integrator with multiple hysteretic dendrites per neuron. Cereb. Cortex 13, 1185–1195 (2003).

    PubMed  Article  PubMed Central  Google Scholar 

  55. 55

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

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  56. 56

    Aksay, E., Gamkrelidze, G., Seung, H.S., Baker, R. & Tank, D.W. In vivo intracellular recording and perturbation of persistent activity in a neural integrator. Nat. Neurosci. 4, 184–193 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57

    Taube, J.S. & Bassett, J.P. Persistent neural activity in head direction cells. Cereb. Cortex 13, 1162–1172 (2003).

    PubMed  Article  PubMed Central  Google Scholar 

  58. 58

    Yoon, K. et al. Specific evidence of low-dimensional continuous attractor dynamics in grid cells. Nat. Neurosci. 16, 1077–1084 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59

    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).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  60. 60

    Seelig, J.D. & Jayaraman, V. Neural dynamics for landmark orientation and angular path integration. Nature 521, 186–191 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61

    Ferrell, J.E. Jr. Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability. Curr. Opin. Cell Biol. 14, 140–148 (2002).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  62. 62

    Cannon, S.C., Robinson, D.A. & Shamma, S. A proposed neural network for the integrator of the oculomotor system. Biol. Cybern. 49, 127–136 (1983).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  63. 63

    Song, P. & Wang, X.J. Angular path integration by moving “hill of activity”: a spiking neuron model without recurrent excitation of the head-direction system. J. Neurosci. 25, 1002–1014 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64

    Monod, J. & Jacob, F. Teleonomic mechanisms in cellular metabolism, growth, and differentiation. Cold Spring Harb. Symp. Quant. Biol. 26, 389–401 (1961).

    CAS  Article  Google Scholar 

  65. 65

    Crick, F. Memory and molecular turnover. Nature 312, 101 (1984).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66

    Lisman, J., Yasuda, R. & Raghavachari, S. Mechanisms of CaMKII action in long-term potentiation. Nat. Rev. Neurosci. 13, 169–182 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67

    Sanhueza, M. et al. Role of the CaMKII/NMDA receptor complex in the maintenance of synaptic strength. J. Neurosci. 31, 9170–9178 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68

    Lisman, J. & Raghavachari, S. Biochemical principles underlying the stable maintenance of LTP by the CaMKII/NMDAR complex. Brain Res. 1621, 51–61 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  69. 69

    Zhou, Y. et al. Interactions between the NR2B receptor and CaMKII modulate synaptic plasticity and spatial learning. J. Neurosci. 27, 13843–13853 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. 70

    Aslam, N., Kubota, Y., Wells, D. & Shouval, H.Z. Translational switch for long-term maintenance of synaptic plasticity. Mol. Syst. Biol. 5, 284 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  71. 71

    Chen, H.X., Otmakhov, N., Strack, S., Colbran, R.J. & Lisman, J.E. Is persistent activity of calcium/calmodulin-dependent kinase required for the maintenance of LTP? J. Neurophysiol. 85, 1368–1376 (2001).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  72. 72

    Hernandez, A.I. et al. Protein kinase M zeta synthesis from a brain mRNA encoding an independent protein kinase C zeta catalytic domain. Implications for the molecular mechanism of memory. J. Biol. Chem. 278, 40305–40316 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  73. 73

    Sacktor, T.C. How does PKMζ maintain long-term memory? Nat. Rev. Neurosci. 12, 9–15 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  74. 74

    Kwapis, J.L. & Helmstetter, F.J. Does PKM(zeta) maintain memory? Brain Res. Bull. 105, 36–45 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  75. 75

    Pastalkova, E. et al. Storage of spatial information by the maintenance mechanism of LTP. Science 313, 1141–1144 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  76. 76

    Ogasawara, H. & Kawato, M. The protein kinase Mζ network as a bistable switch to store neuronal memory. BMC Syst. Biol. 4, 181 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  77. 77

    Jalil, S.J., Sacktor, T.C. & Shouval, H.Z. Atypical PKCs in memory maintenance: the roles of feedback and redundancy. Learn. Mem. 22, 344–353 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. 78

    Volk, L.J., Bachman, J.L., Johnson, R., Yu, Y. & Huganir, R.L. PKM-ζ is not required for hippocampal synaptic plasticity, learning and memory. Nature 493, 420–423 (2013).

    CAS  Article  Google Scholar 

  79. 79

    Lee, A.M. et al. Prkcz null mice show normal learning and memory. Nature 493, 416–419 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. 80

    Hsieh, K., Tsokas, P. & Sacktor, T. Compensation for PKMζ function in spatial long-term memory in mutant mice. Soc. Neurosci. Abstr. 573.01 (2015).

  81. 81

    Tsokas, P. & Sacktor, T. Compensation for PKMζ function in late-LTP in mutant mice. Soc. Neurosci. Abstr. 573.15 (2015).

  82. 82

    Bourne, J.N. & Harris, K.M. Coordination of size and number of excitatory and inhibitory synapses results in a balanced structural plasticity along mature hippocampal CA1 dendrites during LTP. Hippocampus 21, 354–373 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. 83

    Meyer, D., Bonhoeffer, T. & Scheuss, V. Balance and stability of synaptic structures during synaptic plasticity. Neuron 82, 430–443 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  84. 84

    Bartol, T.M. et al. Nanoconnectomic upper bound on the variability of synaptic plasticity. eLife 4, e10778 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  85. 85

    Lim, S. & Goldman, M.S. Balanced cortical microcircuitry for maintaining information in working memory. Nat. Neurosci. 16, 1306–1314 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86

    Boerlin, M., Machens, C.K. & Denève, S. Predictive coding of dynamical variables in balanced spiking networks. PLoS Comput. Biol. 9, e1003258 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  87. 87

    Wiener, N. Cybernetics or Control and Communication in the Animal and the Machine (MIT Press, 1961).

  88. 88

    Sontag, E.D. Mathematical Control Theory: Deterministic Finite Dimensional Systems (Springer Science & Business Media, Berlin, 2013).

  89. 89

    Liddell, E.G.T. & Sherrington, C. Reflexes in response to stretch (myotatic reflexes). Proc. R. Soc. Lond. Biol. 96, 212–242 (1924).

    Article  Google Scholar 

  90. 90

    Alon, U., Surette, M.G., Barkai, N. & Leibler, S. Robustness in bacterial chemotaxis. Nature 397, 168–171 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  91. 91

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

    CAS  Article  Google Scholar 

  92. 92

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

    Article  Google Scholar 

  93. 93

    White, O.L., Lee, D.D. & Sompolinsky, H. Short-term memory in orthogonal neural networks. Phys. Rev. Lett. 92, 148102 (2004).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  94. 94

    Eckert, J.P. Jr. A survey of digital computer memory systems. Proc. IRE 41, 1393–1406 (1953).

    Article  Google Scholar 

  95. 95

    Abeles, M. et al. Local Cortical Circuits (Springer, Berlin, 1982).

  96. 96

    Long, M.A., Jin, D.Z. & Fee, M.S. Support for a synaptic chain model of neuronal sequence generation. Nature 468, 394–399 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97

    Ganguli, S., Huh, D. & Sompolinsky, H. Memory traces in dynamical systems. Proc. Natl. Acad. Sci. USA 105, 18970–18975 (2008).

    CAS  Article  Google Scholar 

  98. 98

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. 99

    van Vreeswijk, C. & Sompolinsky, H. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274, 1724–1726 (1996).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  100. 100

    Brunel, N. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J. Comput. Neurosci. 8, 183–208 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  101. 101

    Murphy, B.K. & Miller, K.D. Balanced amplification: a new mechanism of selective amplification of neural activity patterns. Neuron 61, 635–648 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  102. 102

    Sompolinsky, H., Crisanti, A. & Sommers, H.J. Chaos in random neural networks. Phys. Rev. Lett. 61, 259–262 (1988).

    CAS  Article  Google Scholar 

  103. 103

    Sprott, J.C. Chaotic dynamics on large networks. Chaos 18, 023135 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  104. 104

    Laurent, G. Olfactory network dynamics and the coding of multidimensional signals. Nat. Rev. Neurosci. 3, 884–895 (2002).

    CAS  Article  Google Scholar 

  105. 105

    Hahnloser, R.H.R., Kozhevnikov, A.A. & Fee, M.S. An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature 419, 65–70 (2002).

    CAS  Article  Google Scholar 

  106. 106

    Shen, L. Neural integration by short term potentiation. Biol. Cybern. 61, 319–325 (1989).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  107. 107

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

    CAS  Article  Google Scholar 

  108. 108

    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  Article  Google Scholar 

  109. 109

    Aksay, E. et al. Functional dissection of circuitry in a neural integrator. Nat. Neurosci. 10, 494–504 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  110. 110

    Kalmbach, B.E., Chitwood, R.A., Dembrow, N.C. & Johnston, D. Dendritic generation of mGluR-mediated slow afterdepolarization in layer 5 neurons of prefrontal cortex. J. Neurosci. 33, 13518–13532 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. 111

    Toyama, B.H. et al. Identification of long-lived proteins reveals exceptional stability of essential cellular structures. Cell 154, 971–982 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  112. 112

    Si, K., Lindquist, S. & Kandel, E.R. A neuronal isoform of the aplysia CPEB has prion-like properties. Cell 115, 879–891 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  113. 113

    Stephan, J.S. et al. The CPEB3 protein is a functional prion that interacts with the actin cytoskeleton. Cell Rep. 11, 1772–1785 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  114. 114

    Fioriti, L. et al. The persistence of hippocampal-based memory requires protein synthesis mediated by the prion-like protein CPEB3. Neuron 86, 1433–1448 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  115. 115

    McEvoy, M. et al. Cytoplasmic polyadenylation element binding protein 1–mediated mRNA translation in Purkinje neurons is required for cerebellar long-term depression and motor coordination. J. Neurosci. 27, 6400–6411 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  116. 116

    Miller, P. & Wang, X.-J. Stability of discrete memory states to stochastic fluctuations in neuronal systems. Chaos 16, 026109 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  117. 117

    Fiete, I., Schwab, D.J. & Tran, N.M. A binary Hopfield network with 1/log(n) information rate and applications to grid cell decoding. Preprint at http://arxiv.org/abs/1407.6029 (2014).

  118. 118

    Strogatz, S.H. Nonlinear Dynamics and Chaos: with Applications to Physics, Biology, Chemistry, and Engineering (Westview Press, 2014).

  119. 119

    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  Article  Google Scholar 

  120. 120

    Burak, Y. & Fiete, I.R. Fundamental limits on persistent activity in networks of noisy neurons. Proc. Natl. Acad. Sci. USA 109, 17645–17650 (2012).

    CAS  Article  Google Scholar 

  121. 121

    Lim, S. & Goldman, M.S. Noise tolerance of attractor and feedforward memory models. Neural Comput. 24, 332–390 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  122. 122

    Kilpatrick, Z.P., Ermentrout, B. & Doiron, B. Optimizing working memory with heterogeneity of recurrent cortical excitation. J. Neurosci. 33, 18999–19011 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  123. 123

    Monteforte, M. & Wolf, F. Dynamic flux tubes form reservoirs of stability in neuronal circuits. Phys. Rev. X 2, 041007 (2012).

    CAS  Google Scholar 

  124. 124

    Landauer, T.K. How much do people remember? Some estimates of the quantity of learned information in long-term memory. Cogn. Sci. 10, 477–493 (1986).

    Article  Google Scholar 

  125. 125

    Shepard, R.N. Recognition memory for words, sentences, and pictures. J. Verbal Learn. Verbal Behav. 6, 156–163 (1967).

    Article  Google Scholar 

  126. 126

    Brady, T.F., Konkle, T., Alvarez, G.A. & Oliva, A. Visual long-term memory has a massive storage capacity for object details. Proc. Natl. Acad. Sci. USA 105, 14325–14329 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  127. 127

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  128. 128

    Abu-Mostafa, Y.S. & St Jacques, J. Information capacity of the Hopfield model. IEEE Trans. Inf. Theory 31, 461–464 (1985).

    Article  Google Scholar 

  129. 129

    Gardner, E. & Derrida, B. Optimal storage properties of neural network models. J. Phys. A 21, 271 (1988).

    Article  Google Scholar 

  130. 130

    Amit, D.J., Gutfreund, H. & Sompolinsky, H. Storing infinite numbers of patterns in a spin-glass model of neural networks. Phys. Rev. Lett. 55, 1530–1533 (1985).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  131. 131

    Parisi, G. A memory which forgets. J. Phys. A 19, L617 (1986).

    Article  Google Scholar 

  132. 132

    Benna, M.K. & Fusi, S. Computational principles of biological memory. Preprint at <http://arxiv.org/abs/1507.07580> (2015).

  133. 133

    Hillar, C., Tran, N. & Koepsell, K. Robust exponential binary pattern storage in Little-Hopfield networks. Preprint at <http://arxiv.org/abs/1206.2081> (2012).

  134. 134

    Valiant, L.G. The hippocampus as a stable memory allocator for cortex. Neural Comput. 24, 2873–2899 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  135. 135

    Miller, G.A. The magical number seven plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956).

    CAS  Article  Google Scholar 

  136. 136

    Conway, A.R., Kane, M.J. & Engle, R.W. Working memory capacity and its relation to general intelligence. Trends Cogn. Sci. 7, 547–552 (2003).

    PubMed  Article  PubMed Central  Google Scholar 

  137. 137

    Barrouillet, P., De Paepe, A. & Langerock, N. Time causes forgetting from working memory. Psychon. Bull. Rev. 19, 87–92 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  138. 138

    Lewandowsky, S., Oberauer, K. & Brown, G.D.A. No temporal decay in verbal short-term memory. Trends Cogn. Sci. 13, 120–126 (2009).

    PubMed  Article  PubMed Central  Google Scholar 

  139. 139

    Hughes, J.R. Absence seizures: a review of recent reports with new concepts. Epilepsy Behav. 15, 404–412 (2009).

    PubMed  Article  PubMed Central  Google Scholar 

  140. 140

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  141. 141

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  142. 142

    Emiliani, V., Cohen, A.E., Deisseroth, K. & Häusser, M. All-optical interrogation of neural circuits. J. Neurosci. 35, 13917–13926 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  143. 143

    Sterzer, P., Kleinschmidt, A. & Rees, G. The neural bases of multistable perception. Trends Cogn. Sci. 13, 310–318 (2009).

    PubMed  Article  PubMed Central  Google Scholar 

  144. 144

    Colgin, L.L. et al. Attractor-map versus autoassociation based attractor dynamics in the hippocampal network. J. Neurophysiol. 104, 35–50 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  145. 145

    Loewenstein, Y., Yanover, U. & Rumpel, S. Predicting the dynamics of network connectivity in the neocortex. J. Neurosci. 35, 12535–12544 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  146. 146

    Holtmaat, A. & Svoboda, K. Experience-dependent structural synaptic plasticity in the mammalian brain. Nat. Rev. Neurosci. 10, 647–658 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  147. 147

    Dudai, Y. The restless engram: consolidations never end. Annu. Rev. Neurosci. 35, 227–247 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  148. 148

    Nabavi, S. et al. Engineering a memory with LTD and LTP. Nature 511, 348–352 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. 149

    Lisman, J. The challenge of understanding the brain: where we stand in 2015. Neuron 86, 864–882 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  150. 150

    Jonides, J. et al. The mind and brain of short-term memory. Annu. Rev. Psychol. 59, 193–224 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

We thank K. Harris and K. Raab-Graham for discussions on subcellular substrates of memory. We are grateful to S. Fusi, J. Lisman, J. Murray, X.-J. Wang, and to A. Das, I. Kanitscheider, B. Kriener and J. Widloski of the Fiete group for comments on the manuscript. This work was funded in part by grants from the Simons Collaboration on the Global Brain, the ONR-Young Investigator Program (N000141310529), and the McKnight Foundation to I.F.

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Correspondence to Ila Fiete.

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Chaudhuri, R., Fiete, I. Computational principles of memory. Nat Neurosci 19, 394–403 (2016). https://doi.org/10.1038/nn.4237

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