Head-restrained mice were trained on a trace eyeblink conditioning task (Tseng et al., 2004), where they learned to associate a neutral tone stimulus (Conditioned Stimulus–CS) with an aversive puff of air to the eye (Unconditioned Stimulus–US) within a single session (‘Materials and methods-Behavioral training’). Tone and puff were non-overlapping and separated by a 250 ms interval, thus requiring the subject to maintain a representation or ‘trace’ of the tone (CS) in order to associate it with the puff (US). Behavioral responses were measured by recording deflections in eyelid position (Figure 1B).

We found that naïve mice responded to tone presentation with small, but distinct and measurable eyelid movements early in training, even on trials prior to the introduction of the puff stimulus (Figure 1C). Following repeated pairings of tone and puff, however, blink responses to tone increased significantly (>2 standard deviations [SD]) in amplitude and duration, (n = 9 of 18 mice, Figure 1B,D). Conditioned Response (CR) trials were defined as those trials in the training session whose area under the eyelid–position curve, during the interval spanning tone onset to puff onset, was significantly larger than the pre-training baseline (‘Materials and methods-Behavioral training’). We next established a performance score by measuring the ratio of CR blink rates to spontaneous blink rates (‘Materials and methods-Data analysis’, Figure 1E,F). As a learning control, we randomized the relative timing of tone and puff from trial to trial, for a different set of mice. These pseudo-conditioned mice did not demonstrate an increase in blink amplitude or duration (performance scores, trace = 8.74 ± 2.73, pseudo = 1.96 ± 0.44, mean ± standard error of the mean (SEM); Figure 1E,F, two-sample t test, p=0.012). 9 of 18 trace conditioned mice had performance scores higher than those of the pseudo-conditioned mice.

We next examined the performance of each mouse, to assess which individual mice learned the task, and if so, at what trial number in the training session. We used a previously described expectation maximization algorithm to assess whether each individual mouse had learned the association, and to obtain learning curves (Smith et al., 2004). Briefly, the algorithm uses the list of CR trials for a given mouse, along with the chance probability of the occurrence of a well-timed, significant blink to estimate the probability of CR production at each trial in the session (Figure 1G). Furthermore, the trial at which a mouse has learned the task is statistically defined. It is the first trial when the lower 95% confidence interval of the probability of CR production rises and remains above chance. As per this criterion, 9 out of 18 trace conditioned mice learned the association. The mean of the individual learning trials was 26 ± 5 (mean ± SD, n = 9 learners; for six of these learner mice, imaging data was also acquired, Video 1 shows high-speed video of mouse blinks before and after learning). These were the same nine mice that also had higher performance scores than pseudo-conditioned controls.

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Half the mice trained on the trace eyeblink conditioning task failed to learn to criterion. In part, this was because we were restricted to training mice for only a single session due to the limited residence time of calcium indicator dye in cells (Stosiek et al., 2003). These non-learners most likely represent a heterogeneous population of mice at different stages of learning involving multiple brain regions (Kalmbach et al., 2009). In other words, given more training sessions, many of these mice would likely have learned this task to criterion. Consequently, the interpretation of area CA1 calcium imaging data from ‘non-learners’ is complicated by the uncertain state of learning of each mouse. Hence, in most analyses, we have not used data from these mice. However, for completeness, we have included data from non-learners in key figures.

We surgically exposed the left, dorsal hippocampus of naïve mice and bolus loaded a synthetic calcium indicator dye. We then implanted cranial windows through which we imaged calcium responses from cells in area CA1 of the hippocampus (Figure 2A, Figure 2—figure supplement 1A, Video 2 shows calcium responses from a sample field of view). Image acquisition was carried out within a field of view covering 96 ± 29 (mean ± SD) cells per mouse, imaged at frame rates ranging from 11 Hz to 16 Hz. In parallel with calcium imaging, we simultaneously measured eyeblink responses of mice over the entire pre-training and training sections of the conditioning protocol (n = 14 trace conditioned and six pseudo-conditioned mice; ‘Materials and methods-Awake, two photon calcium imaging of area CA1 cells’; Figure 2—figure supplement 1B). Cell bodies were imaged from the visually identified stratum pyramidale layer of the hippocampus (Figure 2—figure supplement 1C), at depths ranging between 135–150 µm below the hippocampal surface (supporting Video 2 shows imaged calcium responses and Video 3 shows a stack of optical, z-section images with the densely labeled cell-body layer visible). To ensure the reliability of our calcium fluorescence data, we carried out two checks. First, the frequency of calcium transients was observed to be 1.3 ± 0.2 Hz (mean ± SD). While this need not accurately reflect spike rates in our experimental system, it falls well within the range of spontaneous spike rates previously observed in CA1 cells, and was un-changed between early and late trials in the session (Figure 2—figure supplement 1D; Czurkó et al., 1999). Second, the summed area under the calcium curve was also calculated for all datasets, and those datasets that showed a significant drift between early and late trials were discarded (n = 1 of 21; Figure 2—figure supplement 1E).

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We first looked for evidence of sustained or sequentially timed neuronal activity in calcium fluorescence traces for individual cells (ΔF/F,‘Materials and methods: Data analysis’), on trials following the CR learning peak. First, we determined whether or not cells showed sustained activity through the entire period of interest by measuring how wide each cell’s response was at half the amplitude of the peak of the response. The mean response width measured in this manner was found to be 120 ms ± 81 ms (mean ± SD, n = 542 cells, Figure 2B). Only three cells (∼0.5%, of all cells) had response widths greater than 600 ms, the length of the period from tone-onset to puff-onset. Based on this analysis, we concluded that sustained cell activation is not the mechanism used by the hippocampus to maintain a stimulus representation. We next looked for indications of sequential activation. Prior to learning, calcium response peaks were not reliably timed across trials, relative to tone onset (Figure 2C, left panel). Post learning, however, we observed that area CA1 cells had activity peaks at fixed time-points relative to tone onset, seen consistently across multiple trials (Figure 2C, right panel). We further characterized this by averaging these ΔF/F activity traces over trials and identifying activity peaks within the time window from tone onset to 200 milliseconds after puff onset. We rank ordered cells based on the timings of their activity peaks and found that small groups of cells firing at each time-point, tiled the entire interval of interest (Figure 2D). When the same procedure (averaging followed by sorting by timing) was carried out on traces from the pre-training dataset, tiling was markedly skewed with most cells (66% in this case) showing activity peaks during the tone period (Figure 2E). Furthermore, if neurons in the pre-training dataset were ordered as per timing of peaks in the training session, no clear tiling was visible (Figure 2F). This indicated that timings of peak activity of area CA1 cells, relative to the onset of tone stimulus, changed during training. Furthermore, at the population level, these peak times appeared to tile the interval of interest between tone and puff.

Having observed that area CA1 cells were active at fixed time-points relative to tone onset, we next wanted to quantify the reliability with which cells fired at these times. For each cell, we defined the timing of the peak in its averaged ΔF/F trace as its peak response time (PT). We then quantified the reliability with which cells fired at their respective PTs, from trial to trial (Figure 3A shows a cell reliably firing at a particular PT). First, we reasoned that if cells show time-aligned activity, the peak of the trial-averaged ΔF/F trace would be higher than if activity were not reliably timed. Hence, we computed the peaks of the averaged ΔF/F traces during the tone-onset to puff-onset period (Figure 3—figure supplement 1A). The mean peak amplitude for cells from trace conditioned mice was significantly higher than for cells from pseudo-conditioned mice or for spontaneous activity data (trace = 0.022 ± 0.007, pseudo = 0.017 ± 0.009, spontaneous = 0.015 ± 0.008; mean ± SEM; one way Analysis of Variance (ANOVA), followed by Tukey Kramer honest significant difference (h.s.d.) p<0.01). The difference seen was a small one, but this measure does not control for differences in cell response sizes or average activity. Hence, to more rigorously characterize activity timing reliability, we computed a reliability score for each cell.

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The reliability score was computed by comparing each cell’s time-tuning to that of control data obtained by artificially disrupting any trial to-trial timing relationships in calcium activity. We achieved this by pseudo-randomly shifting single-trial ΔF/F traces for each cell in time (Figure 3B). For every cell, a reliability score was then computed as the ratio of the peak area of averaged ΔF/F traces to the peak area of averaged, random time-offset traces (Figure 3C). Hence, the reliability score refers to the size of a time-aligned peak, expressed in multiples of that seen after random shifting. To aid in the intuitive understanding of this score, we report here the reliability score of the example neuron whose traces have been shown in Figure 3A, which is clearly firing reliably at a particular time (RS = 2.5). That is, the area under the peak of the averaged curve for this cell is more than double that obtained after giving random offsets to individual trials.

Reliability scores for area CA1 cells in mice that learned the task (n = 542 cells from 6 mice), were significantly higher than reliability scores for neurons from pseudo-conditioned mice (n = 498 cells from 6 mice), non-learners (n = 612 cells from 8 mice) and for spontaneous activity data from the trace learners (trace = 3.12 ± 0.20, pseudo = 1.35 ± 0.08, spontaneous = 0.84 ± 0.07, non- learners = 1.72 ± 0.13; mean ± SEM; ANOVA followed by h.s.d., p<0.01; Figure 3D). Since the peak timing reliability scores for cells from trace learners were significantly higher than those for controls, we concluded that only neurons from trace conditioned, learner mice had reliably time-tuned peaks in the ΔF/F trace. On plotting the distribution of all reliability scores obtained for cells from trace learners, we found that 44% of neurons had reliability scores close to 1, that is they displayed no time-locked activity (Figure 3—figure supplement 1B). Hence, for all subsequent analysis involving time-tuned activity, 50% of the neurons with the lowest reliability scores were discarded so that we could focus on the cells that did have significant time-tuning. The same treatment (calculation of reliability scores and then discarding the poorest 50%) was also given to control data from the spontaneous activity period, from pseudo-conditioned mice, or from non-learner mice.

Having seen that reliable time-tuning emerges only in trace conditioned mice we next examined the emergence of time-tuning during training. We quantified how the reliability in peak-timing changed as the training session progressed. The same reliability score (Figure 3A–C) was calculated for the 50% most reliable neurons in five-trial blocks (‘Materials and methods: Data analysis’). We found that the reliability scores for neurons from trace learners increased over the training session (Figure 3E) and that this increase was significantly greater than that seen in pseudo-conditioned mice or in spontaneous activity data from trace conditioned mice (Figure 3F, ANOVA followed by Tukey–Kramer h.s.d., p<0.01). Interestingly, the first trial bin where the increase in reliability scores is seen is for trials 21–25. This coincides well with the mean behavioral learning trial (24 ± 1 for imaged mice, 26 ± 2 for all mice mean ± SEM).

The peak reliability score seen in Figure 3E is lower than that seen in Figure 3D. This is because the number of trials used to calculate the score in each case is different (bin-widths of 5 and 24 respectively). The size of the peaks in the averaged trace from randomly time-shuffled trials depends on the number of trials averaged together since averaging time-shuffled traces would cause isolated peaks to collapse in proportion to the number of trials averaged. On the other hand, peaks aligned in time across trials would reinforce each other upon averaging. We computed reliability scores for different bin sizes and found that mean reliability score increases with the number of trials used to compute it (Figure 3—figure supplement 1C). This explains the difference in peak reliability scores seen in Figure 3D,E.

We concluded that time-selective area CA1 cell activity emerged progressively, as the mouse learned an association between two stimuli separated in time. Furthermore, the time-course of their emergence matched the average time taken by mice to learn the association.

We next considered two possible phenomena that might account for the improvement of reliability scores with learning. First, a cell may have a fixed activity peak timing from the beginning of the session, but not fire on every trial. In such a case, if the cell gradually started firing at the same, fixed peak-timing on a greater fraction of trials, its reliability score would increase. The cell in Figure 3A is such a cell. Alternatively, the timing of peak activity of the cell might change (e.g., the cells in Figure 2C). Such a cell too, would show an increase in reliability score. We therefore computed scores that distinguished between these phenomena, to establish that the predominant effect of learning involves changes in cell activity peak timings, and not their response probabilities.

First, we assessed the extent to which increases in trial to trial response probability affected reliability scores, ignoring changes in peak timing. We calculated local reliability scores as training progressed (Figure 3—figure supplement 1D). Here, the timings at which reliability scores were calculated were re-estimated for each five-trial bin. Hence, if peak-timing were to shift during training, it would not affect the reliability score. On the other hand, if cells began to show activity on a larger proportion of trials, the score would increase. On average, no training-related increases in local reliability score were detectable. This indicates that most cells do not increase their response probability with training.

We next quantified how local activity peak timings for each neuron differed from the peak timings found after learning. Half the trials after the learning trials were used to estimate post-learning activity peak timings for all cells. Then, we computed local peak timings for 10-trial bins through the entire session and computed the absolute difference between these local peak timings and the post-learning peak timings. These differences served to estimate how far, on average, cell peak timings were from their values after learning. We observed a gradual decrease in absolute peak timing difference as training progressed (Figure 3—figure supplement 1E) for cells from learners. Such a decrease was not seen for peak timing differences computed for cells from pseudo-conditioned mice, spontaneous activity data or non-learners. We also calculated the same peak timing differences, binning all trials before and after learning. The difference in peak timings from the post-learning peak timings was high prior to learning, and reduced to the order of a single frame-time post learning (Figure 3—figure supplement 1F) for trace learners. For mice that learned the association, the absolute difference in peak timings prior to, and post learning, were significantly different (ANOVA followed by Tukey Kramer h.s.d, p<0.01). No such decrease in peak timing difference was seen for any of the control cases. Hence, we concluded that reliability scores increase as learning progresses and that this increase is mainly due to changes in the peak-timings of cell activity during learning.

To characterize the changes in peak timings of cell populations due to learning, we next examined the distributions of the PTs of all neurons pooled across trace conditioned mice, at various stages of learning. The distribution was clearly non-uniform both before (χ² goodness of fit test with uniform expected distribution; χ² = 69.14, p<10⁻⁴, Figure 3G) as well as at an early stage of trace conditioning (χ² = 61.92, p<10⁻⁴, Figure 3H). However, for the group of trials after the learning trial, the distribution was much closer to a uniform distribution (χ² = 20.53, p=0.015, Figure 3I). Thus, the highly non-uniform PT distributions prior to and early in training became more uniform, effectively tiling the entire period of interest as a result of trace conditioning.

If neuronal activity is indeed sequential and time-locked, it should be possible to use activity data to decode time since tone onset. Thus, as an independent test of time-locking of activity, we formulated a simple time-decoding algorithm based on template matching (Fenton and Muller, 1998; Zhang et al., 1998; ‘Materials and methods: Data analysis’; Figure 3—figure supplement 2). The decoder uses single-frame, ΔF/F fluorescence values from cell-populations on single trials to decode time elapsed since tone presentation. The time-decoder prediction accuracy scores (normalized to decoder accuracy for control, time-shuffled data) for data from trace learners were significantly higher than those for data from pseudo-conditioned mice, spontaneous activity data from trace learners and data from non-learners (trace = 2.28 ± 0.27, pseudo = 1.26 ± 0.10, spontaneous = 1.37 ± 0.06, non-learners = 1.59 ± 0.16, mean ± SEM; ANOVA, followed by Tukey Kramer h.s.d., p<0.01; Figure 3J). As with the activity timing reliability score, the time decoder prediction score is also expressed in multiples of the score achieved using temporally randomized, control data. Hence, a decoder score of 2 would indicate prediction capability twice as good as that obtained with randomized data. To aid an intuitive understanding of the score, we also calculated the percentage of decoder frame-number predictions that were within one frame of the correct frame. For activity data from trace learners, the percentage was 70.3%, for pseudo-conditioned mice it was 32.4%, for spontaneous activity data it was 47.6%, and for non-learners it was 53.1%. The mean correct prediction percentage by random chance was 28.8%. The mean performance scores reported in Figure 3J closely match the values obtained by taking the ratio of these prediction percentages to the chance prediction percentage.

We also checked if neurons sharing time-tuning were spatially clustered within the hippocampus. The mean pair-wise distance between neurons sharing time-tuning was not significantly different from the mean distance between random pairs of neurons, that is neurons sharing time-tuning were not spatially clustered together (mean ± SEM distance for same time-tuning 71.27 ± 1.53 µm, random 70.89 ± 0.62 µm; Figure 3—figure supplement 1G,H; two-sample t test p=0.73).

Together, these data suggested that prior to training, area CA1 cells responded in a manner that was stimulus locked. These cells began to show reliably timed, sequential activity when mice were learning a task that required the maintenance of a sensory representation through a trace time-interval. Training caused a shift in the distribution of these activity peak times towards more uniform coverage of the interval of interest, spanning tone and puff stimulus durations as well as the intervening trace period.

Trial by trial correlations in spontaneous activity (noise correlations or NC) have been used previously as a measure of functional connectivity and have been thought to indicate the presence of direct (monosynaptic) or indirect, polysynaptic connections between correlated neurons (Ts’o et al., 1986; Bair et al., 2001; Fujisawa et al., 2008). Noise correlations, as measured by slower calcium recordings, are also thought to be indicative of shared presynaptic input, and therefore, changes in correlations are thought to imply changes in common input (Komiyama et al., 2010). We calculated correlation coefficients between the spontaneous activity traces of pairs of neurons (more than 4 s away from any stimuli), (Figure 4A, dashed, blue curve). Interestingly, for trace conditioned learners, mean NC increased during the training session, with a peak at trial bin 16-20 (19 ± 2, mean ± SEM). This peak precedes the peak in time-tuning (trial bin 21–25, Figure 3E) and the averaged learning trial (24 ± 1 for imaged mice, trial 26 ± 2, for all mice; mean ± SEM). This relationship was also observed at the level of individual mice (Figure 4—figure supplement 1A, mean learning trial = 24 ± 1, trial of peak reliability score = 25 ± 2, peak NC trial = 19 ± 2 mean ± SEM; ANOVA p=0.03).

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Furthermore, the NC calculated for cell-pairs where both cells had the same activity peak timing was significantly higher than for random cell-pairs (same time-tuning = 0.22 ± 0.01, random pairs = 0.14 ± 0.01 (mean ± SEM), ANOVA followed by Tukey–Kramer h.s.d., p<0.01, n = 56 peak-timing points with an average of 25 cell-pairs in each group; Figure 4B). Towards the end of the session, NC declined, although, even at this stage, NC for neurons sharing the same peak times remained significantly higher than for randomly chosen cell pairs (same PT = 0.16 ± 0.01, random pairs = 0.10 ± 0.01; mean ± SEM; ANOVA followed by Tukey–Kramer h.s.d., p<0.01; Figure 4B). Cells from pseudo-conditioned mice showed no such increase in NC (same PT = 0.13 ± 0.02, random pairs = 0.12 ± 0.01; mean ± SEM; ANOVA followed by Tukey–Kramer h.s.d., p>0.01; n = 48 time-tuning points with an average of 25 cell-pairs in each; Figure 4B).

We also checked if the spontaneous activity correlations were higher for cells with high activity peak timing reliability scores. Neurons with high reliability scores had significantly higher noise correlations than neurons with low reliability scores (Figure 4—figure supplement 1B; high RS 0.24 ± 0.03, low RS 0.14 ± 0.02; mean ± SEM; * indicates p<0.01, two sample t test).

Finally, we checked if activity sequences seen during the stimulus period were re-capitulated during spontaneous activity. We gave the spontaneous period activity trace of each neuron a time-offset equal to the relative timing of its activity peak timing within the stimulus period sequence. Then, we calculated the correlation coefficients between all such peak-time offset traces. If the same sequence indeed recurred during spontaneous activity, these correlations would be higher. As a control, we gave these spontaneous activity traces randomly chosen time-offsets from the set of peak-timings observed. Correlations for peak-timing offset traces were not significantly different from those for randomly offset traces (Figure 4—figure supplement 1C; peak-time offset 0.048 ± 0.0012, random offset 0.047 ± 0.0009, means ± SE; two sample t test, p=0.71). However, it must be pointed out that if sequence replay events are rare in comparison to spontaneous activity of area CA1 cells, then the correlation analysis described above, coupled with the limited size of our dataset (to minimize calcium indicator dye photo-bleaching) might not have the statistical power to detect them. Furthermore, spontaneous, sequential replay activity could still be occurring, but at an accelerated time-scale, as has been observed before (Lee and Wilson, 2002). Such fast replay events would not be resolvable, given our temporal resolution capability.

Surprisingly, spontaneous activity correlations for trace learners were observed to drop towards pre-training levels in the second half of the session (Figure 4A,B). This has been observed previously in the context of rats repeatedly exploring a novel track (Cheng and Frank, 2008). Here, the reduction in spontaneous activity correlations was ascribed to a reduction in the novelty of the particular congruence of stimuli related to the track being explored. Hence, the reduction in noise correlations we observe could also be a post-learning phenomenon, related to a reduction in the novelty of the specific congruence of the paired CS and US stimuli. To support this interpretation, we ruled out possible technical contributors to the reduction in correlations. First, the reduction in correlations were not due to a gradual decline in the quality of optical activity recordings, since we found that recordings remained stable through the training session, as discussed in ‘Results: Cells in hippocampal area CA1 imaged from awake, behaving mice exhibit temporal tuning’; Figure 2—figure supplement 1D,E. Second, this effect was not due to mice paying less attention to the task or fatiguing towards the end of the session. We tested this by measuring the sizes and peak latencies of the significant blinks before and after trial 25, where the decline in spontaneous correlations was seen to begin. Mean significant blink sizes before and after trial 25 were not significantly different (mean normalized early blink size = 1.03 ± 0.06, late = 0.95 ± 0.03, mean ± SEM; two sample t test p=0.62; Figure 4—figure supplement 1D). Additionally, mean significant blink peak latency from the time of tone onset did not change from the first to the second half of the session (early latency = 565.3 ± 68.5 ms, late latency = 538.7 ± 92.3 ms; mean ± SEM; two sample t test p=0.80; Figure 4—figure supplement 1E). Hence, we concluded that blink responses remained stable across the two halves of the session, and that a lack of sustained attention was not likely to be the cause of the reduction in noise correlations.

To summarize, spontaneous activity was seen to become more correlated as a result of trace conditioning, following which time-tuning peaked and mice learned the temporal association. Furthermore, neurons that eventually shared the same time-tuning, showed the greatest increases in NC.

We next checked if area CA1 cells could be separated into groups sharing high within-group NC and low across-group NC. We used the previously described ‘meta k-means’ clustering algorithm to identify groups of neurons that showed highly correlated spontaneous activity (Figure 5A–D; Ozden et al., 2008; Dombeck et al., 2009). We observed that neurons clustered into distinct groups. As expected, within-cluster NC was higher than across cluster NC (Figure 5B,D, two-sample t test, p<10⁻⁴). Interestingly, in contrast to the groups of neurons sharing the same activity peak-timings (Figure 3—figure supplement 1G,H), neurons belonging to the same correlation cluster were spatially clustered within the imaged fields of view (examples of peak-timing groups and correlation clusters are shown in Figure 5E). The mean distance between neuron centroids for pairs of neurons belonging to the same cluster was significantly lower than for pairs of neurons taken from different clusters (intra-cluster = 72.10 ± 1.00 µm, n = 1694 pairs, inter-cluster = 106.79 ± 0.90 µm, n = 3418 pairs, mean ± SEM; two-sample t test p<10⁻⁴, Figure 5F). Furthermore, NC and cell–cell distance were also found to be significantly, inversely correlated (correlation coefficient = −0.178, p<10⁻⁴, n = 24,522 cell pairs; Figure 5—figure supplement 1A). This indicates that neuronal NC are indeed spatially organized.

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To examine how these cell-groups or clusters changed over the session, we calculated a similarity score for clusters identified from different sections of the training session (‘Materials and methods: Data analysis’). We considered spontaneous activity data from three segments of the session comprising early, middle and late blocks of trials. We then measured the similarity between pairs of matched clusters obtained using data from different trial blocks. The similarity score measured the mean fraction of neurons belonging to clusters matched across these blocks, normalized to the mean overlap between random cell-clusters of the same sizes. For example, a high similarity score for a cluster (>> 1) would indicate a similarity much greater than chance, implying that this cluster is well preserved from one trial block to the next. Interestingly, we found highly reliable groupings (i.e., high similarity scores) in the pre-training session datasets, indicating stable patterns of input from upstream neuronal populations (example in Figure 6A) prior to training. In contrast, cell-groupings changed drastically during the trace conditioning session (Figure 6A,C), and were significantly less stable than those in the pre-training datasets. For pseudo-conditioned mice, cell groupings were reliable throughout the training session datasets (ANOVA followed by Tukey Kramer h.s.d., p<0.01; Figure 6B,C). Thus, spatially organized, correlated cell-clusters exist prior to and during learning. These correlated cell-groups remain stable in the absence of training, but undergo continuing re-organization during the process of learning the trace conditioning task.

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Are groups of time-tuned neurons organized into these correlation clusters? We calculated a cluster similarity score for neurons grouped by correlated activity and neurons grouped by time-tuning, during the tone to puff period. We found that this similarity was close to chance (mean overlap score = 2.08, SD = 0.6, in multiples of similarity expected by chance; ‘Materials and methods: Data analysis’).

The objective of this study was to shed light on the role played by the hippocampus in the association of stimuli separated in time, specifically during trace eyeblink conditioning. Previous work had identified neurons that were primarily air-puff responsive early in training, but became progressively more tone responsive as training proceeded. This process was thought to ascribe US valence to the CS (McEchron and Disterhoft, 1999). The progressive changes in neuronal activity timing we observed are consistent with this finding, as a small subset of neurons did indeed shift from being tone to puff responsive (Figure 2E,F, Figure 3G–I). However, the predominant learning-related change we observed was the emergence of sequentially timed activity of groups of neurons (Figure 3D,E,J, Figure 3—figure supplement 1D–F). Here, we note that calcium response recordings typically have a time resolution of about 100 ms, a time window within which entire sequences of neuronal activation have been known to occur (Foster and Wilson, 2006). Hence, we interpret our data to be indicative of sequential activity with respect to time-windows comparable to the average frame time (74 ms) of our recordings. Even at our lower time resolution, the total space of possible sequences is very large. Our study thus indicates that a very small fraction of all possible sequences is important for successful trace conditioning. Grouped by activity peaks within ∼70 ms time-windows, neurons might show finer-scale sequences or even fluctuations that would not be visible in our recordings. However, at the time-scale of a few hundred milliseconds relevant to trace conditioning, area CA1 cells show progressively emerging activity sequences after network re-organization during learning.

Another suggested model for stimulus trace representation involves sustained neuronal activity (Solomon et al., 1986). We saw no evidence to support this model. Our observation of the progressive formation of cell activation sequences (Figure 2C,D, 3E,J) significantly extends our understanding by revealing how the representation of the tone stimulus is maintained over the tone-puff interval in the trace conditioning task.

Time-tuned firing has previously been interpreted to imply that neurons encode time (MacDonald et al., 2011) or that they encode a triggering stimulus in the form of a well-timed sequence, effectively bridging two temporally separated stimuli (Itskov et al., 2011; MacDonald et al., 2013). Stimulus representation by sequential activity would also allow for the association of two temporally separated stimuli, by maintaining a representation of the first stimulus until the arrival of the second one. This would then allow Hebbian plasticity to strengthen synapses linking the neurons associated with the non-stationary, sequential stimulus representation. This stimulus maintenance is necessary since Hebbian plasticity cannot occur with stimuli separated by over ∼100 ms (Levy and Steward, 1983).

Simulation studies of the hippocampal CA3 network, have shown that stimuli separated in time can be ‘bridged’ (Wallenstein et al., 1998). Simple recurrent network models of the hippocampal CA3 region have also been trained on a close proxy of the trace conditioning paradigm (Howe and Levy, 2007; Levy et al., 2005). Notably, in these studies, the tone stimulus triggers a well-timed relay of CA3 cell activation that culminates in the activation of US representing neurons. Our experimental observations are in agreement with this model, if we assume that qualitatively, grouping in area CA1 cell activity reflects grouping of upstream CA3 activity (Brivanlou et al., 2004). We suggest that the progressive emergence of sequentially timed activity we observe in our study is a signature of such network changes.

Sequential activity has been observed before, in subjects that have already learnt a temporal memory task (Lee and Wilson, 2002; Pastalkova et al., 2008; MacDonald et al., 2011). While sequentially-timed activity has also been observed in un-trained mice (Luczak et al., 2009; Dragoi and Tonegawa, 2011), these sequences of activation were very short, only 100–200 ms long. Post training, we observed sequential activation over an 800 ms period, which is a task relevant timescale (Figure 2D). Importantly, to our knowledge, our study is the first to monitor the emergence of these sequences over the entire process of learning a temporal memory task (schematic in Figure 7A). As we discuss below, this has allowed us to identify transient, learning-related network changes in spontaneous activity correlations that could underlie the emergence of the longer lasting sequential activity.

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As discussed earlier (‘Results: Mice learn a trace eyeblink conditioning task within a single session’), area CA1 activity data pooled from non-learners is probably confounded by contrary effects due to the uncertain state of learning of each mouse. However, for the sake of completeness, we have included data from non-learners in some key figures. These panels show that the data from non-learners is consistent with our interpretations and that trace learners are significantly different from non-learners as well as from pseudo-conditioned mice, in all cases.

Correlations between neurons during periods of spontaneous activity or noise correlations (NC) have been thought to be indicative either of shared inputs, or of direct or indirect connectivity (Ts’o et al., 1986; Bair et al., 2001; Fujisawa et al., 2008). While high temporal resolution measurements of neuronal activity are required to infer direct, synaptic connectivity, we, in our study, infer changes in NC to be indicative of changes in clustering of inputs to CA1 cells from upstream CA3 cells (Komiyama et al., 2010). This view is consistent with earlier findings where the correlations between place cell pairs were seen to increase as rats explored a novel track (Cheng and Frank, 2008). Importantly, this increase in correlations was dependent on the expression of N-methyl-D-aspartate receptors and, therefore, the capacity for synaptic plasticity in area CA3 (Dragoi and Tonegawa, 2013). We found that cell-pairs that eventually share the same time-tuning show significantly higher increases in NC than do random cell-pairs, though on average, all cell-pairs show a significant increase in pair-wise correlations (Figure 4A,B). We interpret this to be due to increased common input from changing groupings of inter-connected CA3 neurons. However, this increase is temporary as average NC declines near the end of the session, although neurons sharing the same time-tuning still maintain significantly elevated NC. This is consistent with a previous observation that novelty-induced increases in spontaneous correlations gradually reduce with repeated exposure to the novel environment (Cheng and Frank, 2008). The transient, global increase in NC may arise from a network-wide increase in synaptic weights during learning, and the subsequent decline may reflect weakening of mean synaptic weights due to synaptic re-normalization (schematic in Figure 7B). Consistent with this interpretation, we observed that mean NC peaked just prior to the peaks in behavioral responsiveness and reliability of sequential activity, even on an individual mouse basis (Figure 4—figure supplement 1A).

Similar results have also been observed in previous studies. Cortical neurons sharing task-specific tuning were seen to increase their spontaneous correlations as learning progressed on a sensory association task (Komiyama et al., 2010). In recordings from rodent CA1 neurons, place cells were seen to show gradually increasing spontaneous correlations as a novel environment was explored (Cheng and Frank, 2008; Dragoi and Tonegawa, 2013). Furthermore, over extended exposure to the same novel track, these elevated correlations gradually reduced (Cheng and Frank, 2008).

Interestingly, when neurons were divided into clusters that shared high NC, these clusters turned out to be spatially organized even prior to training (Figure 5E,F). This might indicate that area CA1 cells receive spatially segregated input from bundles of area CA3 cells. This is borne out by the observation of spatially segregated connectivity from CA3 to CA1 cells in hippocampal slices (Brivanlou et al., 2004). Importantly, we observed that these spatially segregated clusters changed only during learning, while in pseudo-conditioned mice and in data collected prior to training, NC clusters remained relatively constant (Figure 6C). This indicates that during learning, area CA1 cells (and probably also, by extension, CA3 cells) enter a dynamic state, where neurons change from one spatially-organized, correlated grouping to another (schematic in Figure 7C). In our view, these fluctuating, spatial groupings reflect network changes that underlie the emergence of spatially un-organized, behavior-related sequentially timed activity.

We propose that when learning the trace eyeblink conditioning task, and perhaps more generally when associating temporally separated stimuli, the hippocampus is recruited as follows:

Through a network-wide, transient increase in specific synaptic strengths within the CA3 and in the CA3 to CA1 network, changing groups of CA3 cells remodel the strengths of their connections to CA1 cells (Figure 7B,C). This is reflected in changing clusters of area CA1 cells showing increased noise-correlations. Progressively, these groups stabilize into a sequence of stimulus-activated cell-groups, after which global synaptic re-normalization causes average noise correlations to move back towards baseline levels (Figure 7C). Only neurons sharing the same timing in the sequence maintain significantly elevated noise correlations (Figure 4B, schematic in Figure 7B). Importantly, these spontaneously correlated clusters of cells are also spatially clustered and changes in these clusters underlie the emergence of sequentially activated cell-groups. These sequential groups allow CS-representing neurons to be indirectly connected to US-representing neurons, thereby forming an association between stimuli separated in time.

All experimental procedures were approved by the National Centre for Biological Sciences Institutional Animal Ethics Committee (Protocol number USB–19–1/2011), in accordance with the guidelines of the Government of India (animal facility CPCSEA registration number 109/1999/CPCSEA) and equivalent guidelines of the Society for Neuroscience. All recordings and behavioral experiments were carried out on male, 30 to 45 day old C57BL/6 mice. Data acquired from a total of 35 mice is included here. A total of 18 mice were trace conditioned, and 17 mice pseudo-conditioned. Of the 18 trace conditioned mice, 9 learned the task to criterion and the other 9 failed to learn. Imaging data was acquired for 14 of the trace conditioned mice. Of these, 6 were learners and 8 were non-learners. Of the 17 pseudo-conditioned mice, imaging data was acquired for 6 mice (Figure 2—figure supplement 1B).

1.5 to 2 hr after recovery from anesthesia, mice were head-fixed on a custom-designed clamp (gift from the Albeanu Lab, Cold Spring Habor Laboratory, New York, NY, USA) and mounted onto the stage of a custom-built, two-photon microscope. Left eyelid blink responses were recorded using a custom-made magnetometer (Koekkoek et al., 2002) positioned close to (1–3 mm away) the neodymium magnet fragment glued to the eyelid and was left un-disturbed throughout the experiment. The magnetometer was constructed using a magneto-resistive sensor (HMC1051Z; Honeywell, Linden, NJ, USA), whose output was further amplified (SR560; Stanford Research Systems) and digitized at 10 KHz using a data acquisition card (PCI-6221; National Instruments, Austin, TX, USA). The tone, conditioned stimulus was delivered using a speaker positioned to the left of the mouse. The stimulus was a pure tone of frequency 9.5 KHz, delivered at an intensity of 80–90 dB at the microscope stage and of 350 ms duration. In a pre-training session, only tones were delivered for 30 trials. Blink responses were acquired for 5 s prior to tone onset and terminated 10 s after tone onset. The inter-trial interval was randomized between 15 and 30 s, making the total duration between tone-presentations 30 to 45 s. Thereafter, a single trace conditioning session of 50 trials was carried out. On training trials a 100 ms air-puff (aversive, un-conditioned stimulus), was delivered 250 ms (trace interval) after the end of the tone. Pressurized air at 5 psi was passed through a nozzle of tip-diameter 1–1.5 mm positioned ∼1–2 cm away and aimed at the mouse’s left eye. Air-flow was switched on and off with a computer-controlled electronic, solenoid valve (EV mouse valve, Clippard, Cinncinati, OH, USA). Every fifth trial was a probe trial (on which no puff was delivered) however as these amounted to only 10 probe trials per mouse, data from these trials have not been used for any specific analysis. Only one training session of 50 trials was carried out per mouse. Longer sessions were seen to cause blinking fatigue and discomfort to the mice. Additional sessions over multiple days were not possible due to the acute nature of the imaging preparation (‘Materials and methods: Mice and surgical procedure’). All trained mice were imaged, irrespective of usability of imaging data, while training was carried out. Microscope scan-mirrors continued to oscillate during the inter-trial interval, to remove auditory cues signaling the beginning or end of a trial.

Hippocampal area CA1 cells were imaged in parallel with eye-blink acquisition and training. We used a custom-built two-photon microscope, built around a Tsunami, Ti-Sapphire, 80 MHz pulsed laser (Spectra Physics, Mountain View, CA, USA) tuned to 810 nm for excitation with a water dipping objective lens (5CFI75 LWD 16x, NA 0.8, Nikon, Japan). Emitted fluorescent light was detected using an analog GaAsP PMT (H7422P-40; Hamamatsu, Japan). The amplified signal was binned over 2 µs pixel times. Imaging and training were carried out in complete darkness.

All analysis was carried out using custom-written programs (Matlab, Mathworks, Natick, MA, USA), unless otherwise mentioned. Matlab code for the behavioral learning curves analysis (Wirth et al., 2003; Smith et al., 2004) and meta k-means analysis (Ozden et al., 2008; Dombeck et al., 2009) is available with the authors of the studies to first use it. Code for the main analyses carried out for this study is available for download at https://github.com/mehrabmodi/CA1-sequences-data-analysis-code.git.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

The authors thank Robert Cannon, Sachin Deshmukh, Niraj Dudani and Aditya Gilra for valuable inputs and discussions. The authors thank Dinu Albeanu for inputs and the generous gifts of head bars and head clamps.

Animal experimentation: All experimental procedures were approved by the National Centre for Biological Sciences institutional animal ethics committee (Protocol number: USB - 19 - ½011), in accordance with the guidelines of the Government of India (animal facility CPCSEA registration number: 109/1999/CPCSEA) and equivalent guidelines of the Society for Neuroscience. All surgery was performed under isoflurane anesthesia, and every effort was made to minimize suffering.

© 2014, Modi et al.

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