Intrinsic dynamics of randomly clustered networks generate place fields and preplay of novel environments
- Neuroscience Program, Brandeis University, United States
- Volen National Center for Complex Systems, Brandeis University, United States
- Department of Psychology , Brandeis University, United States
- Department of Biology, Brandeis University, United States
Figures
Figure 1 with 1 supplement
Illustration of the randomly clustered model.
(a) Schematic diagram of prior replay models that rely on preexisting environment-specific structure, wherein each cell receives uniquely tuned Gaussian-shaped feed-forward inputs to define the …
Figure 1—figure supplement 1
Comparison of the randomly clustered network and the canonical Watts-Strogatz small-world network.
(a) A small ring-lattice network. (b) Example small-world networks. Top, a Watts-Strogatz network with re-wiring parameter . Bottom, a randomly clustered network with two clusters and a cluster …
Figure 2
Spatially correlated reactivations in networks without environment-specific connectivity or plasticity.
(a–f) Example activity from the fiducial parameter set (15 clusters, mean cluster participation of 1.25). (a) Example raster plot from one place-field trial. Cells sorted by trial peak. (b) Example …
Figure 3 with 1 supplement
The model produces place fields with similar properties to hippocampal place fields.
(a) Place field statistics for hippocampal place fields recorded in rats upon their first exposure to a W-track (Shin et al., 2019).
Left, place-field peak rate (Hz). Center, place-field specificity (fraction of track). Right, place-field spatial information (bits/spike). (b) Same as (a) but for place fields from a set of 10 simulated networks at one parameter point (15 clusters and mean cluster participation of 1.25). (c) Network parameter dependence of place-field statistics. For each parameter point, the color indicates the mean over all place fields from all networks. Top row: mean statistics corresponding to the same measures of place fields used in panels (a, b). Bottom left: mean firing rate of the inhibitory cells. Bottom center: the KL-divergence of the distribution of place-field peaks relative to a uniform spatial distribution. Bottom right: fraction of place-field peaks peaked in the central third of the track.
Figure 3—figure supplement 1
The simulated cells have greater place information than time information.
(a) Place fields (left) and time fields (right) for an example cell calculated from simulated trajectories that took 2 s (solid line) or 4 s (dotted line) to traverse the track. (b) CDFs of the …
Figure 4 with 4 supplements
Preplay depends on modest cluster overlap.
(a, c) The cumulative distribution function (CDF) of the absolute weighted correlations for actual events (blue line) versus shuffled events (red dashed line) of experimental data from Shin et al., …
Figure 4—figure supplement 1
Example preplay events from the Shin et al., 2019 data.
Example preplay events. Same as Figure 2f but for events from the hipopcampal data from Shin et al., 2019. The height of each plot spans the length of the trajectory used for decoding, divided into …
Figure 4—figure supplement 2
Significant preplay can typically be identified with as few as 50 cells.
(a–c) Results from performing the same Bayesian decoding on the same simulated population burst events (PBEs) in Figure 4c but using only random subsets of the excitatory cells for performing the …
Figure 4—figure supplement 3
Preplay statistics by trajectory for Shin et al., 2019 data.
(a) Same as Figure 4a but separated by results from decoding by each of the 4 trajectories of the W-track individually (trajectory 1, center arm to right arm; trajectory 2, right arm to center arm; …
Figure 4—figure supplement 4
Additional simulations support the consistency and robustness of the model to variations in spatial input forms.
Each row corresponds to a different parameter grid simulation, with statistics calculated as in the corresponding panel from Figure 4. (a) Preplay statistics are similar to the main simulation …
Figure 5 with 1 supplement
Coherent spiking within clusters supports preplay.
(a) Example event. Top, spike rates averaged across neurons of individual clusters: Each firing rate curve is the smoothed mean firing rate across the population of cells belonging to each cluster. …
Figure 5—figure supplement 1
Relationship between cluster activation and preplay.
(a) Out of all events from the fiducial parameter set simulations where three unique clusters were active, the fraction of those events with sequences that match the order of cluster biases on the …
Figure 6
Preplay is abolished when events are decoded with shuffled cell identities but is preserved if cell identities are shuffled only within clusters.
We decoded the population burst events from the fiducial parameter set simulations after randomly shuffling cell identities in three different manners (a-c, 25 replicates for each condition) and …
Figure 7
Place cells’ mean event rank are correlated with their place field location when accounting for decode direction.
(a) Mean within-event relative spike rank of all place cells as a function of the location of their mean place field density on the track for networks at the fiducial parameter set. Left, mean …
Figure 8
The Small-World Index of networks correlates with preplay quality.
(a–c) Left column, the Small-World Index (SWI; plotted as color) is affected by the global E-to-E connection probability, . Red dotted line indicates a contour line of SWI = 0.4. This boundary …
Figure 9
Trajectories decoded from population-burst events are significantly correlated with linear trajectories in arbitrary environments.
(a) Place fields from a single network with simulated runs in both directions of travel on a linear track in two different environments. Each column of panels is the set of place fields for the …
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