Rapid formation of non-spatial hippocampal representations consistent with behavioral timescale synaptic plasticity is modulated by entorhinal input

Domain: neuroscience
Published: 2026-04-10
Editorial outcome: major_revision

Abstract
Behavioral timescale synaptic plasticity (BTSP) is a form of synaptic potentiation where a single plateau potential in hippocampal neurons forms a place field during spatial learning. However, it remains unknown whether BTSP also forms non-spatial responses and what roles the medial and lateral entorhinal cortex (MEC and LEC) play in driving non-spatial BTSP. Using two-photon calcium imaging of CA1 pyramidal neurons in mice learning an odor-cued working memory task, we observed spontaneously-occurring large plateau-like calcium events during odor cues, forming stable odor representations. Using holographic optogenetics, we induced similar plateau-like calcium events in single neurons that were followed by novel odor representations. Chemogenetic inhibition of MEC reduced the frequency of plateau-like events, whereas LEC inhibition reduced their efficiency in forming odor representations. Together, our findings demonstrate that rare large somatic calcium events, consistent with BTSP, precede and drive novel odor representations in a manner differentially regulated by medial and lateral entorhinal cortex.

Introduction
In many situations, learning is not a gradual process. In fact, our ability to make associations after a single experience is critical for survival. In the hippocampus, a region recognized for its importance in learning and memory, behavioral timescale synaptic plasticity (BTSP) has emerged as a robust mechanism for the rapid generation of spatial representations (place fields) following the occurrence of plateau potentials1–11. However, the hippocampus not only represents the location of animals in space12–15, but also non-spatial sensory information16–19. The hippocampus dynamically links these sensory experiences across time through sequential firing that tracks the passage of time after specific events14,19–22. It is unclear whether BTSP also plays a role in the formation of non-spatial sensory-driven or internally generated hippocampal representations.

Hebbian plasticity mechanisms such as spike-timing dependent plasticity require causality and many repetitions to potentiate synapses when presynaptic spikes precede postsynaptic action potentials by a few milliseconds23–26. While this mechanism may play a role in modulating hippocampal responses, BTSP has many features which could make it a more robust and rapid mechanism for the generation of non-spatial receptive fields. During spatial learning tasks, a single calcium plateau potential can serve as the induction event, asymmetrically boosting synaptic inputs that occur up to several seconds before the induction event, leading to a membrane potential ramp and reliable spatial firing on subsequent trials2,5,6. It is not known whether plateau potentials occurring during non-spatial tasks could also boost synaptic inputs at specific timepoints in the task leading to the rapid formation of stable representations of sensory stimuli and time. The rapid induction of these non-spatial hippocampal representations by BTSP could form the basis for rapid learning in the hippocampus.

The role of the entorhinal cortex (EC) in inducing BTSP events7 and relaying sensory information during non-spatial tasks18,27,28 is poorly understood. CA1 receives direct layer III EC input via the temporammonic (TA) pathway and indirect input via the perforant path from layer II EC to dentate gyrus (DG). DG then projects to CA3, which in turn projects to CA129,30. Lateral and medial EC (LEC and MEC) have distinct inputs and behaviorally relevant response properties: LEC robustly represents olfactory information31–34, while MEC mainly encodes visuo-spatial information35–38. Furthermore, the MEC plays a major role in the induction of plateau potential ‘teaching signals’ during BTSP induced during spatial learning tasks7. Yet, whether MEC and LEC play distinct roles in the generation of BTSP during non-spatial tasks remains to be determined.

To address these questions, we investigated multimodal representations within CA1 and EC during a non-spatial olfactory delayed non-match-to-sample (DNMS) working memory task19. We have previously shown that CA1 pyramidal neurons fire sequentially in response to specific odors and across the 5-second delay period during DNMS performance19. We hypothesized that non-spatial BTSP can generate odor representations in CA1 and that this process would be modulated by MEC and LEC inputs. Using two-photon calcium imaging of dorsal CA1, we recorded non-spatial ‘BTSP-like’ events; large somatic calcium ‘plateau-like’ events that preceded formation of odor-specific fields in CA1 during expert performance of the DNMS task. Combining this approach with holographic optogenetic stimulation, we successfully generated odor-specific representations in individual cells following a single stimulation. Recording dorsal CA1 across learning of the task revealed higher rates of BTSP-like events during the first day of learning compared to all subsequent days. In addition, we found that MEC chemogenetic inhibition decreased the frequency of large calcium induction events in CA1, while LEC inhibition reduced the success rate of odor-field generation. Finally, two-photon calcium imaging of temporammonic EC axonal projections to CA1 demonstrated that MEC axonal strong firing to odor presentations likely plays a role in driving large plateau-like calcium events in CA1, while LEC axonal odor-selectivity mediates plasticity in the formation of odor-fields after the large calcium induction event. Therefore, non-spatial ‘BTSP-like’ events form odor-fields during working memory performance and are differentially modulated by lateral and medial entorhinal cortex inhibition.

Results
We used in vivo two-photon calcium imaging to record the activity of neurons in the pyramidal layer of dorsal CA1 while animals performed an olfactory delayed non-match-to-sample (DNMS) working memory task (Fig. 1a-e). Adult male and female mice (n=17) were injected with AAV1-Syn-jGCaMP8f into the right dorsal CA1 and implanted with a 3mm diameter glass-bottomed titanium cannula above the intact alveus after aspiration of the overlying cortex and corpus callosum19 (Fig. 1d-e). After one week of recovery, mice were water-restricted and trained on the olfactory DNMS working memory task19,39, while head-fixed on a spherical treadmill (Fig. 1a-b). Each trial consisted of two 1-second odor presentations separated by a 5-second delay period. One second after the offset of the 2nd odor, there was a 3-second reward period during which the choice of the animal was determined. Mice were trained to lick the lickport to release water during this reward period if the two odors did not match (correct ‘hit’). Mice learned to refrain from licking the lickport if the odors matched (correct ‘rejection’), and overall performance was quantified as the percentage of correct ‘hits’ and correct ‘rejections’ out of all trials. We considered performance above 85% to be expert level. Each session of the DNMS task consisted of 5 blocks of 20 trials, with pseudorandomly distributed odor combinations (Fig 1c). Mice were recorded for 8 days during expert performance for a total of 136 recording sessions yielding an average of 312 ± 125 (mean ± standard deviation) active neurons per day. We successfully imaged the same field of view (FOV) for each of the 8 days for all animals. Calcium signals were extracted and deconvolved using Suite2p40 (see methods).

Non-spatial BTSP-like events in CA1 formed stable odor-specific fields

In our previous work, we found that a population of CA1 hippocampal neurons fired in an odor-specific manner during specific epochs of the DNMS task; some fired during the presentation of specific odors (‘odor fields’) and others at timepoints during the delay period (‘time fields’) after presentation of specific odors19. Here, we observed CA1 neurons with activity patterns consistent with BTSP during expert DNMS performance (Fig. 1f-g and Supp. Fig. S1 and S2). Namely, neurons without a clear odor or time field developed a stable field after a single spontaneous large calcium event.

A single somatic calcium ‘reference event’ was considered an induction event if it preceded the formation of an odor-field. Using a set of strict criteria, we labeled every calcium event as succeeding or failing to form an odor-field (see methods). Larger ‘reference event’ amplitudes coincided with higher success rates of odor-field formation (Fig. 1h), indicating that these induction events preceded the formation of odor-fields. Events with amplitudes > 10 standard deviations (STD) of the mean deconvolved signal yielded a significantly asymmetrical field-formation, with the new odor-field occurring earlier in trial-time on subsequent trials compared to the reference event (Fig. 1i); similarly to spatial BTSP in previous studies1–11. We thus considered a reference calcium event as ‘plateau-like’ when its amplitude was > 10 STD.

Plateau-like events were rare (122,605 out of 16,215,467 total events recorded = 0.76%) and matched the shapes and amplitudes of previously described somatic calcium plateau events3,11 (Supp. Fig. S3a-d). Approximately 8% of plateau like events that occurred during the odor cue or within the ensuing 0.5 seconds preceded a subsequent odor-field formation (294 ‘successes’ out of 3876 total events). This increased to ~15% during the second half of the odor presentation cue (Fig. 1j). Only 26 ‘plateau-like’ events beyond 0.5 seconds after odor offset preceded a subsequent time-field formation (8% of the 320 successful events). The odor-fields preceded by plateau-like events peaked 0.42 ± 0.14 seconds earlier in trial-time compared to the reference event (Fig. 1k). In contrast, only 0.28% of ‘small events’ (<10 STD amplitude) during the odor cue or the ensuing 0.5 seconds (254 out of 91,635 total events) preceded new odor fields (Supp. Fig. S5a), These events represent the noise floor of our detection criteria and the rare odor-field formation that formed without a preceding ‘plateau-like’ event. These rare fields were symmetrically distributed around the reference event timepoint with increased variance (Supp. Fig. S5b). We thus label ‘plateau-like’ events preceding an odor-field as ‘BTSP-like’.

We observed no difference in success rates or asymmetrical field-formation between first and second odor (Supp. Fig. S4). Therefore, all odor cues were pooled for further analysis.

Categorizing BTSP-like events into successes and failures restricted analysis to a narrowly defined type of plasticity event based on formation of significant odor responses. Some responses were reinforced by multiple ‘plateau-like’ events (Supp. Fig. S2). Therefore, we developed a simpler analysis to avoid artificially imposed criteria and sensitivity to binary thresholding. Here, we measure potentiation as the increase in firing rate across 10 trials after versus 10 trials before a reference calcium event (‘firing rate increase’). We observed an asymmetrical ramp of firing rate increase with a peak in subsequent trials 0.25 seconds earlier in trial-time than the reference event timepoint (Fig. 1l). No increase was observed when aligned to small events (Fig. 1l). While the firing rate increase was significant up to 3 seconds earlier and 1 second later in trial-time, we focused on the average increase in the 1-second period preceding the reference event. Similar to the previous binary success rate analysis, we found that a significant firing rate increase only occurred after events greater than 8 STD in amplitude (Fig. 1m). This increase was highly odor selective (Fig. 1m). The firing rate increase peaked during the second half of the odor presentation and remained significant up to halfway through the delay (Fig. 1n), suggesting that time-fields during the delay period may form after several successive plateau potentials. Results were similar for a range of trial numbers averaged before and after the reference event (Supp. Fig. S6a-d). Both analysis methods demonstrate that the formation of odor-fields occurred with non-spatial BTSP-like events that are characterized by an asymmetric firing rate increase ramp.

To determine if locomotion influenced non-spatial BTSP-like events, we recorded treadmill locomotion during the task. Mice exhibited a range of locomotion patterns while performing the task with some mice rarely moving on the treadmill, some primarily flinching or twitching at the onset of some odor presentations, and others with bouts of running throughout the trial. On average, minor jitter movements occurred at the onset of 20% of odors and running occurred in up to 5% of trials (Supp. Fig. S7a-b). On average, animals spent 90.4 ± 2.3 % of the time immobile on the treadmill, 7.0 ± 1.7 % of the time with minor jitter movements, and only 2.6 ± 1.8 % of the time running (Supp. Fig. S7c). As expected41–43, movement increased neural activity. Both minor jitter movements and running increased the rate of small events and ‘plateau-like’ events (Supp. Fig. S7d-e). However, neither of these types of movements during reference ‘plateau-like’ events influenced the success rate of field formation or firing rate increase (Supp. Fig. S7f-g). In total, out of the 323 detected successful events, 235 (73%) occurred during periods without movement, 70 (22%) occurred during minor jitter movements, and only 18 (6%) occurred during running bouts. Moreover, mice spent 13.9 ± 4.2 % of time licking (primarily during the reward period of non-match trials) (Supp. Fig S7h-j), which also increased the rate of ‘plateau-like’ events (Supp. Fig. S7k-l). Yet, the success rate and firing rate increase was lower for licking bouts (Supp. Fig. S7m-n). In total, 289 (89%) occurred during periods without licking, and only 34 (11%) occurred during licking bouts. Collectively, our findings reveal formation of odor-specific fields occurred with non-spatial BTSP-like events that were not correlated with locomotion or licking behavior.

Single-neuron holographical optogenetic stimulation induced odor-fields

To confirm that large calcium events causally drive the formation of odor-specific fields, we performed simultaneous two-photon calcium imaging and single-neuron holographic optogenetic stimulation in three animals genetically expressing GCaMP (two mice GCaMP6s and one mouse GCaMP8s). Mice were injected with AAV8-CaMKIIa-ChRmine-mScarlet- Kv2.1-WPRE into the right dorsal CA1 to drive expression of the excitatory opsin ChrRmine in pyramidal neurons (Fig. 2a). To replicate the size and width of spontaneously recorded somatic calcium ‘plateau-like’ events (Supp. Fig. S3a-d), we stimulated individual CA1 neurons of well-trained mice for one second (10ms pulses at 10Hz) at various timepoints throughout the entire trial period from 1st odor presentation to middle of the reward period. After recording at least 4 baseline trials, individual stimulation points were delivered at least 6 seconds apart during task performance. Across multiple days and different FOVs with up to 72 stimulated neurons per day, we successfully induced ‘BTSP-like’ events yielding new stable odor-fields following a single stimulation (Fig. 2b-c). Induced event amplitudes varied in size (likely due to varying levels of ChRmine expression and/or excitability state of the neuron) (Fig. 2d, Supp. Fig. S3e-l), but all successful induction events were greater than 10 STD, validating our previously used criteria. Using these same criteria as above, we isolated stimulation events that were preceded by minimal activity and no significant field (111 out of 857 total stimulations across 16 recording sessions in 3 mice). Stimulation-induced ‘plateau-like’ events during the odor cue or ensuring 0.5 seconds had an 11% probability of odor-field formation (5 out of 44 events), similar to that of spontaneous plateau-like events (~8%). In contrast, stimulation-induced ‘plateau-like’ events outside of this time window (0 out of 33 events), or those with <10 STD amplitude (0 out of 34 stimulation attempts) were not followed by field formation. Importantly, the success rate of field formation and firing rate increase after stimulation (Fig. 2e-f) were similar to the spontaneous events we recorded in Figure 1. Therefore, large somatic plateau-like events causally drive the formation of odor-fields.

Highest rates of non-spatial BTSP occurred early in learning

Given the role of spatial BTSP in formation of place cells during learning of a novel spatial environment, we next asked if non-spatial BTSP-like events were also more frequent earlier in learning of non-spatial cues. To address this question, we repeated two-photon calcium imaging of pyramidal CA1 neurons in five mice during learning of the olfactory DNMS task. To include learning of the odor cues themselves, we introduced them only at the final training stage. Mice were first trained to lick at the appropriate reward window by providing only auditory clicks at the timepoints used for odor onset and offset. This allowed the mice to learn the temporal structure of the task without receiving any odors (Fig. 3a). Mice licked during the reward period for more than 90% of trials during these ‘odor-free’ shaping days. On the following ‘Day 1’, odors were delivered for the first time and mice continued to lick during most trials yielding chance-level performance on Days 1 and 2. By days 3 and 4, mice began refraining from licking on match trials (Fig. 3b) and gradually increased their performance to expert-level (85%) around Day 5 (Fig. 3c). Imaging was conducted on the last two days of ‘odor-free’ shaping (Days -2 and -1), and 9 days during the final stage of the olfactory DNMS task. The same FOV was used on each day for each animal.

Compared to baseline ‘odor-free’ shaping days, the ‘plateau-like’ event rate strongly increased in Day 1 and remained relatively stable across all subsequent days of learning, and with a weak correlation with performance (Fig. 3d, Supp. Table 1). However, the success rate of these ‘plateau-like’ events generating an odor-field peaked on Day 1 compared to the following days and was anti-correlated with performance (Fig. 3e). We used a linear regression model to isolate the relative predictive strength of days of training and behavioral performance on our recorded measures. We found that both were significantly predictive of success rate of ‘plateau-like’ events during the odor period (Days: t = -3.19, p = 0.003; Performance: t = -2.62, p = 0.012; Supp. Table 2). Analysis of the firing rate increase ramps (Fig. 3f) revealed a similar effect when compared across the days of learning for the same odor trials (Days: t = -1.85, p = 0.072; Performance: t = -2.16, p = 0.037; Fig. 3g, Supp. Table 3). Notably, the very small firing rate increase during the opposite odor trials remained stable through all days of baseline and learning trials (Days: t = -0.95, p = 0.35; Performance: t = -0.36, p = 0.72; Fig. 3h, Supp. Table 4), suggesting that these non-specific increases may be due to other factors such as inputs related to auditory clicks rather than odor-specific information. Smaller firing rate increases observed during the delay period also showed a strong negative correlation with performance for same odor trials (Days: t = -5.03, p = 1.0e-05; Performance: t = -4.71, p = 2.8e-05; Fig. 3i, Supp. Table 5). Importantly, minor jitter movements and running did not change significantly across learning (Supp. Fig. 8, Supp. Tables 8-11), thus they cannot account for differences observed across days. Therefore, despite a stable rate of ‘plateau-like’ events during learning, the rate of non-spatial BTSP-like events was highest early in learning of novel odor cues, likely due partially to both novelty and behavioral performance.

Chemogenetic inhibition of entorhinal cortex disrupted non-spatial BTSP

Entorhinal inputs can drive BTSP induction events during spatial learning tasks7,44. To determine whether entorhinal inputs may also play a role in the generation of ‘plateau-like’ events during non-spatial BTSP, we used chemogenetic inhibition of lateral entorhinal cortex (LEC) or medial entorhinal cortex (MEC), while imaging CA1 calcium activity during expert performance of the working memory task. Mice were injected with AAV1-Syn-jGCaMP8f in the dorsal CA1 and were subsequently implanted with an optical canula over CA1 as in the previous section. These mice also received an injection of AAV5-CaMKII-PSAM4 into either LEC (n=6 mice) or MEC (n=5 mice) to express the potent chemogenetic inhibitor PSAM445 in excitatory neurons of either structure (Fig. 4a). Control mice underwent injections of AAV5-CaMKII-mCherry into either LEC (n=3 mice) or MEC (n=3 mice). Animals were water-restricted, trained on the task, and imaged three weeks after viral expression. Each animal was recorded for eight days after reaching expert level performance. Between 10-20 minutes before two-photon calcium imaging began each day, mice received an intraperitoneal (IP) injection of saline or uPSEM (the effector molecule for PSAM4). Saline and uPSEM injections were alternated daily and animals were counter-balanced such that one half of the mice received injections of uPSEM on the first day and the other half received injections of saline. We compared the activity of matched neurons over four pairs of saline and uPSEM days during expert performance when BTSP-like event rates were stable.

Despite a lack of a behavioral effect (Supp. Fig. S9a), both LEC and MEC inhibition strongly affected the non-spatial ‘BTSP-like’ events during expert performance. MEC, but not LEC, inhibition significantly reduced the number of ‘plateau-like’ events from 1.91 ± 0.98 per neuron per day to 1.60 ± 1.12 per neuron per day (Fig. 4b). In contrast, LEC, but not MEC, dramatically reduced the success rate of ‘plateau-like’ events during odor presentations from 7.20 ± 3.61% to 3.18 ± 3.48% (Fig. 4c). Additionally, only LEC inhibition significantly decreased the firing rate increase ramps during the 1-second window preceding reference ‘plateau-like’ events on uPSEM days (Fig. 4d). For ‘plateau-like’ events during odor presentation of LEC PSAM animals, the firing rate increase was significantly lower on uPSEM days for same odor trials but significantly higher for opposite odor trials (Fig. 4e-f). Importantly, neither LEC nor MEC inhibition affected minor jitter movements, running, or licking, so these effects could not be explained by differences in animal movement (Supp. Fig. S9b-e). Together, these findings suggest that MEC plays a role in the large ‘plateau-like’ events in CA1, while LEC activity increases the likelihood that these events result in successful field generation. The differential effects of firing rate increase between same odor and opposite odor trials suggest that LEC inputs to the hippocampus are necessary for relaying odor-specific information.

LEC inhibition reduced strength of odor representations in dorsal CA1

Given that LEC inputs have been previously shown to encode odor-related information31–34, we hypothesized that they could convey odor-related information to CA1 in our DNMS task. If so, we would expect the inhibition of LEC but not MEC to decrease odor-selectivity in CA1, potentially driving the decrease in success rate of ‘plateau-like’ events in generating odor-fields. Indeed, LEC chemogenetic inhibition significantly decreased the percentage of significantly odor-selective neurons from 24.5 ± 9.6% on saline control days and only 16.2 ± 10.8% on uPSEM inhibition days (Fig. 4g), while having no effect on any of the other animal groups. To further confirm this effect, we performed binary support vector machine (SVM) decoding training and testing on the same day to evaluate the relative strength of odor encoding on saline days compared to uPSEM days subsampling only 100 neurons. Overall, LEC inhibition significantly decreased odor decoding accuracy, while MEC inhibition did not alter it (Fig. 4h). While the effect in LEC experimental PSAM4 animals also was observed during the first half of the delay period, the effect during the odor presentation period was the strongest with decoding accuracy at 76.4 ± 10.2% on saline control days and only 66.7 ± 14.3% on uPSEM inhibition days. Odor decoding of MEC experimental PSAM4 and control animals expressing mCherry was unaffected by uPSEM administration. Collectively, LEC inhibition strongly decreased odor-selectivity and decodability in pyramidal CA1 neurons, which may have played a role in the reduction in the success rate of ‘plateau-like’ events in generating odor-fields, though additional mechanisms could potentially contribute to this effect. Additionally, these results are limited to expert performance, so we are unable to determine if these manipulations would affect the previously found novelty and learning results.

Two-photon calcium imaging of temporammonic entorhinal cortical axons in dorsal CA1 revealed differential sequential activity in LEC and MEC inputs

The EC is the primary cortical input to the hippocampus; CA1 receives direct layer III EC input via the temporammonic (TA) pathway and indirect input via the perforant path from layer II EC to dentate gyrus, which then projects to CA3, which in turn projects to CA129,30. Given the contribution of MEC in driving ‘plateau-like’ events, we asked if there are differences in timing of LEC and MEC TA inputs. Also, given the strong differences in odor decodability observed in dorsal CA1 with LEC versus MEC inhibition, we asked whether TA inputs from LEC and MEC differ in the olfactory information they convey to CA1.

To address these questions, we performed two-photon calcium imaging of LEC or MEC TA axons in dorsal CA1 during expert performance of the DNMS task. Adult male and female mice were injected with AAV1-CaMKII-Cre and AAV1-CAG-FLEX-jGCaMP7s in either LEC (n=8) or MEC (n=8) (Fig. 5a,c). Mice were implanted with hippocampal windows as in the previous experiments. After three weeks of expression, there was extensive GCaMP7s axonal expression of TA inputs within the stratum lacunosum-moleculare (SLM) layer, as well as layer II EC perforant path axons ramifying deeper within the stratum moleculare (MOL) layer of the dentate gyrus (Fig. 5a,c). In vivo, we were able to selectively image TA EC axons 300-400 µm beneath the alveus. Post-hoc histology after two-photon imaging experiments also confirmed that all mice had extensive expression of GCaMP7s in axons within the SLM layer of hippocampus and somatic expression restricted to either LEC or MEC. Recordings were processed with Suite2p40 using parameters optimized for axonal imaging, followed by post-hoc fusion of axon segments with highly correlated activity which were branches of the same axon (see methods) (Fig. 5b,d). Only behavioral sessions with expert-performance, with no difference between the groups, were used for the following analysis (Supp. Fig. S10c).

A proportion of LEC and MEC TA axons responded reliably at specific timepoints with some having significant peaks during the odor presentation, some during odor offset, and others during the delay period (Fig. 5e-f). Altogether, the LEC or MEC axonal populations had sequential activity that tiled the entire first odor presentation and delay period. However, these sequences differed drastically between LEC and MEC (Fig. 5g and Supp. Fig. S10a-b). A much higher percentage of MEC axons had significant peaks during odor presentations and their offset compared to LEC axons (MEC with 28.0 ± 6.5% axons with activity peaking during odor presentations compared to only 9.5 ± 6.5% for LEC; MEC with 4.6 ± 2.2% axons with activity peaking during odor offset compared to only 2.7 ± 1.0% for LEC) (Fig. 5h). MEC axons with activity peaking during odor presentations also showed greater trial reliability compared to LEC (Supp. Fig. S10d). In contrast, a higher percentage of LEC axons had significant activity peaks during the delay period (LEC with 5.8 ± 1.3% axons with activity peaking during delay period compared to only 2.4 ± 0.7% for MEC). Given that MEC inhibition reduced the rate of ‘plateau-like’ events in CA1, we hypothesize that this strong MEC input timed to the odor presentation possibly important for driving ‘plateau-like’ events.

LEC temporammonic axonal activity encoded stronger odor-specific information than MEC axonal activity

Although a higher proportion of MEC axons were tuned to firing during the odor presentation, examination of sequential firing patterns suggested that these axons were firing with less odor-specificity (Fig. 5g and Supp. Fig. S10a-b). LEC axons with a significant peak during the odor presentation had larger odor-selectivity values than MEC (LEC with an average of 0.261 ± 0.068 compared to only 0.179 ± 0.033 for MEC) (Fig. 5h). Again, to confirm this difference in odor-selectivity, we performed binary support vector machine (SVM) decoding training and testing on the same day using only the most selective 10 axons from each recording session. First odor decoding accuracy was higher in LEC compared to MEC for both the odor/offset period and the remaining 4 seconds of the delay period (68.4 ± 5.6% for LEC and 62.3 ± 7.2% for MEC during odor/offset 0-2 second window; 61.5 ± 9.5% for LEC and 53.9 ± 3.3% for MEC during odor/offset 2-6 second window). Altogether, these findings suggest that LEC temporammonic axonal odor representations were stronger than MEC axonal odor representations, and this may possibly explain the decrease in success rate and firing rate increases with LEC inhibition, although strong odor information is also likely being relayed indirectly to the CA1 via the perforant path as well.

Methods
Animals

All of the experiments were conducted according to the National Institute of Health (NIH) guidelines and with the approval of the Chancellor’s Animal Research Committee of the University of California, Los Angeles. A total of 9 adult male and 8 female mice (8-16 weeks old, C57BL/6J The Jackson Laboratory 000664) were used for simultaneous in vivo calcium CA1 imaging and EC chemogenetic experiments (Figures 1 and 4). These mice were divided into 4 groups: LEC mCherry n=3, MEC mCherry n=3, LEC PSAM4 n=6, MEC PSAM4 n=5. A total of 3 adult female mice (25-35 weeks old) were used for simultaneous in vivo calcium CA1 imaging and holographic optogenetic stimulation experiments (Figure 2). These 3 positive offspring were chosen from a breeding of B6;DBA-Tg(tetO-GCaMP6s)2Niell/J (The Jackson Laboratory 024742) or B6;D2-Tg(tetO-GCaMP8s)1Genie/J (The Jackson Laboratory 037717) crossed with B6.Cg-Tg(Camk2a-tTA)1Mmay/DboJ (The Jackson Laboratory 007004). A total of 2 adult male and 3 female mice (8-16 weeks old, C57BL/6J The Jackson Laboratory 000664) were used for in vivo calcium CA1 imaging during learning experiments (Figure 3). A total of 7 adult male and 9 female mice (8-16 weeks old, C57BL/6J The Jackson Laboratory 000664) were used for in vivo calcium EC axon imaging experiments (Figure 5). These mice were divided into 2 groups: LEC n=8, MEC n=8. All were experimentally naïve and housed in the vivarium under a 12-hour light/dark cycle. All mice were group housed (2-4 per cage) except for 2 that had to be separated following surgery because of fighting.

Surgical Procedures

Animals were assigned groups before surgery pseudorandomly to also control for the covariates of sex and group housing. Mice were subcutaneously administered pre-operative drugs (carprofen 5 mg/kg, dexamethasone 0.2 mg/kg, lidocaine 5 mg/kg) 30 minutes before surgery. Mice were anaesthetized with isoflurane (5% induction, 1-2% for maintenance), and anesthesia was continuously monitored and adjusted as necessary. The scalp was shaved, and mice were placed into a stereotactic frame (David Kopf Instruments, Tujunga, CA) on a feedback-controlled heating pad (Harvard Apparatus) set to maintain body temperature at 37◦C. Eyes were protected from desiccation using artificial tear ointment. The surgical incision site was cleaned three times with 10% povidone-iodine and 70% ethanol. Fascia was removed by applying hydrogen peroxide, connective tissue was cleared from the skull, and the skull was scored to facilitate effective bonding with adhesives at the end of surgery. After stereotactically aligning the skull, a single or several burr holes were made depending on the experiment performed and virus was injected.

CA1 imaging with EC chemogenetics experiments: Control virus (500 nL of 1:5 saline dilution of pAAV1-CaMKIIa-mCherry into all 4 sites) or experimental virus (500 nL of 1:5 saline dilution of AAV5-CaMKII-PSAM4-GlyR-IRES-EGFP into all 4 sites) was injected into LEC (bilaterally 3.4 and 3.9 mm posterior, 4.35 mm lateral, and 4.3 ventral from bregma) or MEC (bilaterally 4.7 mm posterior, 3.35 mm lateral, and 3.8 and 3.0 mm ventral from bregma). Additionally, pGP-AAV1-syn-jGCaMP8f-WPRE (1000nL of 1:5 saline dilution) was injected into the right dorsal CA1 (2.0 mm posterior from bregma,1.8 lateral from bregma, and 1.3 ventral from dura).

CA1 imaging with holographic optogenetics experiments: AAV8-CaMKIIa-ChRmine-mScarletKv2.1-WPRE (1000nL of 1:10 saline dilution) was injected into the right dorsal CA1 (same coordinates as above).

CA1 imaging during learning experiments: pGP-AAV1-syn-jGCaMP8f-WPRE (1000nL of 1:5 saline dilution) was injected into the right dorsal CA1 (same coordinates as above).

EC axon imaging experiments: pENN.AAV1.CaMKII.0.4.Cre.SV40 and pGP-AAV1-CAG-FLEX-jGCaMP7f-WPRE were mixed immediately before the injection (500 nL of 1:1 mix) into right LEC (3.5 mm posterior, 4.35 mm lateral, and 4.3 ventral from bregma) or right MEC (4.7 mm posterior, 3.35 mm lateral, and 3.5 mm ventral from bregma). All viruses were injected using a Nanoject II microinjector (Drummond Scientific) at 60nL per minute.

For mice in all experiments, following virus injection, a circular craniotomy (3 mm diameter) was made centered around a point made 2.0 mm posterior and 1.8 lateral to bregma. Dura beneath the craniotomy was removed and cortical tissue above dorsal CA1 was carefully aspirated using a 27-gauge blunt needle. Corpus callosum was spread to the sides of the craniotomy to expose the alveus. Cortex buffer (NaCl = 7.88g/L, KCl = 0.372g/L, HEPES = 1.192g/L, CaCl = 0.264g/L, MgCl = 0.204g/L, at a pH of 7.4) was continuously flushed during aspiration and until bleeding stopped. A titanium ring with a 3 mm diameter circular thin #0 coverglass attached to its bottom was implanted into the aspirated craniotomy and the overhanging flange was secured to the skull with vetbond (3M). A custom-made lightweight stainless-steel headbar was attached to posterior skull and secured with cyanoacrylate glue. Dental cement (Ortho-Jet, Lang Dental) was applied to seal and cover any remaining skull, and to form a small well around the titanium ring for holding immersion water for the objective during imaging. Following surgery, all animals were given post-operative care (carprofen 5 mg/kg and dexamethasone 0.2 mg/kg for 48 hours after surgery) and provided amoxicillin-treated water at 0.5 mg/mL for 7 days. All mice recovered for 7-14 days before experiments began.

Experimental setup

The entire behavioral setup is as described in Taxidis et al.19. Mice were head-fixed above an 8-inch spherical Styrofoam ball (Graham Sweet) which can rotate about one axis for 1D locomotion that was recorded with a sensor (Avago ADNS-9500). A continuous stream of clean air (∼1 L/min) was delivered toward the animal’s nose via Tygon PVC clear tubing and a custom-made port that held the air tube and water port. At the onset of the odor presentation period, a dual synchronous 3-way valve (NResearch) switched to the odorized one for 1 second. Odorized air was created by using a 4-ports olfactometer (Rev. 7c; Biology Electronics, Caltech) supplying air to either of two glass vials containing odor A (70% isoamyl acetete basis, FCC; Sigma Aldrich) or odor B ((-)-a-Pinene ≥ 97%, FCC; Sigma Aldrich), which were both diluted in mineral oil at 5% concentration. Water droplets (∼10µl) were released by a 3-way solenoid valve (Lee Company), and licks were detected by using a custom battery-operated circuit board with one end of the circuit connected to the headbar and the other to the lickport. The behavioral rig was controlled with custom written software (MATLAB) and through a data acquisition board (USB-6341: National Instruments).

Behavioral training

After 7-14 days recovering from surgery, mice were handled and began water-restriction to 85% of their original weight before water-restriction. After one day of handling, mice were habituated to being head-fixed above the spherical treadmill for two days.

All experiments except CA1 imaging during learning: On the 4th day of training, mice began learning to lick from the lickport as water was automatically delivered at the beginning of the reward period following only non-matched odor trials (AB or BA, with water delivery at time point of 8 seconds). This phase was always 2 days except for the rare mouse that needed one extra day to reach motivation level and lick water from the port for at least 50 trials. In the next phase, water was only delivered if the mouse licked during the response period, and mice learned to reliably lick in anticipation of the reward following the 2nd odor. This phase was also 2 or 3 days, dependent on the mouse licking during the response period of at least 50 trials. The final phase was the full delayed non-match-to-sample (DNMS) task in which matched odor trials (AA and BB) were introduced and mice learned to refrain from licking the port following these trials.

CA1 imaging during learning experiments: All previous training steps were identical, except that the olfactometer remained off until Day 1 (which was the first day that mice received any air or odors). Two-photon calcium imaging was conducted on the last 2 days when water was only delivered if the mouse licked during the response period (baseline days referred to as Day -2 and -1 in the text and figures). For all 11 imaging days, a total of 100 trials delivered in five blocks of 20 trials were given each day. All 9 imaging days across learning of the DNMS task were conducted consecutively and began within 3 hours of the start time of the previous day.

CA1 imaging with EC chemogenetics experiments: Mice underwent 5-7 days of learning the full DNMS task before recording began. Two-photon calcium imaging only began after the mouse had 2 consecutive days of ‘expert performance’ (at least 85%). For all imaging days, a total of 100 trials delivered in five blocks of 20 trials were given each day. All 8 imaging days were conducted consecutively and began within 3 hours of the start time of the previous day.

CA1 imaging with holographic optogenetics experiments: Mice underwent at least 7 days of learning the full DNMS task before recording began. For all imaging days, 60 trials (three blocks of 20 trials) were given for each FOV (up to 3 FOV sessions per day). Imaging days were not consecutive because we did not match FOVs and performance was stable.

EC axon imaging experiments: Imaging was conducted for 7 days and began before expert performance of the DNMS task. However, only sessions with expert performance were included in analysis. All 7 imaging days were conducted consecutively and began within 3 hours of the start time of the previous day.

All experiments: Trials were delivered in blocks of 20 trials. There was no punishment or timeout following an incorrect lick; the water was simply not delivered.

In vivo two-photon imaging

All two-photon calcium imaging without holographic optogenetics was conducted using a resonant scanning two-photon microscope (Scientifica) fitted with a 16x 0.80 NA objective (Nikon) to record 512x512 pixel frames at 30.9 Hz. CA1 imaging fields of view were 500x500 µm and axonal imaging fields were 250x250 µm. Excitation light was delivered with a Ti:sapphire excitation laser (Chameleon Ultra II, Coherent), operated at 920 nm. GCaMP8f and GCaMP7s fluorescence was recorded with a green channel gallium arsenide photomultiplier tube (GaAsP PMT; 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 and olfactory stimulation digital signals at 1 kHz, using WinEDR software (Strathclyde Electrophysiology Software).

CA1 imaging experiments: A single field of view (FOV) of size 500x500 µm was imaged for all recordings. To maximize SNR, FOVs were selected roughly in the center of our window implants (which were centered around the right dorsal CA1 coordinate 2.0 mm posterior and 1.8 lateral to bregma). Careful attention was given to aligning the FOV to the previous day’s FOV as closely as possible. Animals were not included in analysis if successful alignment was not possible. We used rotating stages, a motor for adjusting mouse head angle, and a tiltable objective attachment with two degrees of freedom to fine-tune the alignment.

EC axon imaging experiments: A single field of view (FOV) of size 250x250 µm was imaged for all recordings. Again to maximize SNR, FOVs were selected roughly in the center of our window implants (which were centered around the right dorsal CA1 coordinate 2.0 mm posterior and 1.8 lateral to bregma). The same alignment was always attempted for 7 consecutive days, but the extra difficulty of alignment made it not always possible. Therefore, axon segments were not registered between days; however, FOVs were typically very similar. Laser power and PMT settings were kept consistent between days, except for rare occasions when it was necessary to maintain similar signal-to-noise. Out of the 16 axonal imaging animals included in analysis (each recorded for 7 days), 42 recording sessions were not included because of sub-expert performance and 7 sessions were not included because of poor signal-to-noise (resulting in a total of 63 included recording sessions across the 16 animals).

For each day of recording, imaging was halted between each of the 5 blocks of 20 trials. This allowed fine-tuning of alignment, and it also prevented brain heating or photo-toxicity. Laser power was kept as minimal as possible (60-80mW for CA1, and 100-200mW for EC axons) without sacrificing signal-to-noise ratio, and only mild photo-bleaching was observed in some axonal imaging animals.

In vivo two-photon imaging with holographic optogenetic stimulation

Two-photon calcium imaging with holographic optogenetics was conducted using a resonant scanning two-photon microscope (Ultima 2Pplus, Bruker ) fitted with a 16x 0.8 NA objective (Nikon) to record 512x512 pixel frames at 30 Hz. CA1 imaging FOVs were 556x556 µm. GCaMP excitation light was delivered with an excitation laser (InSight X3, SpectraPhysics-Newport) operated at 920 nm. GCaMP6s and GCaMP8s fluorescence was recorded with a green channel gallium arsenide photomultiplier tube (GaAsP PMT; Hamamatsu). ChRmine excitation light was delivered with another excitation laser (Spirit One 1040-8, SpectraPhysics-Newport) operated at 1040 nm. Timing of stimulation was triggered from the behavioral setup that also triggered odor and water delivery.

After locating a suitable FOV, individual neurons were tested for ChRmine excitation by delivering one or two small stimulations (five 10ms pulses at 10Hz). Laser power for each stimulation was kept at a minimum level that would induce stimulation. If the neuron responded with a noticeable increase in fluorescence, a point was placed. This was repeated until up to 8 neurons had been selected in the ROI manager. Then behavior was started and stimulation points were delivered one-by-one starting on the 5th trial. These large stimulations (ten 10ms pulses at 10Hz lasting one second) were meant to induce large-amplitude, long-duration calcium events resembling plateau potentials. The timing of stimulation was predetermined with a higher concentration near the offset of odors and an even distribution through the delay and reward periods. Another different 8 neurons were selected for the next block of 20 trials for the FOV, and the steps were repeated. With up to 3 sessions (60 trials) per FOV, up to 24 neurons were stimulated. If additional FOVs were suitable, the previous steps were repeated, and up to 3 FOVs were imaged in a single day (for up to 72 neurons total across 3 sessions of 60 trials each). Mice were given at least one rest day between recordings, and a maximum of 3 days were conducted per animal.

Chemogenetic inhibition

All CA1 imaging with EC chemogenetics animals received subcutaneous injections of saline for at least 5 days prior to imaging to habituate them to the injection prior to being head-fixed. For the 8 days of imaging, mice received alternating injections of saline and uPSEM (ultrapotent PSEM 792 hydrochloride binds to PSAM4 to cause strong inhibition). Half of the mice started with saline and the other half started with uPSEM on the first day of imaging. The experimenter was blind to animal group during recordings, but not to drug identity because the drug was prepared immediately before delivery. The uPSEM powder was dissolved into saline at a concentration of 0.3 mg/mL, and injections were administered to achieve 3 mg/kg. After weighing the mouse to calculate the appropriate volume of saline or uPSEM, the mouse was injected intraperitoneally and head-fixed under the microscope. 10-20 minutes elapsed between the injection and the start of behavior and imaging.

Histology

Following all experiments, mice were deeply anaesthetized under isoflurane and transcardially perfused with 30 mL 1x PBS followed by 30 mL 4% paraformaldehyde in 1x PBS at a rate of approximately 4 mL/min. After perfusion, the brains were extracted and post-fixed in 4% paraformaldehyde. Sections of 80 µm were collected using a vibratome, 24-48 hours after perfusion. For animals with LEC viral expression, coronal sections were taken, while sagittal sections were taken from animals with MEC viral expression. The sections were mounted onto glass slides and cover-slipped with DAPI mounting medium. Images were acquired on an Apotome2 microscope (Zeiss; 5x, 10x, 20x objectives) to confirm proper expression and location of viral expression. For chemogenetic experiments, sufficient PSAM4 or mCherry expression was found restricted to either LEC or MEC. In axonal imaging experiments, somatic GCaMP7s was confirmed to be restricted to only LEC or MEC, and axonal expression was found in the SLM layer of dorsal hippocampus. Mice with insufficient PSAM4/mCherry expression or PSAM4/mCherry/GCaMP7s that spread outside of their desired target were excluded from analysis.

Calcium imaging data pre-processing

CA1 imaging with EC chemogenetics experiments: The 8 days of recordings were divided into 4 pairs of days, so that each pair consisted of one saline day and one uPSEM day. Both recordings from a single pair were concatenated before processing so that the same neurons could be detected within the pair of imaging days. Concatenated movies were processed using the Python implementation of Suite2P 0.9.240 to perform non-rigid motion registration, neuron segmentation, extraction of fluorescence signals, and deconvolution with parameters optimized to our GCaMP8f CA1 recordings. We used the default classifier and an ‘iscell’ threshold of 0.1 to only include masks that were likely neurons.

CA1 imaging during learning experiments: All parameters were the same as previous, but days were processed separately.

CA1 imaging with holographic experiments: Each FOV was processed separately. GCaMP6s and GCaMP8s recordings had the same parameters are the previous sections except for ‘tau’ (timescale of sensor), was set to 1.25 and 0.5, instead of 0.25. Stimulation ROIs were manually verified. During some stimulations, more than one neuron responded to a single stimulation. This was due to brain movement and the high density of CA1 neurons. Any neighboring neuron that also responded to the stimulation was included as a stimulated neuron. Some neurons failed to respond at all (left bin in Fig. 2d).

EC axon imaging experiments: The 7 days of recordings were all processed separately. Movies were also processed using Suite2P but with parameters optimized to our GCaMP7s axonal recordings. An additional step of axon merging was taken to decrease the number of duplicates (as an axon could appear as multiple segments within the FOV); this also increased signal-to-noise by increasing the number of pixels for a single mask. By visualizing axon correlation values and their fluorescence traces within the Suite2P GUI, we chose axon segments to merge based on correlation values and footprint distributions. Using custom Python code with functions from Suite2P’s source code, we ‘merged’ axons by generating new ROIs with these new pixels. The old axon segments were then eliminated from analysis and deconvolution was run on the new axon masks.

For all experiments, deconvolved signals were taken as the selected output from Suite2P and analyzed further in MATLAB 2021a. Deconvolved signals were smoothed by a rolling mean of 10 frames (0.32 seconds), then z-scored, and finally values below 2 were set to zero. The resulting signal was used for all analysis and referred to as ‘firing rate (STD)’ as a proxy for spiking activity. Signals were aligned to the trial structure (odor presentations, reward period, lick timing) and the recorded locomotion as mice ran on the spherical ball.

Binary BTSP event detection and analysis

First, to be consistent with our previous work quantifying ‘odor cells’ in CA1 during the same task19, we use a 6-second period and 95th percentile threshold for determining significant odor fields. We extracted each odor presentation period (2 seconds before odor and 3 seconds after) and divided for Odor A and Odor B regardless of whether it was the first or second odor presented in the trial. Since each recording had 5 blocks of 20 trials, we have 100 odor presentations of each odor per neuron per day. Next, we identified every single calcium event, which we defined as a group of consecutive timepoints with a non-zero deconvolved signal. The size and timing of that event are counted as the peak value within the event and that timepoint’s time relative to the odor, respectively.

Next, we identified which events satisfied criteria to be considered as a possible induction event. This detection was performed separately for Odor A and Odor B presentations. Events in the first 10 or last 10 odor presentations were not considered for analysis, because we needed enough odor presentations before and after the event to detect BTSP events. There were two criteria for an event to be considered a possible induction event: during the previous 3 odor presentations the neuron must show no activity within 2 seconds before or after the event in question, and there must not have been a firing field in the top 10% of shuffle data. To determine the significance of a firing field, we took 6-second periods of all previous odor presentations and found the peak of the average activity. We then circularly shuffled each odor presentation and found the peak of the average activity from this shuffled data. This was repeated 2000 times to generate 2000 peak values from shuffled data. For a possible induction event, the real peak of average activity must not have been greater than the 90th percentile of the shuffle. This threshold was chosen to be extremely conservative by excluding all events that are preceded by activity in the top 10% of shuffled data.

If an event passed the previous criteria, we considered it as a possible induction event. We then analyzed if it was successful in forming a field. There were four criteria for a successful field formation: 1. The resulting field must have been significant above the 95th percentile of the shuffle; 2. The resulting field occurred within 2 seconds of the peak of the induction event; 3. The neuron must have fired (have value above 2 STD) within 0.5 seconds of the resulting field for the next 3 odor presentations; 4. The neuron must have fired within 0.5 seconds of the resulting field for at least 7 out of the next 10 odor presentations. The strict criteria for activity in the previous 3, following 3, and following 10 odor presentations improves the likelihood that the event in question does induce a stable field. The ± 2 second window for the difference between the event peak and field peak allowed us to look for asymmetrical formation without any bias. The lack of any criteria regarding the amplitude of the induction event allowed us to probe the relationship of amplitude to success rate and asymmetrical formation. Success rate increased continuously with amplitude (Fig. 1i), but only events with amplitude above 10 STD had statistically significant asymmetrical formation.

Therefore, we use the following operational definitions:

‘plateau-like’ event: any recorded somatic calcium event greater than 10 STD.

‘small event’: any recorded somatic calcium event smaller than 10 STD.

‘possible induction event’: an event of any size that satisfies the first 2 criteria: during the previous 3 odor presentations the neuron must show no activity within 2 seconds before or after the event in question, and there must not have been a firing field in the top 10% of shuffle data.

‘odor-field’: a significant activity peak above the 95th percentile of the shuffle

‘success’: a possible induction event of any size that is followed by an odor-field and satisfies 3 other criteria: the resulting field occurred within 2 seconds of the peak of the induction event; the neuron must have fired (have value above 2 STD) within 0.5 seconds of the resulting field for the next 3 odor presentations; the neuron must have fired within 0.5 seconds of the resulting field for at least 7 out of the next 10 odor presentations.

‘failure’: any possible induction event that does not satisfy all the criteria for a ‘success’.

‘success rate’: percentage of all possible induction events that are successful.

‘BTSP-like’ event: any ‘plateau-like’ event that is a ‘success’.

Firing rate increase analysis

As in the previous section, every single calcium event was identified. Events were not included in analysis if there were not at least 10 trials of the same and 10 trials of the opposite trial type both before and after the trial of the reference event. For the ‘same odor’ analysis, the previous 10 trials of the same odor type were averaged and subtracted from the average of the following 10 trials of the same odor type. The same was done for ‘opposite odor’ analysis but with the trials of the opposite trial type. The firing rate increase ramps were aligned to the peak of the reference event and averaged for all references of the desired specifications. Displayed ramps are not binned and show datapoints from each recorded frame.

When the firing rate increase value is collected as a single value (not displaying ramp), the 30 frames of the 1 second period from -1 to 0 (aligned to reference event) were averaged. This value was then averaged for all events within a single recording session.

For figures showing the ramps or firing rate increases across the trial structure, the entire trial structure was used for the calculation. For figures showing the effect only during the odor period, odor presentations were isolated as done in the previous section (ignoring whether the odor came first or second).

Linear Regression Models

For figures that display a scatter of dots with performance on the x-axis and a measurement on the y-axis, we used a linear regression model to isolate the effects of training day and behavioral performance with mouse as a random effect using the MATLAB function ‘fitlme’ (Measurement ~ 1 + Days*Performance + (1 + Days | Mouse)). Each of the 5 animals had a performance and measurement value for each of the 9 days. All table outputs are displayed in Supplementary Tables 1-11, and main figure scatter plots display t- and p- values that correspond to the fixed effect for performance on the measure.

Locomotion analysis

1D locomotion that was recorded with a sensor (Avago ADNS-9500) at 1kHz was binned to match the frame rate of calcium imaging. The signals were binned to 1/6 seconds, z-scored, and finally values between -1 and 1 were set to 0. Any bin with a locomotion value of 0 was determined to a period of ‘no movement’. A bin or group of bins with values not equal to 0 and lasting for less than one second were considered ‘minor jitter movement’. This included positive and negative values and the minor jitter movements included small ball movements in both directions. A group of bins with values less than -1 and lasting for at least one second were considered bout of ‘running’. The negative is indicative of the ball rolling backwards (which is caused by the mouse running forward).

Selectivity analysis

We calculated the odor-selectivity index value for each ROI as: SI = (R - R ) / (R + R ); where R is the firing rate at a given bin for Odor A trials and R is the same for Odor B trials. Bin sizes were always 0.5 seconds, and all trials were included. For each ROI, a distribution of 2000 shuffled index values were also calculated by randomly shuffling the trial type assignment 2000 times for each bin. The maximal absolute value index is chosen from all the bins (for the real ROI and all 2000 shuffles), and the bin is noted. ROI’s with an absolute value index value above the 95th percentile of absolute value shuffled index values are considered to be ‘significantly selective’. For figures displaying the actual odor-selectivity index value (ranging from 0 to 1), the values of the 2 bins during odor presentations were simply averaged.

Support vector machine decoding

Binary support vector machine (SVM) decoding was performed in MATLAB 2021a (default parameters) using bin sizes of 0.5 seconds (averaging the deconvolved signal for those frames within the bin).

CA1 imaging with EC chemogenetics experiments: The number of ROIs was controlled by randomly subsampling 100 ROIs out of all possible ROIs. The result of 20 subsamples of ROIs were averaged for each data point. For each bin and subsample, 80% of trials were used for training the decoder, and the remaining 20% were used for testing. This was repeated 4 more times so that each block of 20 trials was used as the 20% for testing. For each training of the decoder, another training was done with a shuffled assignment of trial type to confirm a shuffle comparison of data yields decoder accuracy of ∼50%. For odor decoding, the trials were broken down into odor presentations (same as in BTSP detection analysis) to evaluate odor decoder accuracy regardless of the order of the odors. When specific timepoints were mentioned, such as ‘during odor presentation’ or ‘during delay period’, the average accuracies of the 0.5 second bins were averaged (not trained/tested with larger bins).

EC axon imaging experiments: To control the number of axon segments, the 10 most odor-selective neurons were selected from the previous analysis (those with the highest percentile values). All other parameters remained the same.

Sequence-axon detection and analysis

To evaluate peak firing timing in EC axons, we performed sequence-axon detection similar to the previously described approach in CA1 neurons in our DNMS task, Taxidis et al. [20]. First, trials that begin with Odor A and those that begin with Odor B were separated, and the one with a larger peak of the average activity was considered further. Additionally, only the 6-second period including first odor presentation and the delay period was considered. In the same way as described in BTSP-event detection, the peak of average activity within this period and a given trial type was determined to be significant if the peak was greater than the 95th percentile of 2000 circular shuffles. The neuron must also have had a trial reliability of at least 20% (have fired above 2 STD for 20% of the preferred trials within 0.5 seconds of the peak frame found in the previous step). If an ROI passed both criteria, it was considered to be a ‘sequence-axon’ regardless of its odor-selectivity, as that was a separate analysis. An ROI was considered an ‘odor axon’ if its peak was within the odor presentation period. An ROI was considered an ‘offset axon’ if its peak was in the first second of the delay period. An ROI was considered a ‘delay axon’ if its peak was during the last 4 seconds of the seconds of the delay. This was done to not include the large population of ROIs that fired to the off set of the odor (likely the auditory cue of the clicking of the valve).

Statistical analysis

All statistics were performed in MATLAB 2021a and described in figure legends. In all cases in the text, values were written in the format ‘mean ± standard deviation’ (STD), while error bars in all figures show the mean and standard error of the mean (SEM). For all figures, no asterisks were shown if p ≥ 0.05, 1 asterisk if p < 0.05, 2 asterisks if p < 0.01, 3 asterisks if p < 0.001. If the p ≥ 0.1, ‘n.s.’ is displayed, but if 0.05 ≤ p < 0.1 the p-value was typically displayed in the figure. On occasions when single asterisks were displayed above a curve or trace, p-values were corrected for multiple comparisons using the false discovery rate Benjamini-Hochberg procedure. No statistical methods were used to determine appropriate sample sizes but were chosen as being comparable to sizes used in similar publications.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary\nThe manuscript Behavioral timescale synaptic plasticity in the hippocampus creates non-spatial representations during\nlearning and is modulated by entorhinal inputs by Dorian et al. proposes that large, rare “plateau-like” somatic calcium\ntransients in CA1—detected as ≥10 SD ΔF/F events with minimal preceding activity—drive the rapid formation of odor-\nselective firing fields during a two-odor head-fixed DNMS working-memory task. Chemogenetic silencing of MEC is reported\nto reduce event frequency, while LEC silencing lowers the probability that an event yields a field and impairs odor\ndecoding. Single-cell holographic stimulation of ChRmine is used to “write” odor fields on demand. The authors conclude that\nbehavioral-timescale synaptic plasticity (BTSP) underlies non-spatial odor learning and that MEC and LEC provide\ndissociable “teaching” and “content” signals.\nThe study elegantly employs modern optical tools to explore non-spatial BTSP, but several fundamental issues currently\nundermine its central conclusions. First, the authors claim to investigate dendritic plateau potentials yet rely exclusively on\nslow somatic calcium imaging—equating asymmetric somatic ΔF/F transients with true dendritic plateau events without any\ndirect tuft recordings or voltage measurements. Second, their single-cell “causation” paradigm uses a Kv2.1-targeted, non-\nselective ionic-conductance opsin delivered to the soma and proximal dendrite; driving strong proximal depolarization and\nlarge somatic calcium signals after tens of holographic pulses does not demonstrate integration at distal dendrites or\ngenuine BTSP mechanisms. Third, the use of a delayed non-match-to-sample working-memory task conflates one-shot\nnovelty learning with sustained maintenance, decision, and reward processes—yet analyses focus mainly on expert\nperformance rather than the critical first exposures. Fourth, despite distinct sample versus test epochs, delay periods, and\nreward contexts, all odor presentations are pooled, masking potentially divergent network and neuromodulatory states. Fifth,\nonly two fixed odors in a rigid DNMS structure are tested, preventing assessment of BTSP’s role in richer, more flexible\nolfactory learning or generalization. Finally, methodological and statistical weaknesses—including an asymmetric shuffle\nnull, liberal uncorrected thresholds, selective event filtering, small sample sizes/large variance for critical induction\nexperiments, and seemingly false errorbars, eg, Fig. 4c/g—as well as the complete omission of oscillatory-phase\nconsiderations, further erode confidence in the claimed mechanism. This is accompanied by a reported lack of any\ncorrelating effect on the relation to behavior.\nA thorough reanalysis that validates genuine dendritic plateau detection and tightens statistical controls—and that tempers\ncausal language or adds methodologically suitable experiments to control that—is required before the manuscript can be\nconsidered for publication at Nat. comms.\nMajor Points\nTask choice versus claimed mechanism\nUsing a delayed non-match-to-sample working-memory task conflates encoding, maintenance, decision and reward\nprocesses, yet the key novelty claim hinges on “one-shot” learning. The analysis centers on expert performance rather than\nthe critical initial exposures. A simpler multi-odor novelty paradigm would help to support the claim and a critical discussion\nof the lack a behavioral correlation would be helpful; alternatively the framing must shift to steady-state experience\ndependence rather than one-shot encoding.\nPooling of sample and test presentations\nCombining encoding (sample) and retrieval/decision (test) odor epochs ignores distinct neuromodulatory and network\nstates, potentially conflating mechanisms that differ between presentation positions. Separate analyses of event frequency,\namplitude, success rate and field selectivity for sample versus test are required—and pooling justified only if no significant\ndifferences exist.\nUnvalidated somatic proxy for dendritic plateaus\nEquating somatic ΔF/F transients ≥10 SD with dendritic plateau potentials is unsubstantiated. Electrotonic filtering blurs and\nattenuates distal events, and high-amplitude somatic bursts can masquerade as plateaus. The authors should add direct\nmeasurements to support the claim of dendritic integration and ideally provide tuft imaging or voltage recordings—or\nexplicitly temper mechanistic claims.\nIncomplete reporting of event–field contingencies\nKey denominators are missing: the number of large events that do not produce fields and the number of fields arising without\na detected event. Without a full contingency analysis and permutation or time-shift controls, the strength and specificity of the\nevent→field link cannot be assessed objectively.\nCircular shuffle null mismatch and liberal thresholds for event detection\nThe “circular shuffle” null appears to include all odor windows (varying multiple odor trials depending where in the trial\nnumber the event appeared (larger than 10, smaller than 90)—even those filtered out of the real data—which violates\nexchangeability and likely biases p-values. Coupled with an uncorrected 90th-percentile threshold (allowing up to 10% false\npositives per test) and no multiple-comparison control, the field-significance method risks substantial type I error. A matched\nfiltered null, FDR correction, and simulation-based false positive rates are required.\nSelective pre-exclusion of recent activity\nRequiring no activity ±2 s in the three preceding odor presentations excludes neurons with ramping or overlapping events,\nbiasing the sample toward “clean” silent-to-active transitions. This pre-selection may inflate apparent specificity and overlook\nbiologically relevant induction contexts. The impact of this filter must be quantified and, if necessary, replaced with a\ncontinuous covariate rather than binary exclusion.\nHolographic induction paradigm limitations\nSingle-cell optogenetic stimulation results are shown for very few neurons and mice, with no clear reporting of total\nstimulation attempts or failure rates. The use of Kv2.1-targeted ChRmine biases activation to proximal compartments, likely\ndiffering from natural tuft-driven plateaus. Negative controls (sub-threshold, off-epoch, opsin-negative) and direct comparison\nof evoked versus spontaneous event waveforms are essential before claiming sufficiency for field writing.\nAbsence of oscillatory-phase context\nBTSP and dendritic excitability are known to be gated by theta phase. Without LFP recordings or phase analysis, the role of\nrhythmic coordination is speculative. The authors should either provide phase-locking data or explicitly limit mechanistic\nconclusions.\nMinor Points\n• Limited odor set: Only two odors are tested. Generalization to richer odor spaces requires additional stimuli or tempered\nscope.\n• Temporal window choice: The use of a fixed 6 s window and occasional 1 s pre-event bins is never justified; analyses\nshould demonstrate robustness to different window lengths.\n• Anatomical sampling: CA1 exhibits proximodistal and radial innervation gradients from LEC versus MEC; imaging\ncoordinates should be reported and included as covariates.\n• Potentially wrong depiction of errorbars: carefully check errorbar sizes in the figures, most obviously in Fig4c/g; large\nvariance groups seem to have identical or smaller errorbars compared to low variance groups, despite comparable sample\nsize.\n• Terminology clarity: Clear operational definitions for “odor field,” “odor cell,” “plateau-like event” and “success rate” should\nbe provided in Methods.\n• Causal language: Phrases such as “plateaus drive fields” should be replaced with “precede” or “are associated with”\nunless necessity is demonstrated.\n• Figure legends: Denote whether n refers to neurons, events or mice; show per-mouse data points where possible.\n• Threshold justification: Percentile cutoffs for induction and field detection (90th/95th) must be rationalized or chosen via\nROC/FDR frameworks.", "reviewer_2": "The study by Dorian et al. investigates whether behavioral timescale plasticity (BTSP) can drive the formation of memory-\nrelated non-spatial hippocampal representations. This question is addressed by performing calcium imaging from\nhippocampal CA1 cells as well as from LEC and MEC axons in CA1 while mice are performing an olfactory working memory\ntask. In addition, optogenetic induction of BTSPs in CA1 and chemogenetic manipulations of LEC and MEC are performed\nto establish that BTSPs are causal in generating odor-related responses and that LEC and MEC are contributing to the\nsuccess and frequency of BTSPs. Furthermore, recordings during task acquisition show that the relative frequency of BTSP\nis increased early in training on the odor task. The series of experiments is performed with high rigor, and appropriate\nstatistical methods are used. Importantly, the experiments yield a highly novel result, which is that BTSP is a general\nplasticity mechanism and not limited to the formation of spatial fields. In addition to reporting this key result, the study also\ncharacterizes some mechanism of how BTSPs contribute to the encoding of non-spatial representations. As with any novel\nphenomenon, the study of course raises a number of interesting questions for future work, such as the limited effectiveness\nof BTSPs to generate fields later in the delay. The study is nonetheless comprehensive as well as exceptional in its impact\nand novelty. There are only a few minor points that, if addressed, would further improve the manuscript.\nMinor points:\n(1) The sentence in the abstract ‘Using holographic stimulation … ‘ is somewhat imprecise because spontaneously-\noccurring events do not need stimulation.\n(2) Fig. 1c. The blue bars are too small to be legible.\n(3) Panels in Fig. 1g, S1 and S2 seem to show BTSP events later in the delay periods that do not result in fields. Although\nthe low effectiveness of these events to result in fields is pointed out in the text, this could also be more explicitly labelled in\nthe figure panels and mentioned in the figure legends.\n(4) Fig. 1i and S3. Although the results that small events rarely form odor fields is clear, it is less obvious why the timing of\nthe small events shows a value around zero distance from the reference field. If these were completely random events there\nshould at the minimum be an increase variance because the event time and field time are unrelated. Is it possible that ‘small\nevents’ correspond to an odor fields that occur at a low rate, but nonetheless repeatedly around the same time? Perhaps this\npoint is so obvious that it is not explicitly mentioned. It would nonetheless be helpful for non-experts to provide some\nexplanation for the zero distance other than referring to randomness.\n(5) Fig. 3 d, e, g and i, Fig. S4f and g, Fig. S6b, d, f and h. All the correlations with performance % (or the lack thereof) seem to be a somewhat trivial consequence of effects occurring (or not occurring) early in training. Performance is obviously lower\nearly in training, and any effect that varies with training days would thus necessarily also show a correlation with\nperformance. If the question is asked whether performance is a key predictor in addition to training days, one would have to\ndo much more sophisticated analysis than a simple regression. The relative effect of these two variables (training day and\nperformance) would need to be dissociated, for example by a partial correlation or an analysis of variance that considers the\nrelative contribution of each of the two factors (training day and performance). Appropriate statistical methods should\ntherefore be used, or all of the panels plotting a variable against performance % should be removed along with the\ncorresponding text.\n(6) The use of the term ‘odor axon’ for MEC axons can be somewhat misleading. While the activity of these axons is clearly\ntimed with regard to stimulus onset and offset, few have selectivity for a particular odor. For comparison, we would not use\nthe term ‘place cell’ if a cell were not selective for a particular place.\n(7) The very last phrase in the Results section ‘being relayed indirectly through the CA1 perforant path as well’ does not\nsound correct. First, the sentence seems to focus on LEC inputs, but the perforant path through DG and CA3 would be\nconveying merged MEC and LEC inputs. Second, there is no perforant path with a direct connection to CA1 and the\nmeaning of a ‘CA1 perforant path’ is therefore not evident.\n(8) In the Results and in the Discussion, it would be more precise to distinguish two potential types of effects as a\nconsequence of manipulating entorhinal inputs to CA1. One type is plasticity/BTSP-related, and although detectable in well-\ntrained mice, may no longer be as behaviorally relevant as it would be if the manipulation were done earlier in training.\nThe second type are effects on hippocampal activity patterns as a consequence of loss of information from LEC or MEC. With\nany manipulation of a brain region, effects on plasticity and from loss of inputs to connected circuits can often not be fully\ndissociated. It is nonetheless important to conceptually distinguish between these types of effects and to acknowledge that\nboth contribute to differences in neuronal firing patterns during/after manipulations."}, "author_response": "We thank the reviewers for their careful consideration of our manuscript. The reviewers’ comments\nand suggestions led us to substantially improve the manuscript. We are grateful that the reviewers felt\nthat our “comprehensive” study was performed “elegantly”, and that the experiments and results were\n“highly novel”. Yet, the reviewers had some concerns that required us to perform further analyses.\nThese new analyses have further strengthened our conclusions, and we have made appropriate\nchanges to temper mechanistic claims. We have addressed all the points raised by the reviewers and\noutline our response below.\nReviewer #1: Summary:\nThe manuscript Behavioral timescale synaptic plasticity in the hippocampus creates non-spatial\nrepresentations during learning and is modulated by entorhinal inputs by Dorian et al. proposes that\nlarge, rare “plateau-like” somatic calcium transients in CA1—detected as ≥10 SD ΔF/F events with\nminimal preceding activity—drive the rapid formation of odor-selective firing fields during a two-odor\nhead-fixed DNMS working-memory task. Chemogenetic silencing of MEC is reported to reduce event\nfrequency, while LEC silencing lowers the probability that an event yields a field and impairs odor\ndecoding. Single-cell holographic stimulation of ChRmine is used to “write” odor fields on demand.\nThe authors conclude that behavioral-timescale synaptic plasticity (BTSP) underlies non-spatial odor\nlearning and that MEC and LEC provide dissociable “teaching” and “content” signals. The study\nelegantly employs modern optical tools to explore non-spatial BTSP, but several fundamental issues\ncurrently undermine its central conclusions. First, the authors claim to investigate dendritic plateau\npotentials yet rely exclusively on slow somatic calcium imaging—equating asymmetric somatic ΔF/F\ntransients with true dendritic plateau events without any direct tuft recordings or voltage\nmeasurements. Second, their single-cell “causation” paradigm uses a Kv2.1-targeted, non-selective\nionic-conductance opsin delivered to the soma and proximal dendrite; driving strong proximal\ndepolarization and large somatic calcium signals after tens of holographic pulses does not\ndemonstrate integration at distal dendrites or genuine BTSP mechanisms. Third, the use of a delayed\nnon-match-to-sample working-memory task conflates one-shot novelty learning with sustained\nmaintenance, decision, and reward processes—yet analyses focus mainly on expert performance\nrather than the critical first exposures. Fourth, despite distinct sample versus test epochs, delay\nperiods, and reward contexts, all odor presentations are pooled, masking potentially divergent\nnetwork and neuromodulatory states. Fifth, only two fixed odors in a rigid DNMS structure are tested,\npreventing assessment of BTSP’s role in richer, more flexible olfactory learning or generalization.\nFinally, methodological and statistical weaknesses—including an asymmetric shuffle null, liberal\nuncorrected thresholds, selective event filtering, small sample sizes/large variance for critical\ninduction experiments, and seemingly false errorbars, eg, Fig. 4c/g—as well as the complete\nomission of oscillatory-phase considerations, further erode confidence in the claimed mechanism.\nThis is accompanied by a reported lack of any correlating effect on the relation to behavior. A\nthorough reanalysis that validates genuine dendritic plateau detection and tightens statistical\ncontrols—and that tempers causal language or adds methodologically suitable experiments to control\nthat—is required before the manuscript can be considered for publication at Nat. comms.\nWe thank the reviewer for their detailed and thoughtful feedback. We have addressed all the\nanalytical concerns, expanded discussions of caveats and complexity, and tempered some of the\ncausal language. With these revisions, we believe the manuscript is much stronger and precise with\nits novelty. We address all these issues in a point-by-point discussion below.\nReviewer #1: Major Points:\nThe key novelty claim hinges on “one-shot” learning. The analysis centers on expert performance rather\nthan the critical initial exposures. A simpler multi-odor novelty paradigm would help to support\nthe claim and a critical discussion of the lack a behavioral correlation would be helpful; alternatively\nthe framing must shift to steady-state experience dependence rather than one-shot encoding.\nWe agree with the reviewer that the increased rate of BTSP-like events primarily on the first\nday of learning suggests that this rate increase may be largely driven by novelty. We have removed\nmentions of “one-shot learning” as it pertains to our findings. Additionally, we argue that the primary\nnovelty of our findings is the discovery of these BTSP-like events in a non-spatial task. Our\nexperiments were not designed to investigate the plasticity mechanism of BTSP itself, but rather to\ncharacterize the phenomenon in a new context of non-spatial representations during expert\nperformance, which suggests that BTSP may underly the formation of other hippocampal\nrepresentations beyond place cells.\nTo disentangle the contribution of novelty and performance, we have reanalyzed the relevant\ndata. Rather than performing a simple correlation between rates of BTSP-like events and\nperformance, we now use a linear regression model that isolates the effect of day or performance on\nthe measure. We now display the t- and p-values associated with only the effect of performance for all\nscatter plots (Figure 3d,e,g,h,I, Supplementary Figure S4f,g, and Supplementary Figure S6b,d,f,h).\nAdditionally, we include the results of each linear regression model in Supplementary Tables 1-11 to\ndemonstrate the relative effect of day, performance, and the interaction between the two factors.\nAlthough there are changes from the originally displayed p-values, our overall conclusions about the\ncombined effect of day and performance are unaffected. We also include a more thorough discussion\nof the new statistical approach and results.\nPooling of sample and test presentations\nCombining encoding (sample) and retrieval/decision (test) odor epochs ignores distinct\nneuromodulatory and network states, potentially conflating mechanisms that differ between\npresentation positions. Separate analyses of event frequency, amplitude, success rate and field\nselectivity for sample versus test are required—and pooling justified only if no significant differences\nexist.\nThis is an important point as the first (sample) and second (test) odors could be encoded\ndifferently in our CA1 neurons. We have added Supplementary Figure S4 (also included below) that\ncompares results when restricting analysis to either only the first or second odor. We find no\ndifference, which aligns with previous work in our lab studying odor representations in CA1 during the\nsame task (Figure S2 in (Taxidis et al., 2020)).\nDetection analysis was re-run using either the first odors or second odor presentations. Complementing\nprevious work in the lab using the same task19, we find no difference between the two odors. a, Success rates\nof small events are not different between first and second odors (paired t-test, p = 0.4342). Dots and lines\nrepresent each animal (17 animals, which are the average for each of their 8 recording sessions). Error bars\nrepresent the mean and SEM. b, Same as (a), but for ‘plateau-like’ events, which are also not different (paired t-\ntest, p = 0.2303). c, Similar to (a), but calculating the field time from reference as in Figure 1i for all newly\nformed odor-fields (paired t-test, p = 0.2429). d, same as (c), but for ‘plateau-like’ events (paired t-test, p =\n0.7371).\nUnvalidated somatic proxy for dendritic plateaus\nEquating somatic ΔF/F transients ≥10 SD with dendritic plateau potentials is unsubstantiated.\nElectrotonic filtering blurs and attenuates distal events, and high-amplitude somatic bursts can\nmasquerade as plateaus. The authors should add direct measurements to support the claim of\ndendritic integration and ideally provide tuft imaging or voltage recordings—or explicitly temper\nmechanistic claims.\nWhile our calcium imaging approach has some limitations, the restriction to somatic events is\nnot unique, and it aligns with previous spatial BTSP research that recorded ‘BTSP-like’ events using\nsomatic calcium imaging (Grienberger & Magee, 2022; Priestley et al., 2022; Sumegi et al., 2025).\nThe standard for measuring BTSP in place cell formation has been performing intracellular recordings\nfrom somata and the main induction mechanism has been injecting current into the soma. In\nGrienberger & Magee, 2022, a threshold of 3STD is used for CA1 place cell detection. Our use of\n10STD is extremely conservative and limits analysis to only the very largest calcium somatic events\nthat likely propagate through most dendrites to many synapses (Milstein et al., 2021; Takahashi &\nMagee, 2009). Furthermore, characterization of the BTSP-like events in Figure 1 is displayed across\na range of threshold values to demonstrate the relationship between event amplitude and odor field\nmeasurements.\nTo address the reviewer’s point, we now refer to calcium events larger than 10STD to be\n‘plateau-like’ events, and we have tempered mechanistic claims throughout the manuscript by\nremoving causal language. For example, we have replaced phrases such as “induction events drive\nthe formation of odor-fields” with “induction events precede the formation of odor-fields”. In the\nDiscussion, we also now discuss the challenge of understanding a theorized dendritic mechanism by\nrecording somatic events:\n“Our somatic calcium imaging approach has the advantage of recording many neurons to find rare\n‘BTSP-like’ events, but experiments to investigate dendritic dynamics will be valuable for further\nunderstanding the underlying mechanisms of CA3 and EC inputs in driving BTSP. By focusing our\nanalysis to only the largest calcium events, we likely isolate events that propagate through most\ndendrites to many synapses, but we recognize that some of these events may be large somatic\nspiking bursts rather than those originating from dendritic plateau-potentials. Voltage and calcium\nrecordings of dendritic tufts at different depths with the stratum radiatum and stratum lacunosum-\nmoleculare will be necessary to evaluate the contributions of CA3 and EC inputs to the CA1 neurons.”\nIncomplete reporting of event–field contingencies\nKey denominators are missing: the number of large events that do not produce fields and the number\nof fields arising without a detected event. Without a full contingency analysis and permutation or time-\nshift controls, the strength and specificity of the event→field link cannot be assessed objectively.\ndenominators for key findings. We have clarified the text in the results section. We now state:\n“We thus considered a reference calcium event as ‘plateau-like’ when its amplitude was > 10\nSTD. Plateau-like events were rare (122,605 out of 16,215,467 total events recorded = 0.76%) and\nmatched the shapes and amplitudes of previously described somatic calcium plateau events (Supp.\nFig. S3a-d). Approximately 8% of plateau like events that occurred during the odor cue or within the\nensuing 0.5 seconds preceded a subsequent odor-field formation (294 ‘successes’ out of 3876 total\nevents). This increased to ~15% during the second half of the odor presentation cue (Fig. 1j). Only 26\n‘plateau-like’ events beyond 0.5 seconds after odor offset preceded a subsequent time-field formation\n(8% of the 320 successful events). The odor-fields preceded by plateau-like events peaked 0.42 ±\n0.14 seconds earlier in trial-time compared to the reference event (Fig. 1k). In contrast, only 0.28%\n“small events” (<10 STD amplitude) during the odor cue or the ensuing 0.5 seconds (254 out of\n91,635 total events) preceded new odor fields (Supp. Fig. S5a), These events represent the noise\nfloor of our detection criteria and the rare odor-field formation that formed without a preceding\n‘plateau-like’ event. These rare fields were symmetrically distributed around the reference event\ntimepoint with increased variance (Supp. Fig. S5b). We thus label ‘plateau-like’ events preceding an\nodor-field as ‘BTSP-like’. We observed no difference in success rates or asymmetrical field-formation\nbetween first and second odor (Supp. Fig. S4). Therefore, all odor cues were pooled for further\nanalysis.”\nCircular shuffle null mismatch and liberal thresholds for event detection\nThe “circular shuffle” null appears to include all odor windows (varying multiple odor trials depending\nwhere in the trial number the event appeared (larger than 10, smaller than 90)—even those filtered\nout of the real data—which violates exchangeability and likely biases p-values. Coupled with an\nuncorrected 90th-percentile threshold (allowing up to 10% false positives per test) and no multiple-\ncomparison control, the field-significance method risks substantial type I error. A matched filtered null,\nFDR correction, and simulation-based false positive rates are required.\nWe regret the misunderstanding caused by misleading language in our methodological details.\nWe have now improved our description of our Binary BTSP-like events detection, and state the\nfollowing:\n“There were two criteria for an event to be considered a possible induction event: during the\nprevious 3 odor presentations the neuron must show no activity within 2 seconds before or after the\nevent in question, and there must not have been a firing field in the top 10% of shuffle data. To\ndetermine the significance of a firing field, we took 6-second periods of all previous odor\npresentations and found the peak of the average activity. We then circularly shuffled each odor\npresentation and found the peak of the average activity from this shuffled data. This was repeated\n2000 times to generate 2000 peak values from shuffled data. For a possible induction event, the real\npeak of average activity must not have been greater than the 90th percentile of the shuffle.\nThis threshold was chosen to be extremely conservative by excluding all events that are preceded by\nactivity in the top 10% of shuffled data. If an event passed the previous criteria, we considered it as a\npossible induction event. We then analyzed if it was successful in forming a field. There were four\ncriteria for a successful field formation: 1. The resulting field must have been significant above the\n95th percentile of the shuffle; 2. The resulting field occurred within 2 seconds of the peak of the\ninduction event; 3. The neuron must have fired (have value above 2 STD) within 0.5 seconds of the\nresulting field for the next 3 odor presentations; 4. The neuron must have fired within 0.5 seconds of\nthe resulting field for at least 7 out of the next 10 odor presentations. The strict criteria for activity in\nthe previous 3, following 3, and following 10 odor presentations improves the likelihood that the event\nin question does induce a stable field. The ± 2 second window for the difference between the event\npeak and field peak allowed us to look for asymmetrical formation without any bias. The lack of any\n4\ncriteria regarding the amplitude of the induction event allowed us to probe the relationship of\namplitude to success rate and asymmetrical formation.”\nOne form of our analysis uses thresholds to split the induction events into two groups:\n‘successes’ and ‘failures’. We based this analysis on previous published work on spatial BTSP\n(Grienberger & Magee, 2022) and modified the criteria to fit our olfactory DNMS task with binary\ncategorization of odor-fields as in previous work in our lab using this task (Taxidis et al., 2020). Of\nnote, we made the criteria for detection of BTSP-like events even more conservative than this\nprevious study, since we can record from many more neurons.\nWe required the ‘possible induction event’ to have occurred within trials 11-90 because 10\ntrials before and after are needed for the analysis. No data is filtered out; we simply do not include\nanalysis of induction events that occur in the first or last 10 trials because we need 10 trials before\nand after for our criteria. This requirement of trials before and after the induction event is consistent\nwith previous spatial BTSP research that exclude events in the beginning and/or end of recording\nsessions (Grienberger & Magee, 2022; Sumegi et al., 2025). The 90th percentile threshold is used\nonly when finding possible induction events that occur after quiet periods without odor information.\nWe chose the 90th percentile as being more conservative than the 95th percentile because we\nexclude from analysis all induction events that have an odor field greater than the 90th percentile in\nthe 10 trials preceding the induction event. Once an event passes criteria for being a possible\ninduction event with minimal activity in the preceding trials, we continue with a separate step for\ndetermining odor field formation.\nOur detection for odor field formation is very conservative as well. As compared to previous\nanalysis such as Grienberger and Magee, 2022, who only use 40% reliability in the following 5 trials,\nwe use a 95th percentile cutoff for the shuffle and also require 70% reliability in the following 10 trials.\nOnce again, the extremely minimal number of ‘successes’ from small events represent the baseline\nlevel or noise floor for our analysis.\nDespite our confidence in these conservative criteria, we agree that binarizing events is\ninherently difficult and are dependent on threshold settings. While this binarized analysis is useful for\ncomparing our work with published spatial BTSP work, we recognize that non-binarized analysis is\nalso necessary. Therefore, we developed a threshold-free analysis in which we compare firing rate\nincreases across sets of trials before and after an induction event (Figure 1 L-N). We find a consistent\nresult across all possible sets of trials used for this analysis (Supplementary Figure S6 A-B). For all\nexperiments of learning and EC silencing, we include now both the binary event detection and\nparameter-free firing rate increase measure.\nSelective pre-exclusion of recent activity\nRequiring no activity ±2 s in the three preceding odor presentations excludes neurons with ramping or\noverlapping events, biasing the sample toward “clean” silent-to-active transitions. This pre-selection\nmay inflate apparent specificity and overlook biologically relevant induction contexts. The impact of\nthis filter must be quantified and, if necessary, replaced with a continuous covariate rather than binary\nexclusion.\nThe binary event detection serves to demonstrate the rapid formation of odor-fields in a single\ntrial, aligning with the classic definition of BTSP and with previous BTSP research focused on large\nsomatic events preceded by laps with minimal activity. However, we agree with the reviewer that\nsetting thresholds binarizes the analysis. To address this, we developed the aforementioned\nthreshold-free firing rate increase measure where no pre-exclusion of recent activity is imposed.\nHolographic induction paradigm limitations\nreporting of total stimulation attempts or failure rates. The use of Kv2.1-targeted ChRmine biases\nactivation to proximal compartments, likely differing from natural tuft-driven plateaus. Negative\ncontrols (sub-threshold, off-epoch, opsin-negative) and direct comparison of evoked versus\nspontaneous event waveforms are essential before claiming sufficiency for field writing.\nWe agree that these optogenetic stimulations are likely different from naturally occurring\nevents; however, we attempted to replicate the standard in the spatial BTSP field, where current is\ninjected into the soma during whole-cell recordings to strongly depolarize the cell. The Kv2.1-targeted\nChRmine approach strongly depolarizes the soma in a similar way. In addition to the histograms of all\nevents, we have now included detailed numbers of attempts and successes in the results section. We\nhave now also included numbers of stimulation attempts that occurred outside the odor period and\nwhen stimulation did not yield an observable calcium response (opsin-negative or non-responsive).\nWe find that only large calcium events > 10STD preceded odor field formation. No fields were formed\nfollowing weak responses, non-responses, or strong responses beyond one second after the odor.\nAdditionally, we now include a new supplementary figure (Supplementary Figure S3, also\nincluded below) that compares the shapes and sizes of spontaneous and stimulated events. These\nresults align with other works characterizing template shape and size of somatic calcium events in\nresponse to dendritic events (O’Hare et al., 2025). Our large somatic deconvolved events are\nextremely rare, have larger and longer Delta F/F traces. Our optogenetic stimulation generated a\nrange of responses from 0 to 0.6 Delta F/F. The durations as measured by the full-width at half-\nmaximum of average traces were similar to spontaneously occurring events (between 1 and 1.5\nseconds). The result section now states:\n“Using these same criteria as above, we isolated stimulation events that were preceded by\nminimal activity and no significant field (111 out of 857 total stimulations across 16 recording sessions\nin 3 mice). Stimulation-induced ‘plateau-like’ events during the odor cue or ensuring 0.5 seconds had\nan 11% probability of odor-field formation (5 out of 44 events), similar to that of spontaneous plateau-\nlike events (~8%). In contrast, stimulation-induced ‘plateau-like’ events outside of this time window (0\nout of 33 events), or those with <10 STD amplitude (0 out of 34 stimulation attempts) were not\nfollowed by field formation. Importantly, the success rate of field formation and firing rate increase\nafter stimulation (Fig. 2e-f) were similar to the spontaneous events we recorded in Figure 1.\nTherefore, large somatic plateau-like events causally drive the formation of odor-fields similarly to our\nobservations of spontaneously occurring events.”\nrecorded deconvolved spontaneous events in the 17 animals from Figure 1 (136 recording sessions).\nLogarithmic scale shows rare occurrence of large events compared to smaller events. b, Delta F/F traces for all\nevents binned into the same deconvolved STD values as in (a) and aligned to the start of the deconvolved\nevent. Colors are matched with (a). c, Peak amplitude of average Delta F/F traces from (b). Each line represents\none of the 17 animals. Error bar line plot represents mean and SEM. d, Full-width at half-maximum (FWHM)\nfor average Delta F/F traces from (b). Lines and error bars are the same as (c). e-h, Same as (a-d), but for all\nspontaneous events recorded during holographic optogenetics experiments from Figure 2 (3 animals, 16\nrecording sessions). i-l, same as (e-h), but only for optogenetically stimulated events from Figure 2. Standard y-\naxis for (i) instead of logarithmic.\nAbsence of oscillatory-phase context\nBTSP and dendritic excitability are known to be gated by theta phase. Without LFP recordings or\nphase analysis, the role of rhythmic coordination is speculative. The authors should either provide\nphase-locking data or explicitly limit mechanistic conclusions.\nour current study. To address it here, we adjusted our Discussion and added the following statement\nabout the importance of this rhythmic coordination for future BTSP research:\n“Furthermore, spatial BTSP and dendritic excitability is known to be gated by theta phase, so future\nexperiments must evaluate this relationship across a variety of tasks and types of stimuli.”\nReviewer #1: Minor Points:\nLimited odor set: Only two odors are tested. Generalization to richer odor spaces requires additional\nstimuli or tempered scope.\nAdding more odors to study BTSP in response to more complex sensory stimuli is a very interesting\ndirection but would require a different behavioral setup and training protocols. This goes beyond the\nscope of the current project which is to describe BTSP-like events outside of a spatial task. However,\nto address this point, we have adjusted our claims to more closely follow the actual experiments\nconducted. We also include this new statement in the Discussion:\n“Additionally, our task only uses two different odors, so future work should expand this work to other\nodor spaces and non-spatial stimuli.”\nTemporal window choice: The use of a fixed 6 s window and occasional 1 s pre-event bins is never\njustified; analyses should demonstrate robustness to different window lengths.\nWe used a 6 second window and 95th percentile for odor-field detection to remain consistent with\nprevious research of odor fields in this exact task from our lab (Taxidis et al., 2020). This detail is now\nincluded in the text. Again, our threshold-free analysis finds firing rate increase ramps that do not rely\non any window.\nAnatomical sampling: CA1 exhibits proximodistal and radial innervation gradients from LEC versus\nMEC; imaging coordinates should be reported and included as covariates.\nWe agree that this information is important to include. We now state this more clearly in the methods\nsection:\n“A single field of view (FOV) of size 500x500 µm was imaged for all recordings. To maximize SNR,\nFOVs were selected roughly in the center of our window implants (which were centered around the\nright dorsal CA1 coordinate 2.0 mm posterior and 1.8 lateral to bregma).” The same language is\nrepeated for axon imaging but changed to reflect the smaller FOV size of 250x250 µm.\nPotentially wrong depiction of errorbars: carefully check errorbar sizes in the figures, most obviously\nin Fig4c/g; large variance groups seem to have identical or smaller errorbars compared to low\nvariance groups, despite comparable sample size.\nWe thank the reviewer for noticing this issue. Due to a coding error, we had plotted the same standard\nerror value for both saline and uPSEM groups. We have now updated Figure 4b,c,e,f,g,i,\nSupplementary Figure S4i,j, and Supplementary Figure S7a with the correct error bars. All other\nstatistics and visualizations were not affected by this mistake.\nTerminology clarity: Clear operational definitions for “odor field,” “odor cell,” “plateau-like event” and\n“success rate” should be provided in Methods.\nanalysis”. In the methods, it now reads:\n“We use the following operational definitions:\n‘plateau-like’ event: any recorded somatic calcium event greater than 10 STD.\n‘small event’: any recorded somatic calcium event smaller than 10 STD.\n‘possible induction event’: an event of any size that satisfies the first 2 criteria: during the previous 3\nodor presentations the neuron must show no activity within 2 seconds before or after the event in\nquestion, and there must not have been a firing field in the top 10% of shuffle data.\n‘odor-field’: a significant activity peak above the 95th percentile of the shuffle\n‘success’: a possible induction event of any size that is followed by an odor-field and satisfies 3 other\ncriteria: the resulting field occurred within 2 seconds of the peak of the induction event; the neuron\nmust have fired (have value above 2 STD) within 0.5 seconds of the resulting field for the next 3 odor\npresentations; the neuron must have fired within 0.5 seconds of the resulting field for at least 7 out of\nthe next 10 odor presentations.\n‘failure’: any possible induction event that does not satisfy all the criteria for a ‘success’.\n‘success rate’: percentage of all possible induction events that are successful.\n‘BTSP-like’ event: any ‘plateau-like’ event that is a ‘success’.”\nCausal language: Phrases such as “plateaus drive fields” should be replaced with “precede” or “are\nassociated with” unless necessity is demonstrated.\nWe have adjusted the language used throughout the manuscript to more closely reflect our findings.\nFor example, in the Results, we now state: “A single somatic calcium ‘reference event’ was\nconsidered an induction event if it preceded the formation of an odor-field.” Also, “formation of odor-\nfields occurred with non-spatial BTSP-like events that are characterized by an asymmetric firing rate\nincrease ramp.”\nFigure legends: Denote whether n refers to neurons, events or mice; show per-mouse data points\nwhere possible.\nFor all figures, we now include these details in the figure legends. All suitable panels were already\ndisplaying individual mouse lines and dots, but this has now been made clearer for the reader.\nThreshold justification: Percentile cutoffs for induction and field detection (90th/95th) must be\nrationalized or chosen via ROC/FDR frameworks.\nAs mentioned in response to a previous point, we based these criteria to match previous publications\n(Grienberger & Magee, 2022; Taxidis et al., 2020) with our BTSP-like event and odor field detection.\nThe 95th percentile was used exactly as done in previous publications (Taxidis et al., 2020), and the\n90th percentile is the more conservative value to discard cells that already had an odor field in the top\n10% of a shuffle distribution. The main text and methods text now includes these details for\njustification.\nReviewer #2: Summary:\nThe study by Dorian et al. investigates whether behavioral timescale plasticity (BTSP) can drive the\nformation of memory-related non-spatial hippocampal representations. This question is addressed by\nperforming calcium imaging from hippocampal CA1 cells as well as from LEC and MEC axons in CA1\nwhile mice are performing an olfactory working memory task. In addition, optogenetic induction of\nBTSPs in CA1 and chemogenetic manipulations of LEC and MEC are performed to establish that\nthe success and frequency of BTSPs. Furthermore, recordings during task acquisition show that the\nrelative frequency of BTSP is increased early in training on the odor task. The series of experiments is\nperformed with high rigor, and appropriate statistical methods are used. Importantly, the experiments\nyield a highly novel result, which is that BTSP is a general plasticity mechanism and not limited to the\nformation of spatial fields. In addition to reporting this key result, the study also characterizes some\nmechanism of how BTSPs contribute to the encoding of non-spatial representations. As with any\nnovel phenomenon, the study of course raises a number of interesting questions for future work, such\nas the limited effectiveness of BTSPs to generate fields later in the delay. The study is nonetheless\ncomprehensive as well as exceptional in its impact and novelty. There are only a few minor points\nthat, if addressed, would further improve the manuscript.\nWe thank the reviewer for their positive words and careful review of our manuscript. Overall, the\nreviewer’s comments led to additional analysis and improved language that contribute to a stronger\nmanuscript.\nReviewer #2: Minor Points:\n(1) The sentence in the abstract ‘Using holographic stimulation … ‘ is somewhat imprecise because\nspontaneously-occurring events do not need stimulation.\nWe agree. We have adjusted this sentence in the abstract to say:\n“Using two-photon calcium imaging of CA1 pyramidal neurons in mice learning an odor-cued working\nmemory task, we find spontaneously-occurring plateau-like calcium events during odor cues, forming\nstable odor representations. These BTSP-like events were more frequent during early stages of task\nlearning, suggesting that BTSP is important for encoding novelty during learning. Additionally, using\nholographic optogenetics, we induced plateau-like calcium events which were followed by stable odor\nrepresentations.”\n(2) Fig. 1c. The blue bars are too small to be legible.\nWe have made the bars wider and brighter.\n(3) Panels in Fig. 1g, S1 and S2 seem to show BTSP events later in the delay periods that do not\nresult in fields. Although the low effectiveness of these events to result in fields is pointed out in the\ntext, this could also be more explicitly labelled in the figure panels and mentioned in the figure\nlegends.\nWe have added an extra arrow in Figure 1g and described the ‘failures’ in the legends of each figure.\n(4) Fig. 1i and S3. Although the results that small events rarely form odor fields is clear, it is less\nobvious why the timing of the small events shows a value around zero distance from the reference\nfield. If these were completely random events there should at the minimum be an increase variance\nbecause the event time and field time are unrelated. Is it possible that ‘small events’ correspond to an\nodor fields that occur at a low rate, but nonetheless repeatedly around the same time? Perhaps this\npoint is so obvious that it is not explicitly mentioned. It would nonetheless be helpful for non-experts to\nprovide some explanation for the zero distance other than referring to randomness.\nThe reviewer raises an important point that we now clarify with the following statements in the Results\nsection:\n“In contrast, only 0.28% “small events” (<10 STD amplitude) during the odor cue or the ensuing 0.5\nseconds (254 out of 91,635 total events) preceded new odor fields (Supp. Fig. S5a), These events\nrepresent the noise floor of our detection criteria and the rare odor-field formation that formed without\n10\na preceding ‘plateau-like’ event. These rare fields were symmetrically distributed around the reference\nevent timepoint with increased variance (Supp. Fig. S5b).”\n(5) Fig. 3 d, e, g and i, Fig. S4f and g, Fig. S6b, d, f and h. All the correlations with performance % (or\nthe lack thereof) seem to be a somewhat trivial consequence of effects occurring (or not occurring)\nearly in training. Performance is obviously lower early in training, and any effect that varies with\ntraining days would thus necessarily also show a correlation with performance. If the question is\nasked whether performance is a key predictor in addition to training days, one would have to do much\nmore sophisticated analysis than a simple regression. The relative effect of these two variables\n(training day and performance) would need to be dissociated, for example by a partial correlation or\nan analysis of variance that considers the relative contribution of each of the two factors (training day\nand performance). Appropriate statistical methods should therefore be used, or all of the panels\nplotting a variable against performance % should be removed along with the corresponding text.\nWe are thankful to the reviewer for their great suggestion. To disentangle the contribution of\nnovelty versus performance, we have reanalyzed the relevant data using a linear regression model\nthat is equivalent to an ANOVA with continuous variables. We can isolate the effect of day or\nperformance on each measure of BTSP-like events. We now display the t and p values associated\nwith only the effect of performance for all scatter plots (Figure 3d,e,g,h,I, Supplementary Figure S4f,g,\nand Supplementary Figure S6b,d,f,h). Additionally, we include the results of each linear regression\nmodel in Supplementary Tables 1-11 to demonstrate the relative effect of day, performance, and the\ninteraction between the two factors. Although there are changes from the originally displayed p-\nvalues, our overall conclusions about the combined effect of day and performance are unaffected.\n(6) The use of the term ‘odor axon’ for MEC axons can be somewhat misleading. While the activity of\nthese axons is clearly timed with regard to stimulus onset and offset, few have selectivity for a\nparticular odor. For comparison, we would not use the term ‘place cell’ if a cell were not selective for a\nparticular place.\nWe agree and have adjusted the figures and text to note axons that have their peak activity during the\n1st odor, odor offset, or delay period. For example, the results section now states:\n“A much higher percentage of MEC axons had significant peaks during odor presentations and their\noffset compared to LEC axons (MEC with 28.0 ± 6.5% axons with activity peaking during odor\npresentations compared to only 9.5 ± 6.5% for LEC; MEC with 4.6 ± 2.2% axons with activity peaking\nduring odor offset compared to only 2.7 ± 1.0% for LEC) (Fig. 5h). MEC axons with activity peaking\nduring odor presentations also showed greater trial reliability compared to LEC (Supp. Fig. S10d). In\ncontrast, a higher percentage of LEC axons had significant activity peaks during the delay period\n(LEC with 5.8 ± 1.3% axons with activity peaking during delay period compared to only 2.4 ± 0.7% for\nMEC).”\n(7) The very last phrase in the Results section ‘being relayed indirectly through the CA1 perforant\npath as well’ does not sound correct. First, the sentence seems to focus on LEC inputs, but the\nperforant path through DG and CA3 would be conveying merged MEC and LEC inputs. Second, there\nis no perforant path with a direct connection to CA1 and the meaning of a ‘CA1 perforant path’ is\ntherefore not evident.\nWe fixed the error in the wording to state “… being relayed indirectly to the CA1 via the perforant path\nas well”\n11\n(8) In the Results and in the Discussion, it would be more precise to distinguish two potential types of\neffects as a consequence of manipulating entorhinal inputs to CA1. One type is plasticity/BTSP-\nrelated, and although detectable in well-trained mice, may no longer be as behaviorally relevant as it\nwould be if the manipulation were done earlier in training. The second type are effects on\nhippocampal activity patterns as a consequence of loss of information from LEC or MEC. With any\nmanipulation of a brain region, effects on plasticity and from loss of inputs to connected circuits can\noften not be fully dissociated. It is nonetheless important to conceptually distinguish between these\ntypes of effects and to acknowledge that both contribute to differences in neuronal firing patterns\nduring/after manipulations.\nWe now discuss the difficulty of teasing these two potential effects apart in the Results:\n“Collectively, LEC inhibition strongly decreased odor-selectivity and decodability in pyramidal CA1\nneurons, which may have played a role in the reduction in the success rate of ‘plateau-like’ events in\ngenerating odor-fields, though additional mechanisms could potentially contribute to this effect.\nAdditionally, these results are limited to expert performance, so we are unable to determine if these\nmanipulations would affect the previously found novelty and learning results.”\nWe discuss it in more detail in the Discussion:\n“First, it is important to note that we only observed these effects in expert-performance. Perhaps the\neffects that we observed in expert animals would become behaviorally relevant during early training.\nAlternatively, the loss of information from LEC or MEC may affect CA1 activity regardless of the stage\nof behavioral performance. Future experiments will be necessary to tease apart these possibilities.”\nReviewer #2: (Remarks on code availability):\nI have reviewed the files and all details for running the code seems to be provided. However, I do not\nhave the resources to run the code and did not install it. The authors refer to large data files in their\ncode. It should perhaps be more explicitly states whether the data files will be uploaded to a\nrepository or are available upon request.\nWe thank the reviewer for taking the time to evaluate the code. We have improved language in our\nData and Code Availability statement:\n“Analysis code for reviewers is attached in the supplementary files. Analysis code will be made\navailable on GitHub after manuscript acceptance. All pre-processed data files used to generate\nfigures will be made publicly available on Dryad after manuscript acceptance. Any additional\ninformation necessary to reanalyze the data reported in this paper is available from the lead contacts.”\nGrienberger, C., & Magee, J. C. (2022). Entorhinal cortex directs learning-related changes in CA1\nrepresentations. Nature, 611(7936), 554–562. https://doi.org/10.1038/s41586-022-05378-6\nMilstein, A. D., Li, Y., Bittner, K. C., Grienberger, C., Soltesz, I., Magee, J. C., & Romani, S. (2021).\nBidirectional synaptic plasticity rapidly modifies hippocampal representations. eLife, 10,\ne73046. https://doi.org/10.7554/eLife.73046\nO’Hare, J. K., Wang, J., Shala, M. D., Polleux, F., & Losonczy, A. (2025). Distal tuft dendrites predict\nproperties of new hippocampal place fields. Neuron, 113(12), 1969-1982.e7.\nhttps://doi.org/10.1016/j.neuron.2025.03.029\nPriestley, J. B., Bowler, J. C., Rolotti, S. V., Fusi, S., & Losonczy, A. (2022). Signatures of rapid\nplasticity in hippocampal CA1 representations during novel experiences. Neuron, 110(12),\n1978-1992.e6. https://doi.org/10.1016/j.neuron.2022.03.026\nSumegi, M., Olah, G., Lukacs, I. P., Blazsek, M., Makara, J. K., & Nusser, Z. (2025). Diverse calcium\ndynamics underlie place field formation in hippocampal CA1 pyramidal cells. eLife, 13.\nhttps://doi.org/10.7554/eLife.103676.2\nTakahashi, H., & Magee, J. C. (2009). Pathway Interactions and Synaptic Plasticity in the Dendritic\nTuft Regions of CA1 Pyramidal Neurons. Neuron, 62(1), 102–111.\nhttps://doi.org/10.1016/j.neuron.2009.03.007\nTaxidis, J., Pnevmatikakis, E. A., Dorian, C. C., Mylavarapu, A. L., Arora, J. S., Samadian, K. D.,\nHoffberg, E. A., & Golshani, P. (2020). Differential Emergence and Stability of Sensory and\nTemporal Representations in Context-Specific Hippocampal Sequences. Neuron, 108(5), 984-\n998.e9. https://doi.org/10.1016/j.neuron.2020.08.028\nWe thank Reviewer #1 for their additional constructive feedback and acknowledgement of our\n“detailed rebuttal and substantial revisions”. We have updated the manuscript and addressed all\nconcerns in the point-by-point discussion below.\nReviewer #1:\nThank you for the detailed rebuttal and substantial revisions. Several of my earlier concerns are now\nat least partially addressed, especially the change in terminology to “plateau-like” events, the added\ndiscussion of somatic imaging caveats, and the corrections to error bars. However, I still feel that the\nstrength and scope of the main claims exceed what the data can support, and I would strongly\nencourage further tempering of the mechanistic and causal language, as well as clearer\nacknowledgment of alternative interpretations.\nWe thank the reviewer for their thoughtful feedback. We have now addressed these concerns\nby improving our statistical tests and further tempering mechanistic and causal language while more\nclearly discussing alternative interpretations of our results.\nMechanistic and causal claims\nWhile the authors have removed some explicit causal phrases and now refer to “plateau-like” rather\nthan “BTSP-like” events, the title, abstract, and discussion still largely present the results as\ndemonstrating that BTSP is a general plasticity mechanism for non-spatial representations. In\nparticular, the concluding statement in the abstract that the findings “demonstrate a causal role of\nBTSP” is not supported by the present data. Given that all inferences are based on somatic calcium\nsignals and an operational definition of ≥10 SD events, this remains too strong. The same somatic\nsignature could arise from different underlying mechanisms (e.g. bursts, mixed dendritic/somatic\nevents), and the data demonstrate correlation and temporal precedence rather than necessity or\nsufficiency. I would recommend consistently rephrasing to something more neutral, such as “rare,\nlarge somatic calcium events consistent with BTSP models can precede the formation of some odor\nfields in this task,” and avoiding language that implies that BTSP per se has been established as the\nmechanism.\nWe have made several changes to address these concerns.\nFirst, we have changed the Title to read: “Rapid formation of non-spatial hippocampal\nrepresentations consistent with behavioral timescale synaptic plasticity is modulated by entorhinal\ninput”\nSecond, we have changed the concluding sentence in the Abstract to read: “Together, our\nfindings demonstrate that rare large somatic calcium events, consistent with BTSP, precede and drive\nnovel odor representations during learning, in a manner differentially regulated by medial and lateral\nentorhinal cortex.”\nThird, to be consistent with this new language, we have also adjusted similar summarizing\nsentences in the Discussion.\nLast, while we agree that tempering claims is appropriate in regard to the mechanism\nunderlying the formation of odor representations, we would like to point out that experimental results\nshown in Figure 2 suggest sufficiency since we were able to optogenetically-induce odor\nrepresentations with somatic excitation. We now acknowledge that BTSP is likely just one of several\nmechanisms potentially driving formation of hippocampal representations. Furthermore, we now more\nfully discuss the difficult problem of testing necessity of BTSP in the Discussion.\nThe revised text better documents numerators/denominators and threshold choices, but the core\nconcern about circularity remains. “Possible induction events” are defined using stringent criteria (low\nprior activity, no strong field in the preceding trials, restricted trial window), and “success” is then\ndefined as a new, reliable field emerging near the event in time. This effectively preselects events in\nwhich a silent-to-active transition occurs with the desired temporal asymmetry and discards cases\nwhere fields emerge more gradually, overlap existing activity, or arise without a qualifying event. The\nbinary analysis therefore builds the BTSP-like pattern into the inclusion criteria and will, by\nconstruction, emphasize one-shot, asymmetric field formation. The more continuous “threshold-free”\nanalysis is a welcome addition, but the manuscript should more clearly acknowledge that the\nbinary/“induction” analysis is conditional on these strong filters and cannot be taken as an unbiased\nestimate of how often plateau-like events genuinely drive fields versus other scenarios.\nWe have now further revised the text to emphasize the differences between the two types of\nanalysis. In the Results (page 4), it now reads “Categorizing BTSP-like events into successes and\nfailures restricted analysis to a narrowly defined type of plasticity event based on formation of\nsignificant odor responses. Some responses were reinforced by multiple ‘plateau-like’ events (Supp.\nFig. S2). Therefore, we developed a simpler analysis to avoid artificially imposed criteria and\nsensitivity to binary thresholding.”\nIn the Discussion, it now reads “analysis that binarizes complex dynamics into successes and\nfailures is sensitive to chosen thresholds, and this makes it inherently difficult to compare across\ncontexts and recording methods. Similar discrepancies can even be seen within spatial tasks, as the\nreported percentages of CA1 pyramidal neurons that are classified as place cells can vary\ndramatically between 20% and 90%50. Therefore, our reported values for BTSP success cannot be\nextrapolated to BTSP rates beyond our experimental and analytical parameters. For this reason, we\nalso developed and utilized the threshold-free analysis of firing rate increase.”\nAs before, we argue that the binary analysis is still useful because of its widespread use in\nspatial BTSP research classifying the formation of place fields, but we have now tempered our\nexplanations in the Results and Discussion.\nGenerality and time representations\nThe manuscript still suggests that BTSP underlies non-spatial representations more broadly, including\ntime. However, the data show that only a very small minority of plateau-like events occurring after\nodor offset precede time fields, and optogenetic induction in the delay period did not reliably generate\nfields. The discussion now notes that delay-related effects are weaker and rare, but the claims about\ngeneralization to time and “internally generated” codes still read as if they are an established\nextension of the mechanism rather than an open question. I would recommend explicitly stating that\nthe present data provide only limited support for BTSP-like contributions to time fields and that the\nrole of such events in temporal coding remains unresolved.\nWe agree with the reviewer. The noted section in the Discussion now reads “Although we\nobserved small firing rate increases and rare BTSP-like events that form odor-specific fields during\nthe delay period, our findings only provide limited support of BTSP-like events contributing to\ntemporal coding. Future studies should investigate if recurrent CA3 networks capable of generating\ntemporal codes55–57 can be activated coincidentally with LEC and MEC to drive BTSP for internally\ngenerated representations.”\nBehavioral relevance and EC silencing\nodor decoding in CA1, but have no detectable effect on behavioral performance in the expert animals.\nThis is now documented more clearly, but the manuscript still leans toward a causal interpretation in\nwhich BTSP-dependent representations are central for task solution. Given the lack of behavioral\neffect in the regime actually recorded, a more balanced formulation would be that LEC and MEC\nmanipulations alter neural representations and event statistics during expert performance, but their\nnecessity for behavior under these conditions is not demonstrated. Any stronger inference about\n“BTSP creating behaviorally relevant representations” should be softened accordingly or explicitly\nframed as a hypothesis for future work (e.g. during acquisition or more demanding variants of the\ntask).\nWe have removed statements that mention ‘behavioral relevance’ in the Results and\nDiscussion. Additionally, we have expanded the section in the Discussion to further discuss this\nimportant idea: “Perhaps the effects that we observed in expert animals would be stronger if tested\nduring early training. Alternatively, the loss of information from LEC or MEC may affect CA1 activity\nregardless of the stage of behavioral performance. Furthermore, the lack of behavioral performance\ndeficits with either LEC or MEC inhibition suggests that these representations are not necessary for\nthe performance of this specific task. Future experiments with inhibition at different stages of learning\nand across various tasks will be necessary to isolate the behavioral relevance of LEC and MEC\ninputs driving BTSP-like events in CA1.”\nStatistical issues\n- The authors state that they now use linear regression to separate day and performance, and\nthey have corrected the chemogenetic error bars, which is appreciated. Nonetheless, several\nstatistical concerns remain:\n- Regression analyses treat each animal–day as an independent data point without modeling\nmouse as a random effect. With only five mice followed over multiple days, this violates the\nhierarchical structure of the data and likely underestimates uncertainty on day/performance\neffects.\nWe thank the reviewer for pointing out this error and have now updated all regression figures,\nstatistics, and supplemental tables after modeling mouse as a random effect (slope and intercept).\nThis approach is better suited to repeated measurements for each mouse, better captures animal\nvariability, has more power, and thus strengthens our findings. All corresponding figure panels now\ndisplay larger t-values and smaller p-values, including ‘Plateau-like’ Event Rate in Figure 3D which\nis now significant but weaker than all other effects. We now note this difference in the text.\nSupplemental Tables contain all MATLAB Table outputs regarding the model and results.\n- Event-level analyses still appear to treat individual events as independent observations in\nsome tests, even though many events come from the same cells and animals, again inflating\neffective n and p-values.\nThe reviewer’s point applies to Figures 1K and 2D depicting histograms for visualization. All\nstatistics and other figure panels are performed on animal averages or session averages. We\ndenote this in the legend text with statements such as “Individual thin gray lines represent each of\nthe 17 animals (which are the average for each of their 8 recording sessions)”. The only other use\nof individual events is when we summarize total counts in the main text (e.g. “294 ‘successes’ out\nof 3876 total events”), which are summary quantifications without statistics involved.\n- The multi-factor ANOVAs in the chemogenetic experiments are based on very small group\nsizes (e.g. n = 3 controls), where parametric assumptions are fragile and effect sizes or\nconfidence intervals would be more informative than marginal p-values.\nabout effect robustness and for avoiding strong mechanistic or causal claims based on borderline\nstatistics.\nWe appreciate this suggestion. In each figure legend corresponding to figure panels with three-way\nANOVAs we have now included the 95% confidence interval for each significant difference. This is\nincluded for Figure 4B, 4C, 4E, 4F, 4G, 4I, and Supplementary Figure S6I.\nContext from Sumegi et al.\nThe authors now cite Sumegi et al. as using somatic calcium to study BTSP-like phenomena.\nImportantly, that work also shows that large somatic Ca² events with BTSP-like signatures are\nneither necessary nor sufficient for place field formation and that diverse calcium dynamics can\nunderlie new fields. This does not contradict the present ⁺study, but it reinforces the idea that large\nsomatic “plateau-like” events are one of several possible routes to field formation. In my view, this\nmakes it even more important that the current manuscript frame its main conclusion as the\nidentification of one correlated pathway in a specific non-spatial task, rather than as establishing\nBTSP as the general mechanism for non-spatial representations.\nWe have edited the first summary paragraph of the Discussion to read “These events are\nconsistent with models of behavioral timescale synaptic plasticity (BTSP), as they demonstrate\nasymmetrical formation observed previously during spatial tasks1–11. This suggests that BTSP may be\none plasticity mechanism capable of driving rapid formation of hippocampal representations during\nboth spatial and non-spatial cognition.”\nAdditionally, we have expanded a section to discuss the diversity of calcium events as seen in\nSumegi et al. It now reads “Additionally, alternative mechanisms may also play a role, consistent with\nrecent research demonstrating a variety of somatic calcium dynamics preceding formation of place\nfields51. Indeed, the rapid formation of odor responses after optogenetically-induced somatic plateau-\nlike events suggests sufficiency, but the low success rate suggests other mechanisms could also be\ninvolved. Furthermore, tests of necessity are difficult due to our present limited knowledge of the\nmechanisms underlying BTSP.”\nIn summary, the additional analyses and revisions improve the manuscript, but I still recommend a\nfurther toning down of mechanistic and causal language, more explicit discussion of alternative\nmechanisms and the limitations of the operational definition, clearer acknowledgment of the lack of\nbehavioral impact of EC silencing under the conditions tested, and a more cautious treatment of\ngeneralization to time fields.\nWe thank the reviewer for their suggestions. We have addressed each of these concerns with\nrevisions of text throughout the Abstract, Results, and Discussion sections.\n4"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you for the detailed rebuttal and substantial revisions. Several of my earlier concerns are now at least partially addressed, especially the change in terminology to “plateau-like” events, the added discussion of somatic imaging caveats, and the corrections to error bars. However, I still feel that the strength and scope of the main claims exceed what the data can support, and I would strongly encourage further tempering of the mechanistic and causal language, as well as clearer acknowledgment of alternative interpretations.\nMechanistic and causal claims\nWhile the authors have removed some explicit causal phrases and now refer to “plateau-like” rather than “BTSP-like” events, the title, abstract, and discussion still largely present the results as demonstrating that BTSP is a general plasticity mechanism for non-spatial representations. In particular, the concluding statement in the abstract that the findings “demonstrate a causal role of BTSP” is not supported by the present data. Given that all inferences are based on somatic calcium signals and an operational definition of ≥10 SD events, this remains too strong. The same somatic signature could arise from different underlying mechanisms (e.g. bursts, mixed dendritic/somatic events), and the data demonstrate correlation and temporal precedence rather than necessity or sufficiency. I would recommend consistently rephrasing to something more neutral, such as “rare, large somatic calcium events consistent with BTSP models can precede the formation of some odor fields in this task,” and avoiding language that implies that BTSP per se has been established as the mechanism.\nOperational definition and circularity\nThe revised text better documents numerators/denominators and threshold choices, but the core concern about circularity remains. “Possible induction events” are defined using stringent criteria (low prior activity, no strong field in the preceding trials, restricted trial window), and “success” is then defined as a new, reliable field emerging near the event in time. This effectively preselects events in which a silent-to-active transition occurs with the desired temporal asymmetry and discards cases where fields emerge more gradually, overlap existing activity, or arise without a qualifying event. The binary analysis therefore builds the BTSP-like pattern into the inclusion criteria and will, by construction, emphasize one-shot, asymmetric field formation. The more continuous “threshold-free” analysis is a welcome addition, but the manuscript should more clearly acknowledge that the binary/“induction” analysis is conditional on these strong filters and cannot be taken as an unbiased estimate of how often plateau-like events genuinely drive fields versus other scenarios.\nGenerality and time representations\nThe manuscript still suggests that BTSP underlies non-spatial representations more broadly, including time. However, the data show that only a very small minority of plateau-like events occurring after odor offset precede time fields, and optogenetic induction in the delay period did not reliably generate fields. The discussion now notes that delay-related effects are weaker and rare, but the claims about generalization to time and “internally generated” codes still read as if they are an established extension of the mechanism rather than an open question. I would recommend explicitly stating that the present data provide only limited support for BTSP-like contributions to time fields and that the role of such events in temporal coding remains unresolved.\nBehavioral relevance and EC silencing\nThe chemogenetic manipulations clearly modulate plateau-like event rates, success probabilities, and odor decoding in CA1, but have no detectable effect on behavioral performance in the expert animals. This is now documented more clearly, but the manuscript still leans toward a causal interpretation in which BTSP-dependent representations are central for task solution. Given the lack of behavioral effect in the regime actually recorded, a more balanced formulation would be that LEC and MEC manipulations alter neural representations and event statistics during expert performance, but their necessity for behavior under these conditions is not demonstrated. Any stronger inference about “BTSP creating behaviorally relevant representations” should be softened accordingly or explicitly framed as a hypothesis for future work (e.g. during acquisition or more demanding variants of the task).\nStatistical issues\n- The authors state that they now use linear regression to separate day and performance, and they have corrected the chemogenetic error bars, which is appreciated. Nonetheless, several statistical concerns remain:\n- Regression analyses treat each animal–day as an independent data point without modeling mouse as a random effect. With only five mice followed over multiple days, this violates the hierarchical structure of the data and likely underestimates uncertainty on day/performance effects.\nEvent-level analyses still appear to treat individual events as independent observations in some tests, even though many events come from the same cells and animals, again inflating effective n and p-values.\n- The multi-factor ANOVAs in the chemogenetic experiments are based on very small group sizes (e.g. n = 3 controls), where parametric assumptions are fragile and effect sizes or confidence intervals would be more informative than marginal p-values.\nThese points do not overturn the qualitative trends, but they further argue for conservative statements about effect robustness and for avoiding strong mechanistic or causal claims based on borderline statistics.\nContext from Sumegi et al.\nThe authors now cite Sumegi et al. as using somatic calcium to study BTSP-like phenomena. Importantly, that work also shows that large somatic Ca²⁺ events with BTSP-like signatures are neither necessary nor sufficient for place field formation and that diverse calcium dynamics can underlie new fields. This does not contradict the present study, but it reinforces the idea that large somatic “plateau-like” events are one of several possible routes to field formation. In my view, this makes it even more important that the current manuscript frame its main conclusion as the identification of one correlated pathway in a specific non-spatial task, rather than as establishing BTSP as the general mechanism for non-spatial representations.\nIn summary, the additional analyses and revisions improve the manuscript, but I still recommend a further toning down of mechanistic and causal language, more explicit discussion of alternative mechanisms and the limitations of the operational definition, clearer acknowledgment of the lack of behavioral impact of EC silencing under the conditions tested, and a more cautious treatment of generalization to time fields.", "reviewer_2": "All of the previously raised points have been thoroughly addressed."}, "author_response": ""}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have addressed all of my points. Congratulations to the authors on the thorough revisions. I recommend acceptance for publication."}, "author_response": ""}]

Decision letter