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  • Review Article
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Illuminating dendritic function with computational models

Nature Reviews Neuroscience volume 21, pages 303–321 (2020)Cite this article

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Abstract

Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal to the beautiful recordings of today, neuroscientists have been striving to unravel the mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires — and drives — new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities.

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Fig. 1: Levels of description in single-cell and circuit models with dendrites.
Fig. 2: Selected modelling predictions that have received experimental support.
Fig. 3: Examples of experimentally supported modelling predictions of dendritic contributions to circuit functions.

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References

  1. Golgi, C. Sulla Fina Anatomia Degli Organi Centrali del Sistema Nervoso. 1885. Reprinted in: On the fine structure of the pes Hippocampi major (with plates XIII–XXIII). Brain Res. Bull. 54, 473 (2001).

    Google Scholar 

  2. Ramon y Cajal, S. Neue Darstellung vom histologischen Bau des Centralnervensystems. Arch. Anat. Physiol. Anat. Abt. Suppl. 319–428 (1893).

  3. Deisseroth, K. Optogenetics. Nat. Methods 8, 26–29 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Grienberger, C. & Konnerth, A. Imaging calcium in neurons. Neuron 73, 862–885 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Brecht, M. et al. Novel approaches to monitor and manipulate single neurons in vivo. J. Neurosci. 24, 9223–9227 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Suzuki, M. & Larkum, M. E. General anesthesia decouples cortical pyramidal neurons. Cell 180, 666–676.e13 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Takahashi, N., Oertner, T. G., Hegemann, P. & Larkum, M. E. Active cortical dendrites modulate perception. Science 354, 1587–1590 (2016). This work is one of the very few experimental studies that causally link dendritic activity in a primary sensory area with a high-level cognitive function, namely sensory perception.

    Article  CAS  PubMed  Google Scholar 

  8. McBride, T. J., Rodriguez-Contreras, A., Trinh, A., Bailey, R. & Debello, W. M. Learning drives differential clustering of axodendritic contacts in the barn owl auditory system. J. Neurosci. 28, 6960–6973 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. De Schutter, E. & Bower, J. M. An active membrane model of the cerebellar Purkinje cell. I. Simulation of current clamps in slice. J. Neurophysiol. 71, 375–400 (1994).

    Article  PubMed  Google Scholar 

  10. Neubig, M., Ulrich, D., Huguenard, J. & Destexhe, A. in Computational Neuroscience (ed. Bower, J. M.) 233–238 (Springer, 1998).

  11. Bower, J. M. The 40-year history of modeling active dendrites in cerebellar Purkinje cells: emergence of the first single cell “community model”. Front. Comput. Neurosci 9, 129 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Laurent, G. & Borst, A. in Dendrites (eds Stuart, G., Spruston, N. & Häusser, M.) 441–463 https://doi.org/10.1093/acprof:oso/9780198566564.001.0001 (Oxford Univ. Press, 2007).

  13. Rall, W. Theory of physiological properties of dendrites. Ann. N. Y. Acad. Sci. 96, 1071–1092 (1962).

    Article  CAS  PubMed  Google Scholar 

  14. Rall, W. in Neural Theory and Modeling (ed. Reiss, R.) 73–97 (Stanford Univ. Press, 1964).

  15. Rall, W. in Dendrites 429–438 https://doi.org/10.1093/acprof:oso/9780198745273.003.0014 (Oxford Univ. Press, 2016).

  16. Cash, S. & Yuste, R. Linear summation of excitatory inputs by CA1 pyramidal neurons. Neuron 22, 383–94 (1999).

    Article  CAS  PubMed  Google Scholar 

  17. Abrahamsson, T., Cathala, L., Matsui, K., Shigemoto, R. & DiGregorio, D. A. Thin dendrites of cerebellar interneurons confer sublinear synaptic integration and a gradient of short-term plasticity. Neuron 73, 1159–1172 (2012).

    Article  CAS  PubMed  Google Scholar 

  18. Tran-Van-Minh, A., Abrahamsson, T., Cathala, L. & DiGregorio, D. A. Differential dendritic integration of synaptic potentials and calcium in cerebellar interneurons. Neuron 91, 837–850 (2016).

    Article  CAS  PubMed  Google Scholar 

  19. Euler, T., Detwiler, P. B. & Denk, W. Directionally selective calcium signals in dendrites of starburst amacrine cells. Nature 418, 845–852 (2002).

    Article  CAS  PubMed  Google Scholar 

  20. Branco, T., Clark, B. a. & Häusser, M. Dendritic discrimination of temporal input sequences in cortical neurons. Science 329, 1671–1675 (2010). This study, by combining experiments with biophysical modelling, reveals that dendrites can detect the activation order of their incoming inputs, confirming Rall’s (1964) early theoretical prediction, owing to the non-linear activation of NMDARs and the impedance gradient along the somato-dendritic axis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Segev, I. What do dendrites and their synapses tell the neuron? J. Neurophysiol. 95, 1295–1297 (2006).

    Article  PubMed  Google Scholar 

  22. Herz, A. V. M., Gollisch, T., Machens, C. K. & Jaeger, D. Modeling single-neuron dynamics and computations: a balance of detail and abstraction. Science 314, 80–85 (2006).

    Article  CAS  PubMed  Google Scholar 

  23. Stuart, G. J. & Spruston, N. Dendritic integration: 60 years of progress. Nat. Neurosci. 18, 1713–1721 (2015).

    Article  CAS  PubMed  Google Scholar 

  24. Nevian, T., Larkum, M. E., Polsky, A. & Schiller, J. Properties of basal dendrites of layer 5 pyramidal neurons: a direct patch-clamp recording study. Nat. Neurosci. 10, 206–214 (2007).

    Article  CAS  PubMed  Google Scholar 

  25. Larkum, M. E., Nevian, T., Sandler, M., Polsky, A. & Schiller, J. Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: a new unifying principle. Science 325, 756–760 (2009).

    Article  CAS  PubMed  Google Scholar 

  26. Wei, D.-S. S. et al. Compartmentalized and binary behavior of terminal dendrites in hippocampal pyramidal neurons. Science 293, 2272–5 (2001).

    Article  CAS  PubMed  Google Scholar 

  27. Llinas, R., Nicholson, C., Freeman, J. A. & Hillman, D. E. Dendritic spikes and their inhibition in alligator Purkinje cells. Science 160, 1132–1135 (1968).

    Article  CAS  PubMed  Google Scholar 

  28. Sjöström, P. J., Rancz, E. A., Roth, A. & Häusser, M. Dendritic excitability and synaptic plasticity. Physiol. Rev. 88, 769–840 (2008).

    Article  PubMed  CAS  Google Scholar 

  29. Larkum, M. E., Zhu, J. J. & Sakmann, B. A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 398, 338–341 (1999).

    Article  CAS  PubMed  Google Scholar 

  30. Poirazi, P., Brannon, T. & Mel, B. W. Pyramidal neuron as two-layer neural network. Neuron 37, 989–999 (2003). This study, using biophysical modelling and ML, is the first to predict that hippocampal pyramidal neurons act as two-layer ANNs, with their dendrites serving as hidden units and the soma as the output unit.

    Article  CAS  PubMed  Google Scholar 

  31. Katz, Y. et al. Synapse distribution suggests a two-stage model of dendritic integration in CA1 pyramidal neurons. Neuron 63, 171–177 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Tzilivaki, A., Kastellakis, G. & Poirazi, P. Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators. Nat. Commun. 10, 3664 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Branco, T. & Häusser, M. The single dendritic branch as a fundamental functional unit in the nervous system. Curr. Opin. Neurobiol. 20, 494–502 (2010).

    Article  CAS  PubMed  Google Scholar 

  34. Wu, X. E. & Mel, B. W. Capacity-enhancing synaptic learning rules in a medial temporal lobe online learning model. Neuron 62, 31–41 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Poirazi, P. & Mel, B. W. Impact of active dendrites and structural plasticity on the memory capacity of neural tissue. Neuron 29, 779–796 (2001).

    Article  CAS  PubMed  Google Scholar 

  36. Frank, A. C. et al. Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory. Nat. Commun. 9, 1–11 (2018).

    Article  CAS  Google Scholar 

  37. Lee, D., Lin, B.-J. & Lee, A. K. Hippocampal place fields emerge upon single-cell manipulation of excitability during behavior. Science 337, 849–853 (2012).

    Article  CAS  PubMed  Google Scholar 

  38. 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). This study, through the combination of in vivo, in vitro and modelling approaches, shows a dendritic mechanism for one-shot learning: a single strong Ca 2+ plateau potential in neuronal dendrites paired with spatial inputs can be sufficient to produce place cells.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sheffield, M. E. & Dombeck, D. A. Dendritic mechanisms of hippocampal place field formation. Curr. Opin. Neurobiol. 54, 1–11 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Wilson, D. E., Whitney, D. E., Scholl, B. & Fitzpatrick, D. Orientation selectivity and the functional clustering of synaptic inputs in primary visual cortex. Nat. Neurosci. 19, 1003–1009 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lavzin, M., Rapoport, S., Polsky, A., Garion, L. & Schiller, J. Nonlinear dendritic processing determines angular tuning of barrel cortex neurons in vivo. Nature 490, 397–401 (2012).

    Article  CAS  PubMed  Google Scholar 

  42. 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 

  43. Fu, M., Yu, X., Lu, J. & Zuo, Y. Repetitive motor learning induces coordinated formation of clustered dendritic spines in vivo. Nature 483, 92–95 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mainen, Z. F. & Sejnowski, T. J. Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382, 363–366 (1996).

    Article  CAS  PubMed  Google Scholar 

  45. Schaefer, A. T., Larkum, M. E., Sakmann, B. & Roth, A. Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. J. Neurophysiol. 89, 3143–3154 (2003).

    Article  PubMed  Google Scholar 

  46. Golding, N. L., Kath, W. L. & Spruston, N. Dichotomy of action-potential backpropagation in CA1 pyramidal neuron dendrites. J. Neurophysiol. 86, 2998–3010 (2001).

    Article  CAS  PubMed  Google Scholar 

  47. Vetter, P., Roth, A. & Häusser, M. Propagation of action potentials in dendrites depends on dendritic morphology. J. Neurophysiol. 85, 926–37 (2001).

    Article  CAS  PubMed  Google Scholar 

  48. Krichmar, J. L., Nasuto, S. J., Scorcioni, R., Washington, S. D. & Ascoli, G. A. Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study. Brain Res. 941, 11–28 (2002).

    Article  CAS  PubMed  Google Scholar 

  49. Komendantov, A. O. & Ascoli, G. A. Dendritic excitability and neuronal morphology as determinants of synaptic efficacy. J. Neurophysiol. 101, 1847–1866 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Zador, A. M., Agmon-Snir, H. & Segev, I. The morphoelectrotonic transform: a graphical approach to dendritic function. J. Neurosci. 15, 1669–1682 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. van Elburg, R. A. J. & van Ooyen, A. Impact of dendritic size and dendritic topology on burst firing in pyramidal cells. PLOS Comput. Biol. 6, e1000781 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Psarrou, M. et al. A simulation study on the effects of dendritic morphology on layer V prefrontal pyramidal cell firing behavior. Front. Cell. Neurosci. 8, 287 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Ferrante, M., Migliore, M. & Ascoli, G. A. Functional impact of dendritic branch-point morphology. J. Neurosci. 33, 2156–2165 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Jarvis, S., Nikolic, K. & Schultz, S. R. Neuronal gain modulability is determined by dendritic morphology: a computational optogenetic study. PLOS Comput. Biol. 14, 1–21 (2018).

    Article  CAS  Google Scholar 

  55. Cuntz, H., Forstner, F., Borst, A. & Häusser, M. One rule to grow them all: a general theory of neuronal branching and its practical application. PLOS Comput. Biol. 6, e1000877 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Connors, B. W. & Regehr, W. G. Neuronal firing: does function follow form? Curr. Biol. 6, 1560–1562 (1996).

    Article  CAS  PubMed  Google Scholar 

  57. Johnston, D., Magee, J. C., Colbert, C. M., Cristie, B. R. & Christie, B. R. Active properties of neuronal dendrites. Ann. Rev. Neurosci. 19, 165–186 (1996).

    Article  CAS  PubMed  Google Scholar 

  58. Poirazi, P., Brannon, T. & Mel, B. W. Arithmetic of subthreshold synaptic summation in a model CA1 pyramidal cell. Neuron 37, 977–987 (2003).

    Article  CAS  PubMed  Google Scholar 

  59. Branco, T. & Häusser, M. Synaptic integration gradients in single cortical pyramidal cell dendrites. Neuron 69, 885–892 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Mel, B. W. Synaptic integration in an excitable dendritic tree. J. Neurophysiol. 70, 1086–1101 (1993).

    Article  CAS  PubMed  Google Scholar 

  61. Archie, K. a. & Mel, B. W. A model for intradendritic computation of binocular disparity. Nat. Neurosci. 3, 54–63 (2000).

    Article  CAS  PubMed  Google Scholar 

  62. Segev, I. & London, M. Untangling dendrites with quantitative models. Science 290, 744–750 (2000).

    Article  CAS  PubMed  Google Scholar 

  63. Stuart, G. & Spruston, N. Determinants of voltage attenuation in neocortical pyramidal neuron dendrites. J. Neurosci. 18, 3501–10 (1998). This early study combines electrophysiology with biophysical modelling to show that voltage attenuation in pyramidal neurons is high, not due to passive membrane properties but because of the higher density of non-uniformly distributed conductances in the distal apical dendrites.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Hay, E., Hill, S., Schürmann, F., Markram, H. & Segev, I. Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLOS Comput. Biol. 7, e1002107 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Traub, R. D., Buhl, E. H., Gloveli, T. & Whittington, M. A. Fast rhythmic bursting can be induced in layer 2/3 cortical neurons by enhancing persistent Na+ conductance or by blocking BK channels. J. Neurophysiol. 89, 909–921 (2003).

    Article  CAS  PubMed  Google Scholar 

  66. Hoffman, D. A., Magee, J. C., Colbert, C. M. & Johnston, D. K+ channel regulation of signal propagation in dendrites of hippocampal pyramidal neurons. Nature 287, 869–875 (1997).

    Article  Google Scholar 

  67. Migliore, M., Hoffman, D. A., Magee, J. C. & Johnston, D. Role of an A-type K+ conductance in the back-propagation of action potentials in the dendrites of hippocampal pyramidal neurons. J. Comput. Neurosci. 7, 5–15 (1999).

    Article  CAS  PubMed  Google Scholar 

  68. Magee, J. C. & Johnston, D. A synaptically controlled, associative signal for Hebbian synaptic plasticity in hippocampal neurons. Science 275, 209–213 (1997).

    Article  CAS  PubMed  Google Scholar 

  69. Migliore, M., Messineo, L. & Ferrante, M. Dendritic Ih selectively blocks temporal summation of unsynchronized distal inputs in CA1 pyramidal neurons. J. Comput. Neurosci. 16, 5–13 (2004).

    Article  CAS  PubMed  Google Scholar 

  70. Ascoli, G. A., Gasparini, S., Medinilla, V. & Migliore, M. Local control of postinhibitory rebound spiking in CA1 pyramidal neuron dendrites. J. Neurosci. 30, 6434–6442 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Migliore, M. & Migliore, R. Know your current I h: interaction with a shunting current explains the puzzling effects of its pharmacological or pathological modulations. PLOS One 7, e36867 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Pavlov, I., Scimemi, A., Savtchenko, L., Kullmann, D. M. & Walker, M. C. Ih-mediated depolarization enhances the temporal precision of neuronal integration. Nat. Commun. 2, 199 (2011).

    Article  PubMed  CAS  Google Scholar 

  73. Ferrarese, L. et al. Dendrite-specific amplification of weak synaptic input during network activity in vivo. Cell Rep. 24, 3455–3465.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Kim, S., Guzman, S. J., Hu, H. & Jonas, P. Active dendrites support efficient initiation of dendritic spikes in hippocampal CA3 pyramidal neurons. Nat. Neurosci. 15, 600–606 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 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 

  76. Losonczy, A. & Magee, J. C. Integrative properties of radial oblique dendrites in hippocampal CA1 pyramidal neurons. Neuron 50, 291–307 (2006).

    Article  CAS  PubMed  Google Scholar 

  77. Sheffield, M. E. J. & Dombeck, D. A. Calcium transient prevalence across the dendritic arbour predicts place field properties. Nature 517, 200–4 (2015).

    Article  CAS  PubMed  Google Scholar 

  78. Schiller, J., Major, G., Koester, H. J. & Schiller, Y. NMDA spikes in basal dendrites of cortical pyramidal neurons. Nature 404, 285–289 (2000).

    Article  CAS  PubMed  Google Scholar 

  79. Wong, R. K. S., Prince, D. A. & Basbaum, A. I. Intradendritic recordings from hippocampal neurons. Proc. Natl Acad. Sci. USA 76, 986–990 (1979).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Stuart, G. J. & Sakmann, B. Active propagation of somatic action potentials into neocortical pyramidal cell dendrites. Nature 367, 69–72 (1994).

    Article  CAS  PubMed  Google Scholar 

  81. Schiller, J., Schiller, Y., Stuart, G. & Sakmann, B. Calcium action potentials restricted to distal apical dendrites of rat neocortical pyramidal neurons. J. Physiol. 505 (Pt 3), 605–616 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Kamondi, A., Acsady, L. & Buzsaki, G. Dendritic spikes are enhanced by cooperative network activity in the intact hioppocampus. J. Neurosci. 18, 3919–3928 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Waters, J., Larkum, M., Sakmann, B. & Helmchen, F. Supralinear Ca2+ influx into dendritic tufts of layer 2/3 neocortical pyramidal neurons in vitro and in vivo. J. Neurosci. 23, 8558–8567 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Larkum, M. E., Waters, J., Sakmann, B. & Helmchen, F. Dendritic spikes in apical dendrites of neocortical layer 2/3 pyramidal neurons. J. Neurosci. 27, 8999–9008 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Traub, R. D. & Llinas, R. Hippocampal pyramidal cells: significance of dendritic ionic conductances for neuronal function and epileptogenesis. J. Neurophysiol. 42, 476–496 (1979).

    Article  CAS  PubMed  Google Scholar 

  86. Traub, R. D., Wong, R. K., Miles, R. & Michelson, H. A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. J. Neurophysiol. 66, 635–650 (1991).

    Article  CAS  PubMed  Google Scholar 

  87. González, J. F. G., Mel, B. W. & Poirazi, P. Distinguishing linear vs. non-linear integration in CA1 radial oblique dendrites: it’s about time. Front. Comput. Neurosci. 5, 1–12 (2011).

    Article  Google Scholar 

  88. Polsky, A., Mel, B. W. & Schiller, J. Computational subunits in thin dendrites of pyramidal cells. Nat. Neurosci. 7, 621–627 (2004).

    Article  CAS  PubMed  Google Scholar 

  89. Chiovini, B. et al. Dendritic spikes induce ripples in parvalbumin interneurons during hippocampal sharp waves. Neuron 82, 908–924 (2014).

    Article  CAS  PubMed  Google Scholar 

  90. Hu, H. & Vervaeke, K. Synaptic integration in cortical inhibitory neuron dendrites. Neuroscience 368, 115–131 (2018).

    Article  CAS  PubMed  Google Scholar 

  91. Cannon, R. C., O’Donnell, C. & Nolan, M. F. Stochastic ion channel gating in dendritic neurons: morphology dependence and probabilistic synaptic activation of dendritic spikes. PLOS Comput. Biol. 6, e1000886 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Rudolph, M. & Destexhe, A. A fast-conducting, stochastic integrative mode for neocortical neurons in vivo. J. Neurosci. 23, 2466–76 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Poleg-Polsky, A. Effects of neural morphology and input distribution on synaptic processing by global and focal NMDA-spikes. PLOS One 10, e0140254 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Doron, M., Chindemi, G., Muller, E., Markram, H. & Segev, I. Timed synaptic inhibition shapes NMDA spikes, influencing local dendritic processing and global I/O properties of cortical neurons. Cell Rep. 21, 1550–1561 (2017).

    Article  CAS  PubMed  Google Scholar 

  95. Rall, W. & Rinzel, J. Branch input resistance and steady attenuation for input to one branch of a dendritic neuron model. Biophys. J. 13, 648–687 (1973).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Magee, J. C. & Cook, E. P. Somatic EPSP amplitude is independent of synapse location in hippocampal pyramidal neurons. Nat. Neurosci. 3, 895–903 (2000).

    Article  CAS  PubMed  Google Scholar 

  97. Williams, S. R. & Stuart, G. J. Dependence of EPSP efficacy on synapse location in neocortical pyramidal neurons. Science 295, 1907–1910 (2002).

    Article  CAS  PubMed  Google Scholar 

  98. Häusser, M. Synaptic function: dendritic democracy. Curr. Biol. 11, R10–R12 (2001).

    Article  PubMed  Google Scholar 

  99. Cook, E. P. & Johnston, D. Active dendrites reduce location-dependent variability of synaptic input trains. J. Neurophysiol. 78, 2116–2128 (1997).

    Article  CAS  PubMed  Google Scholar 

  100. Williams, S. R. & Stuart, G. J. Site independence of EPSP time course is mediated by dendritic I h in neocortical pyramidal neurons. J. Neurophysiol. 83, 3177–3182 (2000).

    Article  CAS  PubMed  Google Scholar 

  101. Mel, B. W. The clusteron: Toward a simple abstraction for a complex neuron. Adv. Neural Inf. Process. Syst. 4, 35–42 (1992).

    Google Scholar 

  102. Schiller, J. & Schiller, Y. NMDA receptor-mediated dendritic spikes and coincident signal amplification. Curr. Opin. Neurobiol. 11, 343–348 (2001).

    Article  CAS  PubMed  Google Scholar 

  103. Araya, R., Vogels, T. P. & Yuste, R. Activity-dependent dendritic spine neck changes are correlated with synaptic strength. Proc. Natl Acad. Sci. USA 111, E2895–E2904 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Tran-Van-Minh, A. et al. Contribution of sublinear and supralinear dendritic integration to neuronal computations. Front. Cell. Neurosci. 9, 67 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Kastellakis, G., Cai, D. J., Mednick, S. C., Silva, A. J. & Poirazi, P. Synaptic clustering within dendrites: an emerging theory of memory formation. Prog. Neurobiol. 126, 19–35 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Larkum, M. E. & Nevian, T. Synaptic clustering by dendritic signalling mechanisms. Curr. Opin. Neurobiol. 18, 321–331 (2008).

    Article  CAS  PubMed  Google Scholar 

  107. Basak, R. & Narayanan, R. Spatially dispersed synapses yield sharply-tuned place cell responses through dendritic spike initiation. J. Physiol. 596, 4173–4205 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Wybo, W. A. M., Torben-Nielsen, B., Nevian, T. & Gewaltig, M.-O. Electrical compartmentalization in neurons. Cell Rep. 26, 1759–1773.e7 (2019). This study introduces an analytical method for identifying the number of independent dendritic subunits that can co-exist in a dendritic tree and highlights how this number may be dynamically regulated by ongoing synaptic activity.

    Article  CAS  PubMed  Google Scholar 

  109. Eberhardt, F., Herz, A. V. M. & Häusler, S. Tuft dendrites of pyramidal neurons operate as feedback-modulated functional subunits. PLOS Comput. Biol. 15, e1006757 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Destexhe, A., Rudolph, M. & Paré, D. The high-conductance state of neocortical neurons in vivo. Nat. Rev. Neurosci. 4, 739–751 (2003).

    Article  CAS  PubMed  Google Scholar 

  111. Williams, S. R. Spatial compartmentalization and functional impact of conductance in pyramidal neurons. Nat. Neurosci. 7, 961–967 (2004).

    Article  CAS  PubMed  Google Scholar 

  112. Górski, T. et al. Dendritic sodium spikes endow neurons with inverse firing rate response to correlated synaptic activity. J. Comput. Neurosci. 45, 223–234 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  113. London, M. & Segev, I. Synaptic scaling in vitro and in vivo. Nat. Neurosci. 4, 853–855 (2001).

    Article  CAS  PubMed  Google Scholar 

  114. London, M., Schreibman, A., Hausser, M., Larkum, M. E. & Segev, I. The information efficacy of a synapse. Nat. Neurosci. 5, 332–340 (2002).

    Article  CAS  PubMed  Google Scholar 

  115. Farinella, M., Ruedt, D. T., Gleeson, P., Lanore, F. & Silver, R. A. Glutamate-bound NMDARs arising from in vivo-like network activity extend spatio-temporal integration in a L5 cortical pyramidal cell model. PLOS Comput. Biol. 10, e1003590 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Sidiropoulou, K. & Poirazi, P. Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons. PLoS Comput. Biol. 8, e1002489 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Behabadi, B. F., Polsky, A., Jadi, M., Schiller, J. & Mel, B. W. Location-dependent excitatory synaptic interactions in pyramidal neuron dendrites. PLOS Comput. Biol. 8, e1002599 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Major, G., Polsky, A., Denk, W., Schiller, J. & Tank, D. W. Spatiotemporally graded NMDA spike/plateau potentials in basal dendrites of neocortical pyramidal neurons. J. Neurophysiol. 99, 2584–2601 (2008).

    Article  CAS  PubMed  Google Scholar 

  119. Larkum, M. E. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 36, 141–151 (2013).

    Article  CAS  PubMed  Google Scholar 

  120. Gasparini, S., Migliore, M. & Magee, J. C. On the initiation and propagation of dendritic spikes in CA1 pyramidal neurons. J. Neurosci. 24, 11046–56 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Shai, A. S., Anastassiou, C. A., Larkum, M. E. & Koch, C. Physiology of layer 5 pyramidal neurons in mouse primary visual cortex: coincidence detection through bursting. PLOS Comput. Biol. 11, e1004090 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  122. Ariav, G., Polsky, A. & Schiller, J. Submillisecond precision of the input–output transformation function mediated by fast sodium dendritic spikes in basal dendrites of CA1 pyramidal neurons. J. Neurosci. 23, 7750–7758 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Agmon-Snir, H. & Segev, I. Signal delay and input synchronization in passive dendritic structures. J. Neurophysiol. 70, 2066–2085 (1993).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  125. Mainen, Z. F., Malinow, R. & Svoboda, K. Synaptic calcium transients in single spines indicate that NMDA receptors are not saturated. Nature 399, 151–155 (1999).

    Article  CAS  PubMed  Google Scholar 

  126. Koch, C., Poggio, T. & Torre, V. Nonlinear interactions in a dendritic tree: localization, timing, and role in information processing. Proc. Natl Acad. Sci. USA 80, 2799–2802 (1983).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Hao, J., Wang, X.-D., Dan, Y., Poo, M.-M. & Zhang, X.-H. An arithmetic rule for spatial summation of excitatory and inhibitory inputs in pyramidal neurons. Proc. Natl Acad. Sci. USA 106, 21906–21911 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Gidon, A. & Segev, I. Principles governing the operation of synaptic inhibition in dendrites. Neuron 75, 330–341 (2012).

    Article  CAS  PubMed  Google Scholar 

  129. Rhodes, P. The properties and implications of NMDA spikes in neocortical pyramidal cells. J. Neurosci. 26, 6704–15 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Jadi, M., Polsky, A., Schiller, J. & Mel, B. W. Location-dependent effects of inhibition on local spiking in pyramidal neuron dendrites. PLOS Comput. Biol. 8, e1002550 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Wilmes, K. A., Sprekeler, H. & Schreiber, S. Inhibition as a binary switch for excitatory plasticity in pyramidal neurons. PLOS Comput. Biol. 12, e1004768 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  132. Lovett-Barron, M. et al. Regulation of neuronal input transformations by tunable dendritic inhibition. Nat. Neurosci. 15, 423–430 (2012).

    Article  CAS  PubMed  Google Scholar 

  133. Bloss, E. B. et al. Structured dendritic inhibition supports branch-selective integration in CA1 pyramidal cells. Neuron 89, 1016–1030 (2016).

    Article  CAS  PubMed  Google Scholar 

  134. Iascone, D. M. et al. Whole-neuron synaptic mapping reveals spatially precise excitatory/inhibitory balance limiting dendritic and somatic spiking. Neuron https://doi.org/10.1016/j.neuron.2020.02.015 (2020). This elegant study combines experiments with modelling to show a fine-scale balance in the number of inhibitory and excitatory synapses within individual dendrites, which is predicted to dampen dendritic voltage fluctuations and strongly impacts neuronal responses.

    Article  PubMed  PubMed Central  Google Scholar 

  135. Defelipe, J. The evolution of the brain, the human nature of cortical circuits, and intellectual creativity. Front. Neuroanat. 5, 29 (2011).

    PubMed  PubMed Central  Google Scholar 

  136. Mohan, H. et al. Dendritic and axonal architecture of individual pyramidal neurons across layers of adult human neocortex. Cereb. Cortex 25, 4839–4853 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Eyal, G., Mansvelder, H. D., de Kock, C. P. J. & Segev, I. Dendrites impact the encoding capabilities of the axon. J. Neurosci. 34, 8063–8071 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Deitcher, Y. et al. Comprehensive morpho-electrotonic analysis shows 2 distinct classes of L2 and L3 pyramidal neurons in human temporal cortex. Cereb. Cortex 27, 5398–5414 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  139. Beaulieu-Laroche, L. et al. Enhanced dendritic compartmentalization in human cortical neurons. Cell 175, 643–651.e14 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Gidon, A. et al. Dendritic action potentials and computation in human layer 2/3 cortical neurons. Science 367, 83–87 (2020). This study, through a combination of electrophysiology and biophysical modelling, discovers a new type of dendritic action potential that enables human neurons — or, in fact, their dendrites — to solve the XOR problem.

    Article  CAS  PubMed  Google Scholar 

  141. Eyal, G. et al. Human cortical pyramidal neurons: from spines to spikes via models. Front. Cell. Neurosci. 12, 1–24 (2018).

    Article  CAS  Google Scholar 

  142. Boldog, E. et al. Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type. Nat. Neurosci. 21, 1185–1195 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Kaifosh, P. & Losonczy, A. Mnemonic functions for nonlinear dendritic integration in hippocampal pyramidal circuits. Neuron 90, 622–634 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Behabadi, B. F. & Mel, B. W. Mechanisms underlying subunit independence in pyramidal neuron dendrites. Proc. Natl Acad. Sci. USA 111, 498–503 (2014).

    Article  CAS  PubMed  Google Scholar 

  145. Jadi, M., Behabadi, B. F., Poleg-Polsky, A., Schiller, J. & Mel, B. W. An augmented two-layer model captures nonlinear analog spatial integration effects in pyramidal neuron dendrites. Proc. IEEE 102, 782–798 (2014).

    Article  Google Scholar 

  146. Hoffman, D. A. & Johnston, D. Neuromodulation of dendritic action potentials. J. Neurophysiol. 81, 408–411 (1999).

    Article  CAS  PubMed  Google Scholar 

  147. Losonczy, A., Makara, J. K. & Magee, J. C. Compartmentalized dendritic plasticity and input feature storage in neurons. Nature 452, 436–441 (2008).

    Article  CAS  PubMed  Google Scholar 

  148. Li, S. et al. Dendritic computations captured by an effective point neuron model. Proc. Natl Acad. Sci. USA 116, 15244–15252 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Aspart, F., Ladenbauer, J. & Obermayer, K. Extending integrate-and-fire model neurons to account for the effects of weak electric fields and input filtering mediated by the dendrite. PLOS Comput. Biol. 12, e1005206 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  150. Zhou, D., Li, S., Zhang, X. & Cai, D. Phenomenological incorporation of nonlinear dendritic integration using integrate-and-fire neuronal frameworks. PLOS ONE 8, e53508 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Naud, R., Bathellier, B. & Gerstner, W. Spike-timing prediction in cortical neurons with active dendrites. Front. Comput. Neurosci. 8, 90 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  152. Memmesheimer, R.-M. Quantitative prediction of intermittent high-frequency oscillations in neural networks with supralinear dendritic interactions. Proc. Natl. Acad. Sci. 107, 11092–11097 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Kastellakis, G., Silva, A. J. & Poirazi, P. Linking memories across time via neuronal and dendritic overlaps in model neurons with active dendrites. Cell Rep. 17, 1491–1504 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Ujfalussy, B. B., Makara, J. K., Lengyel, M. & Branco, T. Global and multiplexed dendritic computations under in vivo-like conditions. Neuron 100, 579–592.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Kousanakis, E. et al. in 2017 IEEE 25th Annual Int. Symp. Field-Programmable Custom Computing Machines (FCCM) 56–63 https://doi.org/10.1109/FCCM.2017.51 (IEEE, 2017).

  156. Aamir, S.-A. et al. A mixed-signal structured AdEx neuron for accelerated neuromorphic cores. IEEE Trans. Biomed. Circuits Syst. 12, 1027–1037 (2018).

    Article  PubMed  Google Scholar 

  157. Roy, S., Banerjee, A. & Basu, A. Liquid state machine with dendritically enhanced readout for low-power, neuromorphic VLSI implementations. IEEE Trans. Biomed. Circuits Syst. 8, 681–695 (2014).

    Article  PubMed  Google Scholar 

  158. Hussain, S., Liu, S.-C. & Basu, A. Hardware-amenable structural learning for spike-based pattern classification using a simple model of active dendrites. Neural Comput. 27, 845–897 (2015).

    Article  PubMed  Google Scholar 

  159. Sacramento, J., Costa, R. P., Bengio, Y. & Senn, W. Dendritic cortical microcircuits approximate the backpropagation algorithm. Adv. Neural Inf. Process. Syst. 31, 8721–8732 (2018). This work presents circuit-level implementation of a biologically plausible backpropagation algorithm, where the prediction error is produced by a mismatch of feedback excitation and local inhibition in the apical dendrites of model neurons.

    Google Scholar 

  160. Guerguiev, J., Lillicrap, T. P. & Richards, B. A. Towards deep learning with segregated dendrites. eLife 6, 22901 (2017).

    Article  Google Scholar 

  161. Wu, X., Liu, X., Li, W. & Wu, Q. Improved expressivity through dendritic neural networks. Adv. Neural Inf. Process. Syst. 31, 8057–8068 (2018).

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  163. Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Bastos, A. M. et al. Canonical microcircuits for predictive coding. J. Neuron 76, 695–711 (2012).

    CAS  Google Scholar 

  165. 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 

  166. 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 

  167. 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 

  168. Hertäg, L. & Sprekeler, H. Amplifying the redistribution of somato-dendritic inhibition by the interplay of three interneuron types. PLOS Comput. Biol. 15, 1–29 (2019).

    Article  CAS  Google Scholar 

  169. Pouille, F. & Scanziani, M. Routing of spike series by dynamic circuits in the hippocampus. Nature 429, 717–723 (2004).

    Article  CAS  PubMed  Google Scholar 

  170. Naud, R. & Sprekeler, H. Sparse bursts optimize information transmission in a multiplexed neural code. Proc. Natl Acad. Sci. USA 115, E6329–E6338 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Murayama, M. et al. Dendritic encoding of sensory stimuli controlled by deep cortical interneurons. Nature 457, 1137–1141 (2009).

    Article  CAS  PubMed  Google Scholar 

  172. Yang, G. R., Murray, J. D. & Wang, X.-J. A dendritic disinhibitory circuit mechanism for pathway-specific gating. Nat. Commun. 7, 12815 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Zhang, S. et al. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345, 660–665 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Roelfsema, P. R. & Holtmaat, A. Reply to ‘Can neocortical feedback alter the sign of plasticity?’ Nat. Rev. Neurosci. 19, 637–638 (2018).

    Article  CAS  PubMed  Google Scholar 

  176. Williams, L. E. & Holtmaat, A. Higher-order thalamocortical inputs gate synaptic long-term potentiation via disinhibition. Neuron 101, 91–102.e4 (2019).

    Article  CAS  PubMed  Google Scholar 

  177. Attinger, A., Wang, B. & Keller, G. B. Visuomotor coupling shapes the functional development of mouse visual cortex. Cell 169, 1291–1302.e14 (2017).

    Article  CAS  PubMed  Google Scholar 

  178. Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015).

    Article  CAS  PubMed  Google Scholar 

  179. Arkhipov, A. et al. Visual physiology of the layer 4 cortical circuit in silico. PLOS Comput. Biol. 14, e1006535 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  180. Pesaran, B. et al. Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nat. Neurosci. 21, 903–919 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  182. Lindén, H. et al. Modeling the spatial reach of the LFP. Neuron 72, 859–872 (2011).

    Article  PubMed  CAS  Google Scholar 

  183. Reimann, M. W. et al. A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron 79, 375–390 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Ness, T. V., Remme, M. W. H. & Einevoll, G. T. H-type membrane current shapes the local field potential from populations of pyramidal neurons. J. Neurosci. 38, 6011–6024 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. Suzuki, M. & Larkum, M. E. Dendritic calcium spikes are clearly detectable at the cortical surface. Nat. Commun. 8, 276 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  186. Lisman, J. et al. Viewpoints: how the hippocampus contributes to memory, navigation and cognition. Nat. Neurosci. 20, 1434–1447 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  187. Rolls, E. T. The mechanisms for pattern completion and pattern separation in the hippocampus. Front. Syst. Neurosci. 7, 74 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  188. O’Keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Clarendon Press, 1978).

  189. Danielson, N. B. et al. Distinct contribution of adult-born hippocampal granule cells to context encoding. Neuron 90, 101–112 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  190. Chavlis, S., Petrantonakis, P. C. & Poirazi, P. Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity. Hippocampus 27, 89–110 (2017).

    Article  PubMed  Google Scholar 

  191. Cayco-Gajic, N. A. & Silver, R. A. Re-evaluating circuit mechanisms underlying pattern separation. Neuron 101, 584–602 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Migliore, M., Novara, G. & Tegolo, D. Single neuron binding properties and the magical number 7. Hippocampus 18, 1122–1130 (2008).

    Article  PubMed  Google Scholar 

  193. Turi, G. F. et al. Vasoactive intestinal polypeptide-expressing interneurons in the hippocampus support goal-oriented spatial learning. Neuron 101, 1150–1165.e8 (2019). In this synergetic study, experiments show that diverse populations of VIP + interneurons control learning-induced place cell enrichment in CA1; modelling explained that the disinhibitory population of VIP + neurons was responsible for this effect.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  194. Shuman, T. et al. Breakdown of spatial coding and interneuron synchronization in epileptic mice. Nat. Neurosci. 23, 229–238 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  195. Bezaire, M. J., Raikov, I., Burk, K., Vyas, D. & Soltesz, I. Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit. eLife 5, 1–106 (2016).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  197. Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat. Neurosci. 9, 534–42 (2006).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  200. Papoutsi, A., Sidiropoulou, K. & Poirazi, P. Dendritic nonlinearities reduce network size requirements and mediate ON and OFF states of persistent activity in a PFC microcircuit model. PLOS Comput. Biol. 10, e1003764 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  201. Papoutsi, A., Kastellakis, G. & Poirazi, P. Basal tree complexity shapes functional pathways in the prefrontal cortex. J. Neurophysiol. 118, 1970–1983, https://doi.org/10.1152/jn.00099.2017 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  203. Schmidt-Hieber, C. et al. Active dendritic integration as a mechanism for robust and precise grid cell firing. Nat. Neurosci. 20, 1114–1121 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  204. Wang, X.-J., Tegner, J., Constantinidis, C. & Goldman-Rakic, P. S. Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory. Proc. Natl Acad. Sci. USA 101, 1368–1373 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  205. Konstantoudaki, X., Papoutsi, A., Chalkiadaki, K., Poirazi, P. & Sidiropoulou, K. Modulatory effects of inhibition on persistent activity in a cortical microcircuit model. Front. Neural Circuits 8, 7 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  206. Mel, B. W., Ruderman, D. L. & Archie, K. A. Translation-invariant orientation tuning in visual ‘complex’ cells could derive from intradendritic computations. J. Neurosci. 18, 4325–4334 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. Cazé, R. D., Jarvis, S., Foust, A. J. & Schultz, S. R. Dendrites enable a robust mechanism for neuronal stimulus selectivity. Neural Comput. 29, 2511–2527 (2017).

    Article  PubMed  Google Scholar 

  208. Jia, H., Rochefort, N. L., Chen, X. & Konnerth, A. Dendritic organization of sensory input to cortical neurons in vivo. Nature 464, 1307–1312 (2010).

    Article  CAS  PubMed  Google Scholar 

  209. Park, J. et al. Contribution of apical and basal dendrites of L2/3 pyramidal neurons to orientation encoding in mouse V1. Nat. Commun. 10, 5372 (2019). The laser ablation technique introduced in this study allows the causal manipulation of dendritic contributions to function; experiments showed no effect of apical-tree ablation in orientation-tuning properties, whereas modelling predicted that a diverse structure of inputs to the basal dendrites best explains experimental results.

    Article  PubMed  PubMed Central  Google Scholar 

  210. Lee, K. S., Vandemark, K., Mezey, D., Shultz, N. & Fitzpatrick, D. Functional synaptic architecture of callosal inputs in mouse primary visual cortex. Neuron 101, 421–428.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  211. Druckmann, S. et al. Structured synaptic connectivity between hippocampal regions. Neuron 81, 629–640 (2014).

    Article  CAS  PubMed  Google Scholar 

  212. Li, Y., Ibrahim, L. A., Liu, B., Zhang, L. I. & Tao, H. W. Linear transformation of thalamocortical input by intracortical excitation. Nat. Neurosci. 16, 1324–1330 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  213. Hay, E. & Segev, I. Dendritic excitability and gain control in recurrent cortical microcircuits. Cereb. Cortex 25, 3561–3571 (2015).

    Article  PubMed  Google Scholar 

  214. Amsalem, O., Van Geit, W., Muller, E., Markram, H. & Segev, I. From neuron biophysics to orientation selectivity in electrically coupled networks of neocortical L2/3 large basket cells. Cereb. Cortex 26, 3655–3668 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  215. Bono, J., Wilmes, K. A. & Clopath, C. Modelling plasticity in dendrites: from single cells to networks. Curr. Opin. Neurobiol. 46, 136–141 (2017).

    Article  CAS  PubMed  Google Scholar 

  216. Legenstein, R. & Maass, W. Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. J. Neurosci. 31, 10787–10802 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  217. O’Donnell, C. & Sejnowski, T. J. Selective memory generalization by spatial patterning of protein synthesis. Neuron 82, 398–412 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  218. Bono, J. & Clopath, C. Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level. Nat. Commun. 8, 706 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  219. Wu, X., Mel, G. C., Strouse, D. J. & Mel, B. W. How dendrites affect online recognition memory. PLOS Comput. Biol. 15, e1006892 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  221. Carandini, M. From circuits to behavior: a bridge too far? Nat. Neurosci. 15, 507–509 (2012).

    Article  CAS  PubMed  Google Scholar 

  222. Sheffield, M. E. J., Adoff, M. D. & Dombeck, D. A. Increased prevalence of calcium transients across the dendritic arbor during place field formation. Neuron 96, 490–504 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  223. Druckmann, S. et al. Effective stimuli for constructing reliable neuron models. PLOS Comput. Biol. 7, e1002133 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  224. Brookings, T., Goeritz, M. L. & Marder, E. Automatic parameter estimation of multicompartmental neuron models via minimization of trace error with control adjustment. J. Neurophysiol. 112, 2332–2348 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  225. Gerstner, W. & Kistler, W. M. Spiking Neuron Models https://doi.org/10.1017/CBO9780511815706 (Cambridge Univ. Press, 2002).

  226. Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M. & Tolias, A. S. Engineering a less artificial intelligence. Neuron 103, 967–979 (2019).

    Article  CAS  PubMed  Google Scholar 

  227. Richards, B. A. et al. A deep learning framework for neuroscience. Nat. Neurosci. 22, 1761–1770 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  228. Golding, N. L. & Spruston, N. Dendritic sodium spikes are variable triggers of axonal action potentials in hippocampal CA1 pyramidal neurons. Neuron 21, 1189–200 (1998).

    Article  CAS  PubMed  Google Scholar 

  229. Larkum, M. E., Kaiser, K. M. & Sakmann, B. Calcium electrogenesis in distal apical dendrites of layer 5 pyramidal cells at a critical frequency of back-propagating action potentials. Proc. Natl Acad. Sci. USA 96, 14600–14604 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  230. Takahashi, N. et al. Locally synchronized synaptic inputs. Science 335, 353–356 (2012).

    Article  CAS  PubMed  Google Scholar 

  231. Chklovskii, D., Mel, B. W. & Svoboda, K. Cortical rewiring and information storage. Nature 431, 782–788 (2004).

    Article  CAS  PubMed  Google Scholar 

  232. Kazemipour, A. et al. Kilohertz frame-rate two-photon tomography. Nat. Methods 16, 778–786 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  233. Cotton, R. J., Froudarakis, E., Storer, P., Saggau, P. & Tolias, A. S. Three-dimensional mapping of microcircuit correlation structure. Front. Neural Circuits 7, 151 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  234. Piatkevich, K. D. et al. Population imaging of neural activity in awake behaving mice. Nature 574, 413–417 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  235. Abdelfattah, A. S. et al. Bright and photostable chemigenetic indicators for extended in vivo voltage imaging. Science 365, 699–704 (2019).

    Article  CAS  PubMed  Google Scholar 

  236. Kim, J. et al. mGRASP enables mapping mammalian synaptic connectivity with light microscopy. Nat. Methods 9, 96–102 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  237. Kinoshita, N. et al. Genetically encoded fluorescent indicator GRAPHIC delineates intercellular connections. iScience 15, 28–38 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  238. 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 

  239. Redmond, L., Kashani, A. H. & Ghosh, A. Calcium regulation of dendritic growth via CaM kinase IV and CREB-mediated transcription. Neuron 34, 999–1010 (2002).

    Article  CAS  PubMed  Google Scholar 

  240. Zhu, G., Du, L., Jin, L. & Offenhäusser, A. Effects of morphology constraint on electrophysiological properties of cortical neurons. Sci. Rep. 6, 23086 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  241. Blankvoort, S., Witter, M. P., Noonan, J., Cotney, J. & Kentros, C. Marked diversity of unique cortical enhancers enables neuron-specific tools by enhancer-driven gene expression. Curr. Biol. 28, 2103–2114.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank A. Losonczy, N. Takahashi, E. Froudarakis and members of the Poirazi laboratory for critical reading of the manuscript. This work was supported by the Alexander von Humboldt-Stiftung (P.P.), the European Commission FET Open grant (NEUREKA, 863245) (P.P.), and the Brain & Behavior Research Foundation NARSAD Young Investigator Award (27606) (A.P.).

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Authors and Affiliations

  1. Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology — Hellas, Heraklion, Crete, Greece

    Panayiota Poirazi & Athanasia Papoutsi

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  1. Panayiota Poirazi
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  2. Athanasia Papoutsi
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The authors contributed equally to all aspects of the article.

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Correspondence to Panayiota Poirazi.

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Nature Reviews Neuroscience thanks T. Branco, A. Destexhe and H. Sprekeler for their contribution to the peer review of this work.

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Related links

Brain Observatory at the Allen Brain Institute: http://observatory.brain-map.org/visualcoding/

BRIAN2: https://brian2.readthedocs.io/en/stable/

GENESIS: http://genesis-sim.org/

Hippocampome: http://hippocampome.org/php/index.php

ICGenealogy: https://icg.neurotheory.ox.ac.uk/

LFPy: https://lfpy.readthedocs.io/en/latest/

ModelDB: https://senselab.med.yale.edu/modeldb/

MouseLight: https://www.janelia.org/project-team/mouselight

NEURON: https://neuron.yale.edu/neuron/

NeuroMorpho: http://neuromorpho.org/

OpenSourceBrain: https://opensourcebrain.org

The Neocortical Microcircuit Collaboration Portal: https://bbp.epfl.ch/nmc-portal/welcome

Trees toolbox: https://www.treestoolbox.org/

Glossary terms

Coincidence detectors

Parts of a neuron and/or neural circuit that show a supralinear increase of response upon coincident arrival of different input pathways.

Artificial neural networks

(ANNs). Versatile networks with weighted, directed connections organized in layers. ANNs are mathematical models capable of learning and are used mostly for classification tasks.

Ionic channels

Protein structures that span the cell membrane, enabling the (selective) passage of ions from one side of the membrane to the other through the channel pore.

Backpropagation

In neurophysiology, the active regeneration of somatic action potentials travelling backwards into the dendrites.

H-current

An inward current generated by the opening of hyperpolarization-activated cyclic nucleotide-gated cation channels; critical for synaptic integration and plasticity.

Point neuron view

Consideration of a neuron as a summation unit with a non-linear activation function and no internal (dendritic) morphology.

Active dendrites

Dendrites equipped with voltage-dependent ionic conductances.

Fast-spiking (FS) basket cells

Inhibitory neurons characterized by brief and high-frequency action potentials. Usually, FS basket cells innervate the perisomatic region of pyramidal neurons and other interneurons.

Synaptic democracy

Location independence of the efficacy of synaptic inputs in evoking somatic depolarization and/or action potentials.

Place cell

A type of neuron mostly found in the hippocampus that fires at a high rate whenever an animal enters a particular location (place field) within its environment.

Global dendritic spikes

Non-linear depolarizations generated en masse in the dendrites of neurons, usually in response to dispersed input.

AP initiation zones

Specialized domains of a neuron enriched with sodium and potassium channels, where propagated synaptic potentials are summated and an action potential (AP) is initiated.

Inhibitory shunt

Activation of an inhibitory synapse that adds a conductance value to the membrane. This reduces input resistance and thus has a divisive effect on excitatory inputs.

Associative memory engrams

Memory traces that consist of different types of information that become bound together, possibly through their storage in common neurons.

Linear–non-linear (LN) models

Phenomenological models in which the outputs are estimated by successively applying linear temporal filters to the inputs, followed by static non-linear transformations.

Perceptrons

Binary classification algorithms each consisting of weighted inputs, a bias and a thresholding function that generates an output decision.

Predictive coding

The comparison of sensory inputs with prior expectations (to create a ‘prediction’), and propagation of an ‘error’ signal to the brain areas responsible for the expectations.

Short-term depression

(STD). A negative change in postsynaptic potentials following repetitive stimulation of a synapse.

Short-term facilitation

(STF). A positive change in postsynaptic potentials following repetitive stimulation of a synapse.

Field potentials

Extracellular measurements of the activity of a population of neurons, reflecting neuronal transmembrane currents that are mainly due to synaptic activity.

Pattern separation

The process that minimizes the overlap between the neuronal populations that encode for similar input patterns.

Pattern completion

The process during which a learned pattern is recalled upon presentation of a degraded or partial version of the original stimulus.

Theta-cycle phase precession

The advancement of spike timing of a particular place cell to earlier phases of the theta cycle as the animal passes through its place field.

Spike timing-dependent plasticity

A type of Hebbian learning where plasticity is regulated by the relative timing of the presynaptic and postsynaptic action potentials.

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Poirazi, P., Papoutsi, A. Illuminating dendritic function with computational models. Nat Rev Neurosci 21, 303–321 (2020). https://doi.org/10.1038/s41583-020-0301-7

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