Independent computations within dendrites

Cortical pyramidal neurons typically have an elaborate dendritic tree that receives and integrates the many synaptic inputs targeting the neuron. An open question is how information is represented in dendrites in vivo. Otor et al. investigated synaptic computations in the apical tuft of layer 5 pyramidal neurons in the mouse motor cortex using two-photon calcium imaging, behavioral analysis, and cable modeling. Early-branching layer 5 pyramidal neurons showed marked compartmentalization of dendritic calcium signaling, whereas late-branching pyramidal neurons had synchronous tuft activation. N-methyl-d-aspartate spikes and cable properties could explain the varying compartmentalization patterns. Compartmentalized activity between hemi-trees was correlated with behavioral outcome. These results indicate a cell-type-dependent dynamic combinatorial code for motor representation. —PRS


Tuft dendrites of layer 5 pyramidal neurons form specialized compartments important for motor learning and performance, yet their computational capabilities remain unclear. Structural-functional mapping of the tuft tree from the motor cortex during motor tasks revealed two morphologically distinct populations of layer 5 pyramidal tract neurons (PTNs) that exhibit specific tuft computational properties. Early bifurcating and large nexus PTNs showed marked tuft functional compartmentalization, representing different motor variable combinations within and between their two tuft hemi-trees. By contrast, late bifurcating and smaller nexus PTNs showed synchronous tuft activation. Dendritic structure and dynamic recruitment of the N-methyl-d-aspartate (NMDA)–spiking mechanism explained the differential compartmentalization patterns. Our findings support a morphologically dependent framework for motor computations, in which independent amplification units can be combinatorically recruited to represent different motor sequences within the same tree.

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References and Notes

T. Branco, M. Häusser, The single dendritic branch as a fundamental functional unit in the nervous system. Curr. Opin. Neurobiol.20, 494–502 (2010).
X. E. Wu, B. W. Mel, Capacity-enhancing synaptic learning rules in a medial temporal lobe online learning model. Neuron62, 31–41 (2009).
G. J. Stuart, N. Spruston, Dendritic integration: 60 years of progress. Nat. Neurosci.18, 1713–1721 (2015).
G. Major, M. E. Larkum, J. Schiller, Active properties of neocortical pyramidal neuron dendrites. Annu. Rev. Neurosci.36, 1–24 (2013).
S. D. Antic, W. L. Zhou, A. R. Moore, S. M. Short, K. D. Ikonomu, The decade of the dendritic NMDA spike. J. Neurosci. Res.88, 2991–3001 (2010).
J. Cichon, W. B. Gan, Branch-specific dendritic Ca(2+) spikes cause persistent synaptic plasticity. Nature520, 180–185 (2015).
L. M. Palmer, A. S. Shai, J. E. Reeve, H. L. Anderson, O. Paulsen, M. E. Larkum, NMDA spikes enhance action potential generation during sensory input. Nat. Neurosci.17, 383–390 (2014).
L. Beaulieu-Laroche, E. H. S. Toloza, N. J. Brown, M. T. Harnett, Widespread and Highly Correlated Somato-dendritic Activity in Cortical Layer 5 Neurons. Neuron103, 235–241.e4 (2019).
V. Francioni, Z. Padamsey, N. L. Rochefort, High and asymmetric somato-dendritic coupling of V1 layer 5 neurons independent of visual stimulation and locomotion. eLife8, e49145 (2019).
J. Voigts, M. T. Harnett, Somatic and Dendritic Encoding of Spatial Variables in Retrosplenial Cortex Differs during 2D Navigation. Neuron105, 237–245.e4 (2020).
A. Kerlin, B. Mohar, D. Flickinger, B. J. MacLennan, M. B. Dean, C. Davis, N. Spruston, K. Svoboda, Functional clustering of dendritic activity during decision-making. eLife8, e46966 (2019).
N. L. Xu, M. T. Harnett, S. R. Williams, D. Huber, D. H. O’Connor, K. Svoboda, J. C. Magee, Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature492, 247–251 (2012).
C. O. Lacefield, E. A. Pnevmatikakis, L. Paninski, R. M. Bruno, Reinforcement Learning Recruits Somata and Apical Dendrites across Layers of Primary Sensory Cortex. Cell Rep.26, 2000–2008.e2 (2019).
B. B. Ujfalussy, J. K. Makara, M. Lengyel, T. Branco, Global and Multiplexed Dendritic Computations under In Vivo-like Conditions. Neuron100, 579–592.e5 (2018).
R. Naud, B. Bathellier, W. Gerstner, Spike-timing prediction in cortical neurons with active dendrites. Front. Comput. Neurosci.8, 90 (2014).
P. Poirazi, T. Brannon, B. W. Mel, Pyramidal neuron as two-layer neural network. Neuron37, 989–999 (2003).
D. Beniaguev, I. Segev, M. London, Single cortical neurons as deep artificial neural networks. Neuron109, 2727–2739.e3 (2021).
A. Payeur, J. C. Béïque, R. Naud, Classes of dendritic information processing. Curr. Opin. Neurobiol.58, 78–85 (2019).
G. Kastellakis, P. Poirazi, Synaptic Clustering and Memory Formation. Front. Mol. Neurosci.12, 300 (2019).
R. Legenstein, W. Maass, Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. J. Neurosci.31, 10787–10802 (2011).
J. Bono, C. Clopath, Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level. Nat. Commun.8, 706 (2017).
B. W. Mel, in Advances in Neural Information Processing Systems, J. Moody, S. Hanson, R. Lippmann, Eds. (Morgan Kaufmann, 1992), vol. 4, pp. 35–42.
M. N. Economo, S. Viswanathan, B. Tasic, E. Bas, J. Winnubst, V. Menon, L. T. Graybuck, T. N. Nguyen, K. A. Smith, Z. Yao, L. Wang, C. R. Gerfen, J. Chandrashekar, H. Zeng, L. L. Looger, K. Svoboda, Distinct descending motor cortex pathways and their roles in movement. Nature563, 79–84 (2018).
H. Markram, E. Muller, S. Ramaswamy, M. W. Reimann, M. Abdellah, C. A. Sanchez, A. Ailamaki, L. Alonso-Nanclares, N. Antille, S. Arsever, G. A. A. Kahou, T. K. Berger, A. Bilgili, N. Buncic, A. Chalimourda, G. Chindemi, J.-D. Courcol, F. Delalondre, V. Delattre, S. Druckmann, R. Dumusc, J. Dynes, S. Eilemann, E. Gal, M. E. Gevaert, J.-P. Ghobril, A. Gidon, J. W. Graham, A. Gupta, V. Haenel, E. Hay, T. Heinis, J. B. Hernando, M. Hines, L. Kanari, D. Keller, J. Kenyon, G. Khazen, Y. Kim, J. G. King, Z. Kisvarday, P. Kumbhar, S. Lasserre, J.-V. Le Bé, B. R. C. Magalhães, A. Merchán-Pérez, J. Meystre, B. R. Morrice, J. Muller, A. Muñoz-Céspedes, S. Muralidhar, K. Muthurasa, D. Nachbaur, T. H. Newton, M. Nolte, A. Ovcharenko, J. Palacios, L. Pastor, R. Perin, R. Ranjan, I. Riachi, J.-R. Rodríguez, J. L. Riquelme, C. Rössert, K. Sfyrakis, Y. Shi, J. C. Shillcock, G. Silberberg, R. Silva, F. Tauheed, M. Telefont, M. Toledo-Rodriguez, T. Tränkler, W. Van Geit, J. V. Díaz, R. Walker, Y. Wang, S. M. Zaninetta, J. DeFelipe, S. L. Hill, I. Segev, F. Schürmann, Reconstruction and Simu. Cell163, 456–492 (2015).
W. J. Gao, Z. H. Zheng, Target-specific differences in somatodendritic morphology of layer V pyramidal neurons in rat motor cortex. J. Comp. Neurol.476, 174–185 (2004).
Y. Wang, M. Ye, X. Kuang, Y. Li, S. Hu, A simplified morphological classification scheme for pyramidal cells in six layers of primary somatosensory cortex of juvenile rats. IBRO Rep.5, 74–90 (2018).
S. Jiang, Y. Guan, S. Chen, X. Jia, H. Ni, Y. Zhang, Y. Han, X. Peng, C. Zhou, A. Li, Q. Luo, H. Gong, Anatomically revealed morphological patterns of pyramidal neurons in layer 5 of the motor cortex. Sci. Rep.10, 7916 (2020).
G. M. Shepherd, Corticostriatal connectivity and its role in disease. Nat. Rev. Neurosci.14, 278–291 (2013).
A. Groh, H. S. Meyer, E. F. Schmidt, N. Heintz, B. Sakmann, P. Krieger, Cell-type specific properties of pyramidal neurons in neocortex underlying a layout that is modifiable depending on the cortical area. Cereb. Cortex20, 826–836 (2010).
E. J. Kim, A. L. Juavinett, E. M. Kyubwa, M. W. Jacobs, E. M. Callaway, Three Types of Cortical Layer 5 Neurons That Differ in Brain-wide Connectivity and Function. Neuron88, 1253–1267 (2015).
A. M. Hattox, S. B. Nelson, Layer V neurons in mouse cortex projecting to different targets have distinct physiological properties. J. Neurophysiol.98, 3330–3340 (2007).
B. Tasic, Z. Yao, L. T. Graybuck, K. A. Smith, T. N. Nguyen, D. Bertagnolli, J. Goldy, E. Garren, M. N. Economo, S. Viswanathan, O. Penn, T. Bakken, V. Menon, J. Miller, O. Fong, K. E. Hirokawa, K. Lathia, C. Rimorin, M. Tieu, R. Larsen, T. Casper, E. Barkan, M. Kroll, S. Parry, N. V. Shapovalova, D. Hirschstein, J. Pendergraft, H. A. Sullivan, T. K. Kim, A. Szafer, N. Dee, P. Groblewski, I. Wickersham, A. Cetin, J. A. Harris, B. P. Levi, S. M. Sunkin, L. Madisen, T. L. Daigle, L. Looger, A. Bernard, J. Phillips, E. Lein, M. Hawrylycz, K. Svoboda, A. R. Jones, C. Koch, H. Zeng, Shared and distinct transcriptomic cell types across neocortical areas. Nature563, 72–78 (2018).
L. N. Fletcher, S. R. Williams, Neocortical Topology Governs the Dendritic Integrative Capacity of Layer 5 Pyramidal Neurons. Neuron101, 76–90.e4 (2019).
S. Levy, M. Lavzin, H. Benisty, A. Ghanayim, U. Dubin, S. Achvat, Z. Brosh, F. Aeed, B. D. Mensh, Y. Schiller, R. Meir, O. Barak, R. Talmon, A. W. Hantman, J. Schiller, Cell-Type-Specific Outcome Representation in the Primary Motor Cortex. Neuron107, 954–971.e9 (2020).
F. Fuhrmann, D. Justus, L. Sosulina, H. Kaneko, T. Beutel, D. Friedrichs, S. Schoch, M. K. Schwarz, M. Fuhrmann, S. Remy, Locomotion, Theta Oscillations, and the Speed-Correlated Firing of Hippocampal Neurons Are Controlled by a Medial Septal Glutamatergic Circuit. Neuron86, 1253–1264 (2015).
T. W. Chen, T. J. Wardill, Y. Sun, S. R. Pulver, S. L. Renninger, A. Baohan, E. R. Schreiter, R. A. Kerr, M. B. Orger, V. Jayaraman, L. L. Looger, K. Svoboda, D. S. Kim, Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature499, 295–300 (2013).
T. Deneux, A. Kaszas, G. Szalay, G. Katona, T. Lakner, A. Grinvald, B. Rózsa, I. Vanzetta, Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo. Nat. Commun.7, 12190 (2016).
N. Mantel, The detection of disease clustering and a generalized regression approach. Cancer Res.27, 209–220 (1967).
B. Manly, The Statistics of Natural Selection (Chapman & Hall, 1985).
P. W. Mielke, Clarification and Appropriate Inferences for Mantel and Valand’s Nonparametric Multivariate Analysis Technique. Biometrics34, 277 (1978).
B. Engelhard, J. Finkelstein, J. Cox, W. Fleming, H. J. Jang, S. Ornelas, S. A. Koay, S. Y. Thiberge, N. D. Daw, D. W. Tank, I. B. Witten, Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons. Nature570, 509–513 (2019).
P. Ramkumar, B. Dekleva, S. Cooler, L. Miller, K. Kording, Premotor and Motor Cortices Encode Reward. PLOS ONE11, e0160851 (2016).
B. A. Sauerbrei, J.-Z. Guo, J. D. Cohen, M. Mischiati, W. Guo, M. Kabra, N. Verma, B. Mensh, K. Branson, A. W. Hantman, Cortical pattern generation during dexterous movement is input-driven. Nature577, 386–391 (2020).
A. Nashef, O. Cohen, R. Harel, Z. Israel, Y. Prut, Reversible Block of Cerebellar Outflow Reveals Cortical Circuitry for Motor Coordination. Cell Rep.27, 2608–2619.e4 (2019).
M. Lavzin, S. Rapoport, A. Polsky, L. Garion, J. Schiller, Nonlinear dendritic processing determines angular tuning of barrel cortex neurons in vivo. Nature490, 397–401 (2012).
M. E. Larkum, T. Nevian, M. Sandler, A. Polsky, J. Schiller, Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: A new unifying principle. Science325, 756–760 (2009).
M. Larkum, A cellular mechanism for cortical associations: An organizing principle for the cerebral cortex. Trends Neurosci.36, 141–151 (2013).
H. Jia, N. L. Rochefort, X. Chen, A. Konnerth, Dendritic organization of sensory input to cortical neurons in vivo. Nature464, 1307–1312 (2010).
M. E. Sheffield, D. A. Dombeck, Calcium transient prevalence across the dendritic arbour predicts place field properties. Nature517, 200–204 (2015).
L. Beaulieu-Laroche, E. H. S. Toloza, M. S. van der Goes, M. Lafourcade, D. Barnagian, Z. M. Williams, E. N. Eskandar, M. P. Frosch, S. S. Cash, M. T. Harnett, Enhanced dendritic compartmentalization in human cortical neurons. Cell175, 643–651.e14 (2018).
H. Mohan, M. B. Verhoog, K. K. Doreswamy, G. Eyal, R. Aardse, B. N. Lodder, N. A. Goriounova, B. Asamoah, A. B. B Brakspear, C. Groot, S. van der Sluis, G. Testa-Silva, J. Obermayer, Z. S. Boudewijns, R. T. Narayanan, J. C. Baayen, I. Segev, H. D. Mansvelder, C. P. de Kock, Dendritic and axonal architecture of individual pyramidal neurons across layers of adult human neocortex. Cereb. Cortex25, 4839–4853 (2015).
R. Urbanczik, W. Senn, Learning by the dendritic prediction of somatic spiking. Neuron81, 521–528 (2014).
J. Hawkins, S. Ahmad, Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex. Front. Neural Circuits10, 23 (2016).
L. T. Graybuck, T. L. Daigle, A. E. Sedeño-Cortés, M. Walker, B. Kalmbach, G. H. Lenz, E. Morin, T. N. Nguyen, E. Garren, J. L. Bendrick, T. K. Kim, T. Zhou, M. Mortrud, S. Yao, L. A. Siverts, R. Larsen, B. B. Gore, E. R. Szelenyi, C. Trader, P. Balaram, C. T. J. van Velthoven, M. Chiang, J. K. Mich, N. Dee, J. Goldy, A. H. Cetin, K. Smith, S. W. Way, L. Esposito, Z. Yao, V. Gradinaru, S. M. Sunkin, E. Lein, B. P. Levi, J. T. Ting, H. Zeng, B. Tasic, Enhancer viruses for combinatorial cell-subclass-specific labeling. Neuron109, 1449–1464.e13 (2021).
C. J. Roome, B. Kuhn, Chronic cranial window with access port for repeated cellular manipulations, drug application, and electrophysiology. Front. Cell. Neurosci.8, 379 (2014).
M. Kabra, A. A. Robie, M. Rivera-Alba, S. Branson, K. Branson, JAABA: Interactive machine learning for automatic annotation of animal behavior. Nat. Methods10, 64–67 (2013).
A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, M. Bethge, DeepLabCut: Markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci.21, 1281–1289 (2018).
J. Fienup, A. Kowalczyk, Phase retrieval for a complex-valued object by using a low-resolution image. J. Opt. Soc. Am. A Opt. Image Sci. Vis.7, 450 (1990).
C. Stringer, M. Pachitariu, N. Steinmetz, M. Carandini, K. D. Harris, High-dimensional geometry of population responses in visual cortex. Nature571, 361–365 (2019).
M. T. Harnett, N. L. Xu, J. C. Magee, S. R. Williams, Potassium channels control the interaction between active dendritic integration compartments in layer 5 cortical pyramidal neurons. Neuron79, 516–529 (2013).

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Published In

Volume 376 | Issue 6590
15 April 2022

Submission history

Received: 7 November 2021
Accepted: 11 March 2022
Published in print: 15 April 2022


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We thank S. Marom and B. Engelhard for helpful comments on the manuscript. We also thank S. Schwartz for advice in statistical tests and S. Gafniel for movie editing.
Funding: This study was partially supported by the Israeli Science Foundation (J.S. and Y.S.), Prince funds (J.S. and Y.S.), Rappaport Foundation (J.S. and Y.S.), and Zuckerman Postdoctoral Fellowship (N.C.).
Author contributions: Designed research: J.S., N.C., Y.S. Performed experiments: Y.O., N.C., S.A., M.A. Analyzed data: S.A., Y.O., J.S., H.B., O.B., Y.S. Writing – original draft: J.S. Writing – review and editing: J.S., Y.O., S.A., A.P.-P., Y.S. Performed simulations: A.P.-P., S.A., J.S.
Competing interests: The authors declare that they have no competing interests.
Data and materials availability: All data are available in the manuscript or the supplementary materials. Computer code can be found in:



Department of Physiology, Technion Medical School, Bat-Galim, Haifa 31096, Israel.
Roles: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, and Visualization.
Department of Physiology, Technion Medical School, Bat-Galim, Haifa 31096, Israel.
Roles: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, and Visualization.
Department of Physiology, Technion Medical School, Bat-Galim, Haifa 31096, Israel.
Roles: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing - original draft, and Writing - review & editing.
Yale University School of Medicine; Bethany, CT, USA.
Roles: Conceptualization, Formal analysis, Software, Visualization, and Writing - review & editing.
Department of Physiology, Technion Medical School, Bat-Galim, Haifa 31096, Israel.
Roles: Investigation and Validation.
Department of Physiology, Technion Medical School, Bat-Galim, Haifa 31096, Israel.
Roles: Methodology and Supervision.
Department of Physiology, Technion Medical School, Bat-Galim, Haifa 31096, Israel.
Roles: Conceptualization, Formal analysis, and Writing - original draft.
Department of Physiology and Biophysics; University of Colorado School of Medicine, 12800 East 19th Avenue MS8307, Aurora, CO 8004, USA.
Roles: Conceptualization, Data curation, Formal analysis, Methodology, Software, Supervision, Validation, Visualization, Writing - original draft, and Writing - review & editing.
Department of Physiology, Technion Medical School, Bat-Galim, Haifa 31096, Israel.
Roles: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing - original draft, and Writing - review & editing.

Funding Information


Corresponding author. Email: [email protected] (J.S.); [email protected] (A.P.-P.)
These authors contributed equally to this work.

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