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Convergent information flows explain recurring firing patterns in cerebral cortex

Nature Neuroscience volume 29, pages 411–419 (2026) Cite this article

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Abstract

Cortical population events, short-lived patterns of neuronal activity that recur with consistency, are central to sensorimotor coordination. These reproducible firing patterns are often attributed to attractor dynamics, supported by strong mutual connectivity. However, by using multimodal datasets—including two-photon imaging, electrophysiology and electron microscopy—we show that these reproducible patterns do not involve strongly interconnected neurons. Instead, we show that cortical networks exhibit hierarchical modularity, with core neurons serving as high-information-flow nodes at module interfaces. These cores funnel activity but lack the structural signatures of pattern-completion units that are typically found in attractor networks. Using computational models, we find that distance-dependent connectivity is necessary and sufficient to produce the modularity and transient reproducible events observed in cortex. Our findings suggest that cortical networks are preconfigured to support sensorimotor coordination. This work redefines the structural and dynamical basis of cortical activity, with a focus on the relationship between modular structure and function.

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Fig. 1: Reproducible activity patterns occur throughout cortex.
The alternative text for this image may have been generated using AI.
Fig. 2: Cell connectivity does not support functional selectivity and pattern reproducibility.
The alternative text for this image may have been generated using AI.
Fig. 3: Reproducible patterns run through high-information flow nodes of modular networks.
The alternative text for this image may have been generated using AI.
Fig. 4: Distance-dependent connectivity creates high-flow core units.
The alternative text for this image may have been generated using AI.

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Data availability

The datasets analyzed during the current study are available in the CodeOcean capsule hosted by Nature (https://doi.org/10.24433/CO.9782876.v3).

Code availability

The custom code used to preprocess data, conduct analyses and reproduce all figures is available in the CodeOcean capsule (https://doi.org/10.24433/CO.9782876.v3).

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Acknowledgements

This work has been supported by EC Human Brain Project (grant agreement H2020-945539), Virtual Brain Twin project (grant agreement 101137289) and ANR ImpactCom project (CR-CNS program). We express our gratitude to the MICrONS project, Carandini’s, Goard’s, Svoboda’s labs and the Allen Brain Institute for the efforts they make and their commitment to publicly releasing their datasets, without which this study would have not been possible. We would like to thank A. Fairhall, Y. Frègnac and M. Mameli for stimulating discussions and critically reading the paper. We received no specific funding for this work.

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Authors and Affiliations

  1. Department for Integrative and Computational Neuroscience (ICN), Paris-Saclay Institute of Neuroscience (NeuroPSI), Saclay, France

    Domenico Guarino, Anton Filipchuk & Alain Destexhe

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  1. Domenico Guarino
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  2. Anton Filipchuk
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Contributions

D.G. and A.D. conceptualized the study, performed the investigation and contributed to writing, reviewing and editing the final draft of the paper. D.G. and A.F. performed the methodology and validation. D.G. performed the software development and visualization, and wrote the original draft of the paper. A.D. was responsible for funding acquisition, project administration and supervision.

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Correspondence to Domenico Guarino.

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

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Extended data

Extended Data Fig. 1 Reproducible patterns are transient attractors.

a, Top, population events were identified (as discussed in Fig. 1, two examples A and B). Bottom, the dF/F value of each cell participating in the event can be represented as a radial multidimensional vector defining the state of the system. b, An eigenvalue decomposition allows the choice of a suitable reduced set of axes to represent the multidimensional vectors. By looking at their (reduced) state space, the events are grouped in space and colored as the clusters in Fig. 1b (those occurring during stimulus presentations have black edges). Their Silhouette distance confirmed the clustering (see text). c, We can zoom in to each event dividing it into smaller states (left). Each state is characterized by cell dF/F vectors as in a (right). d, The set of states of an event forms a trajectory in the (reduced) state space (red and orange lines). To establish a significant distance, we created surrogate trajectories (gray lines) by shuffling cell vectors, and we measured the multidimensional (Hausdorff) vector distance between population event trajectories against that of surrogate trajectories. e,f, Each event is represented by a dot, colored by its cluster. Its coordinates are the Hausdorff distances within the cluster and within surrogates (black cross at mean trajectory distance, mean surrogate distance). The majority of cortical recordings presented shorter distances (points colored by cluster, with means as crosses) between event trajectories compared to surrogates (e, events from the MICrONS dataset, f events from the Neuropixels dataset, with some confidence intervals shown on the right column of the associated notebook). g, In the MICrONS data, where the ∆F/F data were made available, the proportion of saddle node-contributing neurons overlapping with known cluster cores was calculated for each scan (5 scan depths in µm). Core neurons (green) occurred significantly more inside saddle node events in all scans.

Extended Data Fig. 2 Core functional correlation.

a, Detail from the Allen Brain Observatory experiment ID 540684467 raster plot showing calcium spikes (black circles). Each row contains the calcium spikes for a cell. The minimal interval between two spikes is the two-photon imaging time resolution (30 Hz for the Allen Brain dataset). This interval represents the 1-lag correlation interval to identify cells potentially connected. In the figure, only some cells with 1-lag correlated firing were joined by colored segments to improve legibility. b, The vector of 1-lag correlations for each cell is collected into a matrix (top). Weak correlations (threshold = 0.4) were not considered for further analysis (bottom, correlations for cell ID 237). c, Core neurons are those sharing (nonsignificant, t-score = 1.53, p = 0.12) more high functional correlation across events compared to others.

Extended Data Fig. 3 No specific motifs for core neurons.

We looked at the distribution of motifs—statistically significant connectivity patterns considering groups of three cells. To understand the significance of motif occurrences, we used the ratio of real vs 100 surrogate degree-matched networks. a, Connectivity motifs distribution for all two-photon recorded neurons (top, occurrences count). The ratio followed a known distribution showing an abundance of three-cell mutual motifs. b, Normalized motif occurrence distribution for cores and other neurons. Both made a mixture of mutual and nonmutual connections.

Extended Data Fig. 4 Cores are not recursively connected via multisynaptic feedback.

Even if cores were not directly connected, they could be highly connected via secondary paths, abundant enough to ensure that they are pattern completion units in traditional attractor network terms. A, Core-to-core shortest paths were neither more nor shorter than other-to-other paths (0.27 ± 1.19 vs 0.16 ± 0.92 length, Kruskal–Wallis t = 26.4, p = 2.7 ×10−7, KS = 0.03). B, Core neurons could be part of looped paths, circling back to them, and providing a weak form of recursion. Core-based cycles were more (green, 18,306) compared to other-based cycles (gray, 4,490). But core and noncore cycles had on average the same length (10.34 ± 1.07 and 9.93 ± 1.76 connections, with minimal effect size KS = 0.077), same as the network diameter (d = 10).

Extended Data Fig. 5 Simple centrality measures show no difference between cores and other neurons.

a, The degree—number of connections per neuron—of cores was not significantly higher than other neurons. b, The betweenness—the number of shortest paths between any two nodes passing by a considered node—of cores was not significantly different from other neurons. c, The hub score—the weighted number of outward connections of a node that points to central nodes—of cores was not significantly higher than other neurons.

Extended Data Fig. 6 Cross-correlograms of Neuropixels units.

Cross-correlation was computed over correlated units from the Neuropixels datasets for the areas of interest (top, population correlograms, red squares are the units correlograms shown below). Correlograms were computed as in ref. 89. Briefly, spike trains from pairs of units were convolved using bins of 2 ms over a window of 40 ms (black bars). Then, each spike train was independently jittered within ±5 ms to generate 1,000 surrogate datasets. The surrogate mean (blue curve), 99% pointwise (blue shaded area) and global confidence bands (red lines) are derived from the surrogate distributions. This analysis informed our choice of the time window (1–5 ms) to estimate monosynaptic connections in our functional connectivity analysis (Methods).

Extended Data Fig. 7 Hierarchical modularity across cortices and rewiring of the MICrONS dataset.

a–c, Example hierarchical modularities for other cortices (retrosplenial cortex, from ref. 32, and anterolateral motor cortex, from ref. 33), computed from functional activity using the method discussed in ref. 38. Each cortical region had a different slope (in black the fitting curve for the MICrONS data). This modularity, estimated using functional connectivity, may tend to overestimate large degrees/large clustering coefficient, as evident comparing the same area (VISual primary) functional hierarchical modularity of Fig. 3f and h. d,e, Example hierarchical modularity resulting from rewiring the MICrONS graph. d, rewiring the graph edges with probability p = 0.01 results in a hierarchically modular relationship as that observed in the data. In e, already at p = 0.02 the relationship deteriorates (fitting curve in black is kept in both panels). f, The bow-tie score (ratio of modules with clearly identifiable submodules converging-diverging from a central module over the total number of modules) rapidly deteriorates for increasing rewiring probabilities (s.e.m. reported as gray shaded area).

Extended Data Fig. 8 Comparison of the MICrONS and model basic network properties.

a, Distribution of distances between connected neurons in the MICrONS dataset (gray). Notice that its shape follows the ones found in refs. 76,82,85. We fitted our model (black) to the data, considering the space available given the intercell minimal distance assumed as model spatial and density resolution (1 cells/100 µm2, for a total of 10,000 excitatory cells per mm2, 2,500 inhibitory cells are distributed accordingly to span the same space). b, Normalized count of degrees in the MICrONS dataset (gray, with shaded s.e.m.). In the model (black), the max degree is 75, which we imposed in all models. For small distance-dependent connectivity ranges (from 22 to 42 µm) the chosen intercell minimal distance resulted in a lower number of connections in the model.

Extended Data Fig. 9 Specific clusters for different stimuli.

We tested the ability of our mechanistic model to support pattern completion to show that it can recapitulate phenomena in V1 that have been specifically cited as evidence that it is an attractor network. In many previous models, fixed-point attractors (pattern completion subnetworks) are achieved either through learning or artificially imposed. In our model no learning nor artificial subnetwork was imposed, yet reproducible firing patterns are observed. We used a ‘frozen noise’ input strategy to see whether it leads to same pattern completions. Three spike train matrices (called A, B, C, not shown) were generated using Poisson populations with different seeds (all having on average 5 sp/s). We then created two spike trains matrices (‘frozen 1’ and ‘frozen 2’) in which the first half was identical for both (spiketrain A), and the second half varied (either spiketrain B or C). Then we ran the same model as in main Fig. 4 using the two (frozen) spike train matrices to test whether it reproduced the same events during the identical first half, and different events in the second half. a, Firing rates from the network stimulated with the two spike train matrices (‘frozen 1’, black, and ‘frozen 2’, blue). The first portion of the firing (up to 6 sec, red vertical line) is the same for both runs. The second portion of the firing rate was different (green vertical shades: synchronous events). b,c, The clustering of population events shows the complete reproducibility of activity for the initial portion (left, b and c). Different clusters emerged in the second portion of the stimulus (right, b and c) (colored squares: clusters of vectors sharing neurons beyond a surrogate-based threshold).

Extended Data Fig. 10 Increasing input correlations rescues population event reproducibility but introduces network-wide oscillations.

a, For the same target population (right), we systematically reduced the number of Poisson input drivers (red disks and arrows), to explore how the increase in input correlations affected the correlations between population events. b, Reducing the number of Poisson input drivers (n = (50, 25, 12, 6, 3)) increased the population cross-correlation of spikes toward values characteristic of oscillating regimes (from CC = 0.035 at n = 50, to CC = 0.11 at n = 3). c, The number of Poisson drivers did not significantly alter the population coefficient of variation. d, Reducing the number of Poisson input drivers increased the power for slow oscillations in the Fourier spectra of the firing rates. e, Four 10-s spike rasters for 2,000 example cells, with their correlation matrices, show the changes in firing regimes responsible for the increase in population events correlation. The first correlation matrix on the left shows the absence of reproducibility. In the right correlation matrix, the off-diagonal correlations show the presence of correlations between population events, resulting in higher values of reproducibility.

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Guarino, D., Filipchuk, A. & Destexhe, A. Convergent information flows explain recurring firing patterns in cerebral cortex. Nat Neurosci 29, 411–419 (2026). https://doi.org/10.1038/s41593-025-02128-5

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