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Control of synaptic plasticity in deep cortical networks

Nature Reviews Neuroscience volume 19, pages 166–180 (2018)Cite this article

Subjects

Key Points

  • In addition to presynaptic and postsynaptic mechanisms, synaptic plasticity depends on neuromodulatory substances and feedback connections from higher-order cortical and thalamic brain regions

  • Synaptic plasticity in the brain depends on reward-prediction errors and on selective attention. Neuromodulatory systems code for the reward-prediction errors, and feedback connections from the response-selection stage mediate top-down attention effects

  • The combined influence of feedback connections and neuromodulatory substances on plasticity enables powerful learning rules for the training of 'deep', multilayered neuronal networks

  • Feedback connections project to cortical layers that are distinct from feedforward input, where they impinge on distal dendritic segments, separate excitatory neuronal populations or inhibitory interneurons

  • Feedback connections gate plasticity in cortical pyramidal neurons by promoting NMDA-receptor-driven calcium entry into dendrites and by disinhibiting the cortical column through activation of vasoactive-intestinal-peptide-positive interneurons (among others)

  • Synaptic tags are biochemical processes that make synapses eligible for plasticity. Neuromodulators released later can interact with tagged synapses to increase or decrease synaptic strength

Abstract

Humans and many other animals have an enormous capacity to learn about sensory stimuli and to master new skills. However, many of the mechanisms that enable us to learn remain to be understood. One of the greatest challenges of systems neuroscience is to explain how synaptic connections change to support maximally adaptive behaviour. Here, we provide an overview of factors that determine the change in the strength of synapses, with a focus on synaptic plasticity in sensory cortices. We review the influence of neuromodulators and feedback connections in synaptic plasticity and suggest a specific framework in which these factors can interact to improve the functioning of the entire network.

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Figure 1: Putative control signals that influence synaptic plasticity.
Figure 2: Cortical feedforward, feedback and neuromodulatory information streams.
Figure 3: Effects of learning on neuronal tuning curves.
Figure 4: Attentional selection and eye movement selection during curve tracing.
Figure 5: Gating of plasticity of feedforward connections to the primary somatosensory cortex.
Figure 6: Gating and steering of synaptic plasticity.

References

  1. Sutton, R. S. & Barto, A. G. Reinforcement Learning (MIT Press, 1998).

    Google Scholar 

  2. Littman, M. L. Reinforcement learning improves behaviour from evaluative feedback. Nature 521, 445–451 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. in Parallel Distributed Processing: Explorations in the Microstructure of Cognition (eds Rumelhart, D. E. & McClelland, J. L.) 318–364 (MIT Press, 1986).

    Google Scholar 

  4. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Hebb, D. O. The Organization of Behavior. A Neuropsychological Theory (John Wiley & Sons, 1949).

    Google Scholar 

  6. Martin, S. J., Grimwood, P. D. & Morris, R. G. M. Synaptic plasticity and memory: an evaluation of the hypothesis. Annu. Rev. Neurosci. 23, 649–711 (2000).

    Article  CAS  PubMed  Google Scholar 

  7. Schultz, W. Getting formal with dopamine and reward. Neuron 36, 241–263 (2002).

    Article  CAS  PubMed  Google Scholar 

  8. Niv, Y. & Schoenbaum, G. Dialogues on prediction errors. Trends Cogn. Sci. 12, 265–272 (2008).

    Article  PubMed  Google Scholar 

  9. Baxter, J. & Bartlett, P. L. Infinite-horizon policy-gradient estimation. J. Artif. Intell. Res. 15, 319–350 (2001).

    Article  Google Scholar 

  10. Frémaux, N., Sprekeler, H. & Gerstner, W. Reinforcement learning using a continuous time actor-critic framework with spiking neurons. PLoS Comput. Biol. 9, e1003024 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Izhikevich, E. M. Solving the distal reward problem through linkage of STDP and dopamine signalling. Cereb. Cortex 17, 2443–2452 (2007).

    Article  PubMed  Google Scholar 

  12. Legenstein, R., Pecevski, D. & Maass, W. A learning theory for reward-modulated spike-timing-dependent plasticity with application in biofeedback. PLoS Comput. Biol. 4, e1000180 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Rombouts, J. O., Bohte, S. M., Martinez-trujillo, J., Roelfsema, P. R. & Pieter, R. A learning rule that explains how rewards teach attention. Vis. Cogn. 23, 179–205 (2015).

    Article  Google Scholar 

  14. Yagishita, S. et al. A critical time window for dopamine actions on the structural plasticity of dendritic spines. Science 345, 1616–1620 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Frey, U. & Morris, R. G. M. Synaptic tagging and long-term potentiation. Nature 385, 533–536 (1997).

    Article  CAS  PubMed  Google Scholar 

  16. Montague, P. R., Dayan, P., Person, C. & Sejnowski, T. J. Bee foraging in uncertain environments using predictive Hebbian learning. Nature 377, 725–728 (1995).

    Article  CAS  PubMed  Google Scholar 

  17. Lisman, J., Grace, A. A. & Duzel, E. A neoHebbian framework for episodic memory; role of dopamine-dependent late LTP. Trends Neurosci. 34, 536–547 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Frémaux, N. & Gerstner, W. Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules. Front. Neural Circuits 9, 85 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Pennartz, C. A. M. The ascending neuromodulatory systems in learning by reinforcement: comparing computational conjectures with experimental findings. Brain Res. Rev. 21, 219–245 (1995).

    Article  CAS  PubMed  Google Scholar 

  20. Trabasso, T. & Bower, G. H. Attention in Learning: Theory and Research (Krieger Pub. Co., 1968).

    Google Scholar 

  21. Ahissar, M. & Hochstein, S. Attentional control of early perceptual learning. Proc. Natl Acad. Sci. USA 90, 5718–5722 (1993).

    Article  CAS  PubMed  Google Scholar 

  22. Jiang, Y. & Chun, M. M. Selective attention modulates implicit learning. Q. J. Exp. Psychol. 54, 1105–1124 (2001).

    Article  CAS  Google Scholar 

  23. Vartak, D., Jeurissen, D., Self, M. W. & Roelfsema, P. R. The influence of attention and reward on the learning of stimulus-response associations. Sci. Rep. 7, 9036 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Seitz, A. R., Kim, D. & Watanabe, T. Rewards evoked learning of unconsciously processed visual stimuli in adult humans. Neuron 61, 700–707 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Roelfsema, P. R., van Ooyen, A. & Watanabe, T. Perceptual learning rules based on reinforcers and attention. Trends Cogn. Sci. 14, 64–71 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Jonikaitis, D. & Deubel, H. Independent allocation of attention to eye and hand targets in coordinated eye-hand movements. Psychol. Sci. 22, 339–347 (2011).

    Article  PubMed  Google Scholar 

  27. Moore, T. Shape representations and visual guidance of saccadic eye movements. Science 285, 1914–1917 (1999).

    Article  CAS  PubMed  Google Scholar 

  28. Roelfsema, P. R. & van Ooyen, A. Attention-gated reinforcement learning of internal representations for classification. Neural Comp. 17, 2176–2214 (2005).

    Article  Google Scholar 

  29. Lamme, V. A. F. & Roelfsema, P. R. The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci. 23, 571–579 (2000).

    Article  CAS  PubMed  Google Scholar 

  30. Moore, T. & Armstrong, K. M. Selective gating of visual signals by microstimulation of frontal cortex. Nature 421, 370–373 (2003).

    Article  CAS  PubMed  Google Scholar 

  31. Lillicrap, T. P., Cownden, D., Tweed, D. B. & Akerman, C. J. Random synaptic feedback weights support error backpropagation for deep learning. Nat. Commun. 7, 13276 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Rombouts, J. O., Bohte, S. M. & Roelfsema, P. R. How attention can create synaptic tags for the learning of working memories in sequential tasks. PLoS Comput. Biol. 11, e1004060 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).

    Article  CAS  PubMed  Google Scholar 

  34. Crick, F. The recent excitement about neural networks. Nature 337, 129–132 (1989).

    Article  CAS  PubMed  Google Scholar 

  35. Braitenberg, V. & Schütz, A. Anatomy of the Cortex (Springer-Verlag, 1991).

    Book  Google Scholar 

  36. Mountcastle, V. B. in The Mindful Brain (eds Edelman, G. M. & Mountcastle, V. B.) (MIT Press, 1978).

    Google Scholar 

  37. Felleman, D. J. & Van Essen, D. C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).

    Article  CAS  PubMed  Google Scholar 

  38. Yamins, D. L. K. & DiCarlo, J. J. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19, 356–365 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Sherman, S. M. Thalamus plays a central role in ongoing cortical functioning. Nat. Neurosci. 16, 533–541 (2016).

    Article  CAS  Google Scholar 

  40. Callaway, E. M. Feedforward, feedback and inhibitory connections in primate visual cortex. Neural Networks 17, 625–632 (2004).

    Article  PubMed  Google Scholar 

  41. Harris, K. D. & Mrsic-Flogel, T. D. Cortical connectivity and sensory coding. Nature 503, 51–58 (2013).

    Article  CAS  PubMed  Google Scholar 

  42. Douglas, R. & Martin, K. A. C. Neuronal circuits of the neocortex. Annu. Rev. Neurosci. 27, 419–451 (2004).

    Article  CAS  PubMed  Google Scholar 

  43. Markov, N. T. et al. Cortical high-density counterstream architectures. Science 342, 1238406 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ullman, S. Sequence seeking and counterstreams: a computational model for bidirectional information flow in the visual cortex. Cereb. Cortex 5, 1–11 (1995).

    Article  CAS  PubMed  Google Scholar 

  45. Harris, K. D. & Shepherd, G. M. G. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Feldmeyer, D. Excitatory neuronal connectivity in the barrel cortex. Front. Neuroanat. 6, 24 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Maunsell, J. H. R. & Gibson, J. R. Visual response latencies in striate cortex of the macaque monkey. J. Neurophysiol. 68, 1332–1344 (1992).

    Article  CAS  PubMed  Google Scholar 

  48. Nowak, L. G., Munk, M. H. J., Girard, P. & Bullier, J. Visual latencies in areas V1 and V2 of the macaque monkey. Visual Neurosci. 12, 371–384 (1995).

    Article  CAS  Google Scholar 

  49. van Kerkoerle, T., Self, M. W. & Roelfsema, P. R. Effects of attention and working memory in the different layers of monkey primary visual cortex. Nat. Commun. 8, 13804 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Self, M. W., van Kerkoerle, T., Supèr, H. & Roelfsema, P. R. Distinct roles of the cortical layers of area V1 in figure-ground segregation. Curr. Biol. 23, 2121–2129 (2013).

    Article  CAS  PubMed  Google Scholar 

  51. Constantinople, C. M. & Bruno, R. M. Deep cortical layers are activated by thalamus. Science 340, 1591–1594 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Morgenstern, N. A., Bourg, J. & Petreanu, L. Multilaminar networks of cortical neurons integrate common inputs from sensory thalamus. Nat. Neurosci. 19, 1034–1040 (2016).

    Article  CAS  PubMed  Google Scholar 

  53. Bolz, J. & Gilbert, C. D. Generation of end-inhibition in the visual cortex via interlaminar connections. Nature 320, 362–365 (1986).

    Article  CAS  PubMed  Google Scholar 

  54. Olsen, S. R., Bortone, D. S., Adesnik, H. & Scanziani, M. Gain control by layer six in cortical circuits of vision. Nature 483, 47–52 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Bortone, D. S., Olsen, S. R. & Scanziani, M. Translaminar inhibitory cells recruited by layer 6 corticothalamic neurons suppress visual cortex. Neuron 82, 474–485 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Rockland, K. S. & Virga, A. Terminal arbors of individual 'feedback' axons projecting from area V2 to V1 in the macaque monkey: a study using immunohistochochemistry of anterogradely transported Phaseolus vulgaris-leucoagglutinin. J. Comp. Neurol. 285, 54–72 (1989).

    Article  CAS  PubMed  Google Scholar 

  57. Larkum, M. E. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 36, 141–149 (2013).

    Article  CAS  PubMed  Google Scholar 

  58. Schneider, D. M., Nelson, A. & Mooney, R. A synaptic and circuit basis for corollary discharge in the auditory cortex. Nature 513, 189–194 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Lee, S., Kruglikov, I., Huang, Z. J., Fishell, G. & Rudy, B. A disinhibitory circuit mediates motor integration in the somatosensory cortex. Nat. Neurosci. 16, 1662–1670 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. van Versendaal, D. & Levelt, C. N. Inhibitory interneurons in visual cortical plasticity. Cell. Mol. Life Sci. 73, 3677–3691 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Deschênes, M., Veinante, P. & Zhang, Z. W. The organization of corticothalamic projections: reciprocity versus parity. Brain Res. Rev. 28, 286–308 (1998).

    Article  PubMed  Google Scholar 

  62. Veinante, P., Lavallée, P. & Deschênes, M. Corticothalamic projections from layer 5 of the vibrissal barrel cortex in the rat. J. Comp. Neurol. 424, 197–204 (2000).

    Article  CAS  PubMed  Google Scholar 

  63. Jones, E. G. Thalamus (Cambridge Univ. Press, 2007).

    Google Scholar 

  64. Roth, M. M. et al. Thalamic nuclei convey diverse contextual information to layer 1 of visual cortex. Nat. Neurosci. 19, 299–307 (2016).

    Article  CAS  PubMed  Google Scholar 

  65. Meyer, H. S. et al. Cell type-specific thalamic innervation in a column of rat vibrissal cortex. Cereb. Cortex 20, 2287–2303 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Ohno, S. et al. A morphological analysis of thalamocortical axon fibers of rat posterior thalamic nuclei: a single neuron tracing study with viral vectors. Cereb. Cortex 22, 2840–2857 (2012).

    Article  PubMed  Google Scholar 

  67. Lu, S. M. & Lin, R. C. Thalamic afferents of the rat barrel cortex: a light- and electron-microscopic study using Phaseolus vulgaris leucoagglutinin as an anterograde tracer. Somatosens. Mot. Res. 10, 1–16 (1993).

    Article  CAS  PubMed  Google Scholar 

  68. Mease, R. A., Metz, M. & Groh, A. Cortical sensory responses are enhanced by the higher-order thalamus. Cell Rep. 14, 208–215 (2016).

    Article  CAS  PubMed  Google Scholar 

  69. Groh, A. et al. Convergence of cortical and sensory driver inputs on single thalamocortical cells. Cereb. Cortex 24, 3167–3179 (2014).

    Article  PubMed  Google Scholar 

  70. Ahissar, E., Sosnik, R. & Haidarliu, S. Transformation from temporal to rate coding in a somatosensory thalamocortical pathway. Nature 406, 302–306 (2000).

    Article  CAS  PubMed  Google Scholar 

  71. Moore, J. D., Mercer Lindsay, N., Deschênes, M. & Kleinfeld, D. Vibrissa self-motion and touch are reliably encoded along the same somatosensory pathway from brainstem through thalamus. PLoS Biol. 13, e1002253 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Guo, Z. V. et al. Maintenance of persistent activity in a frontal thalamocortical loop. Nature 545, 181–186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Kwon, S. E., Yang, H., Minamisawa, G. & O'Connor, D. H. Sensory and decision-related activity propagate in a cortical feedback loop during touch perception. Nat. Neurosci. 19, 1243–1249 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Self, M., Kooijmans, R. N., Supèr, H., Lamme, V. A. F. & Roelfsema, P. R. Different glutamate receptors convey feedforward and recurrent processing in macaque V1. Proc. Natl Acad. Sci. USA 109, 11031–11036 (2012).

    Article  CAS  PubMed  Google Scholar 

  75. Daw, N. W., Stein, P. S. G. & Fox, K. The role of NMDA receptors in information processing. Annu. Rev. Neurosci. 16, 207–222 (1993).

    Article  CAS  PubMed  Google Scholar 

  76. Rivadulla, C., Martinez, L. M., Varela, C. & Cudeiro, J. Completing the corticofugal loop: a visual role for the corticogeniculate type 1 metabotropic glutamate receptor. J. Neurosci. 22, 2956–2962 (2002).

    Article  CAS  PubMed  Google Scholar 

  77. Gambino, F. et al. Sensory-evoked LTP driven by dendritic plateau potentials in vivo. Nature 515, 116–119 (2014).

    Article  CAS  PubMed  Google Scholar 

  78. Klink, P. C., Dagnino, B., Gariel-Mathis, M. A. & Roelfsema, P. R. Distinct feedforward and feedback effects of microstimulation in visual cortex reveal neural mechanisms of texture segregation. Neuron 95, 209–220 (2017).

    Article  CAS  PubMed  Google Scholar 

  79. Tritsch, N. X. & Sabatini, B. L. Dopaminergic modulation of synaptic transmission in cortex and striatum. Neuron 76, 33–50 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Picciotto, M. R., Higley, M. J. & Mineur, Y. S. Acetylcholine as a neuromodulator: cholinergic signaling shapes nervous system function and behavior. Neuron 76, 116–129 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Eggermann, E., Kremer, Y., Crochet, S. & Petersen, C. C. H. Cholinergic signals in mouse barrel cortex during active whisker sensing. Cell Rep. 9, 1654–1660 (2014).

    Article  CAS  PubMed  Google Scholar 

  82. Gu, Q. Neuromodulatory transmitter system in the cortex and their role in cortical plasticity. Neuroscience 111, 814–835 (2002).

    Article  Google Scholar 

  83. Lesch, K. P. & Waider, J. Serotonin in the modulation of neural plasticity and networks: implications for neurodevelopmental disorders. Neuron 76, 175–191 (2012).

    Article  CAS  PubMed  Google Scholar 

  84. Froemke, R. C. Plasticity of cortical excitatory-inhibitory balance. Annu. Rev. Neurosci. 38, 195–219 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Hayashi-takagi, A. et al. Labelling and optical erasure of synaptic memory traces in the motor cortex. Nature 525, 333–338 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Xiong, Q., Znamenskiy, P. & Zador, A. M. Selective corticostriatal plasticity during acquisition of an auditory discrimination task. Nature 521, 348–351 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Jin, X. & Costa, R. M. Start/stop signals emerge in nigrostriatal circuits during sequence learning. Nature 466, 457–462 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Liu, B., Huberman, A. D. & Scanziani, M. Cortico-fugal output from visual cortex promotes plasticity of innate motor behaviour. Nature 538, 383–387 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Schoups, A., Vogels, R., Qian, N. & Orban, G. A. Practising orientation identification improves orientation coding in V1 neurons. Nature 412, 549–553 (2001).

    Article  CAS  PubMed  Google Scholar 

  90. Poort, J. et al. Learning enhances sensory and multiple non-sensory representations in primary visual cortex. Neuron 86, 1478–1490 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Goltstein, P. M., Coffey, E. B. J., Roelfsema, P. R. & Pennartz, C. M. A. In vivo two-photon Ca2+ imaging reveals selective reward effects on stimulus-specific assemblies in mouse visual cortex. J. Neurosci. 33, 11540–11555 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Freedman, D. J. & Assad, J. A. Experience dependent representation of visual categories in parietal cortex. Nature 443, 85–88 (2006).

    Article  CAS  PubMed  Google Scholar 

  93. Makino, H. & Komiyama, T. Learning enhances the relative impact of top-down processing in the visual cortex. Nat. Neurosci. 18, 1116–1122 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Bajo, V. M., Nodal, F. R., Moore, D. R. & King, A. J. The descending corticocollicular pathway mediates learning-induced auditory plasticity. Nat. Neurosci. 13, 253–260 (2010).

    Article  CAS  PubMed  Google Scholar 

  95. Brosch, T., Neumann, H. & Roelfsema, P. R. Reinforcement learning of linking and tracing contours in recurrent neural networks. PLoS Comput. Biol. 11, e1004489 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Pooresmaeili, A., Poort, J. & Roelfsema, P. R. Simultaneous selection by object-based attention in visual and frontal cortex. Proc. Natl Acad. Sci. USA 111, 6467–6472 (2014).

    Article  CAS  PubMed  Google Scholar 

  97. Self, M. W. et al. The effects of context and attention on spiking activity in human early visual cortex. PLoS Biol. 14, e1002420 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Houtkamp, R., Spekreijse, H. & Roelfsema, P. R. A gradual spread of attention during mental curve tracing. Percept. Psychophys. 65, 1136–1144 (2003).

    Article  CAS  PubMed  Google Scholar 

  99. Roelfsema, P. R. & Spekreijse, H. The representation of erroneously perceived stimuli in the primary visual cortex. Neuron 31, 853–863 (2001).

    Article  CAS  PubMed  Google Scholar 

  100. Khayat, P. S., Pooresmaeili, A. & Roelfsema, P. R. Time course of attentional modulation in the frontal eye field during curve tracing. J. Neurophysiol. 101, 1813–1822 (2009).

    Article  CAS  PubMed  Google Scholar 

  101. Roelfsema, P. R. Cortical algorithms for perceptual grouping. Annu. Rev. Neurosci. 29, 203–227 (2006).

    Article  CAS  PubMed  Google Scholar 

  102. Reynolds, J. H. & Chelazzi, L. Attentional modulation of visual processing. Annu. Rev. Neurosci. 27, 611–647 (2004).

    Article  CAS  PubMed  Google Scholar 

  103. Zhou, H., Schafer, R. J. & Desimone, R. Pulvinar–cortex interactions in vision and attention. Neuron 89, 209–220 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Purushothaman, G., Marion, R., Li, K. & Casagrande, V. A. Gating and control of primary visual cortex by pulvinar. Nat. Neurosci. 15, 905–912 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Robinson, D. L. & Petersen, S. E. The pulvinar and visual salience. Trends Neurosci. 15, 127–132 (1992).

    Article  CAS  PubMed  Google Scholar 

  106. Chalupa, L. M., Coyle, R. S. & Lindsley, D. B. Effect of pulvinar lesions on visual pattern discrimination in monkeys. J. Neurophysiol. 39, 354–369 (1976).

    Article  CAS  PubMed  Google Scholar 

  107. Manita, S. et al. A top-down cortical circuit for accurate sensory perception. Neuron 86, 1304–1316 (2015).

    Article  CAS  PubMed  Google Scholar 

  108. Xu, N. et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature 492, 247–251 (2012).

    Article  CAS  PubMed  Google Scholar 

  109. Sachidhanandam, S., Sreenivasan, V., Kyriakatos, A., Kremer, Y. & Petersen, C. C. H. Membrane potential correlates of sensory perception in mouse barrel cortex. Nat. Neurosci. 16, 1671–1677 (2013).

    Article  CAS  PubMed  Google Scholar 

  110. Takahashi, N., Oertner, T. G., Hegemann, P. & Larkum, M. E. Active cortical dendrites modulate perception. Science 354, 1587–1590 (2016).

    Article  CAS  PubMed  Google Scholar 

  111. Gambino, F. & Holtmaat, A. Spike-timing-dependent potentiation of sensory surround in the somatosensory cortex is facilitated by deprivation-mediated disinhibition. Neuron 75, 490–502 (2012).

    Article  CAS  PubMed  Google Scholar 

  112. Letzkus, J. J. et al. A disinhibitory microcircuit for associative learning in the auditory cortex. Nature 480, 331–335 (2011).

    Article  CAS  PubMed  Google Scholar 

  113. Letzkus, J. J., Wolff, S. B. E. & Lüthi, A. Disinhibition, a circuit mechanism for associative learning and memory. Neuron 88, 264–276 (2015).

    Article  CAS  PubMed  Google Scholar 

  114. Lee, S., Hjerling-Leffler, J., Zagha, E., Fishell, G. & Rudy, B. The largest group of superficial neocortical GABAergic interneurons expresses ionotropic serotonin receptors. J. Neurosci. 30, 16796–16808 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Wall, N. R. et al. Brain-wide maps of synaptic input to cortical interneurons. J. Neurosci. 36, 4000–4009 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Audette, N. J., Urban-Ciecko, J., Matsushita, M. & Barth, A. L. POm thalamocortical input drives layer-specific microcircuits in somatosensory cortex. Cereb. Cortex https://doi.org/10.1093/cercor/bhx044 (2017).

    Article  Google Scholar 

  117. Pi, H.-J. et al. Cortical interneurons that specialize in disinhibitory control. Nature 503, 521–524 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Fu, Y. et al. A cortical circuit for gain control by behavioral state. Cell 156, 1139–1152 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Pfeffer, C. K., Xue, M., He, M., Huang, Z. J. & Scanziani, M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat. Neurosci. 16, 1068–1076 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Wang, Y. et al. Anatomical, physiological and molecular properties of Martinotti cells in the somatosensory cortex of the juvenile rat. J. Physiol. 561, 65–90 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. van Versendaal, D. et al. Elimination of inhibitory synapses is a major component of adult ocular dominance plasticity. Neuron 74, 374–383 (2012).

    Article  CAS  PubMed  Google Scholar 

  122. Kubota, Y., Hatada, S., Kondo, S., Karube, F. & Kawaguchi, Y. Neocortical inhibitory terminals innervate dendritic spines targeted by thalamocortical afferents. J. Neurosci. 27, 1139–1150 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Palmer, L., Murayama, M. & Larkum, M. Inhibitory regulation of dendritic activity in vivo. Front. Neural Circuits 6, 26 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Chen, S. X., Kim, A. N., Peters, A. J. & Komiyama, T. Subtype-specific plasticity of inhibitory circuits in motor cortex during motor learning. Nat. Neurosci. 18, 1109–1115 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Cichon, J. & Gan, W. Branch-specific dendritic Ca2+ spikes cause persistent synaptic plasticity. Nature 520, 180–185 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Fu, Y., Kaneko, M. K., Tang, Y., Alvarez-Buylla, A. & Stryker, M. P. A cortical disinhibitory circuit for enhancing adult plasticity. eLife 4, e05558 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Higley, M. J. Localized GABAergic inhibition of dendritic Ca2+ signalling. Nat. Rev. Neurosci. 15, 567–572 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Bittner, K. C., Milstein, A. D., Grienberger, C., Romani, S. & Magee, J. C. Behavioral time scale synaptic plasticity underlies CA1 place fields. Science 357, 1033–1036 (2017).

    Article  CAS  PubMed  Google Scholar 

  129. Basu, J. et al. Gating of hippocampal activity, plasticity, and memory by entorhinal cortex long-range inhibition. Science 351, aaa5694 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Smith, S. L., Smith, I. T., Branco, T. & Häusser, M. Dendritic spikes enhance stimulus selectivity in cortical neurons in vivo. Nature 503, 115–120 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Dahmen, J. C., Hartley, D. E. H. & King, A. J. Stimulus-timing-dependent plasticity of cortical frequency representation. J. Neurosci. 28, 13629–13639 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Pawlak, V. & Kerr, J. N. D. Dopamine receptor activation is required for corticostriatal spike-timing-dependent plasticity. J. Neurosci. 28, 2435–2446 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Pawlak, V., Greenberg, D. S., Sprekeler, H., Gerstner, W. & Kerr, J. N. D. Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo. eLife 2, e00012 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  134. Haas, H. L., Sergeeva, O. A. & Selbach, O. Histamine in the central nervous system. Physiol. Rev. 88, 1183–1241 (2008).

    Article  CAS  PubMed  Google Scholar 

  135. Montague, P. R., Hyman, S. E. & Cohen, J. D. Computational roles for dopamine in behavioral control. Nature 431, 760–767 (2004).

    Article  CAS  PubMed  Google Scholar 

  136. Schultz, W. Multiple dopamine functions at different time courses. Annu. Rev. Neurosci. 30, 259–288 (2007).

    Article  CAS  PubMed  Google Scholar 

  137. Bromberg-Martin, E. S., Matsumoto, M. & Hikosaka, O. Dopamine in motivational control: rewarding, aversive, and alerting. Neuron 68, 815–834 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Bao, S., Chan, V. T. & Merzenich, M. M. Cortical remodelling induced by activity of ventral tegmental dopamine neurons. Nature 412, 79–81 (2001).

    Article  CAS  PubMed  Google Scholar 

  139. Lammel, S. et al. Input-specific control of reward and aversion in the ventral tegmental area. Nature 491, 212–217 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Zaborszky, L. et al. Neurons in the basal forebrain project to the cortex in a complex topographic organization that reflects corticocortical connectivity patterns: an experimental study based on retrograde tracing and 3D reconstruction. Cereb. Cortex 25, 118–137 (2015).

    Article  PubMed  Google Scholar 

  141. Kim, J.-H. et al. Selectivity of neuromodulatory projections from the basal forebrain and locus ceruleus to primary sensory cortices. J. Neurosci. 36, 5314–5327 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Kawai, H., Lazar, R. & Metherate, R. Nicotinic control of axon excitability regulates thalamocortical transmission. Nat. Neurosci. 10, 1168–1175 (2007).

    Article  CAS  PubMed  Google Scholar 

  143. Férézou, I. et al. 5-HT3 receptors mediate serotonergic fast synaptic excitation of neocortical vasoactive intestinal peptide/cholecystokinin interneurons. J. Neurosci. 22, 7389–7397 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  144. Pinto, L. et al. Fast modulation of visual perception by basal forebrain cholinergic neurons. Nat. Neurosci. 16, 1857–1863 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Hangya, B., Ranade, S. P., Lorenc, M. & Kepecs, A. Central cholinergic neurons are rapidly recruited by reinforcement feedback. Cell 162, 1155–1168 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Richardson, R. T. & DeLong, M. R. Nucleus basalis of Meynert neuronal activity during a delayed response task in monkey. Brain Res. 399, 364–368 (1986).

    Article  CAS  PubMed  Google Scholar 

  147. Chubykin, A. A., Roach, E. B., Bear, M. F. & Shuler, M. G. H. A cholinergic mechanism for reward timing within primary visual cortex. Neuron 77, 723–735 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Kilgard, M. P. & Merzenich, M. M. Cortical map reorganization enabled by nucleus basalis activity. Science 279, 1714–1718 (1998).

    Article  CAS  PubMed  Google Scholar 

  149. Froemke, R. C. et al. Long-term modification of cortical synapses improves sensory perception. Nat. Neurosci. 16, 79–88 (2013).

    Article  CAS  PubMed  Google Scholar 

  150. Bakin, J. S. & Weinberger, N. M. Induction of a physiological memory in the cerebral cortex by stimulation of the nucleus basalis. Proc. Natl Acad. Sci. USA 93, 11219–11224 (1996).

    Article  CAS  PubMed  Google Scholar 

  151. Froemke, R. C., Merzenich, M. M. & Schreiner, C. E. A synaptic memory trace for cortical receptive field plasticity. Nature 450, 425–429 (2007).

    Article  CAS  PubMed  Google Scholar 

  152. Juliano, S. L., Ma, W. & Eslin, D. Cholinergic depletion prevents expansion of topographic maps in somatosensory cortex. Proc. Natl Acad. Sci. USA 88, 780–784 (1991).

    Article  CAS  PubMed  Google Scholar 

  153. Warburton, E. C. et al. Cholinergic neurotransmission is essential for perirhinal cortical plasticity and recognition memory. Neuron 38, 987–996 (2003).

    Article  CAS  PubMed  Google Scholar 

  154. Easton, A., Ridley, R. M., Baker, H. F. & Gaffan, D. Unilateral lesions of the cholinergic basal forebrain and fornix in one hemisphere and inferior temporal cortex in the opposite hemisphere produce severe learning impairements in rhesus monkeys. Cereb. Cortex 12, 729–736 (2002).

    Article  CAS  PubMed  Google Scholar 

  155. Winkler, J., Suhr, S. T., Gage, F. H., Thal, L. J. & Fisher, L. J. Essential role of neocortical acetylcholine in spatial memory. Nature 375, 484–487 (1995).

    Article  CAS  PubMed  Google Scholar 

  156. Jacobs, B. L. & Azmitia, E. C. Structure and function of the brain serotonin system. Physiol. Rev. 72, 165–229 (1992).

    Article  CAS  PubMed  Google Scholar 

  157. Celada, P., Puig, M. V. & Artigas, F. Serotonin modulation of cortical neurons and networks. Front. Integr. Neurosci. 7, 25 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Nakamura, K., Matsumoto, M. & Hikosaka, O. Reward-dependent modulation of neuronal activity in the primate dorsal raphe nucleus. J. Neurosci. 28, 5331–5343 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Ranade, S. P. & Mainen, Z. F. Transient firing of dorsal raphe neurons encodes diverse and specific sensory, motor, and reward events. J. Neurophysiol. 102, 3026–3037 (2009).

    Article  PubMed  Google Scholar 

  160. Cohen, J. Y., Amoroso, M. W. & Uchida, N. Serotonergic neurons signal reward and punishment on multiple timescales. eLife 4, e06346 (2015).

    Article  CAS  PubMed Central  Google Scholar 

  161. Bromberg-martin, E. S., Hikosaka, O. & Nakamura, K. Coding of task reward value in the dorsal raphe nucleus. J. Neurosci. 30, 6262–6272 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Liu, Z. et al. Dorsal raphe neurons signal reward through 5-HT and glutamate. Neuron 81, 1360–1374 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Nakamura, K. The role of the dorsal raphé nucleus in reward-seeking behavior. Front. Integr. Neurosci. 7, 60 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  164. Jitsuki, S. et al. Serotonin mediates cross-modal reorganization of cortical circuits. Neuron 69, 780–792 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Sara, S. J. & Bouret, S. Orienting and reorienting: the locus coeruleus mediates cognition through arousal. Neuron 76, 130–141 (2012).

    Article  CAS  PubMed  Google Scholar 

  166. Bouret, S. & Richmond, B. J. Sensitivity of locus ceruleus neurons to reward value for goal-directed actions. J. Neurosci. 35, 4005–4014 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Martins, A. R. O. & Froemke, R. C. Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex. Nat. Neurosci. 18, 1483–1492 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Devilbiss, D. M. & Waterhouse, B. D. Phasic and tonic patterns of locus coeruleus output differentially modulate sensory network function in the awake rat. J. Neurophysiol. 105, 69–87 (2011).

    Article  PubMed  Google Scholar 

  169. Pawlak, V., Wickens, J. R., Kirkwood, A. & Kerr, J. N. D. Timing is not everything: neuromodulation opens the STDP gate. Front. Synapt. Neurosci. 2, 146 (2010).

    Article  Google Scholar 

  170. Hu, H. et al. Emotion enhances learning via norepinephrine regulation of AMPA-receptor trafficking. Cell 131, 160–173 (2007).

    Article  CAS  PubMed  Google Scholar 

  171. Johansen, J. P. et al. Hebbian and neuromodulatory mechanisms interact to trigger associative memory formation. Proc. Natl Acad. Sci. 111, E5584–E5592 (2014).

    Article  CAS  PubMed  Google Scholar 

  172. Urbanczik, R. & Senn, W. Reinforcement learning in populations of spiking neurons. Nat. Neurosci. 12, 250–252 (2009).

    Article  CAS  PubMed  Google Scholar 

  173. Seol, G. H. et al. Neuromodulators control the polarity of spike-timing-dependent plasticity. Neuron 55, 919–929 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. He, K. et al. Distinct eligibility traces for LTP and LTD in cortical synapses. Neuron 88, 528–538 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Cassenaer, S. & Laurent, G. Conditional modulation of spike-timing-dependent plasticity for olfactory learning. Nature 482, 47–52 (2012).

    Article  CAS  PubMed  Google Scholar 

  176. Redondo, R. L. & Morris, R. G. M. Making memories last: the synaptic tagging and capture hypothesis. Nat. Rev. Neurosci. 12, 17–30 (2011).

    Article  CAS  PubMed  Google Scholar 

  177. Clopath, C., Ziegler, L., Vasilaki, E., Büsing, L. & Gerstner, W. Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression. PLoS Comput. Biol. 4, e1000248 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Nadim, F. & Bucher, D. Neuromodulation of neurons and synapses. Curr. Opin. Neurobiol. 29, 48–56 (2014).

    Article  CAS  PubMed  Google Scholar 

  179. Blundon, J. A., Bayazitov, I. T. & Zakharenko, S. S. Presynaptic gating of postsynaptically expressed plasticity at mature thalamocortical synapses. J. Neurosci. 31, 16012–16025 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Brzosko, Z., Schultz, W. & Paulsen, O. Retroactive modulation of spike timing-dependent plasticity by dopamine. eLife 4, e09685 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  181. Güçlü, U. & van Gerven, M. A. J. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci. 35, 10005–10014 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Urbanczik, R. & Senn, W. Learning by the dendritic prediction of somatic spiking. Neuron 81, 521–528 (2014).

    Article  CAS  PubMed  Google Scholar 

  183. Schiess, M., Urbanczik, R. & Senn, W. Somato-dendritic synaptic plasticity and error-backpropagation in active dendrites. PLoS Comput. Biol. 12, e1004638 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Scellier, B. & Bengio, Y. Equilibrium propagation: bridging the gap between energy-based models and backpropagation. Front. Comput. Neurosci. 11, 24 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  185. Marblestone, A., Wayne, G. & Kording, K. Towards an integration of deep learning and neuroscience. Front. Comput. Neurosci. 10, 94 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  186. Laramée, M.-E. & Boire, D. Visual cortical areas of the mouse: comparison of parcellation and network structure with primates. Front. Neural Circuits 8, 149 (2015).

    PubMed  PubMed Central  Google Scholar 

  187. Berezovskii, V. K., Nassi, J. J. & Born, R. T. Segregation of feedforward and feedback projections in mouse visual cortex. J. Comp. Neurol. 519, 3672–3683 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  188. Agnati, L. F., Guidolin, D., Guescini, M., Genedani, S. & Fuxe, K. Understanding wiring and volume transmission. Brain Res. Rev. 64, 137–159 (2010).

    Article  PubMed  Google Scholar 

  189. Knott, G. W., Quairiaux, C., Genoud, C. & Welker, E. Formation of dendritic spines with GABAergic synapses by whisker stimulation in induced adult mice. Neuron 34, 265–273 (2002).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank S. Bohte, C. Pennartz, M. Sherman, V. Kehayas and H. Kennedy for helpful input and comments. The work was supported by the Netherlands Organisation for Scientific Research (NWO; ALW grant 823-02-010 to P.R.R.), the European Union Seventh Framework Programme (grant agreement 7202070 'Human Brain Project' to P.R.R. and European Research Council (ERC) grant agreement 339490 'Cortic_al_gorithms' to P.R.R.), the Swiss National Science Foundation (SNF; research grants 31003A-153448 and CRSII3-154453 to A.H. and the National Centre of Competence in Research (NCCR) SYNAPSY grant 51NF40-158776 to A.H.) and the International Foundation for Research in Paraplegia (to A.H.).

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Authors and Affiliations

  1. Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands

    Pieter R. Roelfsema

  2. Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, Netherlands

    Pieter R. Roelfsema

  3. Psychiatry Department, Academic Medical Center, Amsterdam, Netherlands

    Pieter R. Roelfsema

  4. Department of Basic Neurosciences, Geneva Neuroscience Center, Faculty of Medicine, University of Geneva, Geneva, Switzerland

    Anthony Holtmaat

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Contributions

P.R.R. and A.H. researched data for the article, made substantial contributions to discussions of the content, wrote the article and reviewed and/or edited the manuscript before submission.

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Correspondence to Pieter R. Roelfsema.

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PowerPoint slides

Glossary

Reward-prediction errors

(RPEs). Differences between the amount of reward that was expected and the amount that was obtained.

Reinforcement learning

Trial-and-error learning when interacting with an environment and experiencing rewards and punishments as consequences of the chosen actions.

Eligibility traces

Local parameters at the synapses of a network that determine whether they undergo plasticity upon reward-prediction errors during reinforcement learning.

Synaptic tags

Biochemical signals at synapses that determine whether they will undergo plasticity.

Error-backpropagation rule

A mathematical method used to calculate the contribution of connections to the error of a network with multiple layers between input and output.

Derivatives

The derivative of the error function to a synaptic weight is the rate of change of the error when changing the strength of a particular synapse.

Gradient descent

A mathematical optimization method that determines the direction of the vector of changes in all synaptic weights that causes the largest decrease in the error of the network.

Translation invariant

A property of an image processing system whereby the recognition of the object is independent of the object's location relative to the viewer.

Feedback alignment

A process in which, if the feedforward and feedback weights of a neural network are not reciprocal, error backpropagation causes feedforward weights to align; that is, to become more symmetrical.

Optokinetic reflex

The innate reflexive smooth eye movements elicited by large moving visual stimuli.

Frontal eye fields

Area of the frontal cortex involved in the planning of eye movements.

Martinotti cells

Somatostatin-expressing inhibitory interneurons with a characteristic morphology that target the dendritic tufts of pyramidal cells in various cortical layers.

Unsupervised learning

A type of learning in which the structure of unlabelled data is inferred as information about desired categorization is not provided.

Spike-timing-dependent plasticity

(STDP). A plasticity rule whereby the change in the strength of synapses depends on the relative timing of presynaptic and postsynaptic action potentials.

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Roelfsema, P., Holtmaat, A. Control of synaptic plasticity in deep cortical networks. Nat Rev Neurosci 19, 166–180 (2018). https://doi.org/10.1038/nrn.2018.6

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