Abstract
To guide behavior in uncertain environments, the brain must rapidly detect novel or unexpected events. The neocortex, involved with complex perception and decision-making, is thought to contribute to this computation, but underlying mechanisms are poorly understood. Here, we test how a few unanticipated action potentials influence local circuitry in the resting mouse visual cortex. Using targeted holographic stimulation, we evoked sparse “surprise” spikes in single pyramidal neurons and monitored their effects on hundreds of neighboring cells with 2-photon imaging. These novel spikes, distinct from the cortex’s ongoing large-scale activity fluctuations, produced strong, transient recruitment, following a power-law with slope 0.2–0.3, indicating that single neurons can mobilize large fractions of the surrounding network. Ongoing activity was dominated by neuronal avalanches, highly variable, scale-invariant spike cascades characteristic of systems near criticality. Yet, the information regarding the origin of our perturbations remained reliably identifiable and distributed across most of the observed network, as shown using machine-learning classifiers. Cortical network simulations confirmed that the measured scaling and distributed information matches predictions for systems operating near criticality. These results demonstrate two hallmarks of criticality, avalanche organization and amplified responses to small perturbations, suggesting that critical dynamics enhance the cortex’s ability to detect novel events.
Similar content being viewed by others
Data availability
The pre-processed imaging data used in this study are available in the general repository Zenodo using the following link: https://doi.org/10.5281/zenodo.17834168. The source data for all figures and supplemental figures in this study are provided for this paper. Source data are provided in this paper.
Code availability
Computer code used in this study is available at the following GitHub link: https://github.com/plenzd/NoveltyScaling.
References
Allen, W. E. et al. Global representations of goal-directed behavior in distinct cell types of mouse neocortex. Neuron 94, 891–907 (2017).
London, M., Roth, A., Beeren, L., Häusser, M. & Latham, P. E. Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature 466, 123–127 (2010).
Meyer, J. F., Golshani, P. & Smirnakis, S. M. The effect of single pyramidal neuron firing within layer 2/3 and layer 4 in mouse V1. Front. Neural Circuits 12, https://doi.org/10.3389/fncir.2018.00029 (2018).
Chettih, S. N. & Harvey, C. D. Single-neuron perturbations reveal feature-specific competition in V1. Nature 567, 334–340 (2019).
Jouhanneau, J.-S., Kremkow, J. & Poulet, J. F. A. Single synaptic inputs drive high-precision action potentials in parvalbumin expressing GABA-ergic cortical neurons in vivo. Nat. Commun. 9, 1540 (2018).
Hemberger, M., Shein-Idelson, M., Pammer, L. & Laurent, G. Reliable sequential activation of neural assemblies by single pyramidal cells in a three-layered cortex. Neuron 104, 353–369 (2019).
O’Rawe, J. F. et al. Excitation creates a distributed pattern of cortical suppression due to varied recurrent input. Neuron 111, 4086–4101 (2023).
Marshel, J. H. et al. Cortical layer–specific critical dynamics triggering perception. Science 365, eaaw5202 (2019).
Carrillo-Reid, L., Han, S., Yang, W., Akrouh, A. & Yuste, R. Controlling visually guided behavior by holographic recalling of cortical ensembles. Cell 178, 447–457 (2019).
Houweling, A. R. & Brecht, M. Behavioural report of single neuron stimulation in somatosensory cortex. Nature 451, 65–68 (2008).
Wolfe, J., Houweling, A. R. & Brecht, M. Sparse and powerful cortical spikes. Curr. Opin. Neurobiol. 20, 306–312 (2010).
Cheng-yu, T. L., Poo, M. -m & Dan, Y. Burst spiking of a single cortical neuron modifies global brain state. Science 324, 643–646 (2009).
Zucca, S. et al. An inhibitory gate for state transition in cortex. Elife 6, e26177 (2017).
Histed, M. H., Ni, A. M. & Maunsell, J. H. Insights into cortical mechanisms of behavior from microstimulation experiments. Prog. Neurobiol. 103, 115–130 (2013).
Vaadia, E. et al. Dynamics of neuronal interactions in monkey cortex in relation to behavioural events. Nature 373, 515–518 (1995).
Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364, eaav7893 (2019).
Kuhn, A., Aertsen, A. & Rotter, S. Neuronal integration of synaptic input in the fluctuation-driven regime. J. Neurosci. 24, 2345–2356 (2004).
Arieli, A., Sterkin, A., Grinvald, A. & Aertsen, A. Dynamics of ongoing activity: Explanation of the large variability in evoked cortical responses. Science 273, 1868–1871 (1996).
Braitenberg, V. & Schüz, A. Cortex: Statistics and Geometry of Neuronal Connectivity. 2nd Thoroughly Revised Edition. (Springer-Verlag, 1998).
Thomson, A. M., West, D. C., Wang, Y. & Bannister, A. P. Synaptic connections and small circuits involving excitatory and inhibitory neurons in layers 2-5 of adult rat and cat neocortex: triple intracellular recordings and biocytin labelling in vitro. Cereb. Cortex 12, 936–953 (2002).
Levy, R. B. & Reyes, A. D. Spatial profile of excitatory and inhibitory synaptic connectivity in mouse primary auditory cortex. J. Neurosci. 32, 5609 (2012).
Jouhanneau, J.-S., Kremkow, J., Dorrn, A. L. & Poulet, J. F. In vivo monosynaptic excitatory transmission between layer 2 cortical pyramidal neurons. Cell Rep. 13, 2098–2106 (2015).
Campagnola, L. et al. Local connectivity and synaptic dynamics in mouse and human neocortex. Science 375, eabj5861 (2022).
Kumar, A., Rotter, S. & Aertsen, A. Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding. Nat. Rev. Neurosci. 11, 615–627 (2010).
Haider, B., Hausser, M. & Carandini, M. Inhibition dominates sensory responses in the awake cortex. Nature 493, 97–100 (2013).
Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: From cellular properties to circuits. Neuron 91, 260–292 (2016).
Chialvo, D. R., Cannas, S. A., Grigera, T. S., Martin, D. A. & Plenz, D. Controlling a complex system near its critical point via temporal correlations. Sci. Rep. 10, 12145 (2020).
Muñoz, M. A., Dickman, R., Vespignani, A. & Zapperi, S. Avalanche and spreading exponents in systems with absorbing states. Phys. Rev. E 59, 6175–6179 (1999).
Capek, E. et al. Parabolic avalanche scaling in the synchronization of cortical cell assemblies. Nat. Commun. 14, 2555 (2023).
Plenz, D. et al. Self-Organized Criticality in the Brain. Front. Phys. 9, https://doi.org/10.3389/fphy.2021.639389 (2021).
Beggs, J. M. & Plenz, D. Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167–11177 (2003).
Softky, W. R. & Koch, C. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334–350 (1993).
Holmgren, C., Harkany, T., Svennenfors, B. & Zilberter, Y. Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. J. Physiol. 551, 139–153 (2003).
Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. Ser. B 64, 479–498 (2002).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multipletesting. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
Churchland, M. M. et al. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat. Neurosci. 13, 369–378 (2010).
Rajdl, K., Lansky, P. & Kostal, L. Fano factor: a potentially useful information. Front. Comput. Neurosci. 14, 569049 (2020).
Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785–794 (2016).
Ho, T. K. Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition. (1995).
Breiman, L. Random forests. MLear 45, 5–32 (2001).
Shapley, L. S. Notes on the n-person game—ii: The value of an n-person game. (RAND Coorperation, Santa Monica, CA, USA, 1951).
Srinivasan, K., Ribeiro, T. L., Kells, P. & Plenz, D. The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality. Sci. Rep. 14, 19329 (2024).
Miller, S. R., Yu, S. & Plenz, D. The scale-invariant, temporal profile of neuronal avalanches in relation to cortical γ–oscillations. Sci. Rep. 9, 16403 (2019).
Yu, S., Klaus, A., Yang, H. & Plenz, D. Scale-invariant neuronal avalanche dynamics and the cut-off in size distributions. PLoS ONE 9, e99761 (2014).
Marro, J. & Dickman, R. Nonequilibrium Phase Transitions in Lattice Models (2005).
Ribeiro, T. L., Ribeiro, S., Belchior, H., Caixeta, F. & Copelli, M. Undersampled critical branching processes on small-world and random networks fail to reproduce the statistics of spike avalanches. PloS ONE 9, e94992 (2014).
Kinouchi, O. & Copelli, M. Optimal dynamical range of excitable networks at criticality. Nat. Phys. 2, 348–351 (2006).
Histed, M. H., Bonin, V. & Reid, R. C. Direct activation of sparse, distributed populations of cortical neurons by electrical microstimulation. Neuron 63, 508–522 (2009).
Seeman, S. C. et al. Sparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortex. ELife 7, e37349 (2018).
Abeles, M. Corticonics: Neural Circuits of the Cerebral Cortex. Vol. 1 (Cambridge University Press, 1991).
Diesmann, M., Gewaltig, M. O. & Aertsen, A. Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529–533 (1999).
Prechtl, J. C., Cohen, L. B., Pesaran, B., Mitra, P. P. & Kleinfeld, D. Visual stimuli induce waves of electrical activity in turtle cortex. Proc. Natl. Acad. Sci. USA 94, 7621–7626 (1997).
Rubino, D., Robbins, K. A. & Hatsopoulos, N. G. Propagating waves mediate information transfer in the motor cortex. Nat. Neurosci. 9, 1549–1557 (2006).
Singer, W. Cortical dynamics revisited. Trends Cogn. Sci. 17, 616–626 (2013).
Gray, C. M., Konig, P., Engel, A. K. & Singer, W. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337 (1989).
Lisman, J. E. Bursts as a unit of neural information: making unreliable synapses reliable. Trends Neurosci. 20, 38–43 (1997).
Riquelme, J. L., Hemberger, M., Laurent, G. & Gjorgjieva, J. Single spikes drive sequential propagation and routing of activity in a cortical network. ELife 12, e79928 (2023).
Shu, Y., Hasenstaub, A., Badoual, M., Bal, T. & McCormick, D. A. Barrages of synaptic activity control the gain and sensitivity of cortical neurons. J. Neurosci. 23, 10388–10401 (2003).
Tsodyks, M., Uziel, A. & Markram, H. Synchrony generation in recurrent networks with frequency-dependent synapses. J. Neurosci. 20, RC50 (2000).
Molnár, G. et al. Complex events initiated by individual spikes in the human cerebral cortex. PLoS Biol. 6, e222 (2008).
Ferrarese, L. et al. Dendrite-specific amplification of weak synaptic input during network activity in vivo. Cell Rep. 24, 3455–3465 (2018).
Harris, K. D. & Mrsic-Flogel, T. D. Cortical connectivity and sensory coding. Nature 503, 51–58 (2013).
Shadlen, M. N. & Newsome, W. T. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 18, 3870–3896 (1998).
Histed, M. H. & Maunsell, J. H. Cortical neural populations can guide behavior by integrating inputs linearly, independent of synchrony. Proc. Natl. Acad. Sci. USA 111, E178–E187 (2014).
Barth, A. L. & Poulet, J. F. A. Experimental evidence for sparse firing in the neocortex. Trends Neurosci. 35, 345–355 (2012).
Stevens, S. S. Psychophysics: Introduction to its Perceptual, Neural and Social Prospects. (Routledge, 2017).
Assis, V. R. & Copelli, M. Dynamic range of hypercubic stochastic excitable media. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 77, 011923 (2008).
Scott, G. et al. Voltage imaging of waking mouse cortex reveals emergence of critical neuronal dynamics. J. Neurosci. 34, 16611–16620 (2014).
Bellay, T., Klaus, A., Seshadri, S. & Plenz, D. Irregular spiking of pyramidal neurons organizes as scale-invariant neuronal avalanches in the awake state. ELife 4, e07224 (2015).
Bellay, T., Shew, W. L., Yu, S., Falco-Walter, J. J. & Plenz, D. Selective participation of single cortical neurons in neuronal avalanches. Front. Neural Circuits 14, https://doi.org/10.3389/fncir.2020.620052 (2021).
Petermann, T. et al. Spontaneous cortical activity in awake monkeys composed of neuronal avalanches. Proc. Natl. Acad. Sci. USA 106, 15921–15926 (2009).
Beggs, J. M. & Plenz, D. Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures. J. Neurosci. 24, 5216–5229 (2004).
Stewart, C. V. & Plenz, D. Inverted-U profile of dopamine-NMDA-mediated spontaneous avalanche recurrence in superficial layers of rat prefrontal cortex. J. Neurosci. 26, 8148–8159 (2006).
Lombardi, F., Herrmann, H. J., Plenz, D. & de Arcangelis, L. Temporal correlations in neuronal avalanche occurrence. Sci. Rep. 6, 24690 (2016).
Lombardi, F., Herrmann, H., Plenz, D. & de Arcangelis, L. On the temporal organization of neuronal avalanches. Front. Syst. Neurosci. 8, 204 (2014).
Jun, N. Y. et al. Coordinated multiplexing of information about separate objects in visual cortex. ELife 11, e76452 (2022).
Caruso, V. C. et al. Single neurons may encode simultaneous stimuli by switching between activity patterns. Nat. Commun. 9, 2715 (2018).
Levy, M., Sporns, O. & MacLean, J. N. Network analysis of murine cortical dynamics implicates untuned neurons in visual stimulus coding. Cell Rep. 31, 107483 (2020).
Goldey, G. J. et al. Removable cranial windows for long-term imaging in awake mice. Nat. Protocols 9, 2515 (2014).
Dana, H. et al. High-performance calcium sensors for imaging activity in neuronal populations and microcompartments. Nat. Methods 16, 649–657 (2019).
Klapoetke, N. C. et al. Independent optical excitation of distinct neural populations. Nat. Methods 11, 338 (2014).
Juavinett, A. L., Nauhaus, I., Garrett, M. E., Zhuang, J. & Callaway, E. M. Automated identification of mouse visual areas with intrinsic signal imaging. Nat. Protocols 12, 32–43 (2017).
Sit, K. K. & Goard, M. J. Distributed and retinotopically asymmetric processing of coherent motion in mouse visual cortex. Nat. Commun. 11, 1–14 (2020).
Kleiner, M., Brainard, D. & Pelli, D. What’s new in Psychtoolbox-3? Perception 36, 1–16 (2007).
Adesnik, H. & Abdeladim, L. Probing neural codes with two-photon holographic optogenetics. Nat. Neurosci. 24, 1356–1366 (2021).
Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at https://doi.org/10.1101/061507 (2017).
Lecoq, J. et al. Removing independent noise in systems neuroscience data using DeepInterpolation. Nat. Methods 18, 1401–1408 (2021).
Dalgleish, H. W. et al. How many neurons are sufficient for perception of cortical activity? ELife 9, e58889 (2020).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Scott, M. & Su-In, L. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4765–4774 (2017).
Greenberg, J. M. & Hastings, S. P. Spatial patterns for discrete models of diffusion in excitable media. SIAM J. Appl. Math. 34, 515–523 (1978).
Acknowledgements
We thank Craig C. Stewart and members of the Plenz lab for help with animal surgery and care. We thank the members from our U19 BRAIN initiative grant for the many discussions and technical advice. This research was supported by the Division of the Intramural Research Program (DIRP) of the National Institute of Mental Health (NIMH), USA, ZIAMH002797, ZIAMH002971, and the BRAIN initiative Grant U19 NS107464-01. This research utilized the supercomputing resources of the National Institutes of Health (NIH, USA; Biowulf, http://hpc.nih.gov). The contributions of the authors were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements, and are considered Works of the United States Government. However, the findings and conclusions presented in this paper are those of the author(s) and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services.
Funding
Open access funding provided by the National Institutes of Health.
Author information
Authors and Affiliations
Contributions
T.L.R. and D.P. conceived and planned the study; T.L.R., B.G., and V.S. performed experiments; A.V. took the lead in holographic setup design. T.L.R. took the lead in data analysis with support from B.G. and V.S. S.P. and R.S. took the lead in machine learning based decoding of experimental data. All authors contributed to the analyses. T.L.R. and D.P. wrote the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks Taro Toyoizumi (eRef) who co-reviewed with Matthew Farrell (ECR); and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Ribeiro, T.L., Vakili, A., Gifford, B. et al. Critical scaling of novelty in the cortex. Nat Commun (2026). https://doi.org/10.1038/s41467-025-68277-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-025-68277-0
