Differential Emergence and Stability of Sensory and Temporal Representations in Context-Specific Hippocampal Sequences.

RESOURCE AVAILABILITY Lead Contact Further information and requests for resources and reagents should be directed to the Lead Contact, Peyman Golshani ( pgolshani@mednet.ucla.edu ).

Materials Availability

All unique resources generated in this study are available from the Lead Contact with a completed Materials Transfer Agreement.

Data and Code Availability

The data and analysis code generated in this study are available upon reasonable request to the corresponding authors.

EXPERIMENTAL MODEL AND SUBJECT DETAILS Animals

A total of 23 adult male mice (11–31 weeks old) were used for in vivo two-photon calcium imaging experiments: 7 Gad2-Cre:Ai9 mice (Gad2 tm2(cre)Zjh /J crossed with B6.Cg-Gt(ROSA)26Sor tm9(CAG-tdTomato)Hze /J), 14 Gad2-Cre:Ai14 (Gad2 tm2(cre)Zjh /J crossed with B6.Cg-Gt(ROSA)26Sor tm14(CAG-tdTomato)Hze /J) and 2 Gad2-Cre mice. An additional 10 adult male mice (13–25 weeks) were used for behavioral control experiments: 2 Gad2-Cre:Ai32 (Gad2 tm2(cre)Zjh /J crossed with B6;129S-Gt(ROSA)26Sor tm32(CAG-COP4*H134R/EYFP)Hze /J), 2 Gad2-Cre:Ai9 and 6 C57B/6J. All animals were experimentally naïve. All animals were acquired from The Jackson Laboratory and were group housed (2–5 per cage) on a 12 h light/dark cycle. All experimental protocols were approved by the Chancellor’s Animal Research Committee of the University of California, Los Angeles, in accordance with NIH guidelines.

METHOD DETAILS Surgical Procedures

Mice were anaesthetized with isoflurane (3–5% induction, 1.5% maintenance), their scalp was shaved and they were placed into a stereotactic frame (David Kopf Instruments, Tujunga, CA) on a feedback-controlled heating pad (Harvard Apparatus) to maintain body temperature at 37 o C. Eyes were protected from desiccation using artificial tear ointment. The scalp was sterilized with betadine and infiltrated with lidocaine (2%; Akorn, Lake Forest, Illinois) as a local anesthetic. An incision was made to expose the skull from bregma to lambda. Fascia was removed by applying hydrogen peroxide and the skull was stereotactically aligned. A small burr hole was made on the right hemisphere (−2mm posterior and 1.8 mm lateral to bregma), using a dental drill. Using a Nanoject II microinjector (Drummond Scientific), 1500 nl of 1:10 saline-diluted AAV1.Syn.GCaMP6f.WPRE.SV40 virus (diluted immediately prior to surgery; titre: 4.65 × 1013 GC/mL; Penn Vector Core) were injected into dorsal CA1 (1.3 mm ventral from dura) at a 50–100 nl per minute. 60 minutes after the termination of viral injection, a circular craniotomy (3mm diameter) was made around the injection site. Dura over the exposed brain surface was removed and the cortical tissue above the dorsal CA1 was carefully aspirated using a 27 gauge blunt needle. Buffered artificial cerebrospinal fluid (7.888g NaCl, 0.372g KCl, 1.192g HEPES, 0.264g CaCl2, 0.204g MgCl2 per 1000 ml milipore water) was constantly applied throughout the aspiration to prevent desiccation of the tissue. The aspiration ceased after partial removal of the corpus callosum and bleeding terminated, at which point a 3-mm titanium ring with a glass coverslip attached to its bottom was implanted into the aspirated area and its circular flange was secured to the skull surface using vetbond (3M). A custom-made lightweight metal head holder (headbar) was attached to the skull posterior to the implant. Cyanoacrylate glue and black dental cement (Ortho-Jet, Lang Dental) were used to seal and cover the exposed skull. Mice that were used for behavioral experiments were only implanted with a headbar and were not given a craniotomy or aspiration. During recovery (~7 days) mice were administered carprofen (5 mg per kg of body weight) for 3 days as a systemic analgesic and amoxicillin antibiotic (0.25 mg ml −1 in drinking water) through the water supply for 5 days. Their weight was monitored daily.

Experimental Setup

The treadmill consisted of an 8-inch Styrofoam ball (Graham Sweet) suspended through a metal axis, allowing for 1D rotation. It was housed within a dark enclosure. Mice were headfixed on the treadmill by attaching the implanted headbar to a custom-made metal holder. Locomotion was recorded as an independent analog signal, using a custom printed circuit board based on a high sensitivity gaming mouse sensor (Avago ADNS-9500), connected to a microcontroller (Atmel Atmega328) sending data to a dual channel digital-to-analog converter at 100 Sps. A constant stream of clear air (~1 L/min) was transferred to the behavioral rig via Tygon PVC clear tubing and was supplied to the mouse through a custom-made lickport. During odor stimulation a dual synchronous 3-way valve (NResearch), placed ~6 cm away from the mouse, would switch from the clear air stream to the odorized one for 1 sec. Odorized air was created using a 4-ports olfactometer (Rev. 7c; Biology Electronics, Caltech), supplying air to either of two glass vials containing liquid isoamyl acetate (70% isoamyl acetate basis, FCC; Sigma Aldrich) or pinene ((−)-α-Pinene, ≥97%, FCC; Sigma Aldrich) odorants, diluted in mineral oil at 5% concentration (unless stated otherwise). For the non-match-to-long-duration-sample task (NMLS) experiments, all 4 ports were connected to odor vials (see below). Odorized air from each vial would reach the behavioral rig through separate Tygon tubing each leading to a 3-way solenoid valve (Lee Company). For olfactory stimulation, the corresponding olfactometer port started supplying air to its attached odor vial 1 sec prior to actual stimulation to allow for odorized air to travel through the tubing and reach its corresponding solenoid valve. During the 1 sec stimulation, that solenoid turned on allowing the corresponding odorized air to enter a short common path for both odorants (~4cm) ending at the dual synchronous 3-way valve and from there being released to the mouse through the lickport (~10 cm total common path for the two types of odorized air). At the offset of the stimulus, the solenoid turned off and the dual 3-way valve switched the airstream back to clear air, ensuring a constant flow of air to the mouse and a quick clearing of the odorant from the air around the mouse. The odorized and clean air were set to similar airflow values (~1 lt/min), measured with a flowmeter (AWM3300; Honeywell). Odorant concentration in open air was measured through a mini photoionization detector (PID; 200B mini PID; Aurora Scientific), located at the distance of the mouse snout from the air-delivering lickport. Lower ionization threshold for pinene (odor-B) lead to higher deflections in the PID signal during its presentation compared to isoamyl acetate (odor-A). Therefore, potential odor lingering in the small common tube path, during the delay, and mixing with the second odor would result in a larger PID deflection during the second odor in B-A trials (lingering pinene molecules mixing with isoamyl acetate) than in A-A trials (lingering isoamyl acetate mixing with itself). This was observed when creating a very long common path length, but not in our default set up ( Figure S1 ), indicating that no odor mixing is created during the second odor. Licking was detected using a battery operated, custom-made, printed circuit board operating as a lickometer ( Slotnick, 2009 ). One end of the circuit was attached to the metal headbar on the mouse and the other to the metal tube delivering water. Whenever the mouse would lick the tube, an electrical circuit would close, creating a voltage drop that was recorded as a continuous RSE analog signal. Water droplets (~10 μl) were released by a 3-way solenoid valve (Lee Company) and delivered to the mouse through a metal tube on the lickport. At the end of each trial, after the response window, vacuum was applied for 3 sec, through a metal tube underneath the water delivering tube, to clear any lingering water and assist in removing any odorized air. The behavioral rig was controlled with custom written software (Matlab) and through a data acquisition board (USB-6341; National Instruments).

Behavioral training and recording protocol

After the recovery period, mice were briefly handled for two days and water restriction was initiated. They were provided with 1–1.5ml of water daily, either throughout or post training. Their weight was maintained at around 85% of their final weight pre-water restriction initiation. Mice were initially habituated to head fixation on the spherical treadmill for 3 consecutive days. On the second day, the lickport was placed in its normal position for habituation to the metal ports and air flow. Mice were given water drops through the lickport on the third day of habituation. DNMS training consisted of 3–4 days of ‘shaping-stage’ ( Liu et al., 2014 ) where mice were presented with trial-blocks (20 trials each) of non-match trials and the water reward was delivered automatically at the start of the response window in each trial. This was followed by a ‘pre-training’ stage for 2–4 days where mice were presented with non-match trials but had to actively lick the lickport during the response window to release the water drop. They were moved to the final training stage once they performed at >85% hit trials per day. In the ‘training stage’, mice were presented with the full task in a series of trial-blocks per day. Each block again consisted of 20 trials and match trials were intermingled with non-match ones with each group of 4 consecutive trials containing all possible 4 combinations of odors in random order. Mice were not punished if they licked at match trials but no reward was presented. Within ~2–5 days they learned to refrain from licking during match-trials. During training, mice gradually refrained from licking during the delay period as well, and well-trained mice would typically initiate licking during or right after the second odor. Responses were assessed based on licking during the response window only. If any licking occurred during that window, the trial was a hit (non-match trials) or false alarm (match trials) accordingly. In the opposite case, they were labeled as miss or correct rejection respectively. Performance over each trial-block, was quantified as the percentage of hits and correct rejections over the 20 trials. Daily performance was the corresponding ratio over the total number of trials (all blocks) during that day. Mice were typically presented with 5–7 blocks daily. Two-photon imaging sessions for mice under training started as soon as they were ready to move from the ‘pre-training’ stage to ‘training stage’ (N = 9 mice), at ~2–3 weeks post-surgery. 3 mice were first imaged on their first day in ‘training stage’ and 6 mice were first imaged for 2 days while performing ‘pre-training’ trial-blocks (non-match trials only). 3 of them received 3 ‘pre-training’ blocks followed by 3 training blocks on their second day of imaging. The other 3 received 5 pre-training trial-blocks on their second imaging day and were moved to training blocks on their third day of imaging. Finally, 2 mice were imaged only after they had received 5 days of training stage (not having reached well-trained level yet) and they were only imaged at training stage. Some mice (N = 6) were imaged while naive to the task. Only the 3 days of habituation were applied (without any water droplets delivered on day 3 of habituation) before onset of calcium imaging, upon which they were presented with normal DNMS trials for 3 or 6 consecutive days (N = 3 in each case), same as with trained mice. 5/6 naive mice never licked the lickports for a reward. One naive mouse kept licking the tubes and received a series of water rewards, but only early sessions (3 days) where the mouse had not grasped the task were included here. These animals were imaged 2–3 weeks post-surgery to match the time the trained animals were imaged and to allow for GCamp6f to be expressed. Behavioral control experiments Non-match-to-long-duration-sample task training (NMLS; N = 3 mice) consisted of the same training steps as DNMS, but the first odor was presented throughout a 5-sec period and was immediately followed by the second odor. Varying odorant concentration vials were used only for the first odor whereas the second odor was delivered by different olfactometer ports connected with 5% odorant vials. For experiments with odors turned off, the olfactometer ports were deactivated, halting any flow of odorized air, but trials were otherwise normal. For experiments with variable airflow, the airflow for both odor-ports on the olfactometer was altered randomly for each port at the beginning of each trial-block, between ~0.4 −1 lt/min, measured by the flowmeter. In vivo two-photon imaging A resonant scanning two-photon microscope (Scientifica) was used for calcium imaging, recording 512×512 pixel frames at 30.9 Hz, with a 16× 0.80 NA objective (Nikon) yielding a 500×500 μm field of view. Excitation light was delivered with a Ti:sapphire excitation laser (Chameleon Ultra II, Coherent), operated at 920 nm. GCamp6f and Td-Tomato fluorescence were recorded with green and red channel gallium arsenide photomultiplier tubes respectively (GaAsP PMTs; Hamamatsu). Microscope control and image acquisition were performed using LabView-based software (SciScan). Imaging and behavioral data were synchronized by recording TTL pulses, generated at the onset of each imaging frame, as well as olfactory stimulation digital signals at 1 kHz, using the WinEDR software (Strathclyde Electrophysiology Software). A single fixed field of view was imaged every day for each mouse. To ensure that the same neurons were tracked every day, each day’s field of view was overlaid to that of the first day of imaging for each mouse. The reference and live fields of view were aligned using the recording software before recording was initiated. This fixed template reference avoided deviations from the correct z-plane or any slow drifts across days. Moreover, a combination of rotating stages was used to allow tilting of the objective over two degrees of freedom as well as adjusting the mouse head-angle, to fine-tune the alignment of the live field of view to its reference template. Imaging sessions were performed separately for each trial-block, allowing laser power and PMT adjustments in between to ensure optimal imaging conditions and signal-to-noise ratio as well as fine-tuning of the field of view position during the recording session. Time breaks of ~1min were given between trial-blocks to prevent brain heating or photo-toxicity. Laser power was kept to a minimum and no photo-bleaching was observed.

QUANTIFICATION AND STATISTICAL ANALYSIS Calcium imaging data processing

Data was pre-processed in MATLAB using a custom-built pipeline based on the CaImAn package ( Giovannucci et al., 2019 ) for large scale calcium imaging data analysis. To reliably track neurons over multiple days in the presence of changes in the field of view, active neurons were extracted separately for each day and were then registered against each other.

Motion correction and source extraction

Datasets from each day where corrected for motion artifacts using online piecewise rigid registration ( Pnevmatikakis and Giovannucci, 2017 ), where the template obtained during motion correction of trial-block X was used to align data from trial-block X+1. For 3 animals with low signal-to-noise ratio, motion correction took place on spatially downsampled videos and shifts were interpolated and applied on the original recordings. Data from all the trial-blocks each day was then concatenated and downsampled in time by a factor of 5 to increase the signal-to-noise ratio. The spatial footprints of the active sources (ROI) were then extracted from the downsampled data using an implementation of the CNMF algorithm in spatial patches ( Giovannucci et al., 2019 ). Spatial and temporal correlation thresholds for ROI detection were set to 0.6 and 0.8 respectively, acceptable ROI size was limited to 50–150 pixels, and an ROI eccentricity threshold was set at 0.97 to avoid elongated ROI that were axodendritic segments. These values were determined after examination of the relationship between these thresholds and the final accepted ROI spatial footprints and temporal traces, using a custom graphical user interface on a set of our recordings. The spatial footprints were then used to obtain the traces at the original frame rate by solving a non-negative regression problem. The computed traces were first transformed in ΔF/F units and were then deconvolved using the OASIS algorithm for fast nonnegative deconvolution ( Friedrich et al., 2017 ), separately for each trial-block, to correct for variations of laser power between those or any possible bleaching artifacts. Noise levels were computed for each ROI using a power spectral method, and spike thresholds were set to 3 x noise level, except for 4 animals with lower signal-to-noise ratio and sparse activity, were the threshold was lowered to 1.5 and 2 respectively (N = 2 for each). Finally, the deconvolved traces were binned in time using 50% overlapping 320 ms time bins. This signal is used as a proxy of spiking activity per unit of time and is referred to as ‘spiking rate’ throughout the text. This pipeline generated a set of spatial footprints and temporal traces (both in ΔF/F unit and in deconvolved neural activity) for each animal at each day of recordings. Calcium transients decay time, mean ΔF/F amplitudes and baseline fluorescence The decay time of each ROI’s calcium transient during spiking was estimated through the CNMF algorithm ( Pnevmatikakis et al., 2016 ). Under the assumption that the spiking signal comes from a homogeneous Poisson process, the discrete-time calcium concentration dynamics c(t) at time-bin t were approximated using a stable autoregressive process of order p = 2, as: c ( t ) = g 1 c ( t − 1 ) + g 2 c ( t − 2 ) + s ( t ) with s(t) a noise component. The discrete-time calcium transient in response to a spike is thus modeled by two exponential functions with the time constant of the slower function yielding an approximation of the continuous calcium signal’s decay time τ 2 . This is computed as: τ 2 = Δ t / l o g ( r 2 ) , where Δt is the signal’s time-bin width and r 2 is the smallest of the two roots of the polynomial: x 2 − g 1 x − g 2 = 0 . The decay time parameter was computed separately for each trial-block (20 trials) and was averaged across all blocks in a day. To estimate an ROI’s ΔF/F mean amplitude and SD during its activation we isolated the ΔF/F traces during each spiking segment (i.e. continuous set of non-zero values in the deconvolved signal) and used the maximum ΔF/F value at each segment as a measure of the ΔF/F amplitude during that segment. The average and the SD of these maximal values across all spiking segments in a session were computed. Baseline fluorescence of each ROI was computed as the mode value of the raw fluorescence (the weighted average fluorescence from all the pixels in the ROI after removing overlapping components and neuropil contamination and prior to the ΔF/F transformation), separately in each trial-block, and averaged over all blocks of the session. Since cells were activated very sparsely, this mode value corresponded to their baseline, which we confirmed via visual inspections. Inhibitory neuron detection and removal Inhibitory neurons were identified based on their static Td-Tomato fluorescence recorded for 500 frames at the beginning of each imaging day on the red-channel PMT, together with the functional (green) channel. The red channel was first aligned to the green one by using the motion displacement field that was estimated during the motion correction of the green channel. Then the average of the red channel was computed and the resulting image was segmented to obtain contour plots of the inhibitory neurons. The segmentation was performed by using adaptive thresholding (to model different illumination levels within the field of view), with a threshold at each location computed as the Gaussian weighted average of a small neighborhood around the location. The resulting thresholded image was segmented using connected components. For each connected component, the corresponding part in the mean image was then thresholded at 75% of its maximum value, and after this thresholding and another round of connected components analysis, components of small size (0.95 eccentricity) were removed. The remaining components identified the locations and spatial extent of the inhibitory neurons within the field of view. These components were then registered with the CNMF identified components from the functional channel using the procedure described above. Components that were matched corresponded to inhibitory neurons that were active during this imaging session, whereas mismatched components corresponded to inhibitory neurons that were either silent or whose activity did not meet detection criteria by CNMF (slow traces potentially due to prolonged high-frequency spiking). Matched components were removed from the final pool of ROI, so that only pyramidal cell activity from the green channel was analyzed further.

Pairwise Registration

To register components across two different days we followed the following procedure: First the ROI from both days were aligned to the same FOV, by computing a motion transformation between the templates from day 1 to day 2 and using this transformation to align the components from day 1 to the FOV of day 2. Let α 1 1 , α 2 1 , … , α N 1 1 and α 1 2 , α 2 2 , … , α N 2 2 be the set of (aligned) spatial footprints from the two sessions. The components were first transformed into binary masks m 1 2 , m 2 2 , … , m N 2 2 and m 1 2 , m 2 2 , … , m N 2 2 respectively, by thresholding each component at the 10% of its maximum value. An intersection over union metric was used to quantify the distances between the footprints from the different days: d i , j = 1 − | m i 1 ∩ m j 2 | | m i 1 ∪ m j 2 | Based on this metric a matrix of pairwise distances was constructed. Distances between components where the one was a subset of the other (>60% pixels of smallest of the 2 ROIs overlapping with those of the largest) were set to zero, whereas high distances d i,j > 0.98 were set to infinity. Components were then registered to each other using the Hungarian algorithm (a polynomial time algorithm for solving the linear assignment problem). Setting weakly overlapping components to have infinite distance prevented false pairings between components which were instead left unmatched between these two particular sessions.

Calcium and Behavioral Data Analysis

All analysis was performed on the binned spiking rate traces from all pyramidal cells each recording day, using custom written analysis software (Matlab).

Sequence-cell detection and analysis

Whether each cell had an odor or time-field was determined separately over all trials of a session initiated by odor-A or odor-B, respectively, through the following process: First we computed the cell’s average firing rate for the corresponding group of trials within the ‘odor-delay interval’ (from first odor onset up to second odor onset). Only if the cell spiked within that interval at least in 10% or 3 (whichever was greatest) trials of the corresponding type was considered further. The maximum average rate was computed and the time-bin it occurred in was considered the cell’s potential field. Its firing rate trace over the odor-delay interval was then circularly shifted by a random interval up to ± 1/2 x odor-delay interval, separately for each trial and the maximum mean firing rate over the shifted trials was computed. This process was repeated 1000 times, generating a distribution of maximal rate values. The cell was considered to have a significant firing field if its maximum mean firing rate at that time-bin was larger than the 95 th percentile of the shuffled distribution (see additional SI condition below). The time bin of that maximal rate was considered the cell’s field time-point. If it was during the odor stimulation, it was considered an ‘odor-cell’, otherwise a ‘time-cell’. For the three mice receiving a combination of 3 ‘pre-training’ and 3 ‘training-stage’ trial-blocks on their second imaging day (see above), sequence-cell detection was performed separately for each training stage. For experiments where the odors were turned off after the initial trial-blocks, the sequence-cells were computed using only the trials with the odors on. For trials with variable odorant concentrations, they were computed based on all trials. Finally, for imaging days with multiple delays, sequence cells were detected separately over trials of each delay duration. To estimate a cell’s field size, its mean firing rate over preferred trials, from the odor onset to the delay offset, was lowpass filtered (< 1 Hz) and its baseline was approximated by its mode value. We then detected the two time-bins in either side of the field where the filtered rate dropped below a threshold of baseline + 1/2 standard deviation. The time interval between these points was used at the field size. If either of these thresholds was not crossed throughout the whole interval then the odor onset or delay offset were used accordingly. Field sizes longer than 4 sec were disregarded. We note however that, since firing rates here refer to binned deconvolved calcium traces, they do not accurately depict actual levels of activity. Therefore, field sizes should be considered as an approximation of the time interval where the cell had the highest activation probability relative to the rest of the trial, and as such its length represents the variability in activation time for the cell. We computed the selectivity index ( SI ) of each sequence cell as: S I = R f i − R f j R f i + R f j Where R f i is the cell’s mean firing rate at its firing field f over all i -trials, whereas R f j is its mean firing rate at the same time bin but over the opposite type of trials j (with i and j representing odor-A or odor-B accordingly). Cells with a negative SI over the trial type under consideration were discarded from the corresponding sequence (as preferring opposite trials). Therefore, if a cell had a field at the same time-bin during both trial-types, it was assigned to the sequence where its field-rate was highest (positive SI). This condition was removed for analysis of multi-field cells in Figure S2D (the total number of sequence-cells was only increased by 7.6%), but was applied in all other cases. A sequence-cell’s SI over correct or error trials was computed using the above formula but for the corresponding trials only. The cell was included in analysis only if there were at least 10% error preferred-trials and 10% error non-preferred-trials in the session, to allow for adequate sample size and fair comparison of SI over both outcome-types. Cells that were never active in any correct trial, of either odor-type were also removed and the same for those that were never active in any error trials (though including these cells did not alter our results). An odor-cell’s SI at the second odor stimulus was computed using the above formula as well, but for the time-bin that corresponds to its field transferred over the second-odor in a trial. For Figure S11 , it was computed with the same formula but using the cell’s mean firing rate over the two trial types, summed over all time-bins of the second odor. The SI over the first versus the second odor was computed similarly, as: S I 1 − 2 = R m 1 − R m 2 R m 1 + R m 2 where R m 1 is the cell’s mean firing rate summed over all time-bins of the first odor and R m 2 is the corresponding summed rate over the second odor time-bins. Only trials with preferred first or second odor, respectively, were included. The activation probability of a cell in its field was quantified as the percentage of preferred trials where it had a nonzero firing rate inside its firing field. The same holds for the computation over correct or error trials only (again using only sessions where a cell’s preferred trials contained at least 10% errors). Activation probability at the first versus second odor stimulus for an odor-cell was the percentage of trials with nonzero R m 1 and R m 2 respectively. Activation probability at match or non-match trials was computed by computing R m 2 over the corresponding trial groups. Spiking variance was quantified as the variance in the time bin containing a cell’s maximal firing rate at each trial over all trials of a given type. To detect cells encoding match or nonmatch trials or specific odor-combinations, the SI over match versus nonmatch trials was computed for each cell (excluding all cells with an odor or time-field): S I m a t c h = R m a t c h − R n o n m a t c h R m a t c h + R n o n m a t c h where R match and R nonmatch is each cell’s average firing rate across match or non-match trials respectively, summed over all time-bins covering the second odor and the ensuring 1 sec interval, before the response window (to avoid any effects from the water reward in nonmatch trials). Cells with SI match > 0.2 were considered potential match and those with SI match < −0.2 were potential non-match cells. Neurons that spiked within that interval less than 10% or 3 (whichever was greatest) trials of the corresponding type were discarded. For potential match cells, their SI over the same interval was computed for A-A versus B-B trials with the same formula, and for potential non-match cells their SI was computed for A-B versus B-A trials. Potential match cells with SI > 0.5 were considered A-A cells and those with SI < −0.5 were B-B. Similarly, A-B and B-A cells were selected out of all potential non-match cells. The remaining cells with |SI| < 0.5 were the final match and non-match cells accordingly. For plotting, mean firing rates of each sequence cell were normalized by their maximal firing rate at their firing field. The same normalization was applied for both trial types. For spatial distribution analysis, the centroid of each sequence-cell’s spatial footprint was computed. All pairwise centroid Euclidean distances were measured as well as pairwise time distances between firing fields for all time-cells. Place-field distributions were approximated by a two-term power law distribution: f ( x ) = b ∗ x a + c using the trust region reflective algorithm ( fit function in Matlab). The goodness of fit was assessed by creating random sampled distributions from this power law, of equal size to the original, with 2500 repetitions, fitting each sampled distribution with the same algorithm, and comparing the Kolmogorov-Smirnov (KS) statistic of the original distribution with those from the random sampling ( Clauset et al., 2009 ). The P-value corresponds to the ratio of distributions with KS statistic lower than the default distribution.

Motion Analysis

The locomotion signal had its baseline removed (mode value), was binned in the same manner as for the deconvolved calcium traces and its summed value across the odor-delay interval in each trial was computed. For Figure S3 , trials with total motion < 30 (a.u.) during that interval were considered “immobility trials”. Visual inspection of locomotion traces supported this threshold, allowing only some sparse brief motion bouts. An equal number of trials with highest total motion were also selected in each session. The mean firing rates of each sequence-cell during its preferred trials from the two trial groups were compared. Analysis Across Multiple Days ROI were matched across all pairs of imaging days per mouse. ‘Stable sequence-cells’ were defined as two matched ROI that had a significant field in the same sequence both days. Across a series of days, ‘stable sequence-cells’ were detected using the final day’s sequence cells as reference and tracking those that had a match with a cell belonging to the same sequence each other day. To compute field shifts across two consecutive days (Day 1 and 2), we took all sequence cells from Day 1 that were matched to an ROI on Day 2 and computed the maximal firing rate time bin of that matched ROI across the corresponding trials of Day 2 (along the odor-delay interval as usual). Field shifts of recurring sequence-cells were those where the matched ROI was a sequence cell for that trial type as well. If the matching ROI never spiked on Day 2 in the corresponding trials, the cells were discarded from field-shift analysis. When color-plotting average sequence firing rates across days, each cell’s rate was normalized by its mean rate in its field on Day 1. To quantify the FOV alignment quality between two days, we computed a ‘relative correlation’ ρ ¯ i j between the FOVs of days i and j defined as: ρ ¯ i j = ρ i j ρ i ∗ ρ j where ρ ij . is the correlation between the mean FOVs of the two days across all recorded videos and ρ x is the correlation between the mean FOV of the first half of all videos of day x with the second half. The denominator provides an intra-session stability baseline for comparing the inter-session cross-correlation ρ ij . Most pairs of days had high relative correlations with a mean of 〈 ρ ¯ i j 〉 = 0.85 across all pairs per animal. For Figure S11 , we set 〈 ρ ¯ i j 〉 as a threshold for relative correlation and any continuous pair of days under that limit (or day triplet that contained at least one such pair) was removed from analysis (35% of day pairs and 62.5% of triplets removed). For Figure S13 any day that yielded ρ ¯ i j < 〈 ρ ¯ i j 〉 with the first imaging day of an animal (initial template day), was removed from calculating the number of odor/time cells per day (36% of imaging days removed). Analysis Across Multiple Delays and Odor Durations A set of well-trained mice (N = 5) was imaged during both 5 and 10 sec trial-blocks on the same day. Typically, they received a set of 2–3 trial blocks with 5 sec delay followed by the same number of blocks with 10 sec delays. Each animal was exposed to extended delays for 2–3 days in total. For two animals the delay was reduced back to 5 sec after the extension to 10 sec (during 1 day for one animal and 2 days for the other). Removing these imaging days where the delay was reduced again, did not affect our results. A different set of mice (N = 9) was imaged during both 1 sec and 3 sec odor-stimulations on the same day. The delays were set to 5 sec and 3 sec respectively to keep the trial duration constant. Each animal was exposed to extended odors for 2–4 days in total (only 1 day for one animal) and received a set of 4–5 trial blocks with 1 sec delay followed by 3 trial-blocks of extended odor. Separate sequence-cells were detected each day over the 5 sec-delay and the 10 sec-delay trials or the 1 sec-odor and 3 sec-odor trials respectively. ‘Stable cells’ refer to those that have a significant firing field in the same sequence in both delays (though not necessarily in the same time bin), whereas ‘unstable’ cells refer to cells that have a field in one delay but not the other. As in multi-day analysis, for unstable cells we use their maximal average firing rate location over their preferred trials as proxy of a field. If a sequence-cell never spiked in the opposite delay/odor-duration trials it was discarded from field-shift analysis. When color-plotting average sequence firing rates, each cell’s rate was normalized by its mean rate in its default delay (or odor-duration). Analysis over Training Days Performance was calculated separately for each trial-block (20 trials) and was averaged over all blocks per imaging day. Rejection rate was quantified as the ratio of match-trials where the mouse did not lick and is only defined for the training-stage trials (full DNMS). To pool individual mice, data from each mouse (number of cells, control measures, Bayesian decoding measures etc.) were normalized over their mean number across all training-stage days, and are plotted as percentages. Mean total rate was computed by summing the deconvolved calcium trace of each cell over all time-bins during each trial, and averaging over all trials and all cells. Mean motion was computed by summing the motion signal (see above) over all time bins of each trial and averaging over all trials. Inter-spike intervals (ISI) were computed at each trial by making the deconvolved calcium trace of each cell binary (turned to 1 for any value > 0) and computing the time interval between two consecutive 1s following 0s. Mean ISI was computed by averaging each cell’s ISI over each trial, and taking the mean over all trials and all cells. The inflow of new odor- or time-cells in a sequence at any training day d was computed as the ratio of odor- or time-cells, respectively, of day d that did not exhibit a significant field in the same sequence on day d-1 . Stable cells are defined as the complement set of new ones, i.e. the ratio of all odor- or time-cells of day d that also had a field in the same sequence on day d-1 . The outflow of lost odor- or time-cells at any training day d was computed as the ratio of odor- or time-cells, respectively, of day d-1 that did not retain a significant field in the same sequence on day d . Cross-correlation between pairs of odor-cells (stable or new ones accordingly) or time-cells in each session was computed as the average Pearson’s correlation between their rates over all preferred trials. Since cells fired at different timepoints, correlations were computed for lag equal to the interval between the time fields of each cell pair. Co-activation ‘C ij ’ of cells i and j was defined as: C i j = a i j a i ∗ a j where a ij is the number of preferred trials where both cells were active (i.e. non zero rates anywhere over the odor/delay interval) and a x is the number of preferred trials where cell x was active. Therefore, this definition does not consider the timing of activation but only its co-occurrence between two cells over the odor/delay interval. Since ROI segmentation is activity-dependent and cross-day registration is done pairwise, to discern how the sequence-cells of any training Day X spiked maximally on all other days, we traced the matched ROI for each sequence-cell of Day X in every other day. Sequence-cells of Day X that were not matched to another cell on another day, were considered inactive there (zero firing rate). For each matched cell of a Day X sequence-cell, its mean firing rate over trials starting with the same odor as the one on Day X was computed separately for each day. The time-bin within the odor-delay interval yielding the peak mean rate was detected. If the cell was also detected as sequence-cell over those trials that day, this time-bin corresponded to its field location. Otherwise it was considered a non-significant peak location. If a cell was inactive during the odor-delay interval on a day, that data point was ignored. On each day, the absolute time-distance between the corresponding peak rate time-bin and the field location on Day X was computed. Time-distance baseline was computed by shuffling each cell’s firing rate over the odor-delay interval in each trial, just as for significant field detection, computing the new mean rate peak time-bin and repeating the above process. This was done separately for 1000 shuffle repetitions and the mean baseline was computed by averaging over all cells and repetitions.

Principal Components Analysis

Principal component analysis

(PCA) was applied to the collected firing rates of sequence-cells in each imaging day. Each cell’s rate was z-scored over all trials of a given odor and the PCA scores were computed. The explained variances for each PC were computed and PCs explaining >80% mean cumulative variance were kept, separately for each trial type. If that threshold was never reached, then all PCs were kept. The minimum number of the two PC limits was finally applied to both trial types. Trajectories along the multidimensional space of those PCs were computed for each trial and the mean trajectories over the two trial types were kept. The Euclidean distance between those was computed and smoothed with a 0.65 sec-window moving average. Baseline distances were computed for each session by circularly shifting the rates of sequence-cells along time by a random interval up to ± 1/2 x trial duration with 100 repetitions and repeating the PCA analysis for each repetition. Plotted PCA trajectories were smoothed over the 3 PC space with spline interpolation and color coded using linear interpolation over time. Bayesian Decoding A Bayesian decoder was used to assess how well can collective sequence-cell activity predict time during the trial, as well as trial-type ( Zhang et al., 1998 ). A separate decoder was constructed for each imaging day, using the firing rates of all sequence-cells (both sequences pooled), over the odor-delay time interval. For well-trained sessions, only correct trials out of the first 2/3 of all trials of the day were used for training the decoder and the last 1/3 trials were used for decoding. Both correct and error trials were decoded, unless otherwise stated. Each cell’s mean firing rate over each trial-type was smoothed using 1sec-window moving average. In order to decode the trial-type (odor presented), we considered time space to be 2 x odor-delay interval (12 sec long in total). We thus concatenated along the time axis, each cell’s mean rate over the two trial-types. The decoder, trained by these concatenated firing rates (mean rate over odor-A-initiated trials followed by mean rate over odor-B-initiated trials) would thus predict a time point along the doubled time interval 0–12 sec. If the time point was within the first half (0–6 sec) it corresponded to that time point of an A-trial, whereas if it was within the second half (6–12 sec) it corresponded to the analogous time point of a B-trial. Assuming Poisson distributed spiking and statistical independence of sequence cells, the decoded time point T ^ t from activity at time bin t is given by: T ^ t = a r g m a x t ( K ( ∏ c = 1 N τ R m ( c , t ) τ R ( c , t ) ) e x p ( − τ ∑ c = 1 N R m ( c , t ) ) e x p ( | t − T ^ t − 1 | 2 2 σ 2 ) ) where c = 1,…,N are the pooled sequence-cells and R m (c,t) is the mean firing rate of cell c at time bin t (with bins concatenated over both trial types, spanning 12 sec) over all training trials. R(c,t) is the corresponding firing rate at the decoded trial, τ is the bin duration and K is the probability of being at time bin t of a particular trial type which is a constant, proportional to the ratio of trials of that type over all training trials ( Zhang et al., 1998 ). The last term functions as a continuity constraint, limiting the decoded time bin to be in a relative proximity to the previous one 5 , with σ = 3 sec. Time bins with no activity from any sequence cell were not decoded. Chance baselines were computed by randomly shuffling the cell identities for each decoded trial with 1000 repetitions and decoding the shuffled cells for each repetition. Time prediction error refers to the mean absolute time distance between a given timepoint from either trial-types and the decoded one: 〈 | t − t ^ t | 〉 , and does not take the trial-type into account. It thus functions as a measure of the time-information carried by the sequence-cells. Odor prediction accuracy refers to the percentage of correct trial-type decoding at any given timepoint (i.e. correct half of extended time-axis) and functions as a measure of the information on the odor identity carried by the sequence cells. Both measures were averaged over pooled trials from all analyzed sessions, for each decoded time point separately. For cross-days analysis, we applied the Bayesian decoder on the firing rates of the recurring sequence-cells between each pair of days. Day pairs with no common sequence cells were not processed. All correct trials from Day 1 were used to train the decoder and all trials of Day 2 were decoded. Baseline was created separately for each day pair, by randomly shuffling the identities of the recurring cells in Day 2, with 1000 repetitions, and applying to decoder at each repetition. Decoded-time errors over each time point were computed for each day pair, concatenated for both trial types. Errors from all day pairs of any given distance were pooled together and their mean value for that given day distance was computed and plotted. The same process was applied to odor-decoding accuracy as well as the corresponding baselines for both measures. Data points from zero distance between days correspond to decoding all trials from a single day using the same trials (subset of correct trials) for training the decoder. For multi-delay analysis, the Bayesian decoder was trained only with the firing rates of 5 sec-trials time-cells and was used to decode odor/time through the activity of these cells during the 10 sec trials. All correct 5 sec delay trials were used for training and all 10 sec trials for decoding. Only the delay time bins were used. The same method was used for extended odor-duration analysis. Using both correct and error trials for training the decoders did not alter our results. In order to account for the difference in length between training and decoding delays, every other time-bin was decoded, so that firing rates for training and decoding had the same number of time-points. E.g. the 2 nd time-bin in the extended delay was decoded through activity on the 1 st time-bin in the original delay whereas the 10 th time-bin in the extended delay would be decoded by the 5 th time-bin. This scheme inherently assumes that sequences rescale by a constant factor, with late time-cells shifting forward more than early ones. If time-cells are randomly shuffled in the extended delay, this scheme is not expected to yield significant decoding accuracy at any time bin. The opposite approach was used when decoding short delays based on sequences of the extended delays. Baseline was created by randomly shuffling the identities of the cells in the 10 sec delay trials (or the 3 sec extended-odor trials accordingly), with 1000 repetitions and applying to decoder at each repetition. To compute the progress in decoding efficacy over training days, Bayesian decoders were built as before, with all first 2/3 trials per day used for training the decoder and all final 1/3 trials of each day used for decoding. Both correct and error trials were used to avoid sample sizes increasing with behavioral improvement. In naive mice, the same process was followed. To compute the progress in decoding efficacy with a fixed set of cells, a Bayesian decoder was trained on the activity of a subset of sequence-cells of training day 6 that were active in all previous days as well (see above). The decoder was then applied to the activity of the same cells in all previous days. All trials of the pair of days were used for training and decoding respectively. In both cases, baseline was created for each mouse each training day, following the same shuffling procedure as before. Mean baselines were computed by averaging across mice each day. For each day, significance over baseline was extracted by comparing pooled Bayesian data points from all mice with pooled chance values from each shuffling iteration in each mouse. Support Vector Machine A binary support vector machine (SVM) classifier was used to assess the odor stimulus in a trial based on the collective activity of all odor or time-cells. The same method as in Bayesian decoding was used to split trials into the training set and prediction set. All trials were split into two groups based on their odor identity. Again, only the correct trials were used from the training pool, whereas all trials from the decoding pool were decoded. Only sessions with at least 3 training trials of each odor were analyzed. The decoder was applied on the collective firing rates of all sequence cells, averaged either over the entire odor-delay interval, or the odor presentation timepoints or delay timepoints accordingly, or only over the firing field time point of each cell. The corresponding mean rate of each cell was z-scored over all trials. The classifier was trained using a radial basis function kernel with scale σ = 11 and box constrain parameter C = 15505. These values were acquired by Bayesian optimization of the SVM classifier over the two parameters, using the odor-cell firing rates over odor time-bins from one imaging session and a 10-fold cross-validation partition for data. Odor-prediction accuracy refers to the percentage of correct odor-predictions over all predicted trials and is averaged over all imaging days analyzed. Chance baselines were computed by randomly shuffling the cell identities for each predicted trial with 1000 repetitions and applying the SVM classifier on the shuffled data for each repetition. The same classifier parameters and method were applied when using SVM on individual sequence-cells instead of collective sequence-cell groups. Chance baseline for each cell was computed by shuffling the identity of both training and predicted trials with 50 repetitions and applying the SVM classifier on each repetition. For cross-days analysis, we applied the SVM classifier on the firing rates of the recurring sequence-cells between each pair of days, following the same process as with Bayesian decoding (see above). The classifier was applied separately on recurring cells that were odor-cells on Day 1 and those that were time-cells, irrespective of their field on Day 2. Baseline was created by randomly shuffling the odor identity of trials on Day 2, with 1000 repetitions and applying to decoder at each repetition. Decoded-odor accuracy by either cell group was pooled over all day pairs of a given distance and their mean value was computed and plotted. For odor-decoding over correct or error trials, SVM classifiers were trained with the correct trials (correct hits and rejections) out of the first 2/3 trials in a session and either the remaining 1/3 correct trials or all the error trials (false alarms and misses) were decoded using firing rates of only odor-cells or time-cells averaged over the entire odor-delay interval. Due to low number of errors, even in training stages, this resulted in a similar sample size for both decoded outcomes (881 correct versus 773 error trials decoded in total). To decode odors in correct versus error match-trials only, SVM classifiers were trained over all the correct hit trials in a session and either correct rejection or false alarm trials were used for decoding. The same number of correct rejection and false alarm trials were decoded in each session, by randomly downsampling the largest trial group to match the smallest (491 trials total). For SVM outcome-prediction, all trials were split into two groups based on their outcome (correct vs error). Classifiers were trained on 2/3 of error trials and an equal amount of correct trials, chosen randomly, and the outcome of all remaining trials was decoded using firing rates of only odor-cells or time-cells averaged over the entire odor-delay interval. Chance baselines were computed as before in all these cases.

Statistical Analysis

Unless otherwise stated, most statistical tests between distribution averages were performed under the Wilcoxon median test (‘WT’) if the corresponding distributions were not sufficiently close to normality under the Lillieform normality test (p > 0.05). Otherwise, a paired t-test was used. Significance of increasing or decreasing trends in distributions was assessed by a permutation distribution test of the Spearman rank correlation (‘SPT’) between x- and y-axis. P-values were corrected for multiple comparisons wherever necessary. ‘FDR’ across the text refers to FDR-correction. Statistical tests are mentioned in the main text or figure legends. No statistical methods were used to determine sample sizes but those were comparable to sizes used in similar publications.

Materials Availability

All unique resources generated in this study are available from the Lead Contact with a completed Materials Transfer Agreement.

EXPERIMENTAL MODEL AND SUBJECT DETAILS Animals

A total of 23 adult male mice (11–31 weeks old) were used for in vivo two-photon calcium imaging experiments: 7 Gad2-Cre:Ai9 mice (Gad2 tm2(cre)Zjh /J crossed with B6.Cg-Gt(ROSA)26Sor tm9(CAG-tdTomato)Hze /J), 14 Gad2-Cre:Ai14 (Gad2 tm2(cre)Zjh /J crossed with B6.Cg-Gt(ROSA)26Sor tm14(CAG-tdTomato)Hze /J) and 2 Gad2-Cre mice. An additional 10 adult male mice (13–25 weeks) were used for behavioral control experiments: 2 Gad2-Cre:Ai32 (Gad2 tm2(cre)Zjh /J crossed with B6;129S-Gt(ROSA)26Sor tm32(CAG-COP4*H134R/EYFP)Hze /J), 2 Gad2-Cre:Ai9 and 6 C57B/6J. All animals were experimentally naïve. All animals were acquired from The Jackson Laboratory and were group housed (2–5 per cage) on a 12 h light/dark cycle. All experimental protocols were approved by the Chancellor’s Animal Research Committee of the University of California, Los Angeles, in accordance with NIH guidelines.

METHOD DETAILS Surgical Procedures

Mice were anaesthetized with isoflurane (3–5% induction, 1.5% maintenance), their scalp was shaved and they were placed into a stereotactic frame (David Kopf Instruments, Tujunga, CA) on a feedback-controlled heating pad (Harvard Apparatus) to maintain body temperature at 37 o C. Eyes were protected from desiccation using artificial tear ointment. The scalp was sterilized with betadine and infiltrated with lidocaine (2%; Akorn, Lake Forest, Illinois) as a local anesthetic. An incision was made to expose the skull from bregma to lambda. Fascia was removed by applying hydrogen peroxide and the skull was stereotactically aligned. A small burr hole was made on the right hemisphere (−2mm posterior and 1.8 mm lateral to bregma), using a dental drill. Using a Nanoject II microinjector (Drummond Scientific), 1500 nl of 1:10 saline-diluted AAV1.Syn.GCaMP6f.WPRE.SV40 virus (diluted immediately prior to surgery; titre: 4.65 × 1013 GC/mL; Penn Vector Core) were injected into dorsal CA1 (1.3 mm ventral from dura) at a 50–100 nl per minute. 60 minutes after the termination of viral injection, a circular craniotomy (3mm diameter) was made around the injection site. Dura over the exposed brain surface was removed and the cortical tissue above the dorsal CA1 was carefully aspirated using a 27 gauge blunt needle. Buffered artificial cerebrospinal fluid (7.888g NaCl, 0.372g KCl, 1.192g HEPES, 0.264g CaCl2, 0.204g MgCl2 per 1000 ml milipore water) was constantly applied throughout the aspiration to prevent desiccation of the tissue. The aspiration ceased after partial removal of the corpus callosum and bleeding terminated, at which point a 3-mm titanium ring with a glass coverslip attached to its bottom was implanted into the aspirated area and its circular flange was secured to the skull surface using vetbond (3M). A custom-made lightweight metal head holder (headbar) was attached to the skull posterior to the implant. Cyanoacrylate glue and black dental cement (Ortho-Jet, Lang Dental) were used to seal and cover the exposed skull. Mice that were used for behavioral experiments were only implanted with a headbar and were not given a craniotomy or aspiration. During recovery (~7 days) mice were administered carprofen (5 mg per kg of body weight) for 3 days as a systemic analgesic and amoxicillin antibiotic (0.25 mg ml −1 in drinking water) through the water supply for 5 days. Their weight was monitored daily.

Experimental Setup

The treadmill consisted of an 8-inch Styrofoam ball (Graham Sweet) suspended through a metal axis, allowing for 1D rotation. It was housed within a dark enclosure. Mice were headfixed on the treadmill by attaching the implanted headbar to a custom-made metal holder. Locomotion was recorded as an independent analog signal, using a custom printed circuit board based on a high sensitivity gaming mouse sensor (Avago ADNS-9500), connected to a microcontroller (Atmel Atmega328) sending data to a dual channel digital-to-analog converter at 100 Sps. A constant stream of clear air (~1 L/min) was transferred to the behavioral rig via Tygon PVC clear tubing and was supplied to the mouse through a custom-made lickport. During odor stimulation a dual synchronous 3-way valve (NResearch), placed ~6 cm away from the mouse, would switch from the clear air stream to the odorized one for 1 sec. Odorized air was created using a 4-ports olfactometer (Rev. 7c; Biology Electronics, Caltech), supplying air to either of two glass vials containing liquid isoamyl acetate (70% isoamyl acetate basis, FCC; Sigma Aldrich) or pinene ((−)-α-Pinene, ≥97%, FCC; Sigma Aldrich) odorants, diluted in mineral oil at 5% concentration (unless stated otherwise). For the non-match-to-long-duration-sample task (NMLS) experiments, all 4 ports were connected to odor vials (see below). Odorized air from each vial would reach the behavioral rig through separate Tygon tubing each leading to a 3-way solenoid valve (Lee Company). For olfactory stimulation, the corresponding olfactometer port started supplying air to its attached odor vial 1 sec prior to actual stimulation to allow for odorized air to travel through the tubing and reach its corresponding solenoid valve. During the 1 sec stimulation, that solenoid turned on allowing the corresponding odorized air to enter a short common path for both odorants (~4cm) ending at the dual synchronous 3-way valve and from there being released to the mouse through the lickport (~10 cm total common path for the two types of odorized air). At the offset of the stimulus, the solenoid turned off and the dual 3-way valve switched the airstream back to clear air, ensuring a constant flow of air to the mouse and a quick clearing of the odorant from the air around the mouse. The odorized and clean air were set to similar airflow values (~1 lt/min), measured with a flowmeter (AWM3300; Honeywell). Odorant concentration in open air was measured through a mini photoionization detector (PID; 200B mini PID; Aurora Scientific), located at the distance of the mouse snout from the air-delivering lickport. Lower ionization threshold for pinene (odor-B) lead to higher deflections in the PID signal during its presentation compared to isoamyl acetate (odor-A). Therefore, potential odor lingering in the small common tube path, during the delay, and mixing with the second odor would result in a larger PID deflection during the second odor in B-A trials (lingering pinene molecules mixing with isoamyl acetate) than in A-A trials (lingering isoamyl acetate mixing with itself). This was observed when creating a very long common path length, but not in our default set up ( Figure S1 ), indicating that no odor mixing is created during the second odor. Licking was detected using a battery operated, custom-made, printed circuit board operating as a lickometer ( Slotnick, 2009 ). One end of the circuit was attached to the metal headbar on the mouse and the other to the metal tube delivering water. Whenever the mouse would lick the tube, an electrical circuit would close, creating a voltage drop that was recorded as a continuous RSE analog signal. Water droplets (~10 μl) were released by a 3-way solenoid valve (Lee Company) and delivered to the mouse through a metal tube on the lickport. At the end of each trial, after the response window, vacuum was applied for 3 sec, through a metal tube underneath the water delivering tube, to clear any lingering water and assist in removing any odorized air. The behavioral rig was controlled with custom written software (Matlab) and through a data acquisition board (USB-6341; National Instruments).

Behavioral training and recording protocol

After the recovery period, mice were briefly handled for two days and water restriction was initiated. They were provided with 1–1.5ml of water daily, either throughout or post training. Their weight was maintained at around 85% of their final weight pre-water restriction initiation. Mice were initially habituated to head fixation on the spherical treadmill for 3 consecutive days. On the second day, the lickport was placed in its normal position for habituation to the metal ports and air flow. Mice were given water drops through the lickport on the third day of habituation. DNMS training consisted of 3–4 days of ‘shaping-stage’ ( Liu et al., 2014 ) where mice were presented with trial-blocks (20 trials each) of non-match trials and the water reward was delivered automatically at the start of the response window in each trial. This was followed by a ‘pre-training’ stage for 2–4 days where mice were presented with non-match trials but had to actively lick the lickport during the response window to release the water drop. They were moved to the final training stage once they performed at >85% hit trials per day. In the ‘training stage’, mice were presented with the full task in a series of trial-blocks per day. Each block again consisted of 20 trials and match trials were intermingled with non-match ones with each group of 4 consecutive trials containing all possible 4 combinations of odors in random order. Mice were not punished if they licked at match trials but no reward was presented. Within ~2–5 days they learned to refrain from licking during match-trials. During training, mice gradually refrained from licking during the delay period as well, and well-trained mice would typically initiate licking during or right after the second odor. Responses were assessed based on licking during the response window only. If any licking occurred during that window, the trial was a hit (non-match trials) or false alarm (match trials) accordingly. In the opposite case, they were labeled as miss or correct rejection respectively. Performance over each trial-block, was quantified as the percentage of hits and correct rejections over the 20 trials. Daily performance was the corresponding ratio over the total number of trials (all blocks) during that day. Mice were typically presented with 5–7 blocks daily. Two-photon imaging sessions for mice under training started as soon as they were ready to move from the ‘pre-training’ stage to ‘training stage’ (N = 9 mice), at ~2–3 weeks post-surgery. 3 mice were first imaged on their first day in ‘training stage’ and 6 mice were first imaged for 2 days while performing ‘pre-training’ trial-blocks (non-match trials only). 3 of them received 3 ‘pre-training’ blocks followed by 3 training blocks on their second day of imaging. The other 3 received 5 pre-training trial-blocks on their second imaging day and were moved to training blocks on their third day of imaging. Finally, 2 mice were imaged only after they had received 5 days of training stage (not having reached well-trained level yet) and they were only imaged at training stage. Some mice (N = 6) were imaged while naive to the task. Only the 3 days of habituation were applied (without any water droplets delivered on day 3 of habituation) before onset of calcium imaging, upon which they were presented with normal DNMS trials for 3 or 6 consecutive days (N = 3 in each case), same as with trained mice. 5/6 naive mice never licked the lickports for a reward. One naive mouse kept licking the tubes and received a series of water rewards, but only early sessions (3 days) where the mouse had not grasped the task were included here. These animals were imaged 2–3 weeks post-surgery to match the time the trained animals were imaged and to allow for GCamp6f to be expressed. Behavioral control experiments Non-match-to-long-duration-sample task training (NMLS; N = 3 mice) consisted of the same training steps as DNMS, but the first odor was presented throughout a 5-sec period and was immediately followed by the second odor. Varying odorant concentration vials were used only for the first odor whereas the second odor was delivered by different olfactometer ports connected with 5% odorant vials. For experiments with odors turned off, the olfactometer ports were deactivated, halting any flow of odorized air, but trials were otherwise normal. For experiments with variable airflow, the airflow for both odor-ports on the olfactometer was altered randomly for each port at the beginning of each trial-block, between ~0.4 −1 lt/min, measured by the flowmeter. In vivo two-photon imaging A resonant scanning two-photon microscope (Scientifica) was used for calcium imaging, recording 512×512 pixel frames at 30.9 Hz, with a 16× 0.80 NA objective (Nikon) yielding a 500×500 μm field of view. Excitation light was delivered with a Ti:sapphire excitation laser (Chameleon Ultra II, Coherent), operated at 920 nm. GCamp6f and Td-Tomato fluorescence were recorded with green and red channel gallium arsenide photomultiplier tubes respectively (GaAsP PMTs; Hamamatsu). Microscope control and image acquisition were performed using LabView-based software (SciScan). Imaging and behavioral data were synchronized by recording TTL pulses, generated at the onset of each imaging frame, as well as olfactory stimulation digital signals at 1 kHz, using the WinEDR software (Strathclyde Electrophysiology Software). A single fixed field of view was imaged every day for each mouse. To ensure that the same neurons were tracked every day, each day’s field of view was overlaid to that of the first day of imaging for each mouse. The reference and live fields of view were aligned using the recording software before recording was initiated. This fixed template reference avoided deviations from the correct z-plane or any slow drifts across days. Moreover, a combination of rotating stages was used to allow tilting of the objective over two degrees of freedom as well as adjusting the mouse head-angle, to fine-tune the alignment of the live field of view to its reference template. Imaging sessions were performed separately for each trial-block, allowing laser power and PMT adjustments in between to ensure optimal imaging conditions and signal-to-noise ratio as well as fine-tuning of the field of view position during the recording session. Time breaks of ~1min were given between trial-blocks to prevent brain heating or photo-toxicity. Laser power was kept to a minimum and no photo-bleaching was observed.

Surgical Procedures

Mice were anaesthetized with isoflurane (3–5% induction, 1.5% maintenance), their scalp was shaved and they were placed into a stereotactic frame (David Kopf Instruments, Tujunga, CA) on a feedback-controlled heating pad (Harvard Apparatus) to maintain body temperature at 37 o C. Eyes were protected from desiccation using artificial tear ointment. The scalp was sterilized with betadine and infiltrated with lidocaine (2%; Akorn, Lake Forest, Illinois) as a local anesthetic. An incision was made to expose the skull from bregma to lambda. Fascia was removed by applying hydrogen peroxide and the skull was stereotactically aligned. A small burr hole was made on the right hemisphere (−2mm posterior and 1.8 mm lateral to bregma), using a dental drill. Using a Nanoject II microinjector (Drummond Scientific), 1500 nl of 1:10 saline-diluted AAV1.Syn.GCaMP6f.WPRE.SV40 virus (diluted immediately prior to surgery; titre: 4.65 × 1013 GC/mL; Penn Vector Core) were injected into dorsal CA1 (1.3 mm ventral from dura) at a 50–100 nl per minute. 60 minutes after the termination of viral injection, a circular craniotomy (3mm diameter) was made around the injection site. Dura over the exposed brain surface was removed and the cortical tissue above the dorsal CA1 was carefully aspirated using a 27 gauge blunt needle. Buffered artificial cerebrospinal fluid (7.888g NaCl, 0.372g KCl, 1.192g HEPES, 0.264g CaCl2, 0.204g MgCl2 per 1000 ml milipore water) was constantly applied throughout the aspiration to prevent desiccation of the tissue. The aspiration ceased after partial removal of the corpus callosum and bleeding terminated, at which point a 3-mm titanium ring with a glass coverslip attached to its bottom was implanted into the aspirated area and its circular flange was secured to the skull surface using vetbond (3M). A custom-made lightweight metal head holder (headbar) was attached to the skull posterior to the implant. Cyanoacrylate glue and black dental cement (Ortho-Jet, Lang Dental) were used to seal and cover the exposed skull. Mice that were used for behavioral experiments were only implanted with a headbar and were not given a craniotomy or aspiration. During recovery (~7 days) mice were administered carprofen (5 mg per kg of body weight) for 3 days as a systemic analgesic and amoxicillin antibiotic (0.25 mg ml −1 in drinking water) through the water supply for 5 days. Their weight was monitored daily.

Experimental Setup

The treadmill consisted of an 8-inch Styrofoam ball (Graham Sweet) suspended through a metal axis, allowing for 1D rotation. It was housed within a dark enclosure. Mice were headfixed on the treadmill by attaching the implanted headbar to a custom-made metal holder. Locomotion was recorded as an independent analog signal, using a custom printed circuit board based on a high sensitivity gaming mouse sensor (Avago ADNS-9500), connected to a microcontroller (Atmel Atmega328) sending data to a dual channel digital-to-analog converter at 100 Sps. A constant stream of clear air (~1 L/min) was transferred to the behavioral rig via Tygon PVC clear tubing and was supplied to the mouse through a custom-made lickport. During odor stimulation a dual synchronous 3-way valve (NResearch), placed ~6 cm away from the mouse, would switch from the clear air stream to the odorized one for 1 sec. Odorized air was created using a 4-ports olfactometer (Rev. 7c; Biology Electronics, Caltech), supplying air to either of two glass vials containing liquid isoamyl acetate (70% isoamyl acetate basis, FCC; Sigma Aldrich) or pinene ((−)-α-Pinene, ≥97%, FCC; Sigma Aldrich) odorants, diluted in mineral oil at 5% concentration (unless stated otherwise). For the non-match-to-long-duration-sample task (NMLS) experiments, all 4 ports were connected to odor vials (see below). Odorized air from each vial would reach the behavioral rig through separate Tygon tubing each leading to a 3-way solenoid valve (Lee Company). For olfactory stimulation, the corresponding olfactometer port started supplying air to its attached odor vial 1 sec prior to actual stimulation to allow for odorized air to travel through the tubing and reach its corresponding solenoid valve. During the 1 sec stimulation, that solenoid turned on allowing the corresponding odorized air to enter a short common path for both odorants (~4cm) ending at the dual synchronous 3-way valve and from there being released to the mouse through the lickport (~10 cm total common path for the two types of odorized air). At the offset of the stimulus, the solenoid turned off and the dual 3-way valve switched the airstream back to clear air, ensuring a constant flow of air to the mouse and a quick clearing of the odorant from the air around the mouse. The odorized and clean air were set to similar airflow values (~1 lt/min), measured with a flowmeter (AWM3300; Honeywell). Odorant concentration in open air was measured through a mini photoionization detector (PID; 200B mini PID; Aurora Scientific), located at the distance of the mouse snout from the air-delivering lickport. Lower ionization threshold for pinene (odor-B) lead to higher deflections in the PID signal during its presentation compared to isoamyl acetate (odor-A). Therefore, potential odor lingering in the small common tube path, during the delay, and mixing with the second odor would result in a larger PID deflection during the second odor in B-A trials (lingering pinene molecules mixing with isoamyl acetate) than in A-A trials (lingering isoamyl acetate mixing with itself). This was observed when creating a very long common path length, but not in our default set up ( Figure S1 ), indicating that no odor mixing is created during the second odor. Licking was detected using a battery operated, custom-made, printed circuit board operating as a lickometer ( Slotnick, 2009 ). One end of the circuit was attached to the metal headbar on the mouse and the other to the metal tube delivering water. Whenever the mouse would lick the tube, an electrical circuit would close, creating a voltage drop that was recorded as a continuous RSE analog signal. Water droplets (~10 μl) were released by a 3-way solenoid valve (Lee Company) and delivered to the mouse through a metal tube on the lickport. At the end of each trial, after the response window, vacuum was applied for 3 sec, through a metal tube underneath the water delivering tube, to clear any lingering water and assist in removing any odorized air. The behavioral rig was controlled with custom written software (Matlab) and through a data acquisition board (USB-6341; National Instruments).

Behavioral training and recording protocol

After the recovery period, mice were briefly handled for two days and water restriction was initiated. They were provided with 1–1.5ml of water daily, either throughout or post training. Their weight was maintained at around 85% of their final weight pre-water restriction initiation. Mice were initially habituated to head fixation on the spherical treadmill for 3 consecutive days. On the second day, the lickport was placed in its normal position for habituation to the metal ports and air flow. Mice were given water drops through the lickport on the third day of habituation. DNMS training consisted of 3–4 days of ‘shaping-stage’ ( Liu et al., 2014 ) where mice were presented with trial-blocks (20 trials each) of non-match trials and the water reward was delivered automatically at the start of the response window in each trial. This was followed by a ‘pre-training’ stage for 2–4 days where mice were presented with non-match trials but had to actively lick the lickport during the response window to release the water drop. They were moved to the final training stage once they performed at >85% hit trials per day. In the ‘training stage’, mice were presented with the full task in a series of trial-blocks per day. Each block again consisted of 20 trials and match trials were intermingled with non-match ones with each group of 4 consecutive trials containing all possible 4 combinations of odors in random order. Mice were not punished if they licked at match trials but no reward was presented. Within ~2–5 days they learned to refrain from licking during match-trials. During training, mice gradually refrained from licking during the delay period as well, and well-trained mice would typically initiate licking during or right after the second odor. Responses were assessed based on licking during the response window only. If any licking occurred during that window, the trial was a hit (non-match trials) or false alarm (match trials) accordingly. In the opposite case, they were labeled as miss or correct rejection respectively. Performance over each trial-block, was quantified as the percentage of hits and correct rejections over the 20 trials. Daily performance was the corresponding ratio over the total number of trials (all blocks) during that day. Mice were typically presented with 5–7 blocks daily. Two-photon imaging sessions for mice under training started as soon as they were ready to move from the ‘pre-training’ stage to ‘training stage’ (N = 9 mice), at ~2–3 weeks post-surgery. 3 mice were first imaged on their first day in ‘training stage’ and 6 mice were first imaged for 2 days while performing ‘pre-training’ trial-blocks (non-match trials only). 3 of them received 3 ‘pre-training’ blocks followed by 3 training blocks on their second day of imaging. The other 3 received 5 pre-training trial-blocks on their second imaging day and were moved to training blocks on their third day of imaging. Finally, 2 mice were imaged only after they had received 5 days of training stage (not having reached well-trained level yet) and they were only imaged at training stage. Some mice (N = 6) were imaged while naive to the task. Only the 3 days of habituation were applied (without any water droplets delivered on day 3 of habituation) before onset of calcium imaging, upon which they were presented with normal DNMS trials for 3 or 6 consecutive days (N = 3 in each case), same as with trained mice. 5/6 naive mice never licked the lickports for a reward. One naive mouse kept licking the tubes and received a series of water rewards, but only early sessions (3 days) where the mouse had not grasped the task were included here. These animals were imaged 2–3 weeks post-surgery to match the time the trained animals were imaged and to allow for GCamp6f to be expressed.

Supplementary Material 2