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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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).
Ramon y Cajal, S. Neue Darstellung vom histologischen Bau des Centralnervensystems. Arch. Anat. Physiol. Anat. Abt. Suppl. 319–428 (1893).
Deisseroth, K. Optogenetics. Nat. Methods 8, 26–29 (2011).
Grienberger, C. & Konnerth, A. Imaging calcium in neurons. Neuron 73, 862–885 (2012).
Brecht, M. et al. Novel approaches to monitor and manipulate single neurons in vivo. J. Neurosci. 24, 9223–9227 (2004).
Suzuki, M. & Larkum, M. E. General anesthesia decouples cortical pyramidal neurons. Cell 180, 666–676.e13 (2020).
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.
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).
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).
Neubig, M., Ulrich, D., Huguenard, J. & Destexhe, A. in Computational Neuroscience (ed. Bower, J. M.) 233–238 (Springer, 1998).
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).
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).
Rall, W. Theory of physiological properties of dendrites. Ann. N. Y. Acad. Sci. 96, 1071–1092 (1962).
Rall, W. in Neural Theory and Modeling (ed. Reiss, R.) 73–97 (Stanford Univ. Press, 1964).
Rall, W. in Dendrites 429–438 https://doi.org/10.1093/acprof:oso/9780198745273.003.0014 (Oxford Univ. Press, 2016).
Cash, S. & Yuste, R. Linear summation of excitatory inputs by CA1 pyramidal neurons. Neuron 22, 383–94 (1999).
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).
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).
Euler, T., Detwiler, P. B. & Denk, W. Directionally selective calcium signals in dendrites of starburst amacrine cells. Nature 418, 845–852 (2002).
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.
Segev, I. What do dendrites and their synapses tell the neuron? J. Neurophysiol. 95, 1295–1297 (2006).
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).
Stuart, G. J. & Spruston, N. Dendritic integration: 60 years of progress. Nat. Neurosci. 18, 1713–1721 (2015).
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).
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).
Wei, D.-S. S. et al. Compartmentalized and binary behavior of terminal dendrites in hippocampal pyramidal neurons. Science 293, 2272–5 (2001).
Llinas, R., Nicholson, C., Freeman, J. A. & Hillman, D. E. Dendritic spikes and their inhibition in alligator Purkinje cells. Science 160, 1132–1135 (1968).
Sjöström, P. J., Rancz, E. A., Roth, A. & Häusser, M. Dendritic excitability and synaptic plasticity. Physiol. Rev. 88, 769–840 (2008).
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).
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.
Katz, Y. et al. Synapse distribution suggests a two-stage model of dendritic integration in CA1 pyramidal neurons. Neuron 63, 171–177 (2009).
Tzilivaki, A., Kastellakis, G. & Poirazi, P. Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators. Nat. Commun. 10, 3664 (2019).
Branco, T. & Häusser, M. The single dendritic branch as a fundamental functional unit in the nervous system. Curr. Opin. Neurobiol. 20, 494–502 (2010).
Wu, X. E. & Mel, B. W. Capacity-enhancing synaptic learning rules in a medial temporal lobe online learning model. Neuron 62, 31–41 (2009).
Poirazi, P. & Mel, B. W. Impact of active dendrites and structural plasticity on the memory capacity of neural tissue. Neuron 29, 779–796 (2001).
Frank, A. C. et al. Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory. Nat. Commun. 9, 1–11 (2018).
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).
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.
Sheffield, M. E. & Dombeck, D. A. Dendritic mechanisms of hippocampal place field formation. Curr. Opin. Neurobiol. 54, 1–11 (2019).
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).
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).
Xu, N. et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature 492, 247–251 (2012).
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).
Mainen, Z. F. & Sejnowski, T. J. Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382, 363–366 (1996).
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).
Golding, N. L., Kath, W. L. & Spruston, N. Dichotomy of action-potential backpropagation in CA1 pyramidal neuron dendrites. J. Neurophysiol. 86, 2998–3010 (2001).
Vetter, P., Roth, A. & Häusser, M. Propagation of action potentials in dendrites depends on dendritic morphology. J. Neurophysiol. 85, 926–37 (2001).
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).
Komendantov, A. O. & Ascoli, G. A. Dendritic excitability and neuronal morphology as determinants of synaptic efficacy. J. Neurophysiol. 101, 1847–1866 (2009).
Zador, A. M., Agmon-Snir, H. & Segev, I. The morphoelectrotonic transform: a graphical approach to dendritic function. J. Neurosci. 15, 1669–1682 (1995).
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).
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).
Ferrante, M., Migliore, M. & Ascoli, G. A. Functional impact of dendritic branch-point morphology. J. Neurosci. 33, 2156–2165 (2013).
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).
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).
Connors, B. W. & Regehr, W. G. Neuronal firing: does function follow form? Curr. Biol. 6, 1560–1562 (1996).
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).
Poirazi, P., Brannon, T. & Mel, B. W. Arithmetic of subthreshold synaptic summation in a model CA1 pyramidal cell. Neuron 37, 977–987 (2003).
Branco, T. & Häusser, M. Synaptic integration gradients in single cortical pyramidal cell dendrites. Neuron 69, 885–892 (2011).
Mel, B. W. Synaptic integration in an excitable dendritic tree. J. Neurophysiol. 70, 1086–1101 (1993).
Archie, K. a. & Mel, B. W. A model for intradendritic computation of binocular disparity. Nat. Neurosci. 3, 54–63 (2000).
Segev, I. & London, M. Untangling dendrites with quantitative models. Science 290, 744–750 (2000).
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.
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).
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).
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).
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).
Magee, J. C. & Johnston, D. A synaptically controlled, associative signal for Hebbian synaptic plasticity in hippocampal neurons. Science 275, 209–213 (1997).
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).
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).
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).
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).
Ferrarese, L. et al. Dendrite-specific amplification of weak synaptic input during network activity in vivo. Cell Rep. 24, 3455–3465.e5 (2018).
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).
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).
Losonczy, A. & Magee, J. C. Integrative properties of radial oblique dendrites in hippocampal CA1 pyramidal neurons. Neuron 50, 291–307 (2006).
Sheffield, M. E. J. & Dombeck, D. A. Calcium transient prevalence across the dendritic arbour predicts place field properties. Nature 517, 200–4 (2015).
Schiller, J., Major, G., Koester, H. J. & Schiller, Y. NMDA spikes in basal dendrites of cortical pyramidal neurons. Nature 404, 285–289 (2000).
Wong, R. K. S., Prince, D. A. & Basbaum, A. I. Intradendritic recordings from hippocampal neurons. Proc. Natl Acad. Sci. USA 76, 986–990 (1979).
Stuart, G. J. & Sakmann, B. Active propagation of somatic action potentials into neocortical pyramidal cell dendrites. Nature 367, 69–72 (1994).
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).
Kamondi, A., Acsady, L. & Buzsaki, G. Dendritic spikes are enhanced by cooperative network activity in the intact hioppocampus. J. Neurosci. 18, 3919–3928 (1998).
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).
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).
Traub, R. D. & Llinas, R. Hippocampal pyramidal cells: significance of dendritic ionic conductances for neuronal function and epileptogenesis. J. Neurophysiol. 42, 476–496 (1979).
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).
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).
Polsky, A., Mel, B. W. & Schiller, J. Computational subunits in thin dendrites of pyramidal cells. Nat. Neurosci. 7, 621–627 (2004).
Chiovini, B. et al. Dendritic spikes induce ripples in parvalbumin interneurons during hippocampal sharp waves. Neuron 82, 908–924 (2014).
Hu, H. & Vervaeke, K. Synaptic integration in cortical inhibitory neuron dendrites. Neuroscience 368, 115–131 (2018).
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).
Rudolph, M. & Destexhe, A. A fast-conducting, stochastic integrative mode for neocortical neurons in vivo. J. Neurosci. 23, 2466–76 (2003).
Poleg-Polsky, A. Effects of neural morphology and input distribution on synaptic processing by global and focal NMDA-spikes. PLOS One 10, e0140254 (2015).
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).
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).
Magee, J. C. & Cook, E. P. Somatic EPSP amplitude is independent of synapse location in hippocampal pyramidal neurons. Nat. Neurosci. 3, 895–903 (2000).
Williams, S. R. & Stuart, G. J. Dependence of EPSP efficacy on synapse location in neocortical pyramidal neurons. Science 295, 1907–1910 (2002).
Häusser, M. Synaptic function: dendritic democracy. Curr. Biol. 11, R10–R12 (2001).
Cook, E. P. & Johnston, D. Active dendrites reduce location-dependent variability of synaptic input trains. J. Neurophysiol. 78, 2116–2128 (1997).
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).
Mel, B. W. The clusteron: Toward a simple abstraction for a complex neuron. Adv. Neural Inf. Process. Syst. 4, 35–42 (1992).
Schiller, J. & Schiller, Y. NMDA receptor-mediated dendritic spikes and coincident signal amplification. Curr. Opin. Neurobiol. 11, 343–348 (2001).
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).
Tran-Van-Minh, A. et al. Contribution of sublinear and supralinear dendritic integration to neuronal computations. Front. Cell. Neurosci. 9, 67 (2015).
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).
Larkum, M. E. & Nevian, T. Synaptic clustering by dendritic signalling mechanisms. Curr. Opin. Neurobiol. 18, 321–331 (2008).
Basak, R. & Narayanan, R. Spatially dispersed synapses yield sharply-tuned place cell responses through dendritic spike initiation. J. Physiol. 596, 4173–4205 (2018).
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.
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).
Destexhe, A., Rudolph, M. & Paré, D. The high-conductance state of neocortical neurons in vivo. Nat. Rev. Neurosci. 4, 739–751 (2003).
Williams, S. R. Spatial compartmentalization and functional impact of conductance in pyramidal neurons. Nat. Neurosci. 7, 961–967 (2004).
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).
London, M. & Segev, I. Synaptic scaling in vitro and in vivo. Nat. Neurosci. 4, 853–855 (2001).
London, M., Schreibman, A., Hausser, M., Larkum, M. E. & Segev, I. The information efficacy of a synapse. Nat. Neurosci. 5, 332–340 (2002).
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).
Sidiropoulou, K. & Poirazi, P. Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons. PLoS Comput. Biol. 8, e1002489 (2012).
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).
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).
Larkum, M. E. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 36, 141–151 (2013).
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).
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).
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).
Agmon-Snir, H. & Segev, I. Signal delay and input synchronization in passive dendritic structures. J. Neurophysiol. 70, 2066–2085 (1993).
Koch, C., Rapp, M. & Segev, I. A brief history of time (constants). Cereb. Cortex 6, 93–101 (1996).
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).
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).
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).
Gidon, A. & Segev, I. Principles governing the operation of synaptic inhibition in dendrites. Neuron 75, 330–341 (2012).
Rhodes, P. The properties and implications of NMDA spikes in neocortical pyramidal cells. J. Neurosci. 26, 6704–15 (2006).
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).
Wilmes, K. A., Sprekeler, H. & Schreiber, S. Inhibition as a binary switch for excitatory plasticity in pyramidal neurons. PLOS Comput. Biol. 12, e1004768 (2016).
Lovett-Barron, M. et al. Regulation of neuronal input transformations by tunable dendritic inhibition. Nat. Neurosci. 15, 423–430 (2012).
Bloss, E. B. et al. Structured dendritic inhibition supports branch-selective integration in CA1 pyramidal cells. Neuron 89, 1016–1030 (2016).
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.
Defelipe, J. The evolution of the brain, the human nature of cortical circuits, and intellectual creativity. Front. Neuroanat. 5, 29 (2011).
Mohan, H. et al. Dendritic and axonal architecture of individual pyramidal neurons across layers of adult human neocortex. Cereb. Cortex 25, 4839–4853 (2015).
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).
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).
Beaulieu-Laroche, L. et al. Enhanced dendritic compartmentalization in human cortical neurons. Cell 175, 643–651.e14 (2018).
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.
Eyal, G. et al. Human cortical pyramidal neurons: from spines to spikes via models. Front. Cell. Neurosci. 12, 1–24 (2018).
Boldog, E. et al. Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type. Nat. Neurosci. 21, 1185–1195 (2018).
Kaifosh, P. & Losonczy, A. Mnemonic functions for nonlinear dendritic integration in hippocampal pyramidal circuits. Neuron 90, 622–634 (2016).
Behabadi, B. F. & Mel, B. W. Mechanisms underlying subunit independence in pyramidal neuron dendrites. Proc. Natl Acad. Sci. USA 111, 498–503 (2014).
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).
Hoffman, D. A. & Johnston, D. Neuromodulation of dendritic action potentials. J. Neurophysiol. 81, 408–411 (1999).
Losonczy, A., Makara, J. K. & Magee, J. C. Compartmentalized dendritic plasticity and input feature storage in neurons. Nature 452, 436–441 (2008).
Li, S. et al. Dendritic computations captured by an effective point neuron model. Proc. Natl Acad. Sci. USA 116, 15244–15252 (2019).
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).
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).
Naud, R., Bathellier, B. & Gerstner, W. Spike-timing prediction in cortical neurons with active dendrites. Front. Comput. Neurosci. 8, 90 (2014).
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).
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).
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).
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).
Aamir, S.-A. et al. A mixed-signal structured AdEx neuron for accelerated neuromorphic cores. IEEE Trans. Biomed. Circuits Syst. 12, 1027–1037 (2018).
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).
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).
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.
Guerguiev, J., Lillicrap, T. P. & Richards, B. A. Towards deep learning with segregated dendrites. eLife 6, 22901 (2017).
Wu, X., Liu, X., Li, W. & Wu, Q. Improved expressivity through dendritic neural networks. Adv. Neural Inf. Process. Syst. 31, 8057–8068 (2018).
Harris, K. D. & Mrsic-Flogel, T. D. D. Cortical connectivity and sensory coding. Nature 503, 51–58 (2013).
Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016).
Bastos, A. M. et al. Canonical microcircuits for predictive coding. J. Neuron 76, 695–711 (2012).
Fu, Y. et al. A cortical circuit for gain control by behavioral state. Cell 156, 1139–1152 (2014).
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).
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).
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).
Pouille, F. & Scanziani, M. Routing of spike series by dynamic circuits in the hippocampus. Nature 429, 717–723 (2004).
Naud, R. & Sprekeler, H. Sparse bursts optimize information transmission in a multiplexed neural code. Proc. Natl Acad. Sci. USA 115, E6329–E6338 (2018).
Murayama, M. et al. Dendritic encoding of sensory stimuli controlled by deep cortical interneurons. Nature 457, 1137–1141 (2009).
Yang, G. R., Murray, J. D. & Wang, X.-J. A dendritic disinhibitory circuit mechanism for pathway-specific gating. Nat. Commun. 7, 12815 (2016).
Zhang, S. et al. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345, 660–665 (2014).
Pi, H.-J. et al. Cortical interneurons that specialize in disinhibitory control. Nature 503, 521–524 (2013).
Roelfsema, P. R. & Holtmaat, A. Reply to ‘Can neocortical feedback alter the sign of plasticity?’ Nat. Rev. Neurosci. 19, 637–638 (2018).
Williams, L. E. & Holtmaat, A. Higher-order thalamocortical inputs gate synaptic long-term potentiation via disinhibition. Neuron 101, 91–102.e4 (2019).
Attinger, A., Wang, B. & Keller, G. B. Visuomotor coupling shapes the functional development of mouse visual cortex. Cell 169, 1291–1302.e14 (2017).
Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015).
Arkhipov, A. et al. Visual physiology of the layer 4 cortical circuit in silico. PLOS Comput. Biol. 14, e1006535 (2018).
Pesaran, B. et al. Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nat. Neurosci. 21, 903–919 (2018).
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).
Lindén, H. et al. Modeling the spatial reach of the LFP. Neuron 72, 859–872 (2011).
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).
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).
Suzuki, M. & Larkum, M. E. Dendritic calcium spikes are clearly detectable at the cortical surface. Nat. Commun. 8, 276 (2017).
Lisman, J. et al. Viewpoints: how the hippocampus contributes to memory, navigation and cognition. Nat. Neurosci. 20, 1434–1447 (2017).
Rolls, E. T. The mechanisms for pattern completion and pattern separation in the hippocampus. Front. Syst. Neurosci. 7, 74 (2013).
O’Keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Clarendon Press, 1978).
Danielson, N. B. et al. Distinct contribution of adult-born hippocampal granule cells to context encoding. Neuron 90, 101–112 (2016).
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).
Cayco-Gajic, N. A. & Silver, R. A. Re-evaluating circuit mechanisms underlying pattern separation. Neuron 101, 584–602 (2019).
Migliore, M., Novara, G. & Tegolo, D. Single neuron binding properties and the magical number 7. Hippocampus 18, 1122–1130 (2008).
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.
Shuman, T. et al. Breakdown of spatial coding and interneuron synchronization in epileptic mice. Nat. Neurosci. 23, 229–238 (2020).
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).
Goldman-Rakic, P. S. Cellular basis of working memory. Neuron 14, 477–485 (1995).
Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat. Neurosci. 9, 534–42 (2006).
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).
Lisman, J. E., Fellous, J.-M. & Wang, X.-J. A role for NMDA-receptor channels in working memory. Nat. Neurosci. 1, 273–275 (1998).
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).
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).
Wang, M. et al. NMDA receptors subserve persistent neuronal firing during working memory in dorsolateral prefrontal cortex. Neuron 77, 736–749 (2013).
Schmidt-Hieber, C. et al. Active dendritic integration as a mechanism for robust and precise grid cell firing. Nat. Neurosci. 20, 1114–1121 (2017).
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).
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).
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).
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).
Jia, H., Rochefort, N. L., Chen, X. & Konnerth, A. Dendritic organization of sensory input to cortical neurons in vivo. Nature 464, 1307–1312 (2010).
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.
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).
Druckmann, S. et al. Structured synaptic connectivity between hippocampal regions. Neuron 81, 629–640 (2014).
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).
Hay, E. & Segev, I. Dendritic excitability and gain control in recurrent cortical microcircuits. Cereb. Cortex 25, 3561–3571 (2015).
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).
Bono, J., Wilmes, K. A. & Clopath, C. Modelling plasticity in dendrites: from single cells to networks. Curr. Opin. Neurobiol. 46, 136–141 (2017).
Legenstein, R. & Maass, W. Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. J. Neurosci. 31, 10787–10802 (2011).
O’Donnell, C. & Sejnowski, T. J. Selective memory generalization by spatial patterning of protein synthesis. Neuron 82, 398–412 (2014).
Bono, J. & Clopath, C. Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level. Nat. Commun. 8, 706 (2017).
Wu, X., Mel, G. C., Strouse, D. J. & Mel, B. W. How dendrites affect online recognition memory. PLOS Comput. Biol. 15, e1006892 (2019).
Urbanczik, R. & Senn, W. Learning by the dendritic prediction of somatic spiking. Neuron 81, 521–528 (2014).
Carandini, M. From circuits to behavior: a bridge too far? Nat. Neurosci. 15, 507–509 (2012).
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).
Druckmann, S. et al. Effective stimuli for constructing reliable neuron models. PLOS Comput. Biol. 7, e1002133 (2011).
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).
Gerstner, W. & Kistler, W. M. Spiking Neuron Models https://doi.org/10.1017/CBO9780511815706 (Cambridge Univ. Press, 2002).
Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M. & Tolias, A. S. Engineering a less artificial intelligence. Neuron 103, 967–979 (2019).
Richards, B. A. et al. A deep learning framework for neuroscience. Nat. Neurosci. 22, 1761–1770 (2019).
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).
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).
Takahashi, N. et al. Locally synchronized synaptic inputs. Science 335, 353–356 (2012).
Chklovskii, D., Mel, B. W. & Svoboda, K. Cortical rewiring and information storage. Nature 431, 782–788 (2004).
Kazemipour, A. et al. Kilohertz frame-rate two-photon tomography. Nat. Methods 16, 778–786 (2019).
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).
Piatkevich, K. D. et al. Population imaging of neural activity in awake behaving mice. Nature 574, 413–417 (2019).
Abdelfattah, A. S. et al. Bright and photostable chemigenetic indicators for extended in vivo voltage imaging. Science 365, 699–704 (2019).
Kim, J. et al. mGRASP enables mapping mammalian synaptic connectivity with light microscopy. Nat. Methods 9, 96–102 (2011).
Kinoshita, N. et al. Genetically encoded fluorescent indicator GRAPHIC delineates intercellular connections. iScience 15, 28–38 (2019).
Hayashi-Takagi, A. et al. Labelling and optical erasure of synaptic memory traces in the motor cortex. Nature 525, 333–338 (2015).
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).
Zhu, G., Du, L., Jin, L. & Offenhäusser, A. Effects of morphology constraint on electrophysiological properties of cortical neurons. Sci. Rep. 6, 23086 (2016).
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).
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.).
The authors declare no competing interests.
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- 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.
In neurophysiology, the active regeneration of somatic action potentials travelling backwards into the dendrites.
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.
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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