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A synaptic threshold mechanism for computing escape decisions

Nature volume 558, pages 590–594 (2018)Cite this article

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Abstract

Escaping from imminent danger is an instinctive behaviour that is fundamental for survival, and requires the classification of sensory stimuli as harmless or threatening. The absence of threat enables animals to forage for essential resources, but as the level of threat and potential for harm increases, they have to decide whether or not to seek safety1. Despite previous work on instinctive defensive behaviours in rodents2,3,4,5,6,7,8,9,10,11, little is known about how the brain computes the threat level for initiating  escape. Here we show that the probability and vigour of escape in mice scale with the saliency of innate threats, and are well described by a model that computes the distance between the threat level and an escape threshold. Calcium imaging and optogenetics in the midbrain of freely behaving mice show that the activity of excitatory neurons in the deep layers of the medial superior colliculus (mSC) represents the saliency of the threat stimulus and is predictive of escape, whereas glutamatergic neurons of the dorsal periaqueductal grey (dPAG) encode exclusively the choice to escape and control escape vigour. We demonstrate a feed-forward monosynaptic excitatory connection from mSC to dPAG neurons, which is weak and unreliable—yet required for escape behaviour—and provides a synaptic threshold for dPAG activation and the initiation of escape. This threshold can be overcome by high mSC network activity because of short-term synaptic facilitation and recurrent excitation within the mSC, which amplifies and sustains synaptic drive to the dPAG. Therefore, dPAG glutamatergic neurons compute escape decisions and escape vigour using a synaptic mechanism to  threshold threat information received from the mSC, and provide a biophysical model of how the brain performs a critical behavioural computation.

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Fig. 1: Escape behaviour during threats of varying intensity.
Fig. 2: Encoding of threat and escape behaviour in the superior colliculus and periaqueductal gry.
Fig. 3: Optogenetic stimulation shows different roles for mSC and dPAG in escape behaviour.
Fig. 4: Neural circuit and biophysical mechanisms for computing escape behaviour.

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References

  1. Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).

    Article  Google Scholar 

  2. De Franceschi, G., Vivattanasarn, T., Saleem, A. B. & Solomon, S. G. Vision guides selection of freeze or flight defense strategies in mice. Curr. Biol. 26, 2150–2154 (2016).

    Article  PubMed  CAS  Google Scholar 

  3. Yilmaz, M. & Meister, M. Rapid innate defensive responses of mice to looming visual stimuli. Curr. Biol. 23, 2011–2015 (2013).

    Article  PubMed  CAS  Google Scholar 

  4. Kunwar, P. S. et al. Ventromedial hypothalamic neurons control a defensive emotion state. eLife 4, e06633 (2015).

    Article  PubMed Central  Google Scholar 

  5. Shang, C. et al. A parvalbumin-positive excitatory visual pathway to trigger fear responses in mice. Science 348, 1472–1477 (2015).

    Article  ADS  PubMed  CAS  Google Scholar 

  6. Wang, L., Chen, I. Z. & Lin, D. Collateral pathways from the ventromedial hypothalamus mediate defensive behaviors. Neuron 85, 1344–1358 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Wei, P. et al. Processing of visually evoked innate fear by a non-canonical thalamic pathway. Nat. Commun. 6, 6756 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Xiong, X. R. et al. Auditory cortex controls sound-driven innate defense behaviour through corticofugal projections to inferior colliculus. Nat. Commun. 6, 7224 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Vale, R., Evans, D. A. & Branco, T. Rapid spatial learning controls instinctive defensive behavior in mice. Curr. Biol. 27, 1342–1349 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Gross, C. T. & Canteras, N. S. The many paths to fear. Nat. Rev. Neurosci. 13, 651–658 (2012).

    Article  PubMed  CAS  Google Scholar 

  11. Blanchard, R. J., Blanchard, D. C., Rodgers, J. & Weiss, S. M. The characterization and modelling of antipredator defensive behavior. Neurosci. Biobehav. Rev. 14, 463–472 (1990).

    Article  PubMed  CAS  Google Scholar 

  12. Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).

    Article  PubMed  CAS  Google Scholar 

  13. Carandini, M. & Churchland, A. K. Probing perceptual decisions in rodents. Nat. Neurosci. 16, 824–831 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Dean, P., Redgrave, P. & Westby, G. W. M. Event or emergency? Two response systems in the mammalian superior colliculus. Trends Neurosci. 12, 137–147 (1989).

    Article  PubMed  CAS  Google Scholar 

  15. Deng, H., Xiao, X. & Wang, Z. Periaqueductal gray neuronal activities underlie different aspects of defensive behaviors. J. Neurosci. 36, 7580–7588 (2016).

    Article  PubMed  CAS  Google Scholar 

  16. Fotowat, H. & Gabbiani, F. Collision detection as a model for sensory-motor integration. Annu. Rev. Neurosci. 34, 1–19 (2011).

    Article  PubMed  CAS  Google Scholar 

  17. Shea-Brown, E., Gilzenrat, M. S. & Cohen, J. D. Optimization of decision making in multilayer networks: the role of locus coeruleus. Neural Comput. 20, 2863–2894 (2008).

    Article  MathSciNet  PubMed  MATH  Google Scholar 

  18. Silva, B. A. et al. Independent hypothalamic circuits for social and predator fear. Nat. Neurosci. 16, 1731–1733 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Tovote, P. et al. Midbrain circuits for defensive behaviour. Nature 534, 206–212 (2016).

    Article  ADS  PubMed  CAS  Google Scholar 

  20. Wietek, J. et al. An improved chloride-conducting channelrhodopsin for light-induced inhibition of neuronal activity in vivo. Sci. Rep. 5, 14807 (2015).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  21. Pettit, D. L., Helms, M. C., Lee, P., Augustine, G. J. & Hall, W. C. Local excitatory circuits in the intermediate gray layer of the superior colliculus. J. Neurophysiol. 81, 1424–1427 (1999).

    Article  PubMed  CAS  Google Scholar 

  22. Gale, S. D. & Murphy, G. J. Active dendritic properties and local inhibitory input enable selectivity for object motion in mouse superior colliculus neurons. J. Neurosci. 36, 9111–9123 (2016).

    Article  PubMed  CAS  Google Scholar 

  23. Zhao, X., Liu, M. & Cang, J. Visual cortex modulates the magnitude but not the selectivity of looming-evoked responses in the superior colliculus of awake mice. Neuron 84, 202–213 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Schiller,  P. H. in The Handbook of Physiology Vol. 3 (eds Brookhart, J. M. and Mountcastle, V. B.) 457–505 (Lippincott Williams and Wilkins, Pennsylvania, 1984).

  26. Felsen, G. & Mainen, Z. F. Midbrain contributions to sensorimotor decision making. J. Neurophysiol. 108, 135–147 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Cohen, J. D. & Castro-Alamancos, M. A. Neural correlates of active avoidance behavior in superior colliculus. J. Neurosci. 30, 8502–8511 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Horwitz, G. D., Batista, A. P. & Newsome, W. T. Representation of an abstract perceptual decision in macaque superior colliculus. J. Neurophysiol. 91, 2281–2296 (2004).

    Article  PubMed  Google Scholar 

  29. Vong, L. et al. Leptin action on GABAergic neurons prevents obesity and reduces inhibitory tone to POMC neurons. Neuron 71, 142–154 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Vélez-Fort, M. et al. The stimulus selectivity and connectivity of layer six principal cells reveals cortical microcircuits underlying visual processing. Neuron 83, 1431–1443 (2014); erratum 84, 238 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Vélez-Fort, M. et al. A circuit for integration of head- and visual-motion signals in layer 6 of mouse primary visual cortex. Neuron 98, 179–191.e6 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Tervo, D. G. R. et al. A designer AAV variant permits efficient retrograde access to projection neurons. Neuron 92, 372–382 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Mongeau, R., Miller, G. A., Chiang, E. & Anderson, D. J. Neural correlates of competing fear behaviors evoked by an innately aversive stimulus. J. Neurosci. 23, 3855–3868 (2003).

    Article  PubMed  CAS  Google Scholar 

  34. Resulaj, A., Kiani, R., Wolpert, D. M. & Shadlen, M. N. Changes of mind in decision-making. Nature 461, 263–266 (2009).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  35. Ghosh, K. K. et al. Miniaturized integration of a fluorescence microscope. Nat. Methods 8, 871–878 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Guizar-Sicairos, M., Thurman, S. T. & Fienup, J. R. Efficient subpixel image registration algorithms. Opt. Lett. 33, 156–158 (2008).

    Article  ADS  PubMed  Google Scholar 

  37. Aravanis, A. M. et al. An optical neural interface: in vivo control of rodent motor cortex with integrated fiberoptic and optogenetic technology. J. Neural Eng. 4, S143–S156 (2007).

    Article  PubMed  Google Scholar 

  38. Huber, D. et al. Sparse optical microstimulation in barrel cortex drives learned behaviour in freely moving mice. Nature 451, 61–64 (2008).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  39. Isaacson, J. S. & Walmsley, B. Counting quanta: direct measurements of transmitter release at a central synapse. Neuron 15, 875–884 (1995).

    Article  PubMed  CAS  Google Scholar 

  40. del Castillo, J. & Katz, B. Quantal components of the end-plate potential. J. Physiol. (Lond.) 124, 560–573 (1954).

    Article  Google Scholar 

  41. Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  42. Jun, J. J. et al. Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction. Preprint at https://www.bioRxiv.org/content/early/2017/01/30/101030 (2017).

  43. Wall, N. R., Wickersham, I. R., Cetin, A., De La Parra, M. & Callaway, E. M. Monosynaptic circuit tracing in vivo through Cre-dependent targeting and complementation of modified rabies virus. Proc. Natl Acad. Sci. USA 107, 21848–21853 (2010).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  44. Wickersham, I. R. et al. Monosynaptic restriction of transsynaptic tracing from single, genetically targeted neurons. Neuron 53, 639–647 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Franklin, T. B. et al. Prefrontal cortical control of a brainstem social behavior circuit. Nat. Neurosci. 20, 260–270 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Franklin, K. B. J. & Paxinos, G. The Mouse Brain in Stereotaxic Coordinates 3rd edn (Academic Press, 2008).

Download references

Acknowledgements

This work was funded by a Wellcome Trust Henry Dale Fellowship (098400/Z/12/Z), Medical Research Council (MRC) grant MC-UP-1201/1, Wellcome Trust/Gatsby Charitable Foundation SWC Fellowship (T.B.), MRC PhD Studentship (D.A.E., R.V.), Boehringer Ingelheim Fonds PhD fellowship (R.V.), DFG fellowship (A.V.S., S.R.), Marie Skłodowska-Curie Individual Fellowship (706136) and EMBO Long Term Fellowship (Y.L.). We thank P. Latham and members of the Branco laboratory for discussions; S. Sternson, P. Dayan, T. Margrie and T. Mrsic-Flogel for comments on the manuscript; S. Sternson, S. Wiegert, T. Oertner and T. Margrie for gifts of viral vectors; P. Iordanidou, T. Okbinoglu, L. Jin, the LMB and SWC Biological Research Facility and FabLabs for technical support; D. Campagner, T. Harris and N. Steinmetz for help with silicon probe recordings; and K. Betsios for programming the data acquisition software.

Reviewer information

Nature thanks V. Bolshakov, P. Tovote and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Author notes

  1. These authors contributed equally: D. A. Evans, A. V. Stempel.

Authors and Affiliations

  1. MRC Laboratory of Molecular Biology, Cambridge, UK

    Dominic A. Evans, A. Vanessa Stempel, Ruben Vale, Sabine Ruehle, Yaara Lefler & Tiago Branco

  2. UCL Sainsbury Wellcome Centre for Neural Circuits and Behaviour, London, UK

    Dominic A. Evans, A. Vanessa Stempel, Ruben Vale, Sabine Ruehle, Yaara Lefler & Tiago Branco

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  1. Dominic A. Evans
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Contributions

T.B. and D.A.E. conceived the project with input from A.V.S., R.V. and S.R.; D.A.E., A.V.S., R.V. and S.R. performed behavioural and optogenetic experiments. T.B. performed theoretical modelling, D.A.E. performed calcium imaging, A.V.S. and T.B. performed in vitro electrophysiology, Y.L. and R.V. performed single-unit recordings, D.A.E. and A.V.S. performed chemogenetic experiments, A.V.S. performed anatomical tracing. All authors analysed data and contributed to the experimental design. T.B. supervised the project. T.B. wrote the manuscript with help from D.E. and A.V.S.

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Correspondence to Tiago Branco.

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Extended data figures and tables

Extended Data Fig. 1 Behaviour metrics computed over single mice.

a–c, Summary plots for escape behaviour metrics calculated for each mouse individually and averaged. Plots on the left were obtained with data from all trials, and in the plots on the right, trials for each contrast were split in half and the behaviour metrics calculated for each half. There is a significant dependency on contrast for all metrics (reaction time, a: P = 3.5 × 10−8; escape probability, b: P = 2.1 × 10−7; escape vigour, c: P = 1.6 × 10−6, repeated measures ANOVA), and no significant difference between the metrics calculated using the first and second half of the trials (P > 0.4 for a main effect of trial group in all comparisons, two-way repeated measures ANOVA), indicating that behavioural performance was stable across repeated presentations of the stimulus. Error bars and shaded areas are s.e.m. d, Escape probability after the first (as shown in Fig. 1e, calculated by pooling all data) and fifth spot, during the presentation of five consecutive expanding spots.

Extended Data Fig. 2 iChloC activation strongly reduces neuronal firing and disrupts defensive behaviour without affecting basal locomotion.

a, Example voltage traces showing a VGluT2+ dmSC neuron expressing iChloC responding to current steps in control conditions (light off, left) and during continuous illumination with 473-nm light (light on, right). b, Summary of the relationship between current injection and action potential firing showing a strong reduction in firing upon illumination (left, average 87.9 ± 3% reduction across all steps, P = 1.7 × 10−9 for a main effect of light, two-way repeated measures ANOVA; P < 0.05 for simple effects of light on current steps larger than 100 pA), as well as a strong reduction in input resistance (right, 73.2 ± 3% reduction, P = 1.23 × 10−8, t-test). Summary data are pooled from 6 dPAG and 3 dmSC cells. c, For the 18% of trials in which VGluT2+ mice expressing iChloC in the dmSC escape from threat stimuli during continuous illumination (light on), the vigour of escape is significantly lower (77 ± 7% of light off) when compared to escapes elicited without iChloC activation (light off; n = 7 trials, n = 6 out of 9 mice, P = 0.0253, paired t-test). d, Movement during exploration is not affected by iChloC activation in dPAG- or dmSC-targeted mice in the absence of threat, quantified as the maximum speed in the 5-s stimulation period (light on) or control period (light off) as a percentage of the 5-s pre-stimulation period (P = 0.8767 for dPAG, P = 0.3443 for dmSC, U-test). e, Optic fibre placements for all experiments in dPAG (n = 6 mice, blue circles) and dmSC (n = 9 mice, magenta circles), coordinates are in mm and from bregma. Mouse brain images adapted from ref. 46 and reproduced with permission from Elsevier.

Extended Data Fig. 3 Muscimol inactivation of dPAG and mSC abolishes escape while V1 and amygdala have a modulatory effect on escape behaviour.

a, Top, example images of muscimol infusion in the dPAG (left) and mSC (right), and respective speed traces in response to a threatening visual stimulus (bottom) showing a switch from escape to freezing after dPAG inactivation and a loss of defensive responses after mSC muscimol inactivation. b, Summary quantification of the effect of muscimol infusion on threat-evoked defensive behaviour probability in the dPAG (left; n = 7 mice, P = 0.0001 for escape and P = 0.00025 for freezing, U-tests) and mSC (right; n = 10 mice, P = 0.00021 for escape and P = 0.051 for freezing, U-tests). c, Top, images of bilateral muscimol infusion in the amygdala (left) and visual cortex area V1 (right). Respective speed traces during threatening visual stimulus presentation (bottom) show that mice still engage in escape behaviour, but with reduced vigour. d, Summary quantification for escape probability (left) and vigour (right) after amygdala and V1 acute inactivation (amygdala: n = 4 mice, P = 0.37 for escape probability, U-test; P = 0.01 for escape vigour, two-tailed t-test; V1: n = 4 mice, P = 0.5 for escape probability, U-test; P = 0.01 for escape vigour, two-tailed t-test). e, Example speed traces showing that vehicle infusion in the mSC and dPAG does not change threat-evoked escape probability, and respective summary quantification. f, Infusion of mSC and dPAG with vehicle does not affect escape probability (mSC; n = 5 mice, P = 0.21, U-test; dPAG; n = 5 mice, P = 0.21, U-test). g, Infusion of mSC and dPAG with muscimol or vehicle does not affect running speed during exploratory behaviour (mSC: P = 0.8 for vehicle, P = 0.22 for muscimol; dPAG: P = 0.28 for vehicle, P = 0.75 for muscimol, paired t-tests). h, Profile of exploratory behaviour for behavioural sessions lasting at least 40 min, after injection of vehicle or muscimol in the mSC and dPAG. The displacement over time for all conditions is not significantly different to the profile for multiple trials of visual threat stimulation in control conditions (dashed black line, same data as shown in Extended Data Fig. 5e; P > 0.1 for all comparisons with control, two-tailed t-test). Thin lines show individual mice and thick lines show the dataset mean. Box-and-whisker plots show median, IQR and range.

Extended Data Fig. 4 The reliability and fraction of active cells is stable over multiple trials of calcium imaging, activity in the dmSC does not reflect head rotation and rises with different slopes, and dPAG activity is specific to escape.

a, b, Example images of GCaMP6s expression in VGluT2+ cells (green), with schematic showing GRIN lens placement in the dPAG (a) and dmSC (b). c, d, Raster plots showing active (colour squares) and non-active cells (black squares) in a single FOV imaged over multiple trials. A total of 8 FOVs were imaged in the dPAG (c) with a mean of 18 cells per FOV (range = 7–30) and 11 trials per FOV; and in the dmSC (d), 11 FOVs were imaged with a mean of 20 cells per FOV (range = 7–31) and 20 trials per FOV. There was a mean of 7 escape-responding cells per dPAG FOV and 16 escape-responding cells per dmSC FOV. e, f, Reliability of escape-responding cells showing a response over multiple trials for all trials (left) and for the first and second half of trials separately (right). Mean reliability across all trials was 28 ± 3% for dPAG and 35 ± 3% for dmSC, and stable over multiple trials (P = 0.44 for dPAG, P = 0.11 for dmSC, comparison between the two groups of trials, U-test). g, h, Fraction of all cells in a FOV that were active on each trial for all trials (left) and for the first and second half of trials separately (right). The active fraction across all trials was 14 ± 3% for dPAG and 23 ± 6% for dmSC, and stable over multiple trials (P = 0.21 for dPAG, P = 0.08 for dmSC, comparison between the two groups of trials, U-test). i, Correlation between the rise slope of the population activity and escape latency (n = 75 trials, P = 0.0048, Pearson’s r). j, Average population calcium signal in the dmSC for escape trials in response to 98% contrast spots and sound stimuli. The slope of the signal rise is steeper for sound-evoked escape. k, Left, ROC AUC for the dmSC signal before spontaneous escape onset after conditioning (AUC at escape onset = 0.74, significantly above chance 2.1 s before escape, n = 57 trials). Right, average population calcium signal in the dmSC during threat-evoked escape trials where the mouse was already facing the shelter and therefore did not rotate the head (n = 5 trials). l, Summary quantification of dPAG population calcium signals during threat-evoked escape and spontaneous foraging running bouts of similar speed (top; n = 6 escape trials and n = 6 running bouts, speed not significantly different, P = 0.64, t-test), showing that activity increase in the dPAG is specific for escape (bottom; P = 0.0018, t-test). Shaded areas show s.e.m., box-and-whisker plots show median, IQR and range. m, Correlation between the population activity of dPAG (top; n = 39 trials, P = 6.7 × 10−7, Pearson’s r) and dmSC (bottom; n = 64 trials, P = 0.04, Pearson’s r) and escape speed. Each data point is a single trial. n, Placement of GRIN lenses in the dmSC (magenta circles) and dPAG (blue circles), coordinates are in mm and from bregma. Mouse brain images adapted from ref. 46 and reproduced with permission from Elsevier. Box-and-whisker plots show median, IQR and range.

Extended Data Fig. 5 Repeated high-contrast visual stimulation causes place aversion, reduction in exploration and spontaneous escape.

a, Traces and probability distributions for the location of two example mice during free exploration (top), and before and after a high-contrast visual stimulation conditioning paradigm (bottom), showing avoidance of the threat area after conditioning (bottom right). b, Time spent in the threat area decreases with aversive conditioning (35.1 ± 3.5% for naive mice versus 5.1 ± 2.0% after conditioning, n = 7 mice, P = 2.2 × 10−5, two-tailed t-test). c, The frequency of visits to the threat area by the mice decreases significantly after conditioning (1.51 ± 0.10 visits per min for naive mice versus 0.30 ± 0.12 after conditioning, n = 7 mice, P = 1 × 10−4, two-tailed t-test). d, Summary quantification of spontaneous escape probability (left) and single trial speed traces from three mice (right) showing spontaneous escape after conditioning (Pspontaneous escape 3.2 ± 0.8% for naive mice, n = 7 mice, and 12.2 ± 2% after conditioning, n = 13 mice; P = 0.004, two-tailed t-test). e, Profile of exploratory behaviour during behavioural sessions of multiple contrast stimulation (black, data taken from the mice that generated the dataset for Fig. 1) with no stimulation for comparison (orange). Exploration decays over time and the decay is accelerated by visual stimulation, but the two curves are not significantly different over time (2.4 ± 0.3 m min−1 at 40 min for control versus 2.0 ± 0.3 with visual stimulation, P = 0.16, two-tailed t-test). f, Same quantification as in e for sessions of aversive conditioning. Aversive conditioning significantly reduces exploratory behaviour (1.2 ± 0.3 m min−1 after conditioning, P = 0.018 versus no stimulation and P = 0.039 versus multiple contrast stimulation, two-tailed t-test). Thin lines show individual mice monitored for 40 min and thick lines show the dataset mean. Box-and-whisker plots show median, IQR and range.

Extended Data Fig. 6 Optogenetic activation of dPAG and mSC elicits escape over a range of frequencies, and mSC VGluT2::ChR2-evoked escape is abolished by inactivating the PAG, but not the PBGN.

a, Optic-fibre placements for ChR2 stimulation in the dmSC (magenta circles) and dPAG (blue circles), coordinates are in mm and from bregma. Mouse brain images adapted from Franklin and Paxinos46 and reproduced with permission from Elsevier. b, Example speed traces for dPAG (left) and mSC (right) ChR2 stimulation at different frequencies (10 pulses) and high light intensities, showing robust escape behaviour for 5 to 40 Hz stimulation. c, Left, speed traces for 473-nm light stimulation (40 Hz, 30 pulses) of one mouse expressing eYFP in the dPAG (dark green), showing no change in running speed. Light green traces show similar speed profiles for the same mouse entering the stimulation area with the light off. Blue dashed traces are from a different mouse expressing ChR2 in the dPAG (40 Hz, 10 pulses), for comparison. Right, summary data for eYFP control stimulation in dPAG (running speed not significantly different between laser on and off, n = 236 trials from 3 mice, P = 0.48, U-test). d, Image showing expression of ChR2–eYFP in the mSC (green) with projections to the PBGN (yellow) and muscimol infusion (orange). e, Speed traces for spot-evoked escape responses from one mouse before and after acute PBGN inactivation. f, Summary data for escape probability and vigour during mSC optogenetic stimulation and PBGN acute inactivation, showing no difference (n = 3 mice, P = 0.80 for escape probability; P = 0.70 for escape vigour, U-test). g, Image showing expression of ChR2–eYFP in the mSC (green) and muscimol infusion in the PAG (orange). h, i, Speed traces (h) and summary data (i) showing that mSC ChR2-evoked escape is abolished by PAG acute inactivation (n = 3 mice, P = 0.0297 for probability, U-test). Box-and-whisker plots show median, IQR and range.

Extended Data Fig. 7 dPAG neurons receive input from mainly excitatory cells in the SC and do not project back to the SC.

a, Image showing starter dPAG VGlut2+ cells expressing both TVA–GFP and RV–mCherry and presynaptic cells expressing RV–mCherry only (left), and corresponding schematic (right) illustrating the position of starter dPAG (blue) and presynaptic SC cells (pink) across deep, intermediate and superficial SC layers (same as shown in Fig. 4a). b, Kernel density estimation curves for the axial position of presynaptic SC cells for each layer (82.9 ± 2.6% of 1,770 cells are located within the medial bisection of ipsilateral SC, n = 3 mice). c, Image showing presynaptic cells in the mSC infected with rabies virus (red) from starter neurons in the dPAG of a VGluT2::eYFP mouse (left). Box indicates area magnified shown on the right. Yellow cells are VGluT2+ mSC presynaptic neurons. d, Summary quantification of the percentage of presynaptic cells in the mSC that express VGluT2+ (mean = 87.9 ± 1.0%, n = 4 mice). e, Image showing injection of rAAV2-retro in the mSC (left) and no retrogradely labelled cells in the dPAG (bottom, left), while retrograde labelling is present in the auditory cortex for comparison (bottom, right). Similarly, rabies virus injected in the mSC shows a lack of presynaptic cells in the dPAG (right), suggesting a predominantly feed-forward connectivity arrangement between the mSC and dPAG (note, however, that it cannot be excluded that both rAAV2-retro and rabies display selective tropism that prevents labelling of dPAG neurons). f, Summary quantification for retrogradely labelled cells in the dPAG and auditory cortex after mSC rAAV2-retro (n = 3 mice) or rabies infection (n = 3 mice). Box-and-whisker plots show median, IQR and range.

Extended Data Fig. 8 Biophysical properties of excitatory dPAG neurons and synaptic properties of the dmSC-dPAG excitatory connection.

a, Example trace of current step injections in a VGluT2+ dPAG cell (left) and summary current–frequency relationship (right, shaded area is s.e.m.). b, Summary quantification of resting membrane potential (mean = −61.4 ± 2.15), input resistance (mean = 0.55 ± 0.05 GΩ) and membrane time constant (mean = 28.3 ± 3 ms) for VGluT2+ dPAG cells (n = 14 cells, n = 7 mice). c, Example current traces for one dPAG VGluT2+ cell showing optogenetically evoked EPSCs from the dmSC (left) that are blocked by TTX (middle) and recovered by 4-AP (right), confirming the presence of a monosynaptic connection. d, Summary data for peak dmSC–dPAG EPSC amplitudes and connectivity rate in the presence of TTX and 4-AP. e, Summary data showing that the properties of the dmSC–dPAG connection do not change with number of days after viral transfection of ChR2, and remain weak and unreliable (n = 15 mice, P = 0.78, 0.51 and 0.33 for amplitude, failure rate and connectivity rate, respectively, Kruskal–Wallis test). Plots show mean and s.e.m. f, Average waveforms for sEPSCs and mESPCs (recorded in TTX) in one cell, and respective cumulative histogram for peak amplitudes. g, Peak amplitude of sEPSCs and mEPSCs is not significantly different (n = 4 cells, P = 0.18, 0.79, 0.9 and 0.36 respectively, Kolmogorov–Smirnov test for 100 events in each condition per cell). Box-and-whisker plots show median, IQR and range.

Extended Data Fig. 9 Silicon probe anatomical placement and examples of dmSC single units.

a, Example image showing the track left by one probe stained with DiI, superimposed on a bright-field image of a 30-μm sagittal slice. b, Schematic illustrating the probe track in each mouse (sagittal section, 0.6 mm lateral to the midline). Mouse brain image adapted from Franklin and Paxinos46 and reproduced with permission from Elsevier. c, Two examples of dmSC single units (top and bottom). Left, raw voltage trace from the channel with the strongest signal for the unit of interest (black symbols below indicate all spikes detected for the unit). Middle, auto-correlogram of spike times calculated in bins of 1/30 ms. Right, superimposed action potential waveforms chosen randomly from the whole recording (light colour) and average waveform (dark colour).

Extended Data Fig. 10 Controls and cannulae placements for chemogenetic inactivation experiments.

a, Summary in vitro data for hM4D-neurexin/ChR2-expressing VGluT2+ dmSC neurons before (baseline) and after CNO application (CNO), showing no effect of CNO on action potential firing in response to current injection (left, n = 6 cells, P = 0.8738 for main effect of CNO, two-way repeated measured ANOVA; inset shows example traces to two current steps) or to 473-nm light-evoked ChR2 activation (right, n = 9 cells, P = 0.7006 for main effect of CNO, two-way repeated measured ANOVA). Error bars are s.e.m. b, Application of CNO reduces dmSC–dPAG excitatory synaptic transmission by 71 ± 7% (n = 10 cells, P = 6.19 × 10−6, two-tailed t-test between baseline and CNO). c, Disrupting mSC–dPAG synapses with CNO microinfusion in behaving mice blocks visually evoked escape behaviour (n = 3 mice, P = 0.036, U-test). d, Doubling the intensity or frequency of mSC stimulation while locally blocking mSC-dPAG synapses is not sufficient to rescue escape behaviour (n = 5 mice, P = 0.11 for intensity, U-test; P = 0.42 for frequency, U-test; both comparisons against escape probability after local block in baseline conditions shown in Fig. 4l). e, Cannula placements for local inactivation experiments with CNO at the SC–PAG synapse (left) and at the SC–LP synapse (right). The tip of the internal cannulae is indicated by yellow circles (for experiments with optogenetic stimulation of dmSC VGluT2+ cells) and brown circles (for experiments with visual stimulation). Coordinates are in mm and from bregma. Mouse brain images adapted from Franklin and Paxinos46 and reproduced with permission from Elsevier. Box-and-whisker plots show median, IQR and range.

Supplementary information

Supplementary Table 1

Experimental parameters for pharmacological inactivation of circuit elements. This file contains cannulae configurations, stereotaxic coordinates and volumes for muscimol experiments.

Reporting Summary

Video 1

The contrast of expanding spot visual stimuli controls escape behaviour.

Video 2

Optogenetic inactivation of dPAG VGluT2+ neurons during threat.

Video 3

Optogenetic inactivation of dmSC VGluT2+ neurons during threat.

Video 4

Spontaneous escape behaviour after conditioning with high contrast visual stimulation.

Video 5

Optogenetic stimulation of VGluT2+ neurons in mSC and dPAG.

Video 6

Chemogenetic synaptic silencing of the VGluT2+ dmSC-dPAG connection, and controls.

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Evans, D.A., Stempel, A.V., Vale, R. et al. A synaptic threshold mechanism for computing escape decisions. Nature 558, 590–594 (2018). https://doi.org/10.1038/s41586-018-0244-6

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