You are using a browser version with limited support for CSS. Please note there may be errors present which affect the content, and all legal disclaimers apply. Recent error-correction demonstrations on superconducting processors [5–8] have focused primarily on the surface code [9], which offers a high error threshold but poses limitations for logical operations. Measuring these stabilizers in planar architectures like superconducting qubits is challenging, and realizations of color codes [11–19] have not addressed performance scaling with code size on any platform. Scaling the code distance from three to five suppresses logical errors by a factor of Λ3/5 = 1.56(4). Finally, we teleport logical states between color codes using lattice surgery [22]. This is a preview of subscription content, access via your institution Prices may be subject to local taxes which are calculated during checkout N. Lacroix, A. Bourassa, N. Shutty, V. Sivak, A. Bengtsson, M. McEwen, O. Higgott, D. Kafri, J. Claes, A. Morvan, Z. Chen, A. Zalcman, S. Madhuk, R. Acharya, L. Aghababaie Beni, G. Aigeldinger, R. Alcaraz, T. I. Andersen, M. Ansmann, F. Arute, K. Arya, A. Asfaw, J. Atalaya, R. Babbush, B. Ballard, J. C. Bardin, A. Bilmes, J. Bovaird, D. Bowers, L. Brill, M. Broughton, D. A. Browne, B. Buchea, B. Chih, A. Y. Cleland, J. Cogan, R. Collins, P. Conner, W. Courtney, A. L. Crook, B. Curtin, S. Das, S. Demura, L. De Lorenzo, A. Di Paolo, P. Donohoe, I. Drozdov, A. Dunsworth, A. Eickbusch, A. Moshe Elbag, M. Elzouka, C. Erickson, V. S. Ferreira, L. Flores Burgos, E. Forati, A. G. Fowler, B. Foxen, S. Ganjam, G. Garcia, R. Gasca, É. Genois, W. Giang, D. Gilboa, R. Gosula, A. Grajales Dau, D. Graumann, A. Greene, J. Gross, T. Ha, S. Habegger, M. Hansen, M. P. Harrigan, S. D. Harrington, S. Heslin, P. Heu, R. Hiltermann, J. Hilton, S. Hong, H.-Y. Huang, A. Huff, W. J. Huggins, E. Jeffrey, Z. Jiang, X. Jin, C. Joshi, P. Juhas, A. Kabel, H. Kang, A. H. Karamlou, K. Kechedzhi, T. Khaire, T. Khattar, M. Khezri, S. Kim, P. V. Klimov, B. Kobrin, A. N. Korotkov, F. Kostritsa, J. Mark Kreikebaum, V. D. Kurilovich, D. Landhuis, T. Lange-Dei, B. W. Langley, P. Laptev, K.-M. Lau, J. Ledford, K. Lee, B. J. Lester, L. Le Guevel, W. Yan Li, A. T. Lill, W. P. Livingston, A. Locharla, E. Lucero, D. Lundahl, A. Lunt, A. Maloney, S. Mandrà, L. S. Martin, O. Martin, C. Maxfield, J. R. McClean, S. Meeks, A. Megrant, K. C. Miao, R. Molavi, S. Molina, S. Montazeri, R. Movassagh, C. Neill, M. Newman, A. Nguyen, M. Nguyen, C.-H. Ni, M. Y. Niu, L. Oas, W. D. Oliver, R. Orosco, K. Ottosson, A. Pizzuto, R. Potter, O. Pritchard, C. Quintana, G. Ramachandran, M. J. Reagor, R. Resnick, D. M. Rhodes, G. Roberts, E. Rosenberg, E. Rosenfeld, E. Rossi, P. Roushan, K. Sankaragomathi, H. F. Schurkus, M. J. Shearn, A. Shorter, V. Shvarts, S. Small, W. Clarke Smith, S. Springer, G. Sterling, J. Suchard, A. Szasz, A. Sztein, D. Thor, E. Tomita, A. Torres, M. Mert Torunbalci, A. Vaishnav, J. Vargas, S. Vdovichev, G. Vidal, C. Vollgraff Heidweiller, S. Waltman, J. Waltz, S. X. Wang, B. Ware, T. Weidel, T. White, K. Wong, B. W. K. Woo, M. Woodson, C. Xing, Z. Jamie Yao, P. Yeh, B. Ying, J. Yoo, N. Yosri, G. Young, Y. Zhang, N. Zhu, N. Zobrist, H. Neven, S. Boixo, J. Kelly, C. Jones, C. Gidney & K. J. Satzinger You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar Scaling and logic in the color code on a superconducting quantum processor. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.

You are using a browser version with limited support for CSS. You can also search for this author in PubMed Google Scholar At least three universities in Hong Kong are inviting international students at Harvard University to join their institutions, following the United States administration's shock decision last week to ban the prestigious American institution from enrolling foreign students. The University of Hong Kong (HKU) is offering international students at Harvard dedicated scholarships, accommodation assistance and guidance on transferring academic credit, according to a letter addressed to them, which was posted on X yesterday by Zhigang Suo, a materials engineer at Harvard University in Cambridge, Massachusetts. “We recognise that recent developments in the US may have created significant uncertainties for many international students. HKU stands ready to welcome affected students at Harvard who wish to explore options and pathways for continuing their studies with us,” a spokesperson for the university told Nature. The US Department of Homeland Security, which terminated Harvard's ability to enroll and host international students on 22 May, said in a press release that the university had “created an unsafe campus environment by permitting anti-American, pro-terrorist agitators to harass and physically assault individuals, including many Jewish students.” Harvard hosts more than 7,000 international undergraduate and postgraduate students with visas. It is extreme cruelty for the government to make innocent people collateral damage,” says Suo. Harvard has become a target of the Trump administration in recent weeks. US brain drain: the scientists seeking jobs abroad amid Trump's assault on research Landmark air-pollution lab under threat from Trump cuts — can it be saved? Nations urged to ‘pick up the ball' after DEI research cuts US researchers must stand up to protect freedoms, not just funding Goethe University (GU) Frankfurt am Main - Institute of Molecular Systems Medicine IOP is China's premier research institution in condensed matter physics and related fields. US brain drain: the scientists seeking jobs abroad amid Trump's assault on research An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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We found that a single dose of psilocybin altered cortical ensemble turnover and oppositely modulated fear- and extinction-active neurons. Suppression of fear-active neurons and recruitment of extinction-active neurons predicted psilocybin-enhanced fear extinction. In a computational model of this microcircuit, inhibition of simulated fear-active units modulated recruitment of extinction-active units and behavioral variability in freezing, aligning with experimental results. These results suggest that psilocybin enhances behavioral flexibility by recruiting new neuronal populations and suppressing fear-active populations in the retrosplenial cortex. This is a preview of subscription content, access via your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription cancel any time Subscribe to this journal Receive 12 print issues and online access only $17.42 per issue Buy this article Prices may be subject to local taxes which are calculated during checkout The original videos and datasets generated during and/or analyzed during the current study comprise a 5TB dataset and are available from the corresponding authors. Processed data are available on GitHub (https://github.com/sarogers9/Rogers_et_al_2024). Source data are provided with this paper. Custom code generated for this paper is available on GitHub (https://github.com/sarogers9/Rogers_et_al_2024). Castaldelli-Maia, J. M. & Bhugra, D. Analysis of global prevalence of mental and substance use disorders within countries: focus on sociodemographic characteristics and income levels. Article PubMed Nutt, D. & Carhart-Harris, R. The current status of psychedelics in psychiatry. JAMA Psychiatry 78, 121–122 (2021). Article PubMed Griffiths, R. R. et al. Psilocybin occasioned mystical-type experiences: immediate and persisting dose-related effects. Agin-Liebes, G. I. et al. Long-term follow-up of psilocybin-assisted psychotherapy for psychiatric and existential distress in patients with life-threatening cancer. Article PubMed Aday, J. S., Mitzkovitz, C. M., Bloesch, E. K., Davoli, C. C. & Davis, A. K. Long-term effects of psychedelic drugs: a systematic review. Gukasyan, N. et al. Efficacy and safety of psilocybin-assisted treatment for major depressive disorder: prospective 12-month follow-up. Hesselgrave, N., Troppoli, T. A., Wulff, A. B., Cole, A. & Thompson, S. M. Harnessing psilocybin: antidepressant-like behavioral and synaptic actions of psilocybin are independent of 5-HT2R activation in mice. Cameron, L. P. et al. 5-HT2ARs mediate therapeutic behavioral effects of psychedelic tryptamines. Hibicke, M., Kramer, H. M. & Nichols, C. D. A single administration of psilocybin persistently rescues cognitive deficits caused by adolescent chronic restraint stress without long-term changes in synaptic protein gene expression in a rat experimental system with translational relevance to depression. Hernandez-Leon, A. et al. Antidepressant- and anxiolytic-like activities and acute toxicity evaluation of the Psilocybe cubensis mushroom in experimental models in mice. Jones, N. T. et al. Delayed anxiolytic-like effects of psilocybin in male mice are supported by acute glucocorticoid release. Preprint at bioRxiv https://doi.org/10.1101/2020.08.12.248229 (2020). Jones, N. T. et al. Transient elevation of plasma glucocorticoids supports psilocybin-induced anxiolysis in mice. Takaba, R., Ibi, D. & Yoshida, K. et al. Ethopharmacological evaluation of antidepressant-like effect of serotonergic psychedelics in C57BL/6J male mice. Odland, A. U., Kristensen, J. L. & Andreasen, J. T. Investigating the role of 5-HT2A and 5-HT2C receptor activation in the effects of psilocybin, DOI, and citalopram on marble burying in mice. B., Nichols, C. D. & Thompson, S. M. Preclinical perspectives on the mechanisms underlying the therapeutic actions of psilocybin in psychiatric disorders. Brownstien, M. et al. Striking long-term beneficial effects of single dose psilocybin and psychedelic mushroom extract in the SAPAP3 rodent model of OCD-like excessive self-grooming. Passie, T., Seifert, J., Schneider, U. & Emrich, H. M. The pharmacology of psilocybin. Lowe, H. et al. The therapeutic potential of psilocybin. Garcia-Romeu, A., Griffiths, R. R. & Johnson, M. W. Psilocybin-occasioned mystical experiences in the treatment of tobacco addiction. Drug Abuse Rev. Article PubMed Central Bremler, R., Katati, N., Shergill, P., Erritzoe, D. & Carhart-Harris, R. L. Case analysis of long-term negative psychological responses to psychedelics. Simonsson, O., Hendricks, P. S., Chambers, R., Osika, W. & Goldberg, S. B. Prevalence and associations of challenging, difficult or distressing experiences using classic psychedelics. Breeksema, J. J. et al. Adverse events in clinical treatments with serotonergic psychedelics and MDMA: a mixed-methods systematic review. Lewis, C. R., Preller, K. H., Braden, B. B., Riecken, C. & Vollenweider, F. X. Rostral anterior cingulate thickness predicts the emotional psilocybin experience. Halim, H. J., Burk, B. G., Fargason, R. E. & Birur, B. Manic episode following psilocybin use in a man with bipolar II disorder: a case report. Davis, A. K., Barrett, F. S. & Griffiths, R. R. Psychological flexibility mediates the relations between acute psychedelic effects and subjective decreases in depression and anxiety. Article PubMed Doss, M. K. et al. Psilocybin therapy increases cognitive and neural flexibility in patients with major depressive disorder. Torrado Pacheco, A., Olson, R. J., Garza, G. & Moghaddam, B. Acute psilocybin enhances cognitive flexibility in rats. Conn, K. et al. Psilocybin restrains activity-based anorexia in female rats by enhancing cognitive flexibility: contributions from 5-HT1A and 5-HT2A receptor mechanisms. & Duman, R. Neuroplasticity in cognitive and psychological mechanisms of depression: an integrative model. Article PubMed Zhukovsky, P. et al. Perseveration in a spatial-discrimination serial reversal learning task is differentially affected by MAO-A and MAO-B inhibition and associated with reduced anxiety and peripheral serotonin levels. Jacobs, D. S., Bogachuk, A. P., Le Moing, C. L. & Moghaddam, B. Effects of psilocybin on uncertain punishment learning. Vargas, M. V. et al. Psychedelics promote neuroplasticity through the activation of intracellular 5-HT2A receptors. Moliner, R. et al. Psychedelics promote plasticity by directly binding to BDNF receptor TrkB. Carhart-Harris, R. L. & Friston, K. J. REBUS and the anarchic brain: toward a unified model of the brain action of psychedelics. Schmitz, G. P. et al. Psychedelic compounds directly excite 5-HT2A layer 5 pyramidal neurons in the prefrontal cortex through a 5-HT2A Gq-mediated activation mechanism. Preprint at bioRxiv https://doi.org/10.1101/2022.11.15.516655 (2022). Alonso, J. F., Romero, S., Mañanas, M. À. & Riba, J. Serotonergic psychedelics temporarily modify information transfer in humans. Ly, C. et al. Psychedelics promote structural and functional neural plasticity. Ly, C. et al. Transient stimulation with psychoplastogens is sufficient to initiate neuronal growth. Olson, D. E. Psychoplastogens: a promising class of plasticity-promoting neurotherapeutics. et al. Psilocybin induces rapid and persistent growth of dendritic spines in frontal cortex in vivo. Du, Y. et al. Psilocybin facilitates fear extinction in mice by promoting hippocampal neuroplasticity. Vann, S. D., Aggleton, J. P. & Maguire, E. A. What does the retrosplenial cortex do? Summerfield, J. J., Hassabis, D. & Maguire, E. A. Cortical midline involvement in autobiographical memory. Article PubMed Maguire, E. A. Neuroimaging studies of autobiographical event memory. Todd, T. P. & Bucci, D. J. Retrosplenial cortex and long-term memory: molecules to behavior. Todd, T. P., DeAngeli, N. E., Jiang, M. Y. & Bucci, D. J. Retrograde amnesia of contextual fear conditioning: evidence for retrosplenial cortex involvement in configural processing. Article PubMed Miller, A. M. P., Serrichio, A. C. & Smith, D. M. Dual-factor representation of the environmental context in the retrosplenial cortex. Article PubMed Trask, S., Ferrara, N. C., Jasnow, A. M. & Kwapis, J. L. Contributions of the rodent cingulate-retrosplenial cortical axis to associative learning and memory: a proposed circuit for persistent memory maintenance. Trask, S. & Helmstetter, F. J. Unique roles for the anterior and posterior retrosplenial cortices in encoding and retrieval of memory for context. Sousa, A. Fde et al. Optogenetic reactivation of memory ensembles in the retrosplenial cortex induces systems consolidation. Natl Acad. Hattori, R. & Komiyama, T. Context-dependent persistency as a coding mechanism for robust and widely distributed value coding. Sun, W. et al. Context value updating and multidimensional neuronal encoding in the retrosplenial cortex. Mitchell, A. S., Czajkowski, R., Zhang, N., Jeffery, K. & Nelson, A. J. D. Retrosplenial cortex and its role in spatial cognition. Brennan, E. K. W. et al. Thalamus and claustrum control parallel layer 1 circuits in retrosplenial cortex. Cheng, N. et al. Egocentric processing of items in spines, dendrites, and somas in the retrosplenial cortex. Article PubMed Carhart-Harris, R. L. et al. Neural correlates of the LSD experience revealed by multimodal neuroimaging. Natl Acad. Wang, G. et al. Switching from fear to no fear by different neural ensembles in mouse retrosplenial cortex. Article PubMed Zhang, K. et al. The sexually divergent cFos activation map of fear extinction. & Kwan, A. C. Shared and distinct brain regions targeted for immediate early gene expression by ketamine and psilocybin. Kometer, M., Schmidt, A., Jäncke, L. & Vollenweider, F. X. Activation of serotonin 2A receptors underlies the psilocybin-induced effects on α oscillations, N170 visual-evoked potentials, and visual hallucinations. Liu, S., Bubar, M. J., Lanfranco, M. F., Hillman, G. R. & Cunningham, K. A. Serotonin2C receptor (5-HT2CR) localization in GABA neurons of the rat medial prefrontal cortex: implications for understanding the neurobiology of addiction. Pompeiano, M., Palacios, J. M. & Mengod, G. Distribution of the serotonin 5-HT2 receptor family mRNAs: comparison between 5-HT2A and 5-HT2C receptors. Zhang, Y. et al. Resting-state functional connectivity of the raphe nuclei in major depressive disorder: a multi-site study. Corcoran, K. A. et al. NMDA receptors in retrosplenial cortex are necessary for retrieval of recent and remote context fear memory. Corcoran, K. A., Frick, B. J., Radulovic, J. & Kay, L. M. Analysis of coherent activity between retrosplenial cortex, hippocampus, thalamus, and anterior cingulate cortex during retrieval of recent and remote context fear memory. Auguste, A. et al. Distinct brain networks for remote episodic memory depending on content and emotional experience. Article PubMed Todd, T. P., Mehlman, M. L., Keene, C. S., DeAngeli, N. E. & Bucci, D. J. Retrosplenial cortex is required for the retrieval of remote memory for auditory cues. Fournier, D. I. et al. Retrosplenial cortex inactivation during retrieval, but not encoding, impairs remotely acquired auditory fear conditioning in male rats. Kwapis, J. L., Jarome, T. J., Lee, J. L., Gilmartin, M. R. & Helmstetter, F. J. Extinguishing trace fear engages the retrosplenial cortex rather than the amygdala. Article PubMed Trask, S., Ferrara, N. C., Grisales, K. & Helmstetter, F. J. Optogenetic inhibition of either the anterior or posterior retrosplenial cortex disrupts retrieval of a trace, but not delay, fear memory. Han, C. J. et al. Trace but not delay fear conditioning requires attention and the anterior cingulate cortex. Cowansage, K. K. et al. Direct reactivation of a coherent neocortical memory of context. Raybuck, J. D. & Lattal, K. M. Bridging the interval: theory and neurobiology of trace conditioning. Catlow, B. J., Song, S., Paredes, D. A., Kirstein, C. L. & Sanchez-Ramos, J. Effects of psilocybin on hippocampal neurogenesis and extinction of trace fear conditioning. Williams, A. H. et al. Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Pennington, Z. T. et al. ezTrack: an open-source video analysis pipeline for the investigation of animal behavior. Ji, G., Yakhnitsa, V., Kiritoshi, T., Presto, P. & Neugebauer, V. Fear extinction learning ability predicts neuropathic pain behaviors and amygdala activity in male rats. King, G., Scott, E., Graham, B. M. & Richardson, R. Individual differences in fear extinction and anxiety-like behavior. Masella, G. et al. The amygdala NT3-TrkC pathway underlies inter-individual differences in fear extinction and related synaptic plasticity. Presto, P., Ji, G., Junell, R., Griffin, Z. & Neugebauer, V. Fear extinction-based inter-individual and sex differences in pain-related vocalizations and anxiety-like behaviors but not nocifensive reflexes. Russo, A. S. & Parsons, R. G. Neural activity in afferent projections to the infralimbic cortex is associated with individual differences in the recall of fear extinction. Shumake, J., Furgeson-Moreira, S. & Monfils, M. H. Predictability and heritability of individual differences in fear learning. & Geffen, M. N. A circuit model of auditory cortex. Vohryzek, J. et al. Brain dynamics predictive of response to psilocybin for treatment-resistant depression. Woodburn, S. C., Levitt, C. M., Koester, A. M. & Kwan, A. C. Psilocybin facilitates fear extinction: importance of dose, context, and serotonin receptors. Ekins, T. G. et al. Cellular rules underlying psychedelic control of prefrontal pyramidal neurons. Preprint at bioRxiv https://doi.org/10.1101/2023.10.20.563334 (2023). Effinger, D. P. et al. Sex-specific effects of psychedelic drug exposure on central amygdala reactivity and behavioral responding. Tomé, D. F. et al. Dynamic and selective engrams emerge with memory consolidation. Kwan, A. C., Olson, D. E., Preller, K. H. & Roth, B. L. The neural basis of psychedelic action. Rogers, S. A., Heller, E. A. & Corder, G. Psilocybin-enhanced fear extinction linked to bidirectional modulation of cortical ensembles. Preprint at bioRxiv https://doi.org/10.1101/2024.02.04.578811 (2024). Oswell, C. S. et al. Mimicking opioid analgesia in cortical pain circuits. Preprint at bioRxiv https://doi.org/10.1101/2024.04.26.591113 (2025). This work was funded by the National Institute of Health National Institute of General Medical Sciences (DP2GM140923 awarded to G.C. ), and by the National Institute of Mental Health (R01MH126027 awarded to E.A.H). We thank the University Laboratory Animal Resources group at the University of Pennsylvania for assistance with rodent husbandry and veterinary support, including all faculty stationed at both the Translational Research Laboratory. Thanks to M. Geffen (University of Pennsylvania) for advice on model construction. We would also like to thank other members of the Corder Lab, A. Jo (University of Pennsylvania) and R.A.S. Ortega (University of Pennsylvania), for critical discussions and advice on behavioral analysis, data visualization and analysis validation. We would also like to thank C. Mackey for assistance in customizing various Python packages. Finally, we would like to thank the faculty of the Cold Spring Harbor Laboratory course in Neural Data Analysis for critical input on the analysis approach. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Sophie A. Rogers & Gregory Corder Department of Neuroscience, Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Sophie A. Rogers & Gregory Corder Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Sophie A. Rogers & Gregory Corder Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar conceptualized and designed the study. provided key resources, including psilocybin, and assisted with experimental design and behavioral analysis. performed all data collection, analysis and writing. acquired funding, performed data visualization along with S.A.R., and edited and revised the manuscript. Correspondence to Gregory Corder. The authors declare no competing interests. Nature Neuroscience thanks Benjamin Grewe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. a, Center and bottom of implant tracts of all included mice from anterior (left) to posterior (right) granular RSC. b, Individual freezing from data in Fig. 2f with all multiple comparisons shown. From top to bottom: Habituation, Acquisition, Extinction 1, Extinction 2, Extinction 3 (Acquisition: F(trial period)(6,126) = 137.9, P < 0.0001; Extinction 1: F(trial period)(6, 126) = 7.239, P < 0.0001, Extinction 3: F(interaction)(18,126) = 2.582, P = 0.0011, F(trial period)(6,126) = 4.345, P = 0.0005, F(group)(3,21) = 20.53, P < 0.0001; two-way RM ANOVA with Sidak correction; Supplementary Table 1 (rows 6–10)). c, Normalized cross-correlation (NCC) scores of longitudinally registered ROIs with respect to Habituation. Left: heatmap of NCC scores for each ROI across days. Right: histogram of average NCC scores. d, Centroid distances of longitudinally registered ROIs with respect to Habituation. Left, heatmap of centroid distances for each cell across days. Right: histogram of average centroid distances. e,f, Fraction of tone- (e) and trace-responsive (f) cells that are responsive for 1–5 days in each animal. g, Average freezing encoding (auROC) in longitudinally registered neurons across days (F(session)(4,84) = 9.337, P < 0.0001; two-way RM ANOVA with Sidak correction; Supplementary Table 1 (row 17). Source data a, Smoothed distributions (Gaussian, window of 4 for nonimplanted and 3 for implanted mice) of percentage freezing during the trace period in late Extinction 3. Dashed red line represents the inferred threshold from a GMM of both saline and psilocybin datasets. b, Cartoons depicting hypothesized sub-distributions composing saline and psilocybin distributions. c, Probability of assignment to low-freezing groups of mice across 10,000 iterations of GMMs using the leave-one-out method for each animal in each iteration. d, Effect of model choice on key result. Percent freezing during the trace period in early Extinction 3 was compared using thresholds from GMMs trained on only saline mice, psilocybin mice, both or the average threshold of each treatment's GMM. (F(treatment)(1,10) = 9.774, P = 0.0108; mixed effects RM model with Sidak correction; Supplementary Table 2 (rows 1). e. Accuracy of animal classification across session periods. Dashed line is chance (50%). a, Mean fraction of tone-responsive neurons on each day. Insets are proportions of neurons with suppressed, recruited and stable responses (two-way ANOVA, Supplementary Table 3 (rows 1 and 5)). b, Heatmaps displaying significant correlations (Pearson's ρ, P < 0.05) between proportions of total (Tot), suppressed (Sup), recruited (Rec) and stable (Sta) tone-responsive neurons on each day and percentage freezing during the early (E) and late (L) halves of each session (gray rows = Hab freezing and gray columns = fractions of neurons during Hab, red = Acq, yellow = Ext1, green = Ext2, blue = Ext3). c,e,g, Same as a for trace- (c), tone-and-trace (e) and shock-responsive (g) neurons (two-way ANOVA with Sidak correction; Supplementary Table 3 (rows 1 and 5)). d,f,h, Same as b for trace- (d), tone-and-trace (f) and shock-responsive (h) neurons. Data are represented as mean ± s.e.m. Source data a, Diagram of experimental protocol. N = 3 GRIN lens-implanted mice were placed in dark open-field arenas and recorded with infrared cameras and Miniscope. Fifteen minutes into the session, mice were injected with psilocybin. Data for 2–14 minutes pre-injection and 10–42 minutes postinjection were used. b, Immobility bouts per minute pre-injection and postinjection (p = 0.5515, paired t test; Supplementary Table 4 (row 23)). c, Median bout length (p = 0.0459, paired t test; Supplementary Table 5 (row 24)). d, Total time immobile (p = 0.1081, paired t test; Supplementary Table 4 (row 25)). distance between immobility-on and immobility-off trajectories in PC space. f, Single-cell discriminability of immobility vs. motion (median d′) pre-injection and postinjection (p = 0.8208, paired t test; Supplementary Table 4 (row 26)). g, Population discriminability of immobility vs. motion (mean distance in PC space) pre-injection and postinjection (p = 0.2882, paired t test; Supplementary Table 4 (row 27)). Source data a, Normalized temporal factor weights for each component, averaged within groups (Acquisition: F(time)(3,63) = 6.452, p = 0.0007; two-way RM ANOVA; Supplementary Table 5 (rows 4–9)). b, Average activity over trials during Acquisition (left), Extinction 1 (middle) and Extinction 3 (right) in the highest-weighted neuron from each ensemble (top to bottom) identified by rank 5 TCA in a representative animal. Weights of arrows indicate the weight of each neuron in the representative animal's TCA model in the Acq-dominant (top), Ext1-dominant (middle) and Ext3-dominant (bottom) components. c, Example neuron reconstructions from the TCA model of the Acq-only neuron during Acquisition, the Ext1-only neuron during Extinction 1 and the Ext3-only neuron during Extinction 1. d, Correlations between reconstructed neuron activity and real neuron activity in each session for this mouse. a, Session discriminability as a function of model rank-choice in each animal and on average (cyan). Red dashed line indicates the chosen rank. b, Correlations of the temporal (top), neuron (middle) and trial factors (bottom) between the Acq-dominant (left), Ext1-dominant (middle) and Ext3-dominant (right) components of models of ranks 1–10, averaged over animals. Correlations of p > 0.05 were set to 0. Source data a, Schematic of nonshock protocol. Three Miniscope-implanted mice underwent an identical 5-day paradigm to all other mice, with the exception that they received no shock during Acquisition or drug treatment. b, Half-session freezing in nonshock mice (one-way RM ANOVA with Sidak correction; Supplementary Table 7 (row 1)). c, Number of longitudinally registered neurons in nonshock mice. d, Sum of session discriminability index. Because roughly half the number of neurons were recorded in nonshock mice as in the other two groups, pooled tensors from psilocybin responders, nonresponders and saline mice were subsampled to a different, random set of 160 neurons in each of 100 iterations of TCA (F(3,297) = 6694, p < 0.0001, one-way RM ANOVA with Sidak correction; Supplementary Table 7 (row 2). e–g, Overlap of the (e) day 2-dominant ensemble with (f) day 3- and (g) day 5-dominant ensembles in nonshock mice. Bar graphs display the median fraction overlaps. Dots are individual animals. Insets are pie charts displaying total overlap. Stars indicate comparison to low-freezing saline distribution (Acq-dominant: chi square = 10.84, p = 0.0126; Ext1-dominant: chi square = 16.04, p = 0.0011; Ext3-dominant: chi square = 30.50, p < 0.0001; chi-square test; Supplementary Table 7 (rows 3–5)). h–j, Average z score with respect to day 2 of (h) day 2-, (i) day 3- and (j) day 5-dominant ensembles, respectively, during day 3 and 5 in nonshock mice (turquoise) compared to conditioned, saline-administered mice (black; Acq-dominant: F(group)(1,202) = 9.329, p = 0.0026; Ext3-dominant: F(interaction)(1,240) = 5.787, p = 0.0169; F(session)(1,240) = 23.06, p < 0.0001, F(group)(1,240) = 4.534, p = 0.0342; two-way RM ANOVA with Sidak correction; Supplementary Table 7 (rows 4–6)). Data are represented as mean ± s.e.m. Source data a, Change in activity in mean ± s.e.m. from Acquisition in Acq-dominant neurons as a function of factor loading thresholds varying between w = 0-2 during Extinction 1 (left) and Extinction 3 (right). b, Same as a for Ext1-dominant neurons. c, Same as a for Ext3-dominant neurons. d, Peri-stimulus time histogram (PSTH) of an example simulated neuron to determine the null hypothesis factor loading threshold. Tensors of t × c × T size, where c is the number of neurons recorded in a given animal, were created with identically behaving neurons to determine the factor loading threshold in a hypothetical population in which each neuron equally contributes to dynamics, or the null hypothesis factor loading threshold for that animal. e, Reconstruction error and model similarity of varying model ranks for populations of identical neurons. A model of rank 1 yields 0 error in this case. f, Representative rank 1 TCA of a simulated dataset with n = 46 neurons, the median number of neurons recorded in this study. Because variances across trials and neurons were clamped at 0, only the temporal factor varies. g, Data in Fig. 4a plotted as a function of number of neurons recorded. Mean weight of neuron factors across 100 iterations of TCA at the number of cells recorded in each animal. h, Change in activity in mean ± s.e.m. from Acquisition during Extinctions 1 and 3 in Acq-dominant (left), Ext1-dominant (middle) and Ext3-dominant (right) using ensembles determined with the null hypothesis factor loading for each animal. (Acq-dominant: F(group)(3,523) = 8.886, p < 0.0001; Ext1-dominant: F(group)(3,539) = 6.838, p = 0.0002; Ext3-dominant: F(session)(1,523) = 11.12, p < 0.0001; F(group)(3,523) = 8.886, p < 0.0001; two-way RM ANOVA; Supplementary Table 7 (rows 9–11)). Source data a, Overlaps of ensembles within individual animals comprising the mean values in Fig. 4b for the Acq- (top), Ext1- (middle) and Ext3-dominant (bottom) ensembles. b, Fisher decoder performance on Acquisition activity in functionally defined ensembles of cells to distinguish psilocybin groups (black), low-freezing psilocybin vs. saline (light purple) and high-freezing psilocybin vs. low-freezing saline mice (dark purple). Hundred iterations for each comparison. Shuffled values are behind real values. c,d, Three-way Fisher decoder performance low-freezing psilocybin vs. high-freezing psilocybin vs. low-freezing saline mice trained on activity during Extinction 1 (c) and Extinction 3 (d). e–g, Fisher decoder performance classifying psilocybin low- vs. saline low-freezing mice (e), psilocybin high- vs. saline low-freezing mice (f) and psilocybin groups (g) based on ensemble activity during Extinction 1 (top) and 3 (bottom) activity during freezing (salmon), motion (turquoise) and both (black). h–n, Average z-score activity in ensemble neurons in the Acq-only (h), Ext1-only (i), Ext3-only (j), Acq/Ext1 (k), Acq/Ext3 (l), Ext1/Ext3 (m), and Acq/Ext1/Ext3 (n) ensembles from Acquisition during freezing and motion in Extinction 1 (top) and Extinction 3 (bottom; multiple paired t tests for each ensemble within session, FDR = 0.01; Supplementary Table 9 (rows 18–31)). Stars indicate discoveries. Data displayed as mean ± s.e.m. Source data a, Trial diagram for Acquisition. b, Fraction of significantly shock-responsive neurons during shock across all 25 mice (for each neuron, Wilcoxon rank-sumshock-baseline p < 0.01). c, Heatmap of the average fraction of overlap in shock-up neurons between each trial of Acquisition. Average overlap between trials ranges from 16% to 49%. d, Persistence of the response properties of shock-up neurons over the session. Each point y is the fraction of neurons upregulated in response to the shock for x number of trials. Data are represented as mean ± s.e.m. over all 21 mice. Source data Statistical source data. Statistical source data. Statistical source data. Statistical source data Statistical source data Statistical source data. Statistical source data. Statistical source data. Statistical source data. Statistical source data. Statistical source data. Statistical source data. Statistical source data. Statistical source data. Statistical source data. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and permissions & Corder, G. Psilocybin-enhanced fear extinction linked to bidirectional modulation of cortical ensembles. Received: 06 May 2024 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative © 2025 Springer Nature Limited Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.