1932

Abstract

Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long been considered an important component of learning and memory. Computational and engineering work corroborate the power of learning through the directed adjustment of connection weights. Here we review the fundamental elements of four broadly categorized forms of synaptic plasticity and discuss their functional capabilities and limitations. Although standard, correlation-based, Hebbian synaptic plasticity has been the primary focus of neuroscientists for decades, it is inherently limited. Three-factor plasticity rules supplement Hebbian forms with neuromodulation and eligibility traces, while true supervised types go even further by adding objectives and instructive signals. Finally, a recently discovered hippocampal form of synaptic plasticity combines the above elements, while leaving behind the primary Hebbian requirement. We suggest that the effort to determine the neural basis of adaptive behavior could benefit from renewed experimental and theoretical investigation of more powerful directed types of synaptic plasticity.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-neuro-090919-022842
2020-07-08
2024-03-29
Loading full text...

Full text loading...

/deliver/fulltext/neuro/43/1/annurev-neuro-090919-022842.html?itemId=/content/journals/10.1146/annurev-neuro-090919-022842&mimeType=html&fmt=ahah

Literature Cited

  1. Abbott LF, Blum KI. 1996. Functional significance of long-term potentiation for sequence learning and prediction. Cereb. Cortex 6:406–16
    [Google Scholar]
  2. Andersen JA. 1972. A simple neural network generating an interactive memory. Math. Biosci. 14:197–220
    [Google Scholar]
  3. Beaulieu-Laroche L, Harnett MT. 2018. Dendritic spines prevent synaptic voltage clamp. Neuron 97:75–82
    [Google Scholar]
  4. Bell CB, Han V, Sawtell NB 2008. Cerebellum-like structures and their implications for cerebellar function. Annu. Rev. Neurosci. 31:1–24
    [Google Scholar]
  5. Bell CB, Han V, Sugawara Y, Grant K 1997. Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature 387:278–81
    [Google Scholar]
  6. Bengio Y, Lee D, Bornschein J, Mesnard T, Lin Z 2016. Towards biologically plausible deep learning. arXiv:1502.04156 [cs.LG]
  7. Berke JD. 2018. What does dopamine mean?. Nat. Neurosci. 21:787–93
    [Google Scholar]
  8. Bi GQ, Poo MM. 1998. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18:10464–72
    [Google Scholar]
  9. Bittner KC, Grienberger C, Vaidya SP, Milstein AD, Sacklin JJ et al. 2015. Conjunctive input processing drives feature selectivity in hippocampal CA1 neurons. Nat. Neurosci. 18:1133–42
    [Google Scholar]
  10. Bittner KC, Milstein AD, Grienberger C, Romani S, Magee JC 2017. Behavioral timescale synaptic plasticity underlies CA1 place fields. Science 357:1033–36
    [Google Scholar]
  11. Bliss TV, Lomo T. 1973. Long-lasting potentiation of synaptic transmission in the dentate area of the anesthetized rabbit following stimulation of the perforant path. J. Physiol. 232:331–56
    [Google Scholar]
  12. Bloodgood BL, Sabatini BL. 2005. Neuronal activity regulates diffusion across the neck of dendritic spines. Science 310:866–69
    [Google Scholar]
  13. Bloodgood BL, Giessel AJ, Sabatini BL 2009. Biphasic synaptic Ca influx arising from compartmentalized electrical signals in dendritic spines. PLOS Biol 7:9e1000190
    [Google Scholar]
  14. Bloss EB, Cembrowski MS, Karsh B, Colonell J, Fetter RD, Spruston N 2018. Single excitatory axons form clustered synapses onto CA1 pyramidal cell dendrites. Nat. Neurosci. 21:353–63
    [Google Scholar]
  15. Blum K, Abbott L. 1996. A model of spatial map formation in the hippocampus of the rat. Neural Comput 8:85–93
    [Google Scholar]
  16. Boccara C, Nardin M, Stella F, O'Neill J, Csicsvari J 2019. The entorhinal cognitive map is attracted to goals. Science 363:1443–47
    [Google Scholar]
  17. Brito CSN, Gerstner W. 2016. Nonlinear Hebbian learning as a unifying principle in receptive field formation. PLOS Comput. Biol. 12:9e1005070
    [Google Scholar]
  18. Brzosko Z, Zannone S, Schultz W, Clopath C, Paulsen O 2017. Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation. eLife 6:e27756
    [Google Scholar]
  19. Butler WN, Harcastle K, Giocomo LM 2019. Remembered reward locations restructure entorhinal spatial maps. Science 363:1447–52
    [Google Scholar]
  20. Butts DA, Kanold PO. 2010. The applicability of spike time dependent plasticity to development. Front. Synaptic Neurosci. 2:30
    [Google Scholar]
  21. Cadieu CF, Hong H, Yamins DLK, Pinto N, Ardila D et al. 2014. Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLOS Comput. Biol. 10:12e1003963
    [Google Scholar]
  22. Cauller LJ, Clancy B, Connors BW 1998. Backward cortical projections to primary somatosensory cortex in rats extend long horizontal axons in layer I. J. Comp. Neurol. 390:297–310
    [Google Scholar]
  23. Caporale N, Dan Y. 2008. Spike timing–dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31:25–46
    [Google Scholar]
  24. Carrillo-Reid L, Yang W, Bando Y, Peterka DS, Yuste R 2016. Imprinting and recalling cortical ensembles. Science 353:691–94
    [Google Scholar]
  25. Cassenaer S, Laurent G. 2012. Conditional modulation of spike-timing-dependent plasticity for olfactory learning. Nature 482:47–53
    [Google Scholar]
  26. Crick F. 1989. The recent excitement about neural networks. Nature 337:129–32
    [Google Scholar]
  27. Davoudi H, Foster D. 2019. Acute silencing of hippocampal CA3 reveals a dominant role in place field responses. Nat. Neurosci. 22:337–42
    [Google Scholar]
  28. Diamantaki M, Coletta S, Nasr K, Zeraati R, Laturnus S et al. 2018. Manipulating hippocampal place cell activity by single-cell stimulation in freely moving mice. Cell Rep 23:32–38
    [Google Scholar]
  29. Druckmann S, Feng L, Lee B, Yook C, Zhao T et al. 2014. Structured synaptic connectivity between hippocampal regions. Neuron 81:629–40
    [Google Scholar]
  30. Dupret D, O'Neill J, Pleydell-Bouverie B, Csicsvari J 2010. The reorganization and reactivation of hippocampal maps predict spatial memory performance. Nat. Neurosci. 13:995–1002
    [Google Scholar]
  31. El-Boustani S, Ip JPK, Brenton-Provencher V, Knott GW, Okuno H et al. 2018. Locally coordinated synaptic plasticity of visual cortex neurons in vivo. Science 360:1349–54
    [Google Scholar]
  32. Enikolopov A, Abbot LF, Sawtell NB 2018. Internally generated predictions enhance neural and behavioral detection of sensory stimuli in an electric fish. Neuron 99:135–46
    [Google Scholar]
  33. Epsztein J, Brecht M, Lee AK 2011. Intracellular determinants of hippocampal CA1 place and silent cell activity in a novel environment. Neuron 70:109–20
    [Google Scholar]
  34. Erwin E, Miller KD. 1998. Correlation-based development of ocularly matched orientation and ocular dominance maps: determination of required input activities. J. Neurosci. 18:9870–95
    [Google Scholar]
  35. Feldman DE. 2012. The spike-timing dependence of plasticity. Neuron 75:556–71
    [Google Scholar]
  36. Fischler WM, Joshi NR, Devi-Chou V, Kitch L, Schnitzer MJ, Abbott LF, Axel R 2019. Olfactory landmarks and path integration converge to form a cognitive spatial map. bioRxiv 752360. https://doi.org/10.1101/752360
    [Crossref]
  37. Fiser A, Mahringer D, Oyibo HK, Petersen AV, Leinweber M, Keller GB 2016. Experience-dependent spatial expectations in mouse visual cortex. Nat. Neurosci. 19:1658–64
    [Google Scholar]
  38. Fisher SD, Robertson PB, Blak MJ, Redgrave P, Sagar MA et al. 2017. Reinforcement determines the timing dependence of corticostriatal synaptic plasticity in vivo. Nat. Comm. 8:334
    [Google Scholar]
  39. Fremaux N, Gerstner W. 2016. Neuromodulated spike-timing-dependent plasticity and theory of three-factor learning rules. Front. Neural Circuits 9:85
    [Google Scholar]
  40. Fusi S. 2002. Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates. Biol. Cybern. 87:459–70
    [Google Scholar]
  41. Fusi S, Abbott LF. 2007. Limits on the memory storage capacity of bounded synapses. Nat. Neurosci. 10:485–93
    [Google Scholar]
  42. Gambino F, Grimaldo F, Lopez-Inesta E, Mehmani B, Squazzoni F 2014. Sensory-evoked LTP driven by dendritic plateau potentials in vivo. Nature 515:116–19
    [Google Scholar]
  43. Gerstner W, Lehmann M, Liakoni V, Corneil D, Brea J 2018. Eligibility traces and plasticity on behavioral time scales: experimental support of neo Hebbian three-factor learning rules. Front. Neural Circuits 12:53
    [Google Scholar]
  44. Gluck MA, Granger R. 1993. Computational models of the neural bases of learning and memory. Annu. Rev. Neurosci. 16:667–706
    [Google Scholar]
  45. Gluck MA, Myers C. 1993. Hippocampal mediations of stimulus representation: a computational theory. Hippocampus 3:491–516
    [Google Scholar]
  46. Golding NL, Staff NP, Spruston N 2002. Dendritic spikes as a mechanism for cooperative long-term potentiation. Nature 418:326–31
    [Google Scholar]
  47. Grienberger C, Chen X, Konnerth A 2014. NMDA receptor–dependent multidendrite Ca2+ spikes required for hippocampal burst firing in vivo. Neuron 81:1274–81
    [Google Scholar]
  48. Grienberger C, Milstein AD, Bittner KC, Romani S, Magee JC 2017. Inhibitory suppression of heterogeneously tuned excitation enhances spatial coding in CA1 place cells. Nat. Neurosci. 20:417–26
    [Google Scholar]
  49. Grossberg S. 1976. On the development of feature detectors in the visual cortex with applications to learning and reaction-diffusion systems. Biol. Cybern. 21:145–59
    [Google Scholar]
  50. Grunditz A, Holbro N, Tian L, Zuo Y, Oertner TG 2008. Spine neck plasticity controls postsynaptic calcium signals through electrical compartmentalization. J. Neurosci. 28:13457–66
    [Google Scholar]
  51. Gu Q. 2002. Neuromodulatory transmitter systems in the cortex and their role in cortical plasticity. Neuroscience 111:815–35
    [Google Scholar]
  52. Guerguiev J, Lillicrap TP, Richards BA 2017. Towards deep learning with segregated dendrites. eLife 6:e22901
    [Google Scholar]
  53. Gulledge AT, Carnevale NT, Stuart GJ 2012. Electrical advantages of dendritic spines. PLOS ONE 7:e36007
    [Google Scholar]
  54. Harnett MT, Magee JC, Williams S 2015. Distribution and function of HCN channels in the apical dendritic tuft of neocortical pyramidal neurons. J. Neurosci. 79:516–29
    [Google Scholar]
  55. Harnett MT, Makara JK, Spruston N, Kath WL, Magee JC 2012. Synaptic amplification by dendritic spines enhances input cooperativity. Nature 491:599–602
    [Google Scholar]
  56. Harnett MT, Xu NL, Magee JC, Williams S 2013. Potassium channels control the interaction between active dendritic compartments in layer 5 cortical pyramidal neurons. Neuron 79:516–29
    [Google Scholar]
  57. Harvey CD, Collman F, Dombeck DA, Tank DW 2009. Intracellular dynamics of hippocampal place cells during virtual navigation. Nature 461:941–46
    [Google Scholar]
  58. Harvey CD, Svoboda K. 2007. Locally dynamic synaptic learning rules in pyramidal neuron dendrites. Nature 450:1195–200
    [Google Scholar]
  59. He K, Huertas M, Hong SZ, Tie X, Hell JW et al. 2015. Distinct eligibility traces for LTP and LTD in cortical synapses. Neuron 88:528–38
    [Google Scholar]
  60. Hebb D. 1949. The Organization of Behavior New York: Wiley
  61. Hollup SA, Molden S, Donnett JG, Moser M-B, Moser EI 2001. Accumulation of hippocampal place fields at the goal location in an annular watermaze task. J. Neurosci. 21:1635–44
    [Google Scholar]
  62. Hopfield JJ. 1982. Neural networks and physical systems with emergent collective computational abilities. PNAS 79:2554–58
    [Google Scholar]
  63. Houk JC, Adams JL, Barto AG 1995. A model of how the basal ganglia generate and use neural signals that predict reinforcement. Models of Information Processing in the Basal Ganglia JC Houk, JL Davis, DG Beiser 249–70 Cambridge, MA: MIT Press
    [Google Scholar]
  64. Hubel DH, Wiesel TN. 1962. Receptive fields, binocular interactions and functional architecture in the cat's visual cortex. J. Physiol. 160:106–54
    [Google Scholar]
  65. Hubel DH, Wiesel TN. 1968. Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195:215–43
    [Google Scholar]
  66. Hull CL. 1943. Principles of Behavior: An Introduction to Behavior Theory Oxford, UK: Appleton-Century
  67. Humeau Y, Choquet D. 2019. The next generation of approaches to investigate the link between synaptic plasticity and learning. Nat. Neurosci. 22:1536–43
    [Google Scholar]
  68. Issa EB, Cadieu CF, DiCarlo JJ 2018. Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals. eLife 7:e42870
    [Google Scholar]
  69. Ito M. 2008. Control of mental activities by internal models in the cerebellum. Nature 9:304–13
    [Google Scholar]
  70. Izhikevich EM. 2007. Solving the distal reward problem through linkage of STDP and dopamine signaling. Cereb. Cortex 17:2443–52
    [Google Scholar]
  71. Jayant K, Hirtz JJ, Plante IJ, Tsai DM, De Boer WD, Semonche A et al. 2017. Targeted intracellular voltage recordings from dendritic spines using quantum-dot-coated nanopipettes. Nat. Nanotechnol. 12:335–42
    [Google Scholar]
  72. Johnston D, Wu SM-S. 1995. Foundations of Cellular Neurophysiology Cambridge, MA: MIT Press
  73. Kampa B, Letzkus J, Stuart G 2006. Requirement of dendritic calcium spikes for induction of spike-timing-dependent synaptic plasticity. J. Physiol. 574:1283–90
    [Google Scholar]
  74. Kandel ER, Dudai Y, Mayford MR 2014. The molecular and systems biology of memory. Cell 157:163–86
    [Google Scholar]
  75. Kauffman AM, Geiller T, Losonczy A 2020. A role for the locus coeruleus in hippocampal CA1 place cell reorganization during spatial reward learning. Neuron In press. https://doi.org/10.1016/j.neuron.2019.12.029
    [Crossref] [Google Scholar]
  76. Kawato M. 1990. Internal models for motor control and trajectory planning. Curr. Opin. Neurobiol. 9:718–27
    [Google Scholar]
  77. Kerlin A, Mohar B, Flickinger D, MacLennan B, Davis C et al. 2018. Functional clustering of dendritic activity during decision-making. bioRxiv 440396. https://doi.org/10.1101/440396
    [Crossref]
  78. Kim WB, Cho J. 2017. Encoding of discriminative fear memory by input-specific LTP in the amygdala. Neuron 95:1129–46
    [Google Scholar]
  79. Knudsen EI. 1994. Supervised learning in the brain. J. Neurosci. 14:1985–97
    [Google Scholar]
  80. Koester HJ, Sakmann B. 1998. Calcium dynamics in single spines during coincident pre- and postsynaptic activity depend on relative timing of back-propagating action potentials and subthreshold excitatory postsynaptic potentials. PNAS 95:9596–601
    [Google Scholar]
  81. Kohonen T. 1972. Correlation matrix memories. IEEE Trans. Comput. 21:353–59
    [Google Scholar]
  82. Kohonen T. 1982. Self-organized formation of topologically correct feature maps. Biol. Cybern. 43:59–69
    [Google Scholar]
  83. Körding KP, König P. 2001. Supervised and unsupervised learning with two sites of synaptic integration. J. Comput. Neurosci. 11:207–15
    [Google Scholar]
  84. Kostadinov D, Beau M, Pozo MB, Häusser M 2019. Predictive and reactive reward signals conveyed by climbing fiber inputs to cerebellar Purkinje cells. Nat. Neurosci. 22:950–62
    [Google Scholar]
  85. Labarrera C, Deitcher Y, Dudai A, Weiner B, Amichai AK et al. 2018. Adrenergic modulation regulates the dendritic excitability of layer 5 pyramidal neurons in vivo. Cell Rep 23:1034–44
    [Google Scholar]
  86. Lang EJ, Apps R, Bengtsson F, Cerminara NL, De Zeeuw CI et al. 2016. The roles of the olivocerebellar pathway in motor learning and motor control. A consensus paper. Cerebellum 16:230–52
    [Google Scholar]
  87. Larkum ME, Zhu JJ, Sakmann B 1999. A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 389:338–41
    [Google Scholar]
  88. LeCun Y, Bengio Y, Hinton G 2015. Deep learning. Nature 521:436–44
    [Google Scholar]
  89. Lee SR, Escobedo-Lozoya Y, Szatmari EM, Yasuda R 2009. Activation of CaMKII in single dendritic spines during long-term potentiation. Nature 458:299–304
    [Google Scholar]
  90. Levy WB, Steward O. 1983. Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neuroscience 8:791–97
    [Google Scholar]
  91. Li B, Tadross MR, Tsien RW 2016. Sequential ionic and conformational signaling by calcium channels drives neuronal gene expression. Science 351:863–67
    [Google Scholar]
  92. Li Y, Van Hooser SD, Mazurek M, White LE, Fitzpatrick D 2008. Experience with moving visual stimuli drives the early development of cortical direction selectivity. Nature 456:952–56
    [Google Scholar]
  93. Lillicrap TP, Crownden D, Tweed DB, Akerman CJ 2016. Random synaptic feedback weights support error backpropagation for deep learning. Nat. Comm. 7:13276
    [Google Scholar]
  94. Lovett-Barron M, Turi GF, Kaifosh P, Lee PH, Bolze F et al. 2012. Regulation of neuronal input transformations by tunable dendritic inhibition. Nat. Neurosci. 15:3423–30
    [Google Scholar]
  95. Ludvig EA, Sutton RS, Kehoe EJ 2008. Stimulus representation and the timing of reward-prediction errors in models of the dopamine system. Neural Comput 20:3034–54
    [Google Scholar]
  96. Luscher C, Malenka RC. 2012. NMDA receptor-dependent long-term potentiation and long-term depression (LTP/LTD). Cold Spring Harb. Perspect. Biol. 4:6a005710
    [Google Scholar]
  97. Magee JC. 1998. Dendritic hyperpolarization-activated currents modify the integrative properties of hippocampal CA1 pyramidal neurons. J. Neurosci. 18:7613–24
    [Google Scholar]
  98. Magee JC, Carruth M. 1999. Dendritic voltage-gated ion channels regulate the action potential firing mode of hippocampal CA1 pyramidal neurons. J. Neurophysiol. 82:1895–901
    [Google Scholar]
  99. Magee JC, Johnston D. 1997. A synaptically-controlled, associative signal for Hebbian plasticity in hippocampal neurons. Science 275:209–13
    [Google Scholar]
  100. Makino H, Malinow R. 2011. Compartmentalized versus global synaptic plasticity on dendrites controlled by experience. Neuron 72:1001–11
    [Google Scholar]
  101. Malenka RC, Bear M. 2004. LTP and LTD: an embarrassment of riches. Neuron 30:5–21
    [Google Scholar]
  102. Marblestone AH, Wayne G, Kording K 2016. Toward an integration of deep learning and neuroscience. Front. Comput. Neurosci. 10:94
    [Google Scholar]
  103. Markram H, Lubke J, Frotscher M, Sakmann B 1997. Regulation of synaptic efficacy by coincidence of postsynaptic AP and EPSPs. Science 275:213–15
    [Google Scholar]
  104. Marques T, Nguyen J, Fioreze G, Petreanu L 2018. The functional organization of cortical feedback inputs to primary visual cortex. Nat. Neurosci. 21:757–64
    [Google Scholar]
  105. Mayer ML, Westbrook GL, Guthrie PB 1984. Voltage-dependent block by Mg2+ of NMDA responses in spinal cord neurones. Nature 309:261–63
    [Google Scholar]
  106. McKernan MG, Shinnick-Gallagher P. 1997. Fear conditioning induces a lasting potentiation of synaptic currents in vivo. Nature 390:607–11
    [Google Scholar]
  107. Megias M, Emri Z, Freund TF, Gulyas AI 2001. Total number and distribution of inhibitory and excitatory synapses on hippocampal CA1 pyramidal cells. Neuroscience 102:527–40
    [Google Scholar]
  108. Mehta MR, Quirk MC, Wilson MA 2000. Experience-dependent asymmetric shape of hippocampal receptive fields. Neuron 25:707–15
    [Google Scholar]
  109. Meliza CD, Dan Y. 2006. Receptive-field modification in rat visual cortex induced by paired visual stimulation and single-cell spiking. Neuron 49:183–89
    [Google Scholar]
  110. Milstein AD, Bloss EB, Apostolides PF, Vaidya SP, Dilly GA et al. 2015. Inhibitory gating of input comparison in the CA1 microcircuit. Neuron 87:1274–89
    [Google Scholar]
  111. Montague PR, Dayan P, Sejnowski TJ 1996. A framework for mesencephalic dopamine systems based on predictive Hebbian learning. J. Neurosci. 16:51936–47
    [Google Scholar]
  112. Morris RG, Anderson E, Lynch GS, Baudry M 1986. Selective impairment of learning and blockade of long-term potentiation by an N-methyl-d-aspartate receptor antagonist, AP5. Nature 319:774–76
    [Google Scholar]
  113. Muller SZ, Zadina A, Abbott LF, Sawtell NB 2019. Continual learning in a multi-layer network of an electric fish. Cell 179:61382–92.e10
    [Google Scholar]
  114. Nabavi S, Fox R, Proulx CD, Lin JY, Tsien RY, Malinow R 2014. Engineering a memory with LTD and LTP. Nature 511:348–52
    [Google Scholar]
  115. Nadim F, Bucher D. 2014. Neuromodulation of neurons and synapses. Curr. Opin. Neurobiol. 29:48–56
    [Google Scholar]
  116. Nakazawa K, McHugh TJ, Wilson M, Tonegawa S 2004. NMDA receptors, place cells and hippocampal spatial memory. Cell 147:509–24
    [Google Scholar]
  117. Nicoll RA. 2017. A brief history of long-term potentiation. Neuron 93:281–90
    [Google Scholar]
  118. Oja E. 1982. Simplified neuron model as a principal component analyzer. J. Math. Biol. 15:267–73
    [Google Scholar]
  119. Olshausen BA, Field DJ. 1996. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607–9
    [Google Scholar]
  120. Palmer L, Murayama M, Larkum M 2012. Inhibitory regulation of dendritic activity in vivo. Front. Neural Circuits 6:26–32
    [Google Scholar]
  121. Petreanu L, Mao T, Sternson SM, Svoboda K 2009. The subcellular organization of neocortical excitatory connections. Nature 457:1142–45
    [Google Scholar]
  122. Pignatelli M, Bonci A. 2015. Role of dopamine neurons in reward and aversion: a synaptic plasticity perspective. Neuron 86:1145–57
    [Google Scholar]
  123. Rao RPN, Ballard DH. 1999. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2:79–87
    [Google Scholar]
  124. Rao RPN, Sejnowski TJ. 2001. Spike-timing-dependent Hebbian plasticity as temporal difference learning. Neural Comput 13:2221–37
    [Google Scholar]
  125. Rao RPN, Sejnowski TJ. 2002. Predictive coding, cortical feedback, and spike-timing dependent plasticity. Probabilistic Models of the Brain: Perception and Neural Function RPN Rao, BA Olshausen, MS Lewicki 297–315 Cambridge, MA: MIT Press
    [Google Scholar]
  126. Raymond JL, Medina JF. 2018. Computational principles of supervised learning in the cerebellum. Annu. Rev. Neurosci. 41:233–53
    [Google Scholar]
  127. Richards BA, Lillicrap TP. 2019. Dendritic solutions to the credit assignment problem. Curr. Opin. Neurobiol. 54:28–36
    [Google Scholar]
  128. Roelfsema PR, Holtmaat A. 2018. Control of synaptic plasticity in deep cortical networks. Nat. Rev. Neuro 19:166–80
    [Google Scholar]
  129. Rogan MT, Stäubli UV, LeDoux JE 1997. Fear conditioning induces associative long-term potentiation in the amygdala. Nature 390:604–7
    [Google Scholar]
  130. Rosenblatt F. 1959. Two theorems of statistical separability in the perceptron. Mechanisation of Thought Processes: Proceeding of a Symposium Held at the National Physical Laboratory on 24th, 25th, 26th and 27th November 1958, Vol 1:421–56 London: HM Station. Off.
    [Google Scholar]
  131. Royer S, Zemelman BV, Losonczy A, Kim J, Chance F et al. 2012. Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition. Nat. Neurosci. 15:769–75
    [Google Scholar]
  132. Rumelhart DE, Hinton GE, Williams RJ 1986. Learning internal representations by error propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition DE Rumelhart, JL McClelland 318–62 Cambridge, MA: MIT Press
    [Google Scholar]
  133. Ryan TJ, Roy DS, Pignatelli M, Arons A, Tonegawa S 2015. Memory engram cells retain memory under retrograde amnesia. Science 348:1007–13
    [Google Scholar]
  134. Sacramento J, Costa RP, Bengio Y, Senn W 2018. Dendritic cortical microcircuits approximate the backpropagation algorithm. Neural Information Processing Systems 31 (NIPS2018) S Bengio, H Wallach, H Larochelle, K Grauman, N Cesa-Bianchi, R Garnett San Diego, CA: NeurIPS
    [Google Scholar]
  135. Schiller J, Schiller Y, Stuart G, Sakmann B 1997. Calcium action potentials restricted to distal apical dendrites of rat neocortical pyramidal neurons. J. Physiol. 505:605–16
    [Google Scholar]
  136. Schuett S, Bonhoeffer T, Hubener M 2001. Pairing-induced changes of orientation maps in cat visual cortex. Neuron 32:325–37
    [Google Scholar]
  137. Schultz W. 1998. Predictive reward signals of dopamine neurons. J. Neurophysiol. 80:1–27
    [Google Scholar]
  138. Schultz W. 2007. Multiple dopamine functions at different time courses. Annu. Rev. Neurosci. 30:259–88
    [Google Scholar]
  139. Shindou T, Shindou M, Watanabe S, Wickens J 2019. A silent eligibility trace enables dopamine-dependent synaptic plasticity for reinforcement learning in the mouse striatum. Eur. J. Neurosci. 49:726–36
    [Google Scholar]
  140. Sjöström PJ, Häusser M. 2006. A cooperative switch determines the sign of synaptic plasticity in distal dendrites of neocortical pyramidal neurons. Neuron 51:227–38
    [Google Scholar]
  141. Song S, Abbott LF. 2001. Cortical development and remapping through spike-timing-dependent plasticity. Neuron 32:339–50
    [Google Scholar]
  142. Stachenfeld KL, Botvinick MM, Gersman SJ 2017. The hippocampus as a predictive map. Nat. Neurosci. 20:1643–53
    [Google Scholar]
  143. Steinberg E, Keiflin R, Boivin J, Witten I, Deisseroth K, Janak P 2013. A causal link between prediction errors, dopamine neurons and learning. Nat. Neurosci. 16:966–73
    [Google Scholar]
  144. Steward O, Scoville SA. 1976. Cells of origin of entorhinal cortical afferents to the hippocampus and fascia dentata of the rat. J. Comp. Neurol. 169:347–70
    [Google Scholar]
  145. Stuart G, Sakmann B. 1994. Active propagation of somatic action potentials into neocortical pyramidal cell dendrites. Nature 367:69–72
    [Google Scholar]
  146. Stuart G, Spruston N. 1998. Determinants of voltage attenuation in neocortical pyramidal neuron dendrites. J. Neurosci. 18:3501–10
    [Google Scholar]
  147. Suh J, Rivest AJ, Nakashiba T, Tominaga T, Tonegawa S 2011. Entorhinal cortex layer III input to the hippocampus is crucial for temporal association memory. Science 334:1415–20
    [Google Scholar]
  148. Sutton RS. 1988. Learning to predict by the methods of temporal differences. Mach. Learn. 3:9–44
    [Google Scholar]
  149. Sutton RS, Barto AG. 1981. Toward a modern theory of adaptive networks: expectation and prediction. Am. Psych. Assoc. 88:135–70
    [Google Scholar]
  150. Sutton RS, Barto AG. 1990. Time-derivative models of Pavlovian reinforcement. Learning and Computational Neuroscience: Foundations of Adaptive Networks M Gabriel, J Moore 497–537 Cambridge, MA: MIT Press
    [Google Scholar]
  151. Sutton RS, Barto AG. 2018. Reinforcement Learning: An Introduction Cambridge, MA: MIT Press
  152. Suvrathan A. 2019. Beyond STDP—towards diverse and functionally relevant plasticity rules. Curr. Opin. Neurobiol. 54:12–19
    [Google Scholar]
  153. Suvrathan A, Payne HL, Raymond J 2016. Timing rules for synaptic plasticity matched to behavioral function. Neuron 92:959–67
    [Google Scholar]
  154. Takahashi H, Magee JC. 2009. Pathway interactions and synaptic plasticity in the dendritic tuft regions of CA1 pyramidal neurons. Neuron 62:102–11
    [Google Scholar]
  155. Tonegawa S, Liu X, Ramirez S, Redondo R 2015. Memory engram cells have come of age. Neuron 87:918–31
    [Google Scholar]
  156. Tsay D, Dudman JT, Siegelbaum SA 2007. HCN1 channels constrain synaptically evoked Ca2+ spikes in distal dendrites of CA1 pyramidal neurons. Neuron 56:1076–89
    [Google Scholar]
  157. Turi G, Li W, Chavlis S, Bozelos P, Pandi I et al. 2019. Vasoactive intestinal polypeptide-expressing interneurons in the hippocampus support goal-oriented spatial learning. Neuron 101:1150–65
    [Google Scholar]
  158. Turner RW, Meyers DE, Richardson TL, Barker JL 1991. The sire of action potential discharge over the somatodendritic axis of rat hippocampal CA1 pyramidal neurons. J. Neurosci. 11:2270–80
    [Google Scholar]
  159. Urbanczik R, Senn W. 2014. Learning by the dendritic prediction of somatic spiking. Neuron 81:521–28
    [Google Scholar]
  160. von der Malsberg C. 1973. Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14:85–100
    [Google Scholar]
  161. Wayne G, Hung C, Amos D, Mirza M, Ahuja A et al. 2018. Unsupervised predictive memory in a goal-directed agent. arXiv:1803.10760 [cs.LG]
  162. Whittington JCR, Bogacz R 2019. Theories of error back-propagation in the brain. Trends Cogn. Sci. 23:3235–50
    [Google Scholar]
  163. Williams LE, Holtmaat A. 2019. Higher-order thalamocortical inputs gate synaptic long-term potentiation via disinhibition. Neuron 101:91–102
    [Google Scholar]
  164. Williams S, Fletcher LN. 2019. Dendritic substrate for the cholinergic control of neocortical output. Neuron 101:486–99
    [Google Scholar]
  165. Witter MP, Naber PA, van Haeften T, Machielsen WC, Rombouts SA et al. 2000. Cortico-hippocampal communication by way of parallel parahippocampal-subicular pathways. Hippocampus 10:398–410
    [Google Scholar]
  166. Woodrow B, Hoff ME Jr 1960. Adaptive switching circuits. IRE WESCON Conv. Rec. 4:96–104
    [Google Scholar]
  167. Woodrow B, Rumelhart D, Lehr MA 1994. Neural networks: applications in industry, business and science. Commun. ACM 37:93–105
    [Google Scholar]
  168. Xu NL, Harnett MT, Williams SR, Huber D, O'Conner DH et al. 2012. Nonlinear dendritic integration of sensory and motor input produces an object localization signal. Nature 492:247–51
    [Google Scholar]
  169. Yagishita S, Hayashi-Takagi A, Ellis-Davies GCR, Urakubo H, Ishii S, Kasai H 2014. A critical time window for dopamine actions on the structural plasticity of dendritic spines. Science 345:1616–17
    [Google Scholar]
  170. Yao H, Dan Y. 2001. Stimulus timing–dependent plasticity in cortical processing of orientation. Neuron 32:315–23
    [Google Scholar]
  171. Young JM, Waleszczyk WJ, Wang C, Calford MB, Dreher B, Obermayer K 2007. Cortical reorganization consistent with spike timing- but not correlation-dependent plasticity. Nat. Neurosci. 10:887–95
    [Google Scholar]
  172. Yu YC, Bultje RS, Wang X, Shi SH 2009. Specific synapses develop preferentially among sister excitatory neurons in the neocortex. Nature 458:501–4
    [Google Scholar]
  173. Zaremba J, Diamantopoulou A, Danielson N, Grosmark AD, Kaifosh P et al. 2017. Impaired hippocampal place cell dynamics in a mouse model of the 22q11.2 deletion. Nat. Neurosci. 20:1612–23
    [Google Scholar]
  174. Zhao X, Wang Y, Spruston N, Magee JC 2019. Synaptic mechanisms of context-dependent sensory responses in the hippocampus. bioRxiv 624262. https://doi.org/10.1101/624262
    [Crossref]
/content/journals/10.1146/annurev-neuro-090919-022842
Loading
/content/journals/10.1146/annurev-neuro-090919-022842
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error