(A) Tuning curves (Equation (1)) of exemplar place cells covering the whole 3-m-long linear track. (B) Broad, symmetric spike-timing-dependent plasticity (STDP) kernel used in the learning phase. …
(A) Firing rates of exemplar place cells covering the whole 3-m-long linear track. In contrast to the spatial tuning curves shown in Figure 1A (Equation (1)), these are time-dependent rates …
(A) Fitted AdExpIF pyramidal cell (PC) model (blue) and experimental traces (green) are shown in the top panel. The amplitudes of the 800-ms-long step current injections shown at the bottom were as …
(A) Pyramidal cell (PC) raster plot of a 10-s-long simulation, with sequence replays initiating at random time points and positions and propagating either in forward or backward direction on the top …
(A) Posterior matrix of the decoded positions from spikes within a selected high-activity state (first one from Figure 2A). Thick gray lines indicate the edges of the decoded, constant velocity …
(A) Distributions of single pyramidal cell (PC) (A1) and parvalbumin-containing basket cell (PVBC) (A2) firing rates during the 10-s-long simulation shown in Figure 2. (B) Distributions of …
(A) Pyramidal cell (PC) raster plots on top and PC population rates at the bottom for E-E scaling factors 0.9 (A1), 0.95 (A2), and 1.05 (A3). (Scaling factor of 1.0 is equivalent to Figure 2A.) …
(A) Power spectral densities (PSDs) of PC (A1) and parvalbumin-containing basket cell (PVBC) (A2) population rates and estimated local field potential (LFP) (A3). Gray lines correspond to individual …
(A) Learned excitatory recurrent weight matrices. (A1) Weights after learning the first environment. Note that the matrix appears random because neurons are arranged according to their place field …
(A) Asymmetric STDP kernel used in the learning phase. (B) Learned excitatory recurrent weight matrix. (C) Distribution of nonzero synaptic weights in the weight matrix shown in (B). (D) Pyramidal …
(A1) Binarized (largest 3% and remaining 97% nonzero weights averaged separately) recurrent excitatory weight matrix. (Derived from the baseline one shown in Figure 1C.) (A2) Distribution of nonzero …
(A) Voltage traces of fitted AdExpIF (blue) and ExpIF (gray) PC models and experimental traces (green) are shown in the top panel. Insets show the f–I curves of the in vitro and in silico cells. The …
(A) Significant ripple frequency (A1) and ripple power (A2) of a purely PVBC network, driven by (independent) spike trains mimicking pyramidal cell (PC) population activity. Gray color in (A1) means …
1 | In the absence of unified datasets, it was assumed that published parameters from different animals (mouse/rat, strain, sex, age) can be used together to build a general model. |
2 | Connection probabilities were assumed to depend only on the presynaptic cell type and to be independent of distance. |
3 | Each pyramidal cell was assumed to have a place field in any given environment with a probability of 50%. For simplicity, multiple place fields were not allowed. |
4 | When constructing the ‘teaching spike trains’ during simulated exploration, place fields were assumed to have a uniform size, tuning curve shape, and maximum firing rate. |
5 | For simplicity, all synaptic interactions in the network were modeled as deterministic conductance changes. Short-term plasticity was not included, and long-term plasticity was assumed to operate only in the learning phase. |
6 | When considering the nonspecific drive to the network in the offline state, it was assumed that the external input can be modeled as uncorrelated random spike trains (one per cell) activating strong synapses (representing the mossy fibers) in the pyramidal cell population. |
7 | Some fundamental assumptions are inherited from common practices in computational neuroscience; these include modeling spike trains as Poisson processes, capturing weight changes with additive spike-timing-dependent plasticity, describing cells with single-compartmental AdExpIF models, modeling a neuronal population with replicas of a single model, and representing synapses with conductance-based models with biexponential kinetics. |
8 | When comparing our model to in vivo data, an implicit assumption was that the behavior of a simplified model based on slice constraints can generalize to the observed behavior of the full CA3 region in vivo, in the context of studying the link between activity-dependent plasticity and network dynamics. |
Physical dimensions are as follows: : pF; and : nS; , , , , and : mV; and : ms; : pA.
PC | 180.13 | 4.31 | –75.19 | 4.23 | –24.42 | –3.25 | –29.74 | 5.96 | 84.93 | –0.27 | 206.84 |
PC | 344.18 | 4.88 | –75.19 | 10.78 | –28.77 | 25.13 | –58.82 | 1.07 | - | - | - |
PVBC | 118.52 | 7.51 | –74.74 | 4.58 | –57.71 | –34.78 | –64.99 | 1.15 | 178.58 | 3.05 | 0.91 |
Physical dimensions are as follows: : nS; , , and td (synaptic delay): ms; and connection probability is dimensionless. GC stands for the granule cells of the dentate gyrus. ( synapses …
td | ||||||
---|---|---|---|---|---|---|
Sym. | Asym. | |||||
PC PC | 0.1–6.3 | 0–15 | 1.3 | 9.5 | 2.2 | 0.1 |
PC PVBC | 0.85 | 1 | 4.1 | 0.9 | 0.1 | |
PVBC PC | 0.65 | 0.3 | 3.3 | 1.1 | 0.25 | |
PVBC PVBC | 5 | 0.25 | 1.2 | 0.6 | 0.25 | |
GC PC | 19.15 | 21.5 | 0.65 | 5.4 | - | - |