Abstract
The output of cortical columns is routed to different downstream targets via distinct pathways: cortico-cortical and cortico-subcortical. It is as yet unclear what roles these pathways play in perception, and which cellular and circuit mechanisms regulate their gating. We recently showed that activation of the apical dendrites of layer 5 (L5) pyramidal neurons correlates with the threshold for perception, but these neurons come in two classes that target either other cortical or subcortical areas. In the present study, we took advantage of transgenic mouse lines for these L5 subclasses to determine their relative contributions to the perceptual process. We found that the activation of apical dendrites in neurons of the somatosensory cortex, which project to subcortical regions, almost exclusively determined the detection of tactile stimuli in mice. Our results suggest that dendritic activation drives context-dependent interactions between cortex and subcortical regions, including the higher-order thalamus, superior colliculus and striatum, which are crucial for perception.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All data included in this publication are stored on servers of Charité – Universitätsmedizin Berlin and are available from one of the corresponding authors upon reasonable request.
Code availability
Customized MATLAB scripts for data analysis and Igor Pro software for data collection are available from one of the corresponding authors upon reasonable request.
References
Gilbert, C. D. & Sigman, M. Brain states: top-down influences in sensory processing. Neuron 54, 677–696 (2007).
Gilbert, C. D. & Li, W. Top-down influences on visual processing. Nat. Rev. Neurosci. 14, 350–363 (2013).
Xu, N. L. et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature 492, 247–251 (2012).
Takahashi, N., Oertner, T. G., Hegemann, P. & Larkum, M. E. Active cortical dendrites modulate perception. Science 354, 1587–1590 (2016).
Ranganathan, G. N. et al. Active dendritic integration and mixed neocortical network representations during an adaptive sensing behavior. Nat. Neurosci. 21, 1583–1590 (2018).
Williams, S. R. & Stuart, G. J. Mechanisms and consequences of action potential burst firing in rat neocortical pyramidal neurons. J. Physiol. 521(Pt 2), 467–482 (1999).
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).
Larkum, M. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 36, 141–151 (2013).
Lisman, J. Bursts as a unit of neural information: making unreliable synapses reliable. Trends Neurosci. 20, 38–43 (1997).
Cauller, L. Layer I of primary sensory neocortex: where top-down converges upon bottom-up. Behav. Brain Res. 71, 163–170 (1995).
Harris, K. D. & Shepherd, G. M. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).
Koch, C. & Davis, J. L. Large-scale Neuronal Theories of the Brain (MIT Press, 1994).
Bair, W., Koch, C., Newsome, W. & Britten, K. Power spectrum analysis of bursting cells in area MT in the behaving monkey. J. Neurosci. 14, 2870–2892 (1994).
Koch, C., Massimini, M., Boly, M. & Tononi, G. Neural correlates of consciousness: progress and problems. Nat. Rev. Neurosci. 17, 307–321 (2016).
Fregnac, Y. & Bathellier, B. Cortical correlates of low-level perception: from neural circuits to percepts. Neuron 88, 110–126 (2015).
van Vugt, B. et al. The threshold for conscious report: signal loss and response bias in visual and frontal cortex. Science 360, 537–542 (2018).
Llinas, R., Ribary, U., Contreras, D. & Pedroarena, C. The neuronal basis for consciousness. Philos. Trans. R. Soc. Lond. B Biol. Sci. 353, 1841–1849 (1998).
Krauzlis, R. J., Lovejoy, L. P. & Zenon, A. Superior colliculus and visual spatial attention. Annu. Rev. Neurosci. 36, 165–182 (2013).
Parvizi, J. & Damasio, A. Consciousness and the brainstem. Cognition 79, 135–160 (2001).
Aru, J., Suzuki, M., Rutiku, R., Larkum, M. E. & Bachmann, T. Coupling the state and contents of consciousness. Front. Syst. Neurosci. 13, 43 (2019).
Mason, A. & Larkman, A. Correlations between morphology and electrophysiology of pyramidal neurons in slices of rat visual cortex. II. Electrophysiology. J. Neurosci. 10, 1415–1428 (1990).
Larkman, A. & Mason, A. Correlations between morphology and electrophysiology of pyramidal neurons in slices of rat visual cortex. I. Establishment of cell classes. J. Neurosci. 10, 1407–1414 (1990).
Chagnac-Amitai, Y., Luhmann, H. J. & Prince, D. A. Burst generating and regular spiking layer 5 pyramidal neurons of rat neocortex have different morphological features. J. Comp. Neurol. 296, 598–613 (1990).
Wise, S. P. & Jones, E. G. Cells of origin and terminal distribution of descending projections of the rat somatic sensory cortex. J. Comp. Neurol. 175, 129–157 (1977).
Koralek, K. A., Olavarria, J. & Killackey, H. P. Areal and laminar organization of corticocortical projections in the rat somatosensory cortex. J. Comp. Neurol. 299, 133–150 (1990).
Hattox, A. M. & Nelson, S. B. Layer V neurons in mouse cortex projecting to different targets have distinct physiological properties. J. Neurophysiol. 98, 3330–3340 (2007).
Kim, E. J., Juavinett, A. L., Kyubwa, E. M., Jacobs, M. W. & Callaway, E. M. Three types of cortical layer 5 neurons that differ in brain-wide connectivity and function. Neuron 88, 1253–1267 (2015).
Gerfen, C. R., Paletzki, R. & Heintz, N. GENSAT BAC cre-recombinase driver lines to study the functional organization of cerebral cortical and basal ganglia circuits. Neuron 80, 1368–1383 (2013).
Li, N., Chen, T. W., Guo, Z. V., Gerfen, C. R. & Svoboda, K. A motor cortex circuit for motor planning and movement. Nature 519, 51–56 (2015).
Libet, B., Alberts, W. W., Wright, E. W. Jr. & Feinstein, B. Responses of human somatosensory cortex to stimuli below threshold for conscious sensation. Science 158, 1597–1600 (1967).
Arlotta, P. et al. Neuronal subtype-specific genes that control corticospinal motor neuron development in vivo. Neuron 45, 207–221 (2005).
Zolnik, T. A. et al. Layer 6b is driven by intracortical long-range projection neurons. Cell Rep. 30, 3492–3505 (2020).
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).
Helmchen, F., Svoboda, K., Denk, W. & Tank, D. W. In vivo dendritic calcium dynamics in deep-layer cortical pyramidal neurons. Nat. Neurosci. 2, 989–996 (1999).
Gentet, L. J. et al. Unique functional properties of somatostatin-expressing GABAergic neurons in mouse barrel cortex. Nat. Neurosci. 15, 607–612 (2012).
Lacefield, C. O., Pnevmatikakis, E. A., Paninski, L. & Bruno, R. M. Reinforcement learning recruits somata and apical dendrites across layers of primary sensory cortex. Cell Rep. 26, 2000–2008 (2019).
Allen, W. E. et al. Global representations of goal-directed behavior in distinct cell types of mouse neocortex. Neuron 94, 891–907 e896 (2017).
Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. K. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22, 1677–1686 (2019).
Lewis, T. L. Jr., Mao, T., Svoboda, K. & Arnold, D. B. Myosin-dependent targeting of transmembrane proteins to neuronal dendrites. Nat. Neurosci. 12, 568–576 (2009).
Stachniak, T. J., Ghosh, A. & Sternson, S. M. Chemogenetic synaptic silencing of neural circuits localizes a hypothalamus→midbrain pathway for feeding behavior. Neuron 82, 797–808 (2014).
Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014).
Ahissar, E. And motion changes it all. Nat. Neurosci. 11, 1369–1370 (2008).
Shepherd, G. M. Corticostriatal connectivity and its role in disease. Nat. Rev. Neurosci. 14, 278–291 (2013).
Rojas-Piloni, G. et al. Relationships between structure, in vivo function and long-range axonal target of cortical pyramidal tract neurons. Nat. Commun. 8, 870 (2017).
Economo, M. N. et al. Distinct descending motor cortex pathways and their roles in movement. Nature 563, 79–84 (2018).
Beaulieu-Laroche, L., Toloza, E. H. S., Brown, N. J. & Harnett, M. T. Widespread and highly correlated somato-dendritic activity in cortical layer 5 neurons. Neuron 103, 235–241(2019).
Grewe, B. F., Bonnan, A. & Frick, A. Back-propagation of physiological action potential output in dendrites of slender-tufted L5A pyramidal neurons. Front. Cell Neurosci. 4, 13 (2010).
O’Connor, D. H., Peron, S. P., Huber, D. & Svoboda, K. Neural activity in barrel cortex underlying vibrissa-based object localization in mice. Neuron 67, 1048–1061 (2010).
Williams, S. R. & Stuart, G. J. Backpropagation of physiological spike trains in neocortical pyramidal neurons: implications for temporal coding in dendrites. J. Neurosci. 20, 8238–8246 (2000).
Suzuki, M. & Larkum, M. E. General anesthesia decouples cortical pyramidal neurons. Cell 180, 666–676(2020).
Micallef, A. H., Takahashi, N., Larkum, M. E. & Palmer, L. M. A reward-based behavioral platform to measure neural activity during head-fixed behavior. Front. Cell Neurosci. 11, 156 (2017).
Vogelstein, J. T. et al. Fast nonnegative deconvolution for spike train inference from population calcium imaging. J. Neurophysiol. 104, 3691–3704 (2010).
Acknowledgements
We thank C. Koch (Allen Institute for Brain Science, Seattle), M. Murayama (RIKEN CBS, Wako) and J. Aru (Humboldt University of Berlin) for comments on and discussions about the manuscript. The present study was supported by the Deutsche Forschungsgemeinschaft (Exc 257 NeuroCure, LA 3442/3-1, project no. 327654276 SFB1315 TP.A04), the European Union Horizon 2020 Research and Innovation Programme (72070/HBP SGA1, 670118/ERC ActiveCortex) and the Einstein Foundation Berlin (EVF-2017-363).
Author information
Authors and Affiliations
Contributions
N.T. and M.E.L. conceived the project and designed the experiments. N.T. performed in vivo experiments and data analysis. C.E. performed in vitro experiments. J.S.G. and S.M. conducted the histology. S.N. helped with behavioral training. N.T. and M.E.L. wrote the manuscript with comments from all authors.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Cre-driver mouse lines provide genetic access to L5 subpopulations in S1.
a, tdTomato expression in S1 of Sim1-Cre/Ai9 mice (left) and Tlx3-Cre/Ai9 mice (right). A single barrel column is highlighted in magenta. For each transgenic line, similar results were obtained in all brain sections independently tested (n = 5 sections from 2 Sim1-Cre/Ai9 mice; n = 4 sections from 2 Tlx3-Cre/Ai9 mice). b, Images of Sim1-Cre+ neurons expressing GCaMP6f in a section immunostained with an anti-Ctip2 antibody.
Extended Data Fig. 2 Critical frequency for dendritic electrogenesis in L5 pyramidal subclasses.
a, Left, in vitro whole-cell recording from PT (Sim1-Cre+) neurons in S1. Middle, increase in the after-depolarizing potential (ADP) evoked by high-frequency trains of four APs – indicative of dendritic regenerative potentials7. The inset shows the magnified ADPs after the last AP. Right, the sizes of somatic ADP as a function of AP frequency, fitted with a sigmoidal curve. The critical frequency was estimated from the turning point of a sigmoidal fit. b, Same as a, for IT (Tlx3-Cre+) neurons in S1. c,d, Critical frequencies and the changes in ADP size of PT neurons (orange; n = 17 neurons) versus IT neurons (purple; n = 17 neurons) (P = 9.1 × 10−4 for critical frequency, Student’s t-test; P = 2.2 × 10−3 for the change in ADP size, Mann-Whitney U test).
Extended Data Fig. 3 Dendritic Ca2+ activity during whisking behavior.
a, Top, C2 whisker position monitored by a high-speed video camera (500 frames/s). Whisking amplitude is the Hilbert transform of the absolute value of the band-pass filtered (6–30Hz) whisker angle. Bottom, examples of simultaneously-recorded dendritic Ca2+ activity of PT neurons. b, Top, cross-correlograms between whisking amplitude and dendritic Ca2+, sorted according to their peak amplitudes. Bottom, averaged cross-correlogram with s.e.m. (n = 300 dendrites from 4 mice). c, d, Same as a and b, for IT neurons (n = 227 dendrites from 2 mice).
Extended Data Fig. 4 No difference in behavioral performance between Sim1-Cre and Tlx3-Cre mice.
a, Perceptual threshold for detecting whisker stimuli in Sim1-Cre mice (0.61 ± 0.18, n = 12 mice) versus Tlx3-Cre mice (0.70 ± 0.30, n = 9 mice) (P = 0.50, Mann-Whitney U test). b, Reaction time, that is, delay between a whisker stimulus and the first lick, for near-threshold and salient stimuli in Sim1-Cre mice (261 ± 44 ms and 193 ± 40 ms for threshold and salient stimuli, respectively, n = 12 mice) versus Tlx3-Cre mice (252 ± 41 ms and 198 ± 35 ms for threshold and salient stimuli, respectively, n = 9 mice) (P = 0.38, two-way repeated measures ANOVA).
Extended Data Fig. 5 Licking-associated sparse activation of apical dendrites of PT neurons.
a, Top, two-photon Ca2+ imaging from apical dendrites of GCaMP6f-expressing PT neurons in S1 in mice drinking a water reward. Bottom, averaged licking response to a water reward delivered through a spout with a random interval independent of whisker detection (n = 162 trials). b, Two examples of Ca2+ activities in apical dendrites of PT neurons, indicated in a, aligned by the timing of water deliveries. c, Histograms of dendritic Ca2+ responses in PT neurons to a free water reward (light blue; n = 293 dendrites from 3 mice) versus those to a reward upon detection of whisker stimuli, that is, hit trials (dark blue; n = 359 dendrites from 6 mice). Mann-Whitney U test. d, Top, Ca2+ response in reward-only trials versus hit trials aligned by the timing of first licks, averaged across dendrites that were significantly active in those trials compared with catch trials (mean ± s.e.m., n = 95 dendrites from 3 mice and 176 dendrites from 6 mice for reward-only and hit trials, respectively; ***P = 8.1 × 10−6). Bottom, histogram of the Ca2+ onset timings (P = 0.086). Mann-Whitney U test.
Extended Data Fig. 6 Functional characterization of dendrite-targeting ChR2 in vitro.
a, Left, whole-cell recording from ChR2-MBD-expressing PT neurons in S1. Right, somatic voltage response measured during dendritic photostimulation (illumination site is indicated in the right panel). b, Same as a, for ChR2-MBD-expressing IT neurons. c, Somatic voltage changes by dendritic photostimulation of PT neurons (orange; n = 6 neurons) versus IT neurons (purple; n = 6 neurons) (P = 0.39, Mann-Whitney U test).
Extended Data Fig. 7 Neither inactivation of PT outputs to the brainstem nor CNO alone changes animals’ perceptual performance in non-transgenic mice.
a, Subpopulation-unspecific expression of hM4Di-mCherry injected in S1 of wild-type mice. b, Detection probabilities before and after local CNO (10 µM) application in pons in wild-type mice expressing hM4Di in S1 (mean ± s.e.m., n = 3 sessions from 3 mice). c, Same as a, for local CNO application in medulla (n = 3 sessions from 3 mice). d, Detection probabilities before and after systemic CNO (5 mg/kg, i.p.) application in non hM4Di-expressing, wild-type mice (mean ± s.e.m., n = 6 sessions from 4 mice, P = 0.72, two-way repeated measures ANOVA). e–g, Detection probabilities (mean ± s.e.m.) before and after local CNO (10 µM) application in näive mice, targeted in striatum (n = 6 sessions from 4 mice, P = 0.21, e), POm (n = 6 sessions from 4 mice, P = 0.62, f), and SC (n = 6 sessions from 4 mice, P = 0.16, g). Two-way repeated measures ANOVA.
Extended Data Fig. 8 Long-range projections to the superficial layers in S1.
Retrograde labeling of presynaptic neurons (cyan) in different brain regions that project to the superficial layers of S1. Top left, immersion site of Fast Blue (cyan) on S1, overlaid with nuclear staining (red). Similar results were obtained in three independent experiments (n = 3 mice).
Extended Data Fig. 9 Dendritic Ca2+ activity and behavioral context.
a, Standard deviations (SDs) of spontaneous dendritic Ca2+ activity of PT neurons at the baseline in the active versus the passive context (n = 229 dendrites from 4 mice, Wilcoxon signed-rank test). b, Same as a, for IT neurons (n = 367 dendrites from 4 mice). c, Histograms (left) and scatter plot (right) of dendritic Ca2+ responses to whisker deflections in IT neurons in the active (dark green) versus the passive context (light green) (n = 367 dendrites from 4 mice, P = 0.055, Wilcoxon signed-rank test).
Extended Data Fig. 10 Proposed cellular and circuit mechanisms for perceptual tactile detection.
Downstream targets of PT and IT neurons are shown in different brain structures. When a stimulus reaches the suprathreshold intensity, Ca2+ spikes are selectively activated in the apical dendrites of PT neurons in S1 (45.7% of PT dendrites versus 9.5% of IT dendrites). The activation of dendritic Ca2+ spikes is strongly dependent on inputs to the distal apical dendrites carrying contextual information. Ca2+ spikes trigger bursts of high-frequency somatic APs6,7. Boosted AP outputs of PT neurons are broadcasted to subcortical regions, with a critical involvement of higher-order thalamus, superior colliculus, and striatum, for the subsequent perceptual process or motor action (for example, licking). Thus, dendritic Ca2+ spikes drive context-dependent interactions between cortex and subcortical structures that are crucial for perceptual tactile detection.
Supplementary information
Rights and permissions
About this article
Cite this article
Takahashi, N., Ebner, C., Sigl-Glöckner, J. et al. Active dendritic currents gate descending cortical outputs in perception. Nat Neurosci 23, 1277–1285 (2020). https://doi.org/10.1038/s41593-020-0677-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41593-020-0677-8
This article is cited by
-
Pyramidal cell types drive functionally distinct cortical activity patterns during decision-making
Nature Neuroscience (2023)
-
Cortical glutamatergic projection neuron types contribute to distinct functional subnetworks
Nature Neuroscience (2023)
-
Nigrostriatal dopamine modulates the striatal-amygdala pathway in auditory fear conditioning
Nature Communications (2023)
-
How deep is the brain? The shallow brain hypothesis
Nature Reviews Neuroscience (2023)
-
Cortico-cortical feedback engages active dendrites in visual cortex
Nature (2023)