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Origins of choice-related activity in mouse somatosensory cortex

Abstract

During perceptual decisions about faint or ambiguous sensory stimuli, even identical stimuli can produce different choices. Spike trains from sensory cortex neurons can predict trial-to-trial variability in choice. Choice-related spiking is widely studied as a way to link cortical activity to perception, but its origins remain unclear. Using imaging and electrophysiology, we found that mouse primary somatosensory cortex neurons showed robust choice-related activity during a tactile detection task. Spike trains from primary mechanoreceptive neurons did not predict choices about identical stimuli. Spike trains from thalamic relay neurons showed highly transient, weak choice-related activity. Intracellular recordings in cortex revealed a prolonged choice-related depolarization in most neurons that was not accounted for by feed-forward thalamic input. Top-down axons projecting from secondary to primary somatosensory cortex signaled choice. An intracellular measure of stimulus sensitivity determined which neurons converted choice-related depolarization into spiking. Our results reveal how choice-related spiking emerges across neural circuits and within single neurons.

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Figure 1: Choice-related activity in mouse primary somatosensory cortex.
Figure 2: Primary mechanoreceptor afferent neurons do not show choice-related activity.
Figure 3: Transient choice-related activity in VPM thalamus.
Figure 4: Brief cortical response to transient VPM activity.
Figure 5: Choice-related membrane potential dynamics in S1 cortex.
Figure 6: Feedback of choice-related activity from S2 to S1.
Figure 7: Intracellular stimulus sensitivity predicts choice-related spiking.

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References

  1. Britten, K.H., Newsome, W.T., Shadlen, M.N., Celebrini, S. & Movshon, J.A. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci. 13, 87–100 (1996).

    Article  CAS  PubMed  Google Scholar 

  2. Celebrini, S. & Newsome, W.T. Neuronal and psychophysical sensitivity to motion signals in extrastriate area MST of the macaque monkey. J. Neurosci. 14, 4109–4124 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Nienborg, H., Cohen, M.R. & Cumming, B.G. Decision-related activity in sensory neurons: correlations among neurons and with behavior. Annu. Rev. Neurosci. 35, 463–483 (2012).

    Article  CAS  PubMed  Google Scholar 

  4. Cook, E.P. & Maunsell, J.H. Dynamics of neuronal responses in macaque MT and VIP during motion detection. Nat. Neurosci. 5, 985–994 (2002).

    Article  CAS  PubMed  Google Scholar 

  5. Bosking, W.H. & Maunsell, J.H. Effects of stimulus direction on the correlation between behavior and single units in area MT during a motion detection task. J. Neurosci. 31, 8230–8238 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Liu, S., Gu, Y., DeAngelis, G.C. & Angelaki, D.E. Choice-related activity and correlated noise in subcortical vestibular neurons. Nat. Neurosci. 16, 89–97 (2013).

    Article  PubMed  CAS  Google Scholar 

  7. Cohen, M.R. & Newsome, W.T. Estimates of the contribution of single neurons to perception depend on timescale and noise correlation. J. Neurosci. 29, 6635–6648 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Nienborg, H. & Cumming, B.G. Decision-related activity in sensory neurons reflects more than a neuron's causal effect. Nature 459, 89–92 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Nienborg, H. & Cumming, B.G. Macaque V2 neurons, but not V1 neurons, show choice-related activity. J. Neurosci. 26, 9567–9578 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Gu, Y., DeAngelis, G.C. & Angelaki, D.E. A functional link between area MSTd and heading perception based on vestibular signals. Nat. Neurosci. 10, 1038–1047 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Palmer, C., Cheng, S.Y. & Seidemann, E. Linking neuronal and behavioral performance in a reaction-time visual detection task. J. Neurosci. 27, 8122–8137 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Nienborg, H. & Cumming, B.G. Decision-related activity in sensory neurons may depend on the columnar architecture of cerebral cortex. J. Neurosci. 34, 3579–3585 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Romo, R., Hernández, A., Zainos, A., Lemus, L. & Brody, C.D. Neuronal correlates of decision-making in secondary somatosensory cortex. Nat. Neurosci. 5, 1217–1225 (2002).

    Article  CAS  PubMed  Google Scholar 

  14. de Lafuente, V. & Romo, R. Neural correlate of subjective sensory experience gradually builds up across cortical areas. Proc. Natl. Acad. Sci. USA 103, 14266–14271 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Cury, K.M. & Uchida, N. Robust odor coding via inhalation-coupled transient activity in the mammalian olfactory bulb. Neuron 68, 570–585 (2010).

    Article  CAS  PubMed  Google Scholar 

  16. Vallbo, A.B. & Johansson, R.S. in Sensory Functions of the Skin in Primates, With Special Reference To Man: Proceedings of the International Symposium Held in Wenner-Gren Center, Stockholm, January 13–15, 1976 (ed. Zotterman, Y.) 185–199 (Pergamon Press, 1976).

  17. Smolyanskaya, A., Haefner, R.M., Lomber, S.G. & Born, R.T. A modality-specific feedforward component of choice-related activity in MT. Neuron 87, 208–219 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Crochet, S., Poulet, J.F., Kremer, Y. & Petersen, C.C. Synaptic mechanisms underlying sparse coding of active touch. Neuron 69, 1160–1175 (2011).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  20. Mateo, C. et al. In vivo optogenetic stimulation of neocortical excitatory neurons drives brain-state-dependent inhibition. Curr. Biol. 21, 1593–1602 (2011).

    Article  CAS  PubMed  Google Scholar 

  21. Higley, M.J. & Contreras, D. Nonlinear integration of sensory responses in the rat barrel cortex: an intracellular study in vivo. J. Neurosci. 23, 10190–10200 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. O'Connor, D.H. et al. Neural coding during active somatosensation revealed using illusory touch. Nat. Neurosci. 16, 958–965 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Guo, Z.V. et al. Flow of cortical activity underlying a tactile decision in mice. Neuron 81, 179–194 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Siegle, J.H., Pritchett, D.L. & Moore, C.I. Gamma-range synchronization of fast-spiking interneurons can enhance detection of tactile stimuli. Nat. Neurosci. 17, 1371–1379 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ollerenshaw, D.R. et al. Detection of tactile inputs in the rat vibrissa pathway. J. Neurophysiol. 108, 479–490 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Chen, T.W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Zucker, E. & Welker, W.I. Coding of somatic sensory input by vibrissae neurons in the rat's trigeminal ganglion. Brain Res. 12, 138–156 (1969).

    Article  CAS  PubMed  Google Scholar 

  28. Temereanca, S., Brown, E.N. & Simons, D.J. Rapid changes in thalamic firing synchrony during repetitive whisker stimulation. J. Neurosci. 28, 11153–11164 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ollerenshaw, D.R., Zheng, H.J., Millard, D.C., Wang, Q. & Stanley, G.B. The adaptive trade-off between detection and discrimination in cortical representations and behavior. Neuron 81, 1152–1164 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang, Q., Webber, R.M. & Stanley, G.B. Thalamic synchrony and the adaptive gating of information flow to cortex. Nat. Neurosci. 13, 1534–1541 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 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).

    Article  CAS  PubMed  Google Scholar 

  32. Harvey, C.D., Collman, F., Dombeck, D.A. & Tank, D.W. Intracellular dynamics of hippocampal place cells during virtual navigation. Nature 461, 941–946 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Domnisoru, C., Kinkhabwala, A.A. & Tank, D.W. Membrane potential dynamics of grid cells. Nature 495, 199–204 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Petreanu, L. et al. Activity in motor-sensory projections reveals distributed coding in somatosensation. Nature 489, 299–303 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cauller, L. Layer I of primary sensory neocortex: where top-down converges upon bottom-up. Behav. Brain Res. 71, 163–170 (1995).

    Article  CAS  PubMed  Google Scholar 

  36. Nienborg, H. & Cumming, B. Correlations between the activity of sensory neurons and behavior: how much do they tell us about a neuron's causality? Curr. Opin. Neurobiol. 20, 376–381 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Law, C.T. & Gold, J.I. Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat. Neurosci. 11, 505–513 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Pitkow, X., Liu, S., Angelaki, D.E., DeAngelis, G.C. & Pouget, A. How can single sensory neurons predict behavior? Neuron 87, 411–423 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yu, X.J., Dickman, J.D., DeAngelis, G.C. & Angelaki, D.E. Neuronal thresholds and choice-related activity of otolith afferent fibers during heading perception. Proc. Natl. Acad. Sci. USA 112, 6467–6472 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Constantinople, C.M. & Bruno, R.M. Effects and mechanisms of wakefulness on local cortical networks. Neuron 69, 1061–1068 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  42. 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 

  43. Zagha, E., Casale, A.E., Sachdev, R.N., McGinley, M.J. & McCormick, D.A. Motor cortex feedback influences sensory processing by modulating network state. Neuron 79, 567–578 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. de Lafuente, V. & Romo, R. Neuronal correlates of subjective sensory experience. Nat. Neurosci. 8, 1698–1703 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. Vázquez, Y., Zainos, A., Alvarez, M., Salinas, E. & Romo, R. Neural coding and perceptual detection in the primate somatosensory thalamus. Proc. Natl. Acad. Sci. USA 109, 15006–15011 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Shadlen, M.N., Britten, K.H., Newsome, W.T. & Movshon, J.A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci. 16, 1486–1510 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Haefner, R.M., Gerwinn, S., Macke, J.H. & Bethge, M. Inferring decoding strategies from choice probabilities in the presence of correlated variability. Nat. Neurosci. 16, 235–242 (2013).

    Article  CAS  PubMed  Google Scholar 

  48. Huber, D. et al. Multiple dynamic representations in the motor cortex during sensorimotor learning. Nature 484, 473–478 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Safaai, H., von Heimendahl, M., Sorando, J.M., Diamond, M.E. & Maravall, M. Coordinated population activity underlying texture discrimination in rat barrel cortex. J. Neurosci. 33, 5843–5855 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  52. Madisen, L. et al. A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing. Nat. Neurosci. 15, 793–802 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hippenmeyer, S. et al. A developmental switch in the response of DRG neurons to ETS transcription factor signaling. PLoS Biol. 3, e159 (2005).

    PubMed  PubMed Central  Google Scholar 

  54. Redish, A.D. et al. MClust Spike Sorting Toolbox: Documentation for MClust version 4.3 http://redishlab.neuroscience.umn.edu/MClust/MClust-4.3.pdf (2014).

  55. Poulet, J.F., Fernandez, L.M., Crochet, S. & Petersen, C.C. Thalamic control of cortical states. Nat. Neurosci. 15, 370–372 (2012).

    Article  CAS  PubMed  Google Scholar 

  56. Yizhar, O., Fenno, L.E., Davidson, T.J., Mogri, M. & Deisseroth, K. Optogenetics in neural systems. Neuron 71, 9–34 (2011).

    Article  CAS  PubMed  Google Scholar 

  57. Landers, M., Pytte, C. & Zeigler, H.P. Reversible blockade of rodent whisking: Botulinum toxin as a tool for developmental studies. Somatosens. Mot. Res. 19, 358–363 (2002).

    Article  PubMed  Google Scholar 

  58. Kerlin, A.M., Andermann, M.L., Berezovskii, V.K. & Reid, R.C. Broadly tuned response properties of diverse inhibitory neuron subtypes in mouse visual cortex. Neuron 67, 858–871 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank V. Jayaraman, R. Kerr, D. Kim, L. Looger, K. Svoboda and the HHMI Janelia Farm GENIE Project for GCaMP6. We thank R. Gummi for technical assistance; S. Peron for MATLAB software; J. Cohen for discussions and help with optrode construction; T. Shelley for instrument fabrication. We thank J. Cohen, G. Minamisawa, W. Shew, K. Svoboda and S. Yu for comments on the manuscript. This work was supported by the Whitehall Foundation, Klingenstein Fund and the US National Institutes of Health (R01NS089652 (D.H.O.), P30NS050274).

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Authors

Contributions

H.Y., S.E.K. and D.H.O. planned the project. H.Y., S.E.K. and K.S.S. performed experiments. H.Y. and D.H.O. built the apparatus. All authors analyzed data. H.Y., S.E.K. and D.H.O. wrote the paper with comments from K.S.S.

Corresponding author

Correspondence to Daniel H O'Connor.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Neuronal stimulus-response curves and behavioral performance.

(a) Stimulus-response curves (mean ± SEM across recordings) show action potential rate evoked (Methods) by whisker stimuli of different strengths in TG (left; 9 single units from 2 mice), VPM (middle; 15 multi-units from 2 mice) and S1 (right; 16 single units from 2 mice) in awake mice. (b) Behavioral performance (fraction of trials correct) for TG, VPM, S1 whole cell, and calcium imaging (L2/3 soma and S2→S1 axon) recordings. Histogram means (µ, also shown by arrows) and standard deviations (σ) are indicated. TG histogram includes 17 single-neuron recordings from 6 mice. VPM histogram includes 17 multi-unit recordings from 4 mice. S1 whole cell histogram includes 22 recordings from 17 mice. Imaging histogram includes 10 recording sessions total from 10 mice. (c) Behavioral reaction times obtained during TG, VPM, S1 whole cell, and calcium imaging recordings. Each observation is the median reaction time on Hit trials for one recording. Conventions as in (b). (d) Behavioral task performance for two mice (gray and black symbols) over the course of prolonged training. Training sessions occurred daily, typically 6 days per week. Error bars show SEM for individual sessions based on bootstrapping.

Supplementary Figure 2 Task performance is abolished by optogenetic or pharmacological reversible silencing of S1.

(a) Schematic of silencing somatosensory cortex by optogenetic excitation of parvalbumin-positive (PV) GABAergic neurons in PV-IRES-Cre;Ai32 mice. Image adapted from The Allen Mouse Brain Atlas (http://mouse.brain-map.org/). (b) Example traces (high-pass filtered) from a cell-attached recording in an awake PV-IRES-Cre;Ai32 mouse from a putative PV neuron with whisker stimulation alone (black, top trace) or with whisker stimulation plus photostimulation (blue, bottom trace). Photostimulation is indicated by the cyan horizontal bar and bolt below trace (5 ms pulses at 100 Hz for 2.5 s, beginning 0.3 s before onset of whisker stimulus, mean power ~3 mW at brain surface). Arrows: onset of whisker stimulus (0.5 s, 40 Hz sinusoidal rostral-caudal deflection, peak speed ~800 deg/s). (c) Example traces from a neuron that was completely silenced during photostimulation. Conventions as in (b). (d) Example traces from a neuron that was partly silenced during photostimulation. Conventions as in (b). (e) Example spike rasters from the recording in (b) showing spike times from trials without (black, top raster) and with (blue, bottom raster) photostimulation. Photostimulation and conventions as in (b). (f) Example spike rasters from the recording in (c) showing spike times from trials without (black, top raster) and with (blue, bottom raster) photostimulation. Photostimulation and conventions as in (b). (g) Example spike rasters from the recording in (d) showing spike times from trials without (black, top raster) and with (blue, bottom raster) photostimulation. Photostimulation and conventions as in (b). (h) AP rate during the 0.5 s of whisker stimulation was greatly reduced by photostimulation. The response to whisker stimulation (“W”) alone and the response to whisker stimulation plus light (bolt) is shown (12.0 ± 6.0 vs. 1.4 ± 0.87 Hz, respectively, p = 2.4e–7, two-tailed sign test, n = 23). Inset: zoomed region. (i) For each neuron, a “silencing index” was calculated as the ratio of AP rate during the 0.5 s whisker stimulus with and without photostimulation. 0 corresponds to complete silencing, and 1 to completely unaffected AP rate. Histogram shows silencing index for 23 putative non-PV neurons (mean 0.11, median 0.046). 10 out of the 23 neurons are completely silenced and 4 have silencing index > 0.2. (j) Task performance is abolished on a trial-by-trial basis by optogenetically activating PV neurons in PV-IRES-Cre;Ai32 mice (n = 2). Hit rate vs. False Alarm rate plotted for different mice (colors) and conditions (plot symbols). Optogenetic stimulation in somatosensory (S1, circles) but not primary visual cortex (V1, squares) abolished performance relative to no stimulation (ellipses). (k) Schematic of pharmacological silencing using injections of the GABAA receptor agonist muscimol. (l) Muscimol abolishes performance. Hit rate vs. False Alarm rate plotted for different mice (colors) and conditions (plot symbols). Horizontal ellipses: baseline performance before muscimol injection. Filled circles: muscimol injection in S1 (100 nl of 5 µg/µl). Stars: recovery sessions without drug. Square: muscimol injection in primary visual cortex (V1; 300 nl of 5 µg/µl). ***, p < 0.001.

Supplementary Figure 3 Thalamic recording site verification, synchrony analysis and single units.

(a) Coronal diagram showing VPM thalamus, adapted from The Allen Mouse Brain Atlas (http://mouse.brain-map.org/). (b) Example cytochrome oxidase-stained coronal section showing overlaid recording tract marked by DiI fluorescence. (c) TC40, a measure of synchronous spiking, is larger for Hits compared with Misses (172.1 ± 24.6 vs. 141.2 ± 25.2 spikes/s, p = 0.049, two-tailed sign test, n = 17). TC40 is a measure of temporal precision (Pinto, D. J., Brumberg, J. C., & Simons, D. J., 2000; J. Neurophysiology, 83(3):1158–66) that can be applied to multiunit data. We computed TC40 as 40% of the total spike count in a 15 ms window starting 5 ms after stimulus onset, divided by the time taken to reach the first 40% of the total spike count within this window. Right panel shows cumulative histogram of difference in TC40 on Hits compared with Misses. (d) Mean cumulative histogram (± SEM across 17 recordings) of interspike intervals in the post-peak window (Methods) are similar for Hits (blue) and Misses (black; p = 0.82, two-tailed K-S test). (e) Example single unit waveforms (magenta, inset) and cluster (magenta; sorted using MClust; Methods). We obtained 7 single units with estimated false positive and false negative rates of 0.033 ± 0.024 and 0.057 ± 0.100 (mean ± SD), respectively (2 ms refractory period; methods from: Hill, D.N., Mehta, S.B. and Kleinfeld, D, 2011; J. Neuroscience, 31(24):8699-705). (f) All 7 single units show a higher evoked AP rate on Hits compared with Misses during a brief window at the peak of the response (p = 0.016). (g) Mean PSTHs for Hits (solid blue) and Misses (solid black) for the 7 single units. Dashed lines: ± SEM. Arrow: stimulus onset. *, p < 0.05.

Supplementary Figure 4 Simultaneous VPM optogenetic microstimulation and S1 recordings in awake but non–task-performing mice.

(a) Schematic of simultaneous optogenetic microstimulation in VPM and cell-attached recording in S1. Image adapted from The Allen Mouse Brain Atlas (http://mouse.brain-map.org/). (b–f) Results from experiments, independent of those in Fig. 4, in which mice (n = 2) were awake but not performing the behavioral task. (b) Example spike rasters from an S1 recording with whisker stimulation alone (black ticks) or whisker stimulation plus weak (dark blue) or strong (light blue) photostimulation. Arrow: whisker stimulus onset. Dark blue bolt: weak photostimulation. Double light blue bolts: strong photostimulation. Responses to light alone are shown toward the end of the rasters (bolts in the dark blue and light blue rasters). (c) Mean PSTHs (4 ms bins; ± SEM) for n = 21 recordings. Arrow: onset of whisker stimulus followed by light pulse (4 ms delay; Methods). (d) Evoked AP rate is similar for whisker stimulation (“W”) alone vs whisker stimulation plus photostimulation (dark blue bolt, weak light: p = 0.61, n = 21; double light blue bolts, strong light: p = 0.26, two-tailed sign test, n = 21). (e) Evoked AP rate after the peak of the whisker-evoked response showed no difference for whisker-alone vs whisker plus photostimulation (weak light: p = 0.66, two-tailed sign test, n = 21; strong light: p = 0.08, n = 21). (f) Pre-stimulus AP rate is similar for trials with whisker-alone vs whisker plus photostimulation (weak light: p = 0.11, n = 21, two-tailed sign test; strong light: p = 0.28, n = 21). n.s., p > 0.05.

Supplementary Figure 5 Analysis of whole-cell recordings.

(a) Resting potential (Vrest, left), spike threshold (Vspike, middle) and depth from the cortical surface (right) for each primary somatosensory cortex whole cell recording. Depth is estimated as micromanipulator depth reading minus 100 µm to account for dimpling of the brain surface. (b) P-value (red trace) at each time point from two-tailed Wilcoxon signed rank test of difference between Vm on Hit trials (blue trace: mean Vm on Hits) and Miss trials (black trace: mean Vm on Misses). Dashed red line: p = 0.05. Vm for Hits and Misses diverged significantly beginning at 28 ms after stimulus onset (indicated by red arrow). (c) Vm difference between Hits and Misses over the course of the full trial. Mean Vm after AP removal (± SEM; n = 22 neurons) for Hit (blue), Miss (black) and Correct Rejection (red) trials. Same as in Fig. 5c but showing more of the trial. The magenta arrow indicates approximately the period of Hit trials in which licking and reward occur (we did not analyze this period). (d) Detect and stimulus probability over the course of the full trial. Mean time course of detect probability (black) and stimulus probability (gray) across Vm recordings (n = 22). Same as in Fig. 5g but showing more of the trial. Magenta arrow as in panel (c). (e) Mean Vm after AP removal for Hit (solid blue) and Miss (solid black) for a subset of putative L2/3 neurons (cortical depth < 400 µm). Dashed lines: ± SEM (n = 12 neurons). (f) For putative L2/3 neurons, stimulus-evoked change in membrane potential (ΔVm) is larger on Hit trials compared with Miss trials (p < 1e–3, two-tailed sign test, n = 12). (g) Data from Fig. 5d broken down into the first 30 ms (left; p = 0.29, two-tailed sign test, n = 22) and the remaining 70 ms (right; p < 1e–3) of the “evoked” window. n.s., p > 0.05; ***, p < 0.001.

Supplementary Figure 6 Choice-related Vm is not explained by filtering VPM spike trains with an exponential kernel to simulate passive membrane time constant of cortical neurons.

(a) Overlaid plots of VPM mean PSTHs (dashed traces) and mean cortical Vm (solid traces). (b) VPM spike times convolved with a 15 ms exponential kernel (dashed), overlaid with mean Vm (solid). A 15 ms kernel was chosen to simulate the passive membrane time constant of cortical neurons (Oswald, A. M. & Reyes, A. D., 2008; J. Neurophysiology, 99(6):2998-3008). VPM traces are scaled to match the amplitude of the Vm traces. Note that the difference between Hits (blue) and Misses (black) persists in Vm but rapidly diminishes in the filtered VPM traces. Arrows: stimulus onset.

Supplementary Figure 7 Example traces from neurons with high, medium and low detect probability.

Raw Vm traces (with spikes truncated) from individual neurons with high, medium and chance-level action potential-based detect probabilities. High values of AP detect probability arose when a neuron’s reversal potential was relatively close to its spike threshold. In contrast, neurons could show large subthreshold, Vm detect probability, but fail to show any choice-related spiking because the reversal potential was hyperpolarized with respect to spike threshold. Arrows: stimulus onset.

Supplementary Figure 8 Correlation between stimulus probability and detect probability.

(a) Left: schematic of whole cell recording in S1 cortex. Image adapted from The Allen Mouse Brain Atlas (http://mouse.brain-map.org/). Right: spike rate detect probability and spike rate stimulus probability measured from whole cell recordings are correlated (R2 = 0.31, p = 0.008). (b) Top: schematic of two-photon calcium imaging of S1 cortex during the tactile detection task. Bottom panels: detect probability and stimulus probability calculated from ΔF/F0 responses are shown for each neuron (plot symbols) and each mouse (n = 6; separate panels) appearing in Fig. 7d.

Supplementary Figure 9 False alarm versus correct rejection trials.

(a) Example Vm traces for False Alarm (green) and Correct Rejection (red) trials. Cyan dots: licking. (b) In four recordings with at least 2 FA and CR trials each, Vm or AP rate changes were compared (Vm: 5.9 ± 2.5 vs. −0.10 ± 0.13 mV; AP: 12.5 ± 7.5 vs. −0.11 ± 0.44 Hz). Changes were calculated with respect to the same baseline window used elsewhere for Hit and Miss trials (a 200 ms window ending 3 ms before the time of possible stimulus onset). For Correct Rejections, baseline activity was subtracted from activity in a 100 ms window starting 5 ms after stimulus onset. For False Alarms, baseline activity was subtracted from activity in a 100 ms window ending 200 ms before the first lick (period indicated by the gray bar in panel (a)). Trials with premature licking (defined previously) were excluded. Detect probability based on Vm and AP rate are, respectively, 0.88 ± 0.11 (mean ± SD) and 0.76 ± 0.28. (c) Mean time course of detect probability based on Vm (black) and AP rate (orange) across the four whole cell recordings. For False Alarms, time is with respect to the first lick (indicated with cyan arrow). For Correct Rejections, time is with respect to the onset of the window defined above.

Supplementary Figure 10 Sensitivity analysis of windows used to compare evoked responses on hits versus misses.

P-value from two-tailed Wilcoxon signed rank test of difference between Hit and Miss trial evoked responses, as a function of post-stimulus window used to calculate evoked response. Window durations were varied from 10 ms to 100 ms in increments of 5 ms. Window onsets were as previously defined for each set of recordings (Methods). Gray dashed lines mark 0.05 on the y-axis. Hit–Miss difference was never significant at the 0.05 level for TG, was transiently significant for VPM during the initial ~10 ms, and was consistently significant for window sizes > 30 ms for S1.

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Yang, H., Kwon, S., Severson, K. et al. Origins of choice-related activity in mouse somatosensory cortex. Nat Neurosci 19, 127–134 (2016). https://doi.org/10.1038/nn.4183

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