Replay and time compression of recurring spike sequences in the hippocampus

Abstract

Information in neuronal networks may be represented by the spatiotemporal patterns of spikes. Here we examined the temporal coordination of pyramidal cell spikes in the rat hippocampus during slow-wave sleep. In addition, rats were trained to run in a defined position in space (running wheel) to activate a selected group of pyramidal cells. A template-matching method and a joint probability map method were used for sequence search. Repeating spike sequences in excess of chance occurrence were examined by comparing the number of repeating sequences in the original spike trains and in surrogate trains after Monte Carlo shuffling of the spikes. Four different shuffling procedures were used to control for the population dynamics of hippocampal neurons. Repeating spike sequences in the recorded cell assemblies were present in both the awake and sleeping animal in excess of what might be predicted by random variations. Spike sequences observed during wheel running were "replayed" at a faster timescale during single sharp-wave bursts of slow-wave sleep. We hypothesize that the endogenously expressed spike sequences during sleep reflect reactivation of the circuitry modified by previous experience. Reactivation of acquired sequences may serve to consolidate information.

Fig. 1.

Spike sequence extraction methods.Panela, Unit activity was recorded simultaneously from multiple tetrodes. Filtered recordings from a single tetrode are shown (Ch1–Ch4).Panelc, Spike sorting resulted in 4–8 neurons/tetrode. Panelb, Superimposed waveforms of a single cell are shown. Panel d, The parallel spike train (verticaltics; cells 0–4) was analyzed by a sequence-search algorithm for repeating spike sequences. All possible sequences were considered as a template. The duration of the template window (w) was typically 200 msec. The tolerance of spike match (spike window; dt) was 10 or 20 msec. Neur, Neuron. Panele, Spike sequences of neurons a–d are represented as spatiotemporal vectors. For graphical illustrations, repeating sequences are superimposed in subsequent figures. Panelf, The significance of sequence repetition was tested by Monte Carlo statistics. Panelg, Spike triplets were also detected by the JPM method. The distribution of spike triplets (a, b,c; Δt_ab, Δt_ac) within thew time window was investigated by constructing a joint peri-event time histogram. A difference map (D_ij) was created by subtracting chance combinations, as predicted by the corresponding spike doublets, from the joint peri-event time histogram. The pixels of the difference map (JPM) represent the probability of observing a given triplet.

Fig. 2.

Spike-shuffling methods. Panela, Original parallel spike train. Three repetitions of the same spike sequence (0, 1, 2, 3) are shown. Panelb, Elimination of temporal correlation between the spikes by shuffling the interspike intervals (ISI) within each spike train.Graytics indicate the original spikes.Panelc, Spike displacement. Spikes of the original spike train (graytics) are randomly shifted in time by 0–50 msec (Δt;blacktics). Although the interspike intervals may change somewhat by this method, the field modulation of the neurons is better preserved. Paneld,Shuffling of spikes across spike trains. This method preserves population modulation of spike timing but may reduce firing-rate differences between the original spike trains. A fourth method (phase-invariant spike shuffling) is illustrated below (see Fig.5).

Fig. 3.

Relationship between the firing rate during θ behavior and the probability of spike participation in SPW.A, Probability of discharge of single pyramidal neurons in SPW events. Note that the majority of pyramidal neurons discharge <15% of all recorded SPWs. B, Relationship between the firing rate during θ and the probability of discharge during SPW events. Note that increased discharge rate during θ predicts a higher incidence of participation in SPWs.

Fig. 4.

Examples of the spike sequences during sleep (a) and running (b) sessions, detected by the template-matching method. Only spike sequences of neurons, recorded by a single tetrode, are shown. The sleep session preceded the run session. The sequence initiator neuron is indicated by arrows. Recordings during sleep and running sessions were obtained from a single rat. The spike window (dt) was set to 10 msec in these searches. Different colors indicate different patterns. Thegraylines in b, top,indicate all nonrepeating (single) sequences for comparison.Cellnumbers refer to the same cells within the same behavioral category. m, Number of different sequences; r, number of repetitions of a given sequence. Also see: FTP://speedy2.md.huji.ac.il/pub/neuron.mid.

Fig. 5.

Comparison of repeating spike sequences in a parallel spike train, recorded during wheel running, with its shuffled surrogates. a, Peaks of θ oscillation were taken as a reference point, and the spike timing was converted to phase values within the θ cycle. During shuffling, sets of spikes within a given θ cycle were transposed randomly (arrows).b, Phase-normalized spike density histograms during the θ cycle are shown. c, Cross-correlogram between the negative peaks of local θ and unit discharges is shown.d, Spike autocorrelograms of units are shown. Note the similar spike dynamics in the original and shuffled spike trains.e, Repetition curves of spike sequences in the original spike train and in its shuffled surrogates are shown.

Fig. 6.

Comparison of repeating spike sequences in real spike trains (original) and their shuffled surrogates.a–e, Data from five different rats. They-axis indicates the number of different sequences (m), and the x-axis indicates the average number of repeating sequences (r); e.g, 50 different sequences were repeated 16 times on average in rat k12-30 (panel c). Note that the repetition rate in the original spike train is higher than that in any of the 100 shuffled surrogates (p < = 0.01). In these comparisons, shuffling was done across spike trains. Rats are identified in each toprightcorner.

Fig. 7.

Spike triplets detected by the JPM method.a, The JPTH of a spike triplet (3, 2, 0). Summed pixels in the x- and y-axes are also shown.b, “Expected” JPTH, constructed on the assumption that triplets are random coincidences of spike doublets (see Materials and Methods). c, The excess number of triplets expressed as the difference between the observed and expected JPTHs. Significant pixels (Fisher's exact probability test) are framed inboxes. d, Vector representation of 3, 2, 0 sequences extracted by the template-matching method. Note that the latencies of the triplets match the significant pixels in the JPM.e, JPM maps constructed using three different pixel sizes (5, 6.7, and 10 msec) from the original and 100 shuffled surrogates (same original data sets shown in Fig. 6). The number of significant pixels in the surrogate JPMs is expressed as a percentage of the significant pixels in the original JPM. Colorbars, Number of events.

Fig. 8.

Spike sequences during sleep are influenced by previous wheel-running behavior. Histograms of significant triplets common to Sleep1 and Run sessions (a), to Run and Sleep2 sessions (b), and to Sleep1 and Sleep2 sessions (c). A sequence was considered to be “common” if it was significant by the JPM method (Fig. 7) in both behavior sessions regardless of the interspike intervals (e.g., 4-1-2 at 50 and 80 msec and at 5 and 8 msec). Individual triplets are listed on the x-axis. Theupward and downwardbarsat any given location on the x-axis indicate the number of significant pixels of the JPM of a common triplet in the two sessions, respectively. Note that there were almost twice as many triplets common to Run and Sleep2 sessions than to Sleep1 and Run sessions. The r values (Pearson's product moment correlation coefficient) indicate the correlation of the number of common triplets between the respective two sessions.

Fig. 9.

Long and short repeating spike sequences are associated with θ and ripple field activity, respectively. The power spectra of background field activity, associated with short and long sequences, were compared. The first spike of the same long (termination > 100 msec; n = 47) or short (termination < 50 msec; n = 78) spike sequences was regarded as the reference event for extracting field EEG information. a, EEG power in the low-frequency band surrounding long (solidline) and short (interruptedline) repeating spike sequences. Note the increased θ power during long sequences.b, EEG power in the ripple frequency band (100–200 Hz) surrounding long and short repeating spike sequences. Note the large power peak at 160 Hz during short sequences. Insets,Long (in a) and short (in b) sequences of the same neurons. Note the difference in timescale; short sequences are shown at an enhanced timescale.