Fig 1.
Experience-dependent CaMKIIα phosphorylation in the hippocampus during REM.
(A) Normalized hippocampal levels of pCaMKIIα, total CaMKIIα, Zif-268 and Actin (mean densitometric levels, one datapoint per animal). Kruskal-Wallis followed by Dunn’s test between consecutive states revealed a significant increase from SWS to REM (*p<0.05). Normalized levels of total CaMKIIα, Zif-268 and Actin in the hippocampus were not significantly different across states. This was expected because of the slower regulation kinetics of these proteins, in comparison with pCaMKIIα. Numbers on the bottom indicate the curvatures of the quadratic fits in red. (B) Schematic representation of the theoretical predictions for levels of plasticity markers across wake-sleep states. Compare with the quadratic fits on panel A.
Fig 2.
Cortical spindles at the SWS/REM transition correlate with CaMKII phosphorylation in the hippocampus.
(A) Raw LFP recordings of representative SWS animal (top trace) classified according to state (colored hypnogram). Cortical spindles before (middle trace) and after band-pass filtering in the spindle range (bottom trace). (B) Same as (A) for representative REM animal. (C) Spectral map of representative SWS animal, depicting sleep-wake cycle with state clusters and borders. Cortical spindles marked as circles of size proportional to duration, color-coded by hypnogram. (D) Same as (C) for representative REM animal. (E) Cortical spindle counts and hippocampal pCaMKIIα were significantly correlated in the REM group when all spindles were considered (R2 = 0.484, p = 0.0375), i.e. pooled from SWS and IS. No correlation was observed during SWS only (R2 = 0.116, p = 0.369) or IS only (R2 = 0.007, p = 0.825). No correlation was observed in the SWS group for SWS+IS (R2 = 0.00003, p = 0.999), SWS only (R2 = 0.006, p = 0.849) or IS only (R2 = 0.052, p = 0.5560). Plots include both exposed and control animals. See also S1, S2 Figs.
Fig 3.
Statistics of real spikes from the hippocampal CA1 field during WK, SWS or REM.
(A) Probability distributions of spike rates across states, with mean population values represented by dashed lines (left panel). Mean and variance of spike rates recorded during each state (right panel). (B) Square matrix of Pearson's linear correlation coefficient for spiking of 45 neurons during WK, SWS or REM (left panel), and the corresponding mean and variance (left panel). Significant differences of the Pearson's coefficient distribution were found between WK and SWS (Kolmogorov-Smirnov (KS) p = 6.9607e-023), WK and REM (KS, p = 1.0890e-023), and SWS and REM (p = 4.0259e-017). (C) Distribution of durations for intervals separating consecutive REM episodes (left panel, n = 6 rats; 28.8 hours of recordings). Cumulative plot of the REM-to-REM interval durations (right panel). Note that 91.6% of the intervals are shorter than 30 min (dashed line). More examples in S4 Fig; see also S1 Table.
Fig 4.
LTP during sleep leads to marked changes in synaptic weight trajectories.
(A) Hypnogram of representative recording (top panel) and corresponding population spike rate activity over time (bottom panel). Three different simulations were run using real spike data: (B) without LTP; (C) with LTP 1 full SWS model, modulated by the amount of synchronized spikes observed during the SWS episode that preceded the 4th REM period (duration of SWS episode = 658s), and (D) with LTP 2 permissive model, related to the changes in synaptic weight trajectories at the SWS/REM transition. Black arrow indicates the time point at the SWS/REM transition when the LTP Gaussian was triggered (~10,500s; 30s were elapsed for LTP evaluation before bonuses was applied). To focus on the sleep period, the (C) and (D) simulations are plotted from 8,000s to 16,200s. The initial synaptic weight values were uniformly distributed in the range [0..1]; consequently, colors (from dark blue to dark red) are also homogeneously distributed for wij values in [0..1]. Initial color maintained for each synaptic weight trajectory during entire simulation. In (B), bottom panel indicates the period during which the Gaussian curve affected the synaptic values to simulate LTP (blue curve). See also S7 and S8 Figs.
Fig 5.
Synaptic patterns are downscaled during sleep without LTP, but undergo restructuring after sleep-dependent LTP.
Fully connected network of 16 neurons with synapses (cylinders); strength indicated by cylinder radius and colors spanning blue for weak, red for strong. For comparison, all simulations were initialized with the same random uniform distribution of synaptic weights (column t = 1s). (A) Simulations with Poisson inputs. (B) Simulations with real inputs. The rows in each panel represent the No LTP model, LTP1 full SWS model and LTP2 permissive model (top to bottom). The columns represent 5 simulation time-points; Spearman´s correlation values (bottom right) with reference pattern t = 8,000s.
Fig 6.
LTP model variations show that evaluation around the SWS/REM transition leads to pattern restructuring.
(A) Initial and final synaptic weight values across the time interval of LTP simulation (11,000–14,000s) for variations of the LTP 1 and LTP 2 models. Red, green and blue lines represent positive, near-zero and negative slopes, with corresponding percentages indicated by the color bar. 1st panel for LTP 1 model evaluated over an entire SWS episode; 2nd panel for LTP 1 model evaluated only during the last 30s of SWS immediately before REM; 3rd panel for LTP 1 model evaluated using the last 30s SWS period plus the following 30s of REM; 4th panel for LTP 2 model using all positive REM slopes (permissive); 5th panel for LTP 2 model using only positive REM slopes that were also larger than the SWS slope (restrictive). For details of restrictive and permissive LTP 2 models, see S10 Fig. (B) Initial synaptic weight values (x axis) versus difference between initial and last time-points of LTP simulation (ΔWij, y axis). The curves are quadratic fits. Left and right panels for the variations of LTP 1 and LTP 2 models, respectively. (C) Spearman´s correlations over time for variations of LTP 1 and LTP 2 models, comparing the distribution of wij(t) values at the time t with the initial distribution of wij(0). Dashed lines indicate the time range [10,500s to 14,100s] when the LTP Gaussian was applied (boundaries of LTP simulation).
Fig 7.
Group data (n = 6) show pattern restructuring when LTP is evaluated near the SWS/REM transition.
(A) LTP Gaussian triggered near the SWS/REM transition. (B) Mean Spearman´s correlations (red lines) and mean synaptic weight values (black lines ±SEM as shaded area) during the time period of Gaussian curve application (~3,600s). Note the quasi-monotonic decay of Spearman’s correlations from left to right panels, corresponding to the 5 LTP models studied (bottom row); See S10 Fig.
Fig 8.
Canonical hippocampal-cortical model.
(A) Network model with an input layer, a principal neurons layer and an interneurons layer. (B) Input spikes shown for two different input cycles (top). Alternated memories are active in each cycle. Cells #1 to #100 belong to memory A and cells #101 to #200 belong to memory B. Postsynaptic spikes and Inhibitory Postsynaptic Potentials (IPSP) release shown following the above input (bottom). (C) Conductance of all synapses of two representative neurons during a full simulation run: one neuron whose memory selectivity remains stable after a sleep cycle (left); and one neuron whose memory selectivity is switched following the sleep cycle (right). (D) Population activity for all cycles with a specific memory on shown for two conditions of STDP modulation: (top) no Cp/Cd modulation during sleep leads to no change in the response pattern of the population; (bottom) high Cp/Cd modulation leads to complete restructuring of the response pattern of the population.
Fig 9.
Functional relationship between the synaptic homeostasis and embossing hypotheses.
(A) Proportion of neurons with stable memory selectivity after sleep (TS) as a function of STDP modulation factor (γsleep) (mean, n = 50). Colored interval denotes the range by which the measure is not significantly (p>0.05) different from random trials. (B) Proportion of LTP hits (TH) as a function of overall LTP gain (κsleep) (mean, n = 50). Colored interval denotes the range in which the measure is not significantly (p>0.05) different from random trials. (C) Color coded plot of previous metric as a function of both STDP modulation factor (γsleep) and overall LTP gain (κsleep). Yellow to red shades are values with significant number of memory hits. Blue shades cannot be distinguished from random runs.