Research ArticleNEUROSCIENCE

Computing hubs in the hippocampus and cortex

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Science Advances  26 Jun 2019:
Vol. 5, no. 6, eaax4843
DOI: 10.1126/sciadv.aax4843
  • Fig. 1 Unsupervised extraction of states and hubs.

    (A) Cartoon representing the approximate recording locations (mEC and CA1; mPFC and CA1) during two experiment types in anesthesia and sleep. (B) Example LFP trace taken from the 32 channels in CA1 (blue) and 32 channels in mEC (orange). Below are examples of isolated unit activity taken from the same recording. For each time window (t), we extract different features represented by the FeatureVector(t), which has a feature value for each channel or single unit recorded. (C) We consider four features: spectral band averaged powers (from LFP channels), single unit firing rates, information storage, and information sharing. (D) Left: Cartoon representation of Msim. To extract substates and their temporal dynamics, we construct a feature similarity matrix Msim in which the entry Msim(ta, tb) measures Pearson correlation between the vectors FeatureVector(ta) and FeatureVector(tb). Time flows from the top-left corner horizontally to the top-right corner and vertically to the bottom-left corner. A block (square) along the diagonal in the resulting image identifies a period of feature stability, i.e., a substate. A block appearing several times horizontally or vertically indicates that a feature is repeated several times. Middle: Unsupervised clustering identifies the different substates (indicated by a number) and their temporal dynamics (the vertical axis corresponds to that of the similarity matrix). Right: We identify computing hub cells, i.e., neurons that display exceptionally high values for a given feature, associated with given substates. Note that reoccurring states have the same hub cells (state 3 in this example). The (*) corresponds to the neurons that are behaving in the top 5% of the examined feature.

  • Fig. 2 Firing substates.

    Examples of similarity matrices Msim obtained from Firing(t) at different times in mEC during anesthesia (A) and in mPFC during natural sleep (D), measured in two animals. The bar below Msim indicates the transitions occurring between THE/REM (dark blue) and SO/nonREM (light blue). Although there were only two global brain states, six (A) and five (D) firing substates were identified. (B and E) Examples of the firing density of three neurons (a, b, and c) recorded in mEC and mPFC, respectively, with amplitude normalized for visualization. Neurons tended to fire in specific substates, indicated here with a color code. These examples also illustrate the switching between different firing substates inside a given global oscillatory state and their overlap across different global oscillatory states. The analysis of all recordings revealed that a majority of firing substates tended to occur during a preferred global oscillatory state, as indicated by the bimodal histograms during anesthesia (C) and natural sleep (F), respectively.

  • Fig. 3 Information storage substates.

    Examples of similarity matrices Msim obtained from Storage(t) at different times in mEC during anesthesia (A) and CA1 during natural sleep (D). As for firing substates, we identified more storage substates (six and seven, respectively, in the shown examples) than global oscillatory states. We show in (B) and (E) that the participation of three individual neurons to information storage (indicated in arbitrary units for visualization) was substate dependent. The values reported above the plots correspond to the average firing rate of neuron b (green color) during the corresponding epochs within consistent storage substates. The analysis of all recordings showed that storage substates tended to occur during a preferred global oscillatory substate, as indicated by the bimodal histograms for anesthesia (C) and for natural sleep (F).

  • Fig. 4 Information sharing substates.

    The cartoon in (A) shows an example of sharing assembly for a given sharing hub neuron across three nonsequential occurrences of the same substate. The total strength of in- and out-going sharing is equal (large, external arrows) during ta, tb,, and tc while the assembly changes (smaller, internal arrows). The changing size of internal arrows represents the sharing strength of that particular functional connection between the sharing hub and its source and target neurons. (B) Similarity matrices Msim for sharing strengths Sharing_S(t) (top) and sharing assemblies Sharing_A(t) (bottom) in mEC during anesthesia (left) and mPFC during natural sleep (right). We identified a multiplicity of substates within each global oscillatory state as shown by the colored bars below the feature similarity matrices. The similarity matrices for sharing strengths and assemblies have a matching block structure. However, sharing strengths were very stable within a substate (red-hued blocks), while sharing assemblies were highly volatile (light blue–hued blocks). (C) This is quantified for each sharing assembly substate by a liquidity coefficient. For all observed sharing substates across all regions and global oscillatory states in all animals, the liquidity of sharing assemblies was much larger than the one of sharing strengths. (D) Most sharing substates occurred preferentially during a preferred global oscillatory state for both anesthesia and natural sleep combined (see fig. S5 for separated histograms for the two conditions).

  • Fig. 5 A democracy of computing hubs.

    (A) Within every computing substate, some neurons exhibited significantly strong values of information storage or sharing (computing hubs). However, these computing hubs did generally change from one substate to the other, as shown in this example. Different rows correspond to different single units recorded in mEC during anesthesia and different columns correspond to different computing substates (left, storage substates 1 to 6; right, sharing substates 1 to 4). An entry is colored in yellow when the neuron is a computing hub within the corresponding substate. In the example shown, while ~9% of neurons on average were simultaneously acting as computing hub, more than 40% of the recorded units were recruited as hubs for at least one substate when considering all the computing substates together (vertical bar on the right). (B and C) The probability that a neuron acted as hub depended only loosely on its anatomical localization. Panel (B) shows that for all regions and layers, the probability that a neuron acts as computing hub at least once was always larger than 30%. Inhibitory (i) neurons tended to be recruited as hubs more frequently than excitatory (e) neurons. Analogously, panel (C) shows that none of the layers display a specialization in either one of the two processing operations of information storage or sharing. Asterisks denote statistically significant comparisons (lack of overlap between 95% confidence intervals for the probability, reported as vertical ranges on top of the histogram bar). In (C), a yellow horizontal line indicates the fraction of computing hub cells, which also happen to be simultaneously high firing rate cells. Many computing hubs thus have an average or low firing rate. In (B) and (C), in CA1, light blue represents anesthesia and dark blue represents natural sleep.

  • Fig. 6 Complexity of substate sequences.

    State switching found for each feature (firing, storage, and sharing) did not align in time. This can be visualized by state switching tables, whose different rows graphically represent transitions between global brain oscillatory states and firing, storage, and sharing substates. (A) Examples of switching tables for mEC during anesthesia (top) and for mPFC during natural sleep (bottom; note the different time scales). (B) Switching tables were neither perfectly regular (top left) nor random (top right), but they were “complex,” displaying organized patterns escaping simple description (bottom). (C) The complexity of the switching tables was larger for THE/REM than for SO/nonREM for most recordings. We included two recordings from mPFC under anesthesia for comparison. (D) Switching tables were complex in all cases. Complexity values were significantly above the upper threshold for regularity and below the lower threshold for randomness. (E) The increase of complexity was significant for mEC when transitioning from SO to THE and for mPFC from nonREM to REM sleep. This trend in CA1 was not statistically significant [significance assessed in terms of lack of intersection between 95% confidence intervals and threshold values for both (D) and (E)]. Asterisks indicate that the number of recordings in this category was not enough to assess significance, but that the median value lay below or above the considered threshold.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/6/eaax4843/DC1

    Fig. S1. Recording paradigm.

    Fig. S2. Global brain oscillatory states.

    Fig. S3. Effects of global states on unit firing.

    Fig. S4. Firing substates.

    Fig. S5. Information storage is brain state dependent.

    Fig. S6. Substates of information sharing: Additional information.

    Fig. S7. Burstiness and used dictionary fraction explain complexity.

    Table S1. Number of states and their oscillatory mode specificity.

    Table S2. Sharing assembly liquidity across regions and conditions.

    Table S3. Matching between substate sequences of different types across conditions and regions.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Recording paradigm.
    • Fig. S2. Global brain oscillatory states.
    • Fig. S3. Effects of global states on unit firing.
    • Fig. S4. Firing substates.
    • Fig. S5. Information storage is brain state dependent.
    • Fig. S6. Substates of information sharing: Additional information.
    • Fig. S7. Burstiness and used dictionary fraction explain complexity.
    • Table S1. Number of states and their oscillatory mode specificity.
    • Table S2. Sharing assembly liquidity across regions and conditions.
    • Table S3. Matching between substate sequences of different types across conditions and regions.

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