Fig 1.
The schematic of the thalamocortical network model.
The cortical layer was organized in a one-dimensional chain of pyramidal cells (PYs) and inhibitory neurons (INs). The thalamus model included thalamic relay (TC) and reticular thalamic (RE) neurons. Black filled circles and black bars represent excitatory and inhibitory connections between neurons, respectively.
Fig 2.
Network dynamics and sequence learning paradigm.
a) The cortical network activity during transitions from awake state (pink block, top), to N2 sleep (purple block), to N3 sleep (dark green block) and back to the awake. Raster plot (middle) shows membrane voltages of cortical pyramidal cells. Broadband filtered local field potential (LFP, bottom) from the cortical population. The sequence was learned during the training period (orange bar). Grey bar represents the period of sleep. The performance was tested in three test sessions: before training, after training before sleep, and after sleep. b). The expanded view of characteristic spatiotemporal patterns (top), LFP (middle) and single cell activity of neuron #200 (bottom) during awake (left), N2 sleep (middle) and N3 sleep (right) from where pink, purple, dark green bars are shown in a (bottom). The spindle activity during N2 sleep revealed a typical waxing-waning pattern, consisted of 7–14 Hz brief bursts of rhythmic waves. The slow oscillations (<1Hz) during N3 sleep consisted of a typical Up and Down state transitions. c) The characteristic examples of a training session and three test sessions. The training included stimulating sequentially at groups A, B, C, D, and E. The test included stimulating only at group A (“pattern completion”). The sequence started at neuron #200. Each group included five neurons and it was stimulated for 10 ms. The delay between groups was 5 ms. d). The dot represents the string match between an ideal sequence (“ABCDE”) and each recalled sequence during test sessions for one trial. The value one represents a perfect match. The red line and the light red patch error bar represent mean and SEM of a moving average string match (window size = 10) over all trials (n = 10). e). The bar plot of the performance that was defined by the probability of recalled sequence with 80% similarity to the ideal sequence “ABCDE” (SM> = 0.8) during each test session. Error bars indicate standard error of the mean (SEM). For the boxplot in the right panel, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. Left: trained sequence; Right: untrained sequence tested at all other locations. * p<0.05, ** p<0.01, *** p<0.001.
Fig 3.
Spontaneous sequence replay mediates synaptic changes underlying memory consolidation during sleep.
a) The change of synaptic weights relative to the initial values after training (left), N2 (middle) and N3 sleep (right). The synaptic weights between neurons in direction of sequence activation (grey box) were enhanced due to the sequence replay. b) The dynamics of the mean synaptic weights (grey box in a) shows the progressive increase in synaptic strength during normal N2+N3 sleep (left), only N2 sleep (middle right); only N3 sleep (right). Note the lack of synaptic changes when sleep was supplemented by awake state of the same duration (middle left). Orange bar represents training period. The blocks in the top summarize the protocol of each experiment: Pink block—awake, purple block—N2 sleep, dark green block—N3 sleep. The patch error bar represents standard deviation. c) The bar plots of performance during test sessions after training (before sleep) and after sleep in four different experimental conditions corresponding to b. Error bars indicate SEM. * p<0.05, ** p<0.01, *** p<0.001. N.S. represents no significant difference. d) Characteristic examples of sequence (“ABCDE”) replay during sleep spindles and slow oscillations. e) The fraction of correct replayed sequence (“ABCDE”) during four difference experimental protocols. For the boxplot in the right panel, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively.
Fig 4.
The differential spatiotemporal pattern of sleep spindles and slow oscillations.
a) Spatial correlation between neurons at the different distance during sleep spindles and slow oscillations. The patch error bar represents standard deviation. b,c) An example of smoothed spike trains (top left) and the clustered region (top right), the histogram of neuron number (bottom left) that were identified within a cluster and the histogram of cluster numbers (bottom right) during spindle (b) and slow oscillations (c). The grey bar in b) is the histogram of temporally-cooccurring clusters that are monosynaptic connected during spindles.
Fig 5.
The role of slow oscillation during two-sequence learning.
a) The model simulated transitions from awake to N3 sleep, and to awake again. Orange bar represents the duration of training of each sequence (top: Seq1; bottom: Seq2). b) A cartoon of the sequential network stimulation to generate two sequences. The duration of stimulation was 10ms for each group of neurons. The delay between subsequent stimuli of two groups was 5ms. Each group includes five neurons. c) A characteristic example of test and training of Seq1 (“A1B1C1D1E1”). The test was stimulating only at group A1. d) Test and training of Seq2 (“E2D2C2B2A2”). The test was stimulating only at group E2. The Seq1 and Seq2 started at neuron #200 and #300, respectively. e) The bar plots of performance for Seq1 and Seq2 during different test sessions. Error bars indicate SEM. f) A characteristic example of the sequences replay during slow oscillations. g) The bar plots of the total replayed Seq1 (top) and Seq2 (bottom) during N3 sleep in correct and reverse order. Error bars indicate SEM. The correct and reversed orders for Seq1 were “A1B1C1D1E1” and “E1d1C1B1A1”, respectively. The correct and reversed orders for Seq2 were “E2D2C2B2A2” and “A2B2C2D2E2”, respectively. h) The change of synaptic weights relative to the initial values after training (left) and after N3 sleep (right). Note that synaptic weights between neurons in the direction of Seq1 activation (red box) and Seq2 (magenta box) were both enhanced due to the training (left) but the effect decayed for Seq2 after N3 (right). i) The synaptic weights associated with Seq1 (red) were progressively increased during N3, while those associated with Seq2 (magenta) were decreased during N3 due to the interaction from the reactivation of Seq1. When Seq2 was trained alone (no interference) in the same experimental conditions, synaptic weights associated with Seq2 increased during N3 (black). The patch error bar represents standard deviation. * p<0.05, ** p<0.01, *** p<0.001. N.S. represents no significant difference.
Fig 6.
The effect of memory strength on the consolidation during slow oscillations.
a) The dynamics of synaptic weights associated with Seq2 after N3 sleep for the different training duration (memory strength) of Seq2. The black line represents Seq2 trained alone. The dark green line indicates Seq2 trained along with the stronger Seq1. b) The change of Seq2 performance for the different training duration of Seq2. As the memory strength of Seq2 increased (longer training), the impact of interference on the synaptic weights and performance on Seq2 decreased. Error bars indicate SEM.
Fig 7.
The role of sleep spindles during two-sequence learning.
a) The model simulated transitions from awake to N2 sleep, to N3 sleep, and to awake again. Sequence training is the same as in Fig 3. b) The bar plots of performance for Seq1 and Seq2 during test sessions. Note significant increase in Seq2 performance after the sleep. Error bars indicate SEM. c) A characteristic example of sequence replay during slow oscillations. Note, that both Seq1 and Seq2 can be replayed during the same Up state of slow oscillation. d) The bar plots of the replay count for Seq1 and Seq2 during N2 (purple) and N3 (dark green) sleep. Error bars indicate SEM. Note that for both sequences number of correct order replays (“A1B1C1D1E1” for Seq1 and “E2D2C2B2A2” for Seq2) was higher than the number of reversed order replays. e) The change of synaptic weights relative to the initial values after N2 (right) and after subsequent N3 sleep (left). The synaptic change after training is the same as in 4j). The enough amount of sleep spindles enhanced synaptic connections associated with both sequences independently. f). The progressive increase in synaptic weights associated with Seq1 (red), Seq2 (magenta), and Seq2 alone (black). The patch error bar represents standard deviation. * p<0.05, ** p<0.01, *** p<0.001. N.S. represents no significant difference.
Fig 8.
Effect of memory strength on the consolidation during normal N2+N3 sleep.
a,b) The change of synaptic weights associated with Seq2 after N2 (a) and following N3 (b) sleep for the different training duration (memory strength) of Seq2. Importantly, after N2 sleep there is no difference in synaptic changes between Seq2 trained along with the stronger Seq1 and Seq2 trained alone. c) The change of Seq2 recall performance after N2+N3 sleep for the different training duration of Seq2. d) Probability across trials of synaptic weights increase for Seq2, when trained along with Seq1, for different duration of N2 sleep. Error bars indicate SEM.
Table 1.
This table includes the units and description of the parameters used in the model.