We find that trade among RCEP member countries will increase significantly and economic output will expand with the reduction of regional tariffs. However, the results show that complete tariff elimination among RCEP members would increase the yearly global CO2 emissions from fuel combustion by about 3.1%, doubling the annual average growth rate of global CO2 emissions in the last decade. The emissions in some developing members will surge. In the longer run, the burdens can be lessened to some extent by the technological spillover effects of deeper trade liberalization. We stress that technological advancement and more effective climate policies are urgently required to avoid undermining international efforts to reduce global emissions.Regional trade agreements (RTAs) have been widely adopted to facilitate international trade and cross-border investment and promote economic development. However, ex ante measurements of the environmental effects of RTAs to date have not been well conducted. Here, we estimate the CO The Regional Comprehensive Economic Partnership (RCEP) will come into force in January 2022. Here the authors quantify ex ante economic and environmental effects following RCEP tariff reductions. After an 8-year-long negotiation, the ten countries of ASEAN (Association of Southeast Asian Nations), China, Japan, South Korea, Australia, and New Zealand finally concluded the Regional Comprehensive Economic Partnership (RCEP) in November 2020 and it became the largest RTA in the world in terms of both economic size and population. According to the Schedule of Tariff Commitments in the RCEP Agreement, most trade in goods will be duty-free immediately or within ten years after the agreement enters into force. Tariff elimination in the region will reduce trade and production costs, resulting in considerable trade-creation and production-boosting effects.Regional trade agreements (RTAs) have been sweeping the world and have become ubiquitous in facilitating international trade and investment10. That is, international trade increases global or regional emissions if developed economies with cleaner production technology and more stringent environmental policies transfer their polluting industries or production activities to developing countries, leading to emission leakages. Most RCEP member countries are typical developing economies that are less emission efficient in their manufacturing industries. In 2018, the amount of CO2 emitted by RCEP member countries accounted for a high share (39.1%11) of global CO2 emissions from fuel combustion. Therefore, the rapid growth of production activities and trade in an increasing number of less developed nations could impose non-negligible burdens on global and national emission mitigation. Since all the RCEP member countries have also committed under the Paris Agreement and the 2030 Sustainable Development Goals (SDG) of the United Nations12, quantifying both the potential economic gains and environmental burdens following RCEP is vital for balancing economic and environmental development when implementing RTAs.However, increased international production fragmentation has raised concerns about the trade-climate dilemma (or pollution haven effect) of international trade18 and has largely neglected environmental problems. The second strand deals with the environmental side effects of international trade, which has substantially accounted ex post for the large carbon flows between countries via international trade28. The effect of international trade on emission level and intensity has also been intensively studied in the past decade31. Some studies consider the effect of international trade and foreign direct investment (FDI) when studying the energy-economy-environment relationship34. In fact, few studies have ex ante quantified the environmental effects of a trade agreement.There are two main strands of literature relevant to our study. The first strand of literature deals with the economic effects of RTAs by focusing on quantifying the economic welfare effects of RTAs13 from the perspective of global production networks or global value chains (GVCs)36. The results show that the RCEP tariff reduction will unleash trade-creation effects and improve all member countries’ economic welfares. However, we also find that complete tariff elimination within the RCEP bloc would increase the yearly global CO2 emissions by 3.1% if the emission intensities (CO2 emissions per unit of output) keep unchanged. Given that the annual average growth rate of global CO2 emissions from fuel combustion in the last decade was ~1.5%11, 3.1% represents a substantial burden on global CO2 emissions mitigation. The environmental burdens on some developing countries such as Vietnam and Thailand will surge. In the longer run, the learning-by-doing effects of deeper trade integration reduce the emission intensity and thereby lessen the emission burdens to some extent. However, they are not large enough to completely offset the burdens. As a result, we emphasize that technological advancements in reducing pollutant intensity are urgently required in more developing countries to offset the extra emissions caused by the RCEP. We also suggest that more effective climate policies for international trade should be designed and implemented.In this work, we estimate the carbon emission effects after evaluating the economic effects of RCEP tariff reductions. We estimate how and to what extent RCEP tariff reductions affect trade and economic welfare using a multi-sector and multi-country general equilibrium modelIn this subsection, we evaluate how and to what extent trade and welfare are affected by tariff reductions committed in the RCEP Agreement. Our estimation draws on three types of datasets: input–output (I.O.) tables, bilateral trade flows, and bilateral tariff data. Supplementary note Figure Table To reveal how each member’s welfare is improved, we decompose the total welfare effects into the volume of trade effect (columns 3 and 4) and terms of trade effect (columns 5 and 6), which are further decomposed separately into the results of trade with RCEP members versus trade with other economies outside the RCEP Agreement . Columns 3 and 4 show that trade with RCEP members rather than the RoW is the most important contributor to the increase in all members’ volume of trade. Comparing with the results in columns 5 and 6, we can find that creating more trade within the RCEP bloc also makes the most significant contribution to the increase in welfare for member countries—South Korea, Cambodia, Malaysia, the Philippines, Thailand, and Vietnam. In addition, trade reductions with the RoW generate slightly negative welfare effects for most members. The reason for such negative effects stems from the RCEP diverting trade from non-RCEP member countries.Columns 5 and 6 show that the aggregate terms of trade for almost all members improve, whereas South Korea’s and Thailand’s terms of trade deteriorate slightly. This differential performance can be attributed to the divergent changes in the export prices of each country, as terms of trade compare the price of a country’s export with the price of its import. The model shows that export prices are determined by the unit costs of input bundles, namely, the combination of labor costs  and the prices of intermediate inputs. Column 1 shows that the RCEP will increase the real wages of all members, which increases export prices. However, other things being equal, the prices of intermediate inputs will decline with tariffs on imported intermediates. Such effects can further be propagated through input–output linkages. As a result, the change in the prices of intermediate inputs decreases export prices. Ultimately, for most RCEP members, the increase in real wages is larger than the decrease in the prices of intermediate inputs, which results in a positive effect on terms of trade. For other economies, the contrary is the case.Supplementary Tables For each RCEP member, we calculate the sectoral contribution to its aggregated change in volume of trade and terms of trade. First, as shown in Fig. Another notable observation is that a handful of sectors—electrical equipment, machinery and equipment, and motor vehicles—explain a high proportion of the changes in terms of trade in many countries . This is the combined result of tariff reduction, the share of intermediate inputs required in the production process, and intersectoral linkages. Although the tariff reductions in these technology-intensive sectors are not the largest, they are considerable for some countries. More importantly, these sectors use a considerably larger share of intermediate inputs in production than other sectors. They also have stronger input–output linkages with other sectors. Therefore, a decrease in the unit production costs in these sectors has a larger multiplicative effect and thus a larger impact on terms of trade.2 per US dollar in 2015) do not decrease enough to offset the extra emissions caused by the increase in production outputs. Assuming the emission intensities of all countries stay at the same level as that in the year 2015, the global CO2 emissions from fuel combustion would increase by 251.4 million tonnes  when the tariff structure changes to that specified for the first year after the RCEP enters into force. When the tariffs continue to decline to the level in the 5th and 10th years and then to zero, global CO2 emissions will increase by 463.7 Mt (1.4%), 756.4 Mt (2.3%), and 1046.5 Mt (3.1%), respectively. Recalling that global CO2 emissions grew at an annual average rate of 1.5%11 in the last decade, these results indicate substantial burdens on carbon-emission mitigation.The synergy of tariff elimination within the RCEP bloc substantially reduces the costs of intraregional trade and production and thus increases the outputs of the member countries. As a result, carbon emissions will also increase significantly if the emission intensities , followed by the ASEAN economies  and Japan (52.7 Mt CO2). In terms of magnitude, Vietnam will increase the most (16.5%), followed by Malaysia (16.1%) and Thailand (13.6%). The results indicate that the emission intensities in these countries must decrease by the same magnitude to ensure that the CO2 emissions do not increase. The magnitude of the decrease should be larger if countries aim to reduce their emissions. Such an ambitious target could be a non-negligible burden, especially for some developing ASEAN economies.The increased COCO2 Fig. , account2 emission change rate for RCEP members. The ratio gives the welfare gains at the cost of a 1% increase in carbon emissions. Vietnam, Singapore, and Cambodia are the greatest gainers in this ratio, whereas China and Japan rank at the lower end.In Fig. 2 in its increased trade with other RCEP members and reduce carbon emissions because of its decreased trade with non-RCEP economies. We employ the environmentally extended inter-country input–output (ICIO) model to account for the comprehensive carbon-emission changes due to trade changes. The results show that trade changes in the case of all trade in goods within the RCEP bloc becoming duty-free would increase CO2 emissions for China, the ASEAN countries, South Korea, Japan, Australia, and New Zealand by 130.2 Mt, 70.4 Mt, 27.2 Mt, 22.5 Mt, 4.8 Mt, and 0.5 Mt, respectively.The increased carbon emissions are driven by the rise of production for both domestic expenditures and trade. As mentioned above, the RCEP tariff reductions bring mainly trade-creation effects within the RCEP bloc and cause trade diversion between RCEP members and non-RCEP economies. An RCEP member may emit more CO2 (61.2 Mt) emitted by RoW (non-RCEP economies). The emission effects of RCEP on non-RCEP economies are twofold. On the one hand, RCEP tariff reductions reduce the direct exports of some non-RCEP economies to RCEP members for reasons we discussed in the previous section, which will reduce the emissions of some non-RCEP economies. On the other hand, the increased production for trade within the RCEP bloc requires more intermediate inputs from some economies outside the RCEP. The economic activities associated with the increased production of such intermediate products generate more CO2 emissions in non-RCEP economies. Due to the increased international production fragmentation, the emissions generated by these indirect linkages can be substantial. The fact that the RCEP increases overall emissions by economies outside indicates that the indirect effects are larger than the direct effects.We also find that trade changes slightly increase the amount of CO2 emissions due to changes in bilateral trade flows, which tells us who emits increased CO2 for whom. It shows the amount of CO2 emitted by a region of origin for the production of its increased exports to the destination. The largest flow is 44.3 Mt CO2 for China’s increased exports to the ASEAN countries. Other large flows include emissions for the increased exports of the ASEAN countries to China (40.2 Mt CO2), China to Japan (36.1 Mt CO2), and China to South Korea (27.8 Mt CO2). Figure 2) and South Korea (29.3 Mt CO2) generates a relatively small increase in the CO2 emissions of these countries. However, their imports generate considerable increases in CO2 emission (57.8 and 60.3 Mt CO2) in the source countries. The results indicate that developing RCEP members, including China and some ASEAN economies will increase CO2 for the developed members.Figure 2 emission changes generated by trade changes for RCEP members. The exports of electrical equipment contribute the most to China (14.5%), the ASEAN countries (12.7%), Japan (18.7%), and South Korea (23.7%). The other two large contributors are machinery and equipment  and computer, electronic, and optical products . The motor vehicle industry makes the second-largest contribution (17.8%) to Japan, but its contribution to other countries is relatively small, indicating Japan’s strong comparative advantage in this industry. For Australia and New Zealand, the increased trade generates a very small increase in emissions. The major contributors are mining for Australia and agriculture for New Zealand.Figure 37, increased trade in this product will reduce emissions in both countries.The finding that RTAs increase participants’ economic welfare at the cost of environmental burdens can be explained by the fact that in real economic interactions, what, how much, and with whom to trade are still determined based on economic profitability rather than environmental considerations. In a Ricardian world, a country exports more products in which it has a comparative advantage in terms of production. The advantage is defined as using fewer of the resources under consideration. Traditionally, labor was such a resource, and additional trade generated from lower trade costs led to increased welfare and emissions in each trading country. Alternatively, if we consider environmental aspects, the story changes. For instance, if we define the advantage as generating fewer emissions in producing a certain product30. The results of emission burdens presented above focus on the first two channels holding the last channel constant. Next, we examine how trade liberalization following RCEP may affect the emission intensity and whether such effects lessen or intensify the emission burdens driven by the scale effects and the sectoral composition effects. Specifically, we examine the effects of global value chain (GVC) participation on emission intensity. GVC participation indicators measure to what extent countries/industries/firms are involved in globally fragmented production. GVC participation may affect emission intensity via pollution outsourcing arising from imports of intermediate inputs, intensified competition or knowledge spillover which encourages process innovation and technological upgrading, and other channels29. As the deepened GVC participation can be considerably attributed to the reduction of trade costs in the past decades38, we use GVC participation as a proxy of trade liberalization to examine its effects on emission intensity. We distinguish between forward and backward GVC participation because they reflect two different ways of participating in GVCs. Therefore, they may have different effects on the emission intensity of developed countries and developing countries. The forward participation reflects a country’s supplying intermediates to other countries for further production, while the backward GVC participation measures to what extent the country imports intermediate inputs to produce its products39.The main channels through which a country’s emission is determined are the scale effects, the sectoral composition effects, and the technique (intensity) effects40. This process may generate economic gains and increase emission levels but does not necessarily alter emission intensity in developing countries.In Supplementary Note Backward GVC participation has no significant effects until an extended time period (about 3–5 years). The effects do not materialize in the short run. Deeper backward participation indicates that a country increases its imports of intermediate inputs to process into products for domestic consumption and/or export. It does not suggest that the country will necessarily substitute its domestic production with imports. This explains that we do not observe a significant negative effect on emission intensity contemporaneously. In the medium and long run, we observe that increased backward participation reduces emission intensity for both developed and developing countries, and it reduces more of the emission intensity for developing countries than for developed countries. It increases a country’s foreign market access to cleaner intermediate inputs, creating knowledge spillovers, stimulating upgrading, and thus reducing the emission intensity. However, such upgrading-by-doing effects materialize in the long run rather than contemporaneously. The results also suggest that backward participation provides more upgrading opportunities for a developing country. This finding is consistent with the convergence theory that learning-by-doing enables lagging countries to catch up with the leaders of emission efficiency in the longer run.Forward and backward GVC participation focuses on the effects of trade in intermediate inputs on emission intensity. We also examine the effects of trade in final goods on emission level and emission intensity. We find that imports of final goods can substitute domestic production and thus reduce domestic emissions. However, we find no robust significant effect of trade in final goods on emission intensity, which attests that trade in final goods alters a country’s emission level but does not necessarily change the emission intensity.Comprehensively considering the effects of GVC participation and trade in final goods, we conclude that trade liberalization reduces the emission intensity of developed countries to a larger extent. The effects of GVC participation on developing countries materialize mainly via backward participation in the medium and long run. Therefore, we conclude that the intensity effects of deeper GVC participation following RCEP can lessen the emission burdens that we present in the section above for developed members such as Japan and South Korea in a relatively short run. The intensity effects can also lessen the emission burdens for developing countries. However, it takes longer, and the intensity effects are not strong enough to completely offset the burdens. To be specific, our estimations indicates that China’s emission intensity can potentially decline by 0–1.8% due to the deeper GVC participation. This reduction in emission is not large enough to offset China’s burdens, recalling the results above that RCEP increases China’s emissions by 2.7% and 4.0%, respectively, in the 5th and 10th year after it enters into force. A similar situation applies to most developing ASEAN economies.2 emissions caused by the RCEP. However, we emphasize that anti-globalization is far from a possible strategy for global emission mitigation. Although de-globalization could reduce international trade and the corresponding embodied carbon emissions in the short term41, it harms the economic welfare of all countries and threatens international efforts to fight climate change in the longer run. Our regression results show that deeper GVC participation reduces the emission intensities of the participating countries. Returning to autarky cuts off developing countries’ opportunities to participate in GVCs and then upgrade their emission technologies through learning-by-doing. In addition, anti-globalization will hinder international cooperation that aims to mitigate global emissions42.The world trade system has been seriously undermined due to huge shocks, such as the COVID-19 global pandemic, the United States–China trade conflict, and Brexit, which to some extent have stimulated the formation of more RTAs. In this paper, we estimate the economic gains and the corresponding carbon-emission burdens of the RCEP. The results show that RCEP tariff elimination will substantially reduce intraregional trade costs and product prices, increase the comparative advantage of regional products, and ultimately improve the welfare of all member countries. Meanwhile, we point out that policymakers should pay attention to the environmental impacts of the RCEP since our results indicate non-negligible increases in potential CO2 emissions per unit of output) in all member countries should decline to offset the extra emissions caused by the RCEP, particularly for the developing member countries. Our calculations based on data for 2015 show that the emission intensities of China (2.83 times) and most ASEAN economies, such as Vietnam (2.25 times), Malaysia (2.11 times), and Thailand (2.04 times), were more than twice that of Japan. Our regression results based on historical data show that the technological spillover effects of trade integration are not strong enough to offset the emission burdens for developing countries. Therefore, on the one hand, strengthening regional and international coordination between developed RCEP members and developing members are very necessary to accelerate the diffusion of cleaner production technologies to the developing members when implementing the trade agreement. This process will help accelerate improvements in emission performance in developing countries. On the other hand, developing nations should accelerate efforts to reduce their emission intensity gap with developed nations. For example, as the world’s factory and the largest emitter of CO2, in 2021, China pledged to achieve carbon neutrality before 2060, which is largely consistent with the 1.5 °C warming limit43. The country is making greater efforts and taking a series of measures to achieve this challenging goal.Instead, we emphasize that technological advancements in reducing pollutant intensity are urgently required in more developing countries. We find that the carbon-emission intensities  should be directed at relatively greener production and consumption activities across these nations.Since all the RCEP countries have committed to the Paris Agreement and the SDG agenda of the United Nations, these nations are highly motivated to ensure economic development and environmental sustainability in tandem2 for other countries. Some often discussed policies include levying carbon tax on international trade and setting an international carbon price floor with border tax adjustments44. We stress that the premise of such policies is a robust and fair accounting system to assign responsibility for internationally traded emissions. Currently, the production-based accounting (PBA) system is in practice widely adopted to assign responsibilities for global environmental problems to individual countries, but it ignores potential carbon leakages through international trade. Consumption-based accounting (CBA) includes the emissions that are emitted at home or in a foreign country but which are embodied in the final products that are consumed at home. CBA redistributes the emissions from PBA, but it still has its problems37. Several researchers proposed further refinements in CBA for assigning the responsibilities for global emissions52. Despite these efforts in academia, national and global climate policies have not adopted such adjusted accounting systems so far. Therefore, we call for the idea of governments and researchers working together to design robust and fair accounting tools, develop and implement effective global and regional climate policies, and share the responsibility of global emission mitigation56.Our findings also suggest that more effective climate policies for international trade should be designed and implemented as we find that some members will emit increasing CO2.5) associated with fuel burning. Second, we currently measured economic welfare due to tariff reduction following RCEP. We did not incorporate endogenous environmental regulations into the equilibrium model. Therefore, the negative welfare effects caused by pollutant emissions are not included in the welfare. Future studies can extend the model by incorporating environmental regulation into the production function and household utility function31. The extended model can be used to conduct policy analysis by investigating potential environmental regulations that align with economic development goals and optimize the economic and environmental welfares of RCEP members. For example, to explore optimal emission tax levels that maximize the welfares of a specific RCEP member after the agreement enters into force.This study has potential extensions that are worthy of pursuit. First, we did not measure other potential environmental impacts, of which the most important include air pollutants  and a bundle of intermediate inputs from all sectors are used for the production of ωj in country r. The production technology is ωj) denotes the efficiency in producing ωj in country r. ωj) is labor and ωj) are the intermediate inputs from sector i required in the production of ωj. i used as intermediate inputs in the production of ωj, with i, and This section outlines the modelr’s imports of sector j products shipped from country s. It consists of an ad valorem tariff  and trade deficits (Dr). In particular, r’s import of sector j products from country s. The trade deficit of a country is the summation of sectoral deficits, The total expenditure on the products of sector τ is changed to τ′. Instead of solving for two equilibria under τ and τ′, Caliendo and Parro13 propose solving for an equilibrium in relative changes so that it is not necessary to estimate some parameters that are difficult to identify. Let a variable with a circumflex “s and r, and the change in a specific sector j of country r.The next step is to solve for changes in wages and prices given that the tariff structure 62) to account for the carbon-emission changes. Denote the following as the flows of final products among different countries:31, the gross output vector y isI is an (nm × nm) identity matrix, and u is a summation vector of appropriate length with all elements being ones.The RCEP tariff reductions lead to changes in multilateral trade flows resulting in trade-related carbon emissions changes. We adopt the environmentally extended ICIO model (see Supplementary Table w be the CO2 emission coefficient vector, the elements of which provide the emissions per unit of output. Then, the CO2 emission vector e can be written asLet 2 emissions in all sectors in the world, which equals global CO2 emissions. This equation can be adapted to allow us to calculate the CO2 emissions (2 emission coefficients for the sectors in country The left side of Eq.  equals tw in Eq.  with a v2 emissions that are generated by the exports of final products from Japan to final users in China. The production processes for trade between Japan and China may consume intermediate products from Thailand, of which the production emits CO2 in Thailand. Using the global Leontief inverse, we can take fully into account these indirect effects in Eq. (Equation  allows us in Eq. .2 emission changes in country To calculate the COfrom Eq. , we deveSupplementary information


The ability to use sensory cues to inform goal-directed actions is a critical component of behavior. To study how sounds guide anticipatory licking during classical conditioning, we employed high-density electrophysiological recordings from the hippocampal CA1 area and the prefrontal cortex (PFC) in mice. CA1 and PFC neurons undergo distinct learning-dependent changes at the single-cell level and maintain representations of cue identity at the population level. In addition, reactivation of task-related neuronal assemblies during hippocampal awake Sharp-Wave Ripples (aSWRs) changed within individual sessions in CA1 and over the course of multiple sessions in PFC. Despite both areas being highly engaged and synchronized during the task, we found no evidence for coordinated single cell or assembly activity during conditioning trials or aSWR. Taken together, our findings support the notion that persistent firing and reactivation of task-related neural activity patterns in CA1 and PFC support learning during classical conditioning. The ability to react to sensory cues with appropriate behavior is crucial for survival. On the level of neuronal circuits, linking cues to actions likely requires the interplay between a large network of cortical and subcortical brain structures, including the medial prefrontal cortex (PFC) and the CA1 area of the hippocampus . Both arPFC has been suggested to map contextual and sensory information to appropriate actions according to flexible rules . AccordiSimilar to PFC, CA1 responds to sensory cues and displays sustained activity during delay periods to support memory formation .Importantly, CA1 and PFC interact substantially during awake hippocampal Sharp-Wave Ripples (aSWRs) . aSWRs hHere, we investigate how neural activity patterns in CA1 and PFC change throughout learning of sensory guided behavior, if information related to sensory cues is maintained while anticipatory actions are performed, and whether task-related information is reactivated in the CA1–PFC circuit during aSWRs. To this end, we employed high-density silicon probe recordings from both areas in head-fixed mice during appetitive auditory trace conditioning (AATC). Our findings reveal that CA1 and PFC exhibit distinct learning-dependent changes in sensory cue evoked activity, trial type-, and sensory-cue-related sustained activity as well as reactivation of task-related neural assemblies during aSWR.We trained head-fixed mice to associate one of two sounds (CS+ vs CS−) with a liquid reward delivered after a 1-s-long, silent trace period . SuccessWe next investigated how learning shapes neural dynamics in CA1 and PFC. To this end, we performed high-density silicon probe recordings from dorsal CA1 and PFC  = 23.64, p < 0.001; post hoc Wilcoxon rank sum test for CS+, p < 0.001 and CS−, p = 0.008) but were not significantly different from each other postlearning  = 21.49, p < 0.001; post hoc Wilcoxon rank sum test for CS+, p = 0.2 and CS−, p < 0.001) which led to significant differences in postlearning responses between CS+ and CS− stimuli   while avReward evoked activity significantly decreased in both CA1  and PFC   from pren = 77, 4% of all cells; criterion: mean activity during −250 to +250 ms around first lick, one standard deviation above pretrial baseline)   . We also 36, 2%) .However, lick-related activity was not the only driver of single-cell modulation during the interval between CS+ and reward delivery. We discovered a large subset of single cells that exhibited trial type-specific sustained responses during the trace period in CA1 and PFC, similar to what had previously been described during aversive eyeblink trace conditioning .n = 580, 40%) . We also observed a smaller fraction of nonlick cells with sustained increases to CS+ sounds   , bottom 47, 17%) , bottom.47, 17%)  and B.n = 734, 38%)    , top and30, 33%) , bottom.< 0.001) .n = 36, Population Vector Distance vs Licks: correlation coefficient = 0.14, p = 0.42; Population Vector Distance vs movement: correlation coefficient = 0.03, p = 0.85; n = 38, Population Vector Distance vs Licks: correlation coefficient = −0.1, p = 0.52; Population Vector Distance vs movement: correlation coefficient = –0.04, p = 0.74; Because individual cells in CA1 and PFC exhibited sustained activity during the trace period, we hypothesized that these responses might be part of a broader CA1 and PFC population code to maintain a representation of trial identity between CS+ and reward delivery . To test this, we first computed the binned population rate vectors for all simultaneously recorded nonlick cells in both areas during CS+ and CS− trials on a session-by-session basis. We next calculated the Euclidean distance between the population rate vector trajectories during the two stimuli  .We found that postlearning, high-frequency CA1–PFC LFP coherence was increased during CS presentation (y <0.05) . During R2)  .We observed that postlearning CA1 ensemble activity at baseline could be used to predict other individual neurons firing rates in CA1 just as well as the firing rates of neurons in PFC. The PFC ensemble, on the other hand, was much better at predicting the firing rates of other PFC neurons compared to neurons in CA1 , which i< 0.05). Comparing the performance of the full-rank model also did not reveal any significant differences in coordination between CA1 and PFC across task periods   .Learning-dependent reorganization of cortical circuits during memory consolidation has previously been linked to activity during hippocampal SWRs  and reacp = 0.2) . During p = 0.2) . AverageWe next analyzed how reactivation strength changed over time within individual sessions. We found that in CA1 assembly reactivation strength per aSWR increases gradually over the course of individual prelearning sessions for positively modulated assemblies (reactivation+) as well as for negatively modulated assemblies (reactivation−) during aSWRs . This eft-test, compared to pretrial baseline; p < 0.05). Those effects were not observed for the 25 % least reactivated cells.We then sought to determine what type of task-specific information the most strongly SWR-reactivated assemblies represent during the conditioning task in postlearning sessions. In CA1, we found that the 25 % most reactivated assemblies are suppressed during the trace and reward period after CS+ trials compared to CS− trials  Wilcoxo  . HoweverDespite both areas being highly engaged in the task and encoding trial-specific information on a trial-by-trial level, we did not find any evidence for task-specific communication on the single-cell level. CA1 and PFC therefore either process conditioning trials in parallel rather than in series or rely on intermediate structures  for effective communication .Lastly, we found that cell assemblies in CA1 and PFC that are responsive during classical conditioning also strongly reactivate during hippocampal aSWRs. Earlier reports found coordinated aSWR reactivation of behavioral sequences during learning of spatial memory tasks in CA1 and PFC, respectively . In addiNotably, we observed a fast increase in reactivation strength of assemblies derived from conditioning trial activity within CA1 during aSWRs as individual training sessions progressed and a slow increase in reactivation strength in trial and intertrial interval derived assemblies in PFC from prelearning to postlearning sessions. This is well in line with the idea that both areas support learning on different timescales and levels of complexity, with the hippocampus adapting fast to detailed new experiences and the PFC adapting more slowly to behaviorally important variables that remain stable over time . AssemblThis preferential reactivation of CS suppressed assemblies during aSWR, provides additional evidence that suppression in CA1 during CS+ trials plays a pivotal role during trace conditioning and might be relevant to actively encode stimulus identity or to predict upcoming task events.Surprisingly, we did not observe that assemblies, derived from activity during conditioning trials in CA1 and PFC, significantly coreactivated during aSWR.Several studies previously reported single cells in CA1 and PFC with similar spatial firing fields to also be strongly correlated during aSWRs and that synaptic inputs to individual mPFC cells increased if CA1 replay was more coordinated . HoweverOne way to reconcile these findings is that the coordination of aSWRs reactivation within the CA1–PFC circuit might depend on task structure. If animals have to follow rules that are based on specific sequences of behavioral events, for example, in spatial alternation tasks , replay Lastly, it would be intriguing to know how SWRs reactivation of task relevant information during classical conditioning depends on the current state of the animal. A previous study reported significant differences in coordinated CA1–PFC reactivation between wakefulness and sleep . Yet eviFor this experiment, we used a total of 17 male C57Bl/6 mice. The animals were obtained at 10–13 months of age from Charles River Laboratory and all experiments were performed within 2 months after delivery. Seven animals were used for silicon probe recordings from dorsal hippocampus area CA1, six animals were used for recordings from PFC, and an additional four animals were used for combined silicon probe recordings from CA1 and PFC during the AATC task. All animals were group housed until the first surgery after which they were individually housed to prevent damage to the implants. Throughout the experiment the animals were maintained on a reversed 12 h light/dark cycle and received food and water ad libitum until we introduced food restriction 2 days after the first surgery. All experiments were performed during the dark period. This study was approved by the Central Commissie Dierproeven (CCD) and conducted in accordance with the Experiments on Animals Act and the European Directive 2010/63/EU on animal research.Animals were anesthetized using isoflurane (1–2%) and placed in a stereotaxic frame. At the onset of anesthesia, all mice received subcutaneous injections of carprofen (5 mg/kg) as well as a subcutaneous lidocaine injection through the scalp. The animals’ temperature was maintained for the duration of the surgical procedure using a heating blanket. Anesthesia levels were monitored throughout the surgery and the concentration of isoflurane was adjusted so that a breathing rate was kept constant at around 1.8 Hz. We exposed the skull and inserted a skull screw over the cerebellum to serve as combined reference and ground for electrophysiological recordings. We then placed a custom made, circular head plate for head fixation evenly on the skull and fixated it with dental cement (Super-Bond C&B). For CA1 recordings, a craniotomy was performed over the left hippocampus –2.3 mm posterior and +1.5 mm lateral to Bregma and for PFC recordings a craniotomy was performed over left frontal cortex at +1.78 mm anterior and +0.4 mm lateral to Bregma. The exposed skull was covered with a silicon elastomer  until the first recording. All mice were given at least 2 days to recover from the surgery.https://github.com/neurodroid/gnoom). While in head fixation the animals received soy milk as reward which was delivered through a plastic spout that was positioned 0.5 cm anterior to the lower lip. Licks were detected with an infrared beam-break sensor that was positioned right in front of the spout.The head-fixed virtual reality setup consisted of two rods that were screwed onto either side of the implanted head plate and fixated the mice on top of an air-supported spherical treadmill. The motion of the treadmill was recorded using an optical mouse and transformed into movement along a virtual linear track designed with the Blender rendering software. The virtual track was then projected through a mirror into a spherical screen surrounding the head-fixed animal on the treadmill , delivered after 1 s of silence, the so-called ‘trace period’ while ignoring a CS− control sound. We interleaved the CS presentations randomly every 30–45 s. For the two sounds, we choose a 3000 kHz continues pure tone and a 7000 kHz tone pulsating at 10 Hz. We counter balanced the CS+ and CS− sounds evenly between animals throughout the experiment.As the main behavioral outcome measure, we detected the licks of the animals with an infrared beam-break sensor that was mounted in front of the reward spout. Each training session ended as soon as the animals received 50 rewards. We repeated the experiment for at least 10 days.During these behavioral training sessions, the head-fixed animals could freely run on the linear track in the virtual reality which was otherwise not correlated with the AATC task.We performed acute silicon probe recordings from the dorsal CA1 area and/or PFC of head-fixed mice during the first 2 days of the AATC task as well as from day 6 onwards for as long as we were able to achieve stable recordings. We then classified recording sessions from the first 2 days of training as prelearning (Pre) and recording sessions from day 6 onwards and with a significant increase in anticipatory licks as postlearning (Post).At the start of each recording session, we placed the mice in head fixation and removed the silicon elastomer cover to expose the skull. We then used a micromanipulator (Thorlabs) to acutely insert a 128-channel silicon probe into the middle of the previously prepared craniotomy above PFC and/or CA1. For PFC recordings, we then slowly lowered the recording electrode to –2.0 mm ventral to Bregma. For CA1 recordings, we continuously monitored the local field potential (1–600 Hz), ripple frequency signal (150–300 Hz), and spiking activity (600–2000 Hz) during the insertion process and tried to positioned our electrode in a way that the strongest ripple amplitude and spiking activity was 200–250 µm from the base of the probe and 470–520 µm from the tip. In this way, we were able to cover most of dorsoventral extend of CA1 with our recording electrode.Electrophysiological signals were filtered between 1 and 6000 Hz, digitized at 30 kHz using 2 64 channel digitizing heads stages  and acquired with an open-ephys recording system. After each recording session, we retracted the silicon probe and placed a new silicon cover on the skull before releasing the animals back to their respective home cages.t-test between the change in lick rate for all CS+ vs all CS− trials and defined the animal to have learned (postlearning sessions) if this comparison showed a significant difference in lick rate between the two conditions.For every training session and each animal, we compared the change in lick rate between CS+ vs CS− trials. In short, for each trial we took the sum of all licks during the trace period and subtracted the sum of all licks during the baseline period (−1 s to onset of CS). We then computed a https://github.com/cortex-lab/Kilosort) and then manually inspected and curated with the ‘phy’ gui (https://github.com/kwikteam/phy). All following analysis was performed using custom written MATLAB scripts .To identify single-unit activity, the raw voltage signal was automatically spike sorted with Kilosort , smoothed the data with Gaussian-weighted moving average filter (25 bins), and computed the Peri-Stimulus-Time-Histograms (PSTHs) for CS+ and CS− trails.z-scored firing rates in the first 350 ms (postlearning CS) poststimulus interval for each cell and compared population responses in this time window.To assess evoked responses to CS+ and CS− we calculated the To assess lick-related activity, we first defined lick onset as the first lick after CS+ sound onset but before reward delivery that was followed by at least three licks within the next second. We then defined single cells to be lick responsive if the average firing rate during the time window around the first lick (−250 to +250 ms) was increased by at least one standard deviation or decreased by at least one standard deviation from pretrial baseline (−1000 – CS+ onset).z-scored firing rate during the 1-s trace period increased by at least one standard deviation or decreased by at least one standard deviation from baseline during either CS+ or CS− trials.To assess trace period activity, we defined single cells to be trace responsive if the average S-scored responses during the trace period.To quantify group differences, we performed a Kruskal–Wallis (pre- vs postlearning) using average z-scored firing rates during the reward response window (reward delivery − reward delivery +500 ms).For analysis of learning-dependent changes in reward evoked activity, we calculated the n-dimensional space (n = number of simultaneously recorded nonlick cells) for each bin and averaged across sessions.To analyze the CA1 and PFC population response during the trace period, we first computed the average CS+ and CS− PSTHs for all simultaneously recorded nonlick cells in 25-ms bins for every session and smoothed the data with Gaussian-weighted moving average filter (25 bins). For visualization purposes, we then computed the first three principal components of the resulting matrices CS+ and CS− PSTHs and plotted the resulting three-dimensional vectors. To quantify the difference in population activity between CS+ and CS− trials, we calculated the Euclidean distance between CS+ and CS− in In order to predict trial identity by trace period population firing rates using a support vector machine classifier, we first calculated the trial-by-trial firing rates for all simultaneously recorded cells during the trace period. We then split all trials of a single recording session into 20 equal partitions and used 19 of these partitions to train the support vector machine classifier . We then tested the classifier performance on the 20th partition and repeated this process for all other partitions . Classifier performance above chance was determined by comparing the average prediction accuracy across all sessions against chance level using a Wilcoxon sign rank test.R2). We then selected the first model which had mean performance within 1 standard error of the mean (SEM) of the best model, using its rank as the number of predictive dimensions  can be explain another (disjoint) sampled population in the same or in a separate target area, through a simplified, low-dimensional linear model . Brieflymensions . This prmensions .http://www.chronux.org).To assess coherence between mPFC and CA1 during the AATC task, we first selected the CA1 recording channel with the strongest aSWR amplitude (see below) as well at the central channel of the mPFC recording electrodes and downsampled the raw voltage singles to 2000 Hz. Coherence was then analyzed with multitaper Fourier analysis , using tIn order to detected aSWRs during the intertrial periods of the AATC task, we first filtered the local field potential at the top half of our recording electrode  between 150 and 300 Hz, and used common average reference to exclude artifacts affecting all channels. We then identified the recording depth with the strongest average ripple power and used the ripple band signal on this channel for further analysis. For each recording session, we visually inspected the ripple band signal and manually set a low-cut threshold for ripple detection (100 µV in most cases) and a high-cut threshold for artifact rejection (500 µV in most cases). We furthermore, excluded threshold crossings within 200 ms of each other as well as within 200 ms of any licking activity.z-score transformation. Normalization was done using the z-score transformation, which in the case of stimulus-triggered assembly activity only used prestimulus period (of both CS+ and CS−) as baseline (−1000 to 0 s) and in case of ripples-triggered activity only used the period outside the ripple center (50-ms window) as baseline. At last, we defined the assembly reactivation window on SWR as the average z-scored assembly activity in the 50-ms window centered on the aSWR. We then divided each session in 10 equally long-trial blocks, and investigated the reactivation strength of positively (reactivation+) and negatively (reactivation−) modulated assemblies over the course of the session. Statistical comparisons between pre- and post  mean assembly activity were done using a Wilcoxon rank sum test, while comparisons of reactivation within a session was done comparing the reactivation in the first three and last blocks using a Wilcoxon signed-rank test. In t-test.To compute task-related cell assemblies in CA1 and PFC, we first binned the spikes of each trial (−1 to 4 s from stimulus onset) into 20-ms bins. Then, we used independent component analysis to find the coactivation patterns as described previously . The numFor each assembly a trial-type modulation score  was compFirst, to ensure that both areas had significant aSWRs reactivations, we computed the percentage of significant ripples. This was defined as the percentage of reactivations above two standard deviations (computed through the median absolute deviation) from the median. Then, to assess the coordination of CA1 and PFC assembly reactivation (during aSWRs), we counted how often each simultaneously recorded pair (one CA1 and one PFC assembly) reactivated together above the median in both areas and then compared the percentage of coincident high reactivations  with the percentage of high and low reactivations in CA1 and PFC, respectively  were examined with light microscopy to confirm electrode placement in CA1 and mPFC. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Acceptance summary:This paper is of interest to scientists using classical conditioning and others interested in prefrontal-hippocampal interactions in general. In particular, the reactivation of neuronal ensemble activity during non-spatial tasks is novel and fills a critical gap in knowledge.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Kaori Takehara-Nishiuch as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Laura Colgin as the Senior Editor.Thank you for submitting your article "Learning differentially shapes prefrontal and hippocampal activity patterns during classical conditioning" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1. The finding of reactivation during aSWRs in inter-trial intervals (ITI) provides the major novel advance provided by the manuscript. The authors conclude that this reactivation plays a role in learning the task. This claim requires further supporting evidence, including the demonstration of the relationship of reactivated cell assemblies with activity patterns described for CS+ and CS- stimuli as well as details about the rate and distribution of aSWRs over learning. For example, can the authors classify the detected cell assemblies into groups, each of which represents a specific task event ? By running all subsequent analyses separately in each of these groups, it is possible to demonstrate how the timing and incidence of reactivation differ among these groups. Alternatively, similar to CA1 neurons, PFC neurons also change firing patterns depending on contextual information . Can the author identify "control" cell assemblies that co-fired irrespectively to task events by running the same assembly analysis with firings during the ITI period (excluding SWR events)? If the task-related information is preferentially reactivated, these control assemblies should be reactivated less frequently than the assemblies representing task events.2. A few previous studies have reported prefrontal reactivation during behavior  – only one is cited , and not in the context of reactivation. Their analyses focused on the coordination of PFC and CA1 reactivation during aSWRs, which contrasts with the results in Figure 5 that demonstrate the assembly reactivation separately in each region. The authors need to address whether the reactivation is related or coordinated between the two regions and how this may change during learning. For example, do PFC and CA1 assemblies reactivate during the same aSWRs, which can potentially aid task-related plasticity and learning in the CA1-PFC circuit, or are they reactivated separately?3. It is expected that ripples also occurred during the reward consumption period. The ripples here were within an active task phase and should yield more information relevant to the task. Did the authors look at the ripples during the reward consumption period?4. The study is the first to simultaneously record from CA1 and PFC in trace conditioning. Despite this strength, the current form applies the same set of analyses to the two regions in parallel, leaving inter-region relationships and coordinations mostly unexplored. For example, is there any relationship or coordination between Trace-up and Trace-down neurons in PFC and CA1 reported in Figure 3, or is there any evidence of oscillatory coordination in the CA1-PFC circuit? At the minimum, cross-correlation analyses can be added to examine temporal relationships between the firing of PFC and CA1 neurons with distinct response sub-types.5. In the analyses for examining evoked and trace period responses , the authors primarily use a subtractive measure (change in firing rate from baseline) and average across all neurons to infer sustained trace-period suppression in CA1 and enhancement in PFC for CS+ stimuli . This population presumably consists of neurons with variable baseline rates and levels of responsiveness, as seen in the Z-scored figures . Therefore, the same analysis can be done using the Z-scored measure, which can control variable baseline rates and avoid outlier contributions from neurons with high firing rates. The authors also need to confirm that the differing responses of overall suppression in CA1 and enhancement in PFC persist when using alternative measures.6. Firing suppression in CA1 is interesting but expected given "rate-decreasing cells" reported in previous works with trace eyeblink conditioning . Even in the prelimbic cortex, about half of stimulus-responding cells decrease their firing rates in response to the CS . The authors need to relate their results to these existing findings and discuss a model explaining how, despite both Trace-up and Trace-down neurons, the overall effect is suppression in CA1 and enhancement in PFC. This can presumably occur due to the different strength of contributions of these two kinds of neuronal responses in the two regional populations.7. In Figures 3and4, the authors used population vector distance to track the separation between the representations of two different sounds versus time. They all showed a sustained increase from the baseline. First, the distance was expressed as a Z-score. What distribution was the Z-score calculated against? Second, did the distance between the two sounds ever come back to the baseline level, e.g., close to 0, meaning not separable anymore? This could be a way to validate the method or check the stability of the recording.8. As the authors wrote, traditionally sustained firing patterns are considered a neural correlate for the association of temporally discontinuous stimuli. However, recent studies using trace eyeblink conditioning  show that CA1 and the prelimbic region also contain cells transiently increase firing rates during a specific time segment and form a sequential firing pattern that bridges the trace period. These cells will not pass the authors' criterion for "trace-responsive" cells, which is based on the averaged firing rate in the entire 1-second delay period. Given the scarcity of the large-scale neural activity recording during explicitly non-spatial tasks, it is worth investigating whether or not the authors observe similar sequential patterns in either region.9. The medial prefrontal cortex consists of several sub-regions, such as rostral anterior cingulate, prelimbic, and infralimbic cortex. In particular, Hattori et al. (2014) reported substantial differences in firing selectivity between the rostral cingulate and prelimbic area in trace eyeblink conditioning. Therefore, the authors need to show histology and report which sub-region was the primary source of recordings.10. Another concern is how the authors ensure the stability of their recordings. Acute recording is less table than chronic recording. Is there any analysis or method for the authors to only include the stably recorded cells in the analyses?11. In several sections , the authors first used ANOVA and then follow-up with rank-sum tests. Please check the normality of the data and use either parametric or non-parametric tests.eLife. Your article has been reviewed by 3 peer reviewers, including Kaori Takehara-Nishiuch as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Laura Colgin as the Senior Editor.Thank you for submitting your article "Learning differentially shapes prefrontal and hippocampal activity patterns during classical conditioning" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:All reviewers appreciated the authors' effort in conducting additional analyses to strengthen their claims further. In particular, the new results successfully tighten the link between learning and detected neural activity patterns. Although Reviewers 1 and 2 thought their concerns were fully addressed, Reviewer 3 raised several concerns. Among them, we agree that the third comment is critical and invite the authors to include additional discussion on the following points:1) The role of aSWR reactivation in non-spatial associative learning.2) The uncorrelated reactivation between CA1 and mPFC in light of contradicting findings by other groupsIn addition, please address editorial suggestions/concerns raised by all reviewers.Reviewer #3 (Recommendations for the authors):The authors have addressed several concerns in the revised manuscript. In particular, they have clarified key analyses, and added new analyses examining coordination of CA1-PFC ensembles during the trace conditioning task. The revised manuscript does a better job of addressing of how physiological phenomena contribute to learning of the task. There are still a few areas that remain unaddressed and additional clarification can be added.1) The authors add LFP coherence and reduced rank regression (RRR) analyses to examine CA1-PFC interactions during trace conditioning . Here, it will be useful to show examples of raw LFP traces in CA1 and PFC from which the coherence is derived.The RRR analyses is informative about directionality of interactions, and similar CA1-PFC interactions are observed across task periods, suggesting generality of interactions unrelated to the task. The question of whether the different cell type responses, Trace-up and Trace-down neurons, across CA1 and PFC regions are correlated or not is not addressed. Since these response types are shown to be task-specific in Figure 2, examining correlations for specific neuronal response types across regions has the potential to reveal an interaction, and is a missed opportunity.2) The authors also choose not to examine possibility of transient increases in neuronal activity and sequential patterns, which can also drive the population vector trajectories shown in Figure 3.3) The authors have added to the reactivation analyses to examine what kinds of information is reactivated in CA1 and PFC during aSWRs in the inter-trial periods. They report reactivation of CS+ specific information in CA1, and non-specific reactivation of all assemblies in PFC that increases over learning. The authors also report that reactivation across regions remains uncorrelated.In the light of these new findings, the authors can provide additional interpretation regarding the possible role of aSWR reactivation in the learning of this task. It seems that aSWR reactivation is only beneficial for within-region changes during learning? Although role of intermediate regions such as entorhinal cortex is speculated, reactivation in these regions can also be independent.The finding of uncorrelated reactivation and associated analyses can also use additional explanation. As reported in Figure 6D, the proportion of reactivation in each region was compared against the rate of joint reactivation across CA1-PFC assemblies, with the finding that joint reactivation was no more likely than independent reactivation. Here, it is important to report the probability of significant assembly reactivation (reactivation rate per aSWR) in CA1 and PFC. If these probabilities are low, then it is possible that data limitations constrain the statistical probability of observing joint reactivation across assemblies. Although Figure 5 shows the strength of assembly reactivation, the rate of this reactivation per aSWR is not clear.Finally, given that PFC reactivation is detected during hippocampal aSWRs, which drive synchronous reactivation of prefrontal neurons , it will be useful to hear the authors' interpretation of why prefrontal reactivation may be uncorrelated, the possible function of independent reactivation in task learning, and if perhaps post-task sleep SWRs can have a role in driving synchronous CA1-cortical reactivation for learning. Essential revisions:1. The finding of reactivation during aSWRs in inter-trial intervals (ITI) provides the major novel advance provided by the manuscript. The authors conclude that this reactivation plays a role in learning the task. This claim requires further supporting evidence, including the demonstration of the relationship of reactivated cell assemblies with activity patterns described for CS+ and CS- stimuli as well as details about the rate and distribution of aSWRs over learning. For example, can the authors classify the detected cell assemblies into groups, each of which represents a specific task event ? By running all subsequent analyses separately in each of these groups, it is possible to demonstrate how the timing and incidence of reactivation differ among these groups.We thank the reviewers for this insightful comment and we agree that reactivation during aSWR is an important, novel result that invites further investigations.To find evidence for reactivation of task event specific information during aSWR , we first computed the average activity of each assembly for CS+ or CS- stimuli and then computed a trial-type modulation score, defined as the average assembly activation during CS+ trials subtracted by the average activation during CS- trials . Then, for each session, we selected the most positively modulated , the most negatively modulated , and the least modulated “control” assembly. Because we found that most assemblies in CA1 are suppressed during the stimulus , we term assemblies by whether they were more suppressed during CS+ or CS- stimuli.We found that, for CA1, CS+ suppressed assemblies in stimulus and trace periods , were more reactivated during aSWRs  compared to CS- suppressed assemblies and control, non-modulated assemblies. This effect was confirmed by a negative correlation between the trial-type modulation score and the average aSWR reactivation of each assembly . CA1 therefore preferentially reactivates assemblies that encode task specific information. In PFC we did not observe preferential reactivation of CS+ or CS- specific assemblies .Alternatively, similar to CA1 neurons, PFC neurons also change firing patterns depending on contextual information . Can the author identify "control" cell assemblies that co-fired irrespectively to task events by running the same assembly analysis with firings during the ITI period (excluding SWR events)? If the task-related information is preferentially reactivated, these control assemblies should be reactivated less frequently than the assemblies representing task events.In the revised version of the manuscript, we included a new control analysis in which we do as suggested by the reviewer. We detected the assemblies during the inter-trial intervals (excluding aSWRs events) and investigated those assembly reactivations during aSWR .We find that reactivation of assemblies defined during the intertrial interval is similar to reactivation of assemblies defined during the trial. However, groups of cells that are coactive during the trial will often also be coactive during the intertrial interval and vice versa which makes a comparison between “trial” and “ITI” assembly reactivation hard to interpret.In contrast, our findings in new Figure 6 clearly highlight that assemblies that specifically respond to CS+ stimuli with suppression, are more re-activated than assemblies that are not modulated by CS+ stimuli and, in our view, provide solid evidence that in CA1 CS+ specific assemblies are preferentially reactivated.On the other hand, neither analysis produced evidence of CS specific reactivation in PFC. We therefore conclude that increased reactivation from pre to post learning session is not CS specific and instead, general task context dependent and reward related  assemblies are reactivated during aSWR in PFC.2. A few previous studies have reported prefrontal reactivation during behavior  – only one is cited , and not in the context of reactivation. Their analyses focused on the coordination of PFC and CA1 reactivation during aSWRs, which contrasts with the results in Figure 5 that demonstrate the assembly reactivation separately in each region. The authors need to address whether the reactivation is related or coordinated between the two regions and how this may change during learning. For example, do PFC and CA1 assemblies reactivate during the same aSWRs, which can potentially aid task-related plasticity and learning in the CA1-PFC circuit, or are they reactivated separately?Indeed, reactivation in PFC during aSWR is likely an important phenomenon that underlies learning and memory. The literature cited by the reviewer shows that reactivation in PFC has previously been observed in spatial memory tasks and we now emphasize this in the manuscript. However, whether reactivation is coordinated between CA1 and PFC is still a matter of debate . To check whether CA1 and PFC assembly reactivation is coordinated during aSWR during trace conditioning, we counted how often each simultaneously recorded assembly pair (1 CA1 and 1 PFC assembly) reactivated together above the median in both areas and then compared the percentage of coincident high reactivations (Q1) with the percentage of high and low reactivations in CA1 and PFC respectively (Q2). . In line with previous results , we did not find evidence for increased co-reactivation of CA1 and PFC assemblies during aSWRs. In combination, with analysis of single cell coordination between both areas during the trials (see below), this suggests that CA1 and PFC process information during trace-conditioning in parallel rather than in series or by means of an intermediate structure . We now highlight this result and interpretation in a revised version of our discussion.3. It is expected that ripples also occurred during the reward consumption period. The ripples here were within an active task phase and should yield more information relevant to the task. Did the authors look at the ripples during the reward consumption period?We thank the reviewers for raising an interesting point. Indeed, aSWR are elevated at reward locations in spatial navigation studies . However, differences in aSWR rate at the exact moments of chewing/licking behavior compared to non-chewing/licking at the same reward location are difficult to detect in freely moving animals and, to the best of our knowledge, have not been previously reported. Furthermore, aSWR in response to behaviorally relevant sounds have not been extensively described before.In our head-fixed trace-conditioning experiments, we were able to precisely detect instances of licking for reward as well as CS+ and CS- sound presentations and align aSWR rate to these events.To our surprise, we observed a marked decrease in the rate of aSWR during conditioning trials and almost a complete absence of aSWR while mice were licking for the reward. While conceptually interesting, we were therefore not able to perform the analysis suggested by the reviewer above because of a lack of aSWR during the precise reward consumption period.However, the baseline rate of aSWR occurrence during the ITIs in our task was similar to what has previously been reported at familiar reward locations .To emphasize these points, we included a Supplementary Figure 5.2 that shows the distribution of aSWR during trials, sessions and learning stages.4. The study is the first to simultaneously record from CA1 and PFC in trace conditioning. Despite this strength, the current form applies the same set of analyses to the two regions in parallel, leaving inter-region relationships and coordinations mostly unexplored. For example, is there any relationship or coordination between Trace-up and Trace-down neurons in PFC and CA1 reported in Figure 3, or is there any evidence of oscillatory coordination in the CA1-PFC circuit? At the minimum, cross-correlation analyses can be added to examine temporal relationships between the firing of PFC and CA1 neurons with distinct response sub-types.We thank the reviewers for this insightful comment. In fact, we originally set up parallel recordings from CA1 and PFC because we hypothesized that these two areas which have been shown to interact on the level of single cells and field potentials in various other memory tasks would also show coordinated activity during trace conditioning. We are excited that the reviewers share our interest in this topic and decided to include an additional main Figure 4 in the manuscript.Heightened oscillatory coherence between CA1 and PFC has previously been described during spatial working memory tasks. We therefore decided to check for oscillatory coherence between CA1 and PFC. We found a significant increase in CA1-PFC coherence during the trace-period after CS+ compared to CS- trials (12-140 Hz) .CA1 – PFC coherence during spatial working memory tasks was reported predominantly in the Theta range (4-12Hz). In our experiments mice were mostly stationary . Expectedly, we therefore did not detect strong theta nor a specific peak in theta coherence.2) . We observed that CA1 ensemble activity at baseline could be used to predict other individual neurons firing rates in CA1 just as well as the firing rates of neurons in PFC. The PFC ensemble on the other hand was much better at predicting the firing rates of other PFC neurons over neurons in CA1 which indicates a directionality of information flow between both areas at baseline.We next checked whether we could find evidence for single cell coordination during conditioning trials . To this end we computed a reduced-rank regression (RRR) to assess how well the activity of a sampled population in one of the areas (source area) can be used to explain the activity of another (disjoint) sampled population in the same area or in a separate target area, through a simplified, low-dimensional linear model. We then used cross-validation to estimate the optimal dimensionality (rank) of each RRR and its performance , indicating that communication between the two areas might depend on more general, context dependent variables instead of specific conditioning trial specific information and that both areas process trial type specific information in parallel rather than in series. We had similar results when comparing the performance of the full-rank model across different periods  We updated our result section, figures and discussion to present these new findings accordingly.5. In the analyses for examining evoked and trace period responses , the authors primarily use a subtractive measure (change in firing rate from baseline) and average across all neurons to infer sustained trace-period suppression in CA1 and enhancement in PFC for CS+ stimuli . This population presumably consists of neurons with variable baseline rates and levels of responsiveness, as seen in the Z-scored figures . Therefore, the same analysis can be done using the Z-scored measure, which can control variable baseline rates and avoid outlier contributions from neurons with high firing rates. The authors also need to confirm that the differing responses of overall suppression in CA1 and enhancement in PFC persist when using alternative measures.We thank the reviewers for this insightful comment and we agree that representing changes in firing rates as Z-scores consistently throughout the text improves the interpretability of our data. We therefore updated Figures 1 and 2 as well as the corresponding paragraphs in the text accordingly. Overall, we had very similar results, however, sound evoked firing rates in CA1 post-learning were found to be not significantly different from each other when comparing Z-scored firing rates .6. Firing suppression in CA1 is interesting but expected given "rate-decreasing cells" reported in previous works with trace eyeblink conditioning . Even in the prelimbic cortex, about half of stimulus-responding cells decrease their firing rates in response to the CS . The authors need to relate their results to these existing findings and discuss a model explaining how, despite both Trace-up and Trace-down neurons, the overall effect is suppression in CA1 and enhancement in PFC. This can presumably occur due to the different strength of contributions of these two kinds of neuronal responses in the two regional populations.We thank the reviewer for bringing up this important point and we have consequently elaborated on the distribution of rate increasing and decreasing cells across both areas as well as on how the numbers that we find in our study relate to previous work by adding an additional supplementary figure  and by expanding on this point in our discussion.We also substantially revised the first half of the Discussion section to properly contrast our results to previous findings in the trace-conditioning field:“This study characterizes changes in neural activity in the CA1-PFC network while mice learn to use predictive sounds to anticipate future rewards. We show that activity in both areas is strongly shaped by learning and that task specific information is reactivated in a complex pattern across CA1 and PFC during aSWR.While CA1 and PFC are highly active during aversive eyeblink trace conditioning, evidence for a similar involvement during appetitive trace-conditioning had been missing. In fact, several previous studies have pointed out differences in the mechanisms underlying both types of learning .Despite these differences, we found that single cells in CA1 and mPFC during appetitive trace-conditioning behave similarly to what had previously been reported during aversive trace-conditioning. Both areas display long lasting sustained activity that bridges the temporal gap between CS+ offset and reward delivery. In both areas, these sustained responses are composed of a mix of Trace-Up and Trace-down cells, i.e. cells that display sustained excitation and inhibition respectively. In CA1, higher numbers and stronger inhibition of Trace-Down cells result in overall suppression of the entire area during the trace period, while in PFC higher numbers and stronger activation of Trace-Up cells resulted in overall excitation.Similar to our study, abundant Trace-Down like responses and spares Trace-Up like responses have been described during aversive trace-conditioning in CA1. This distinct pattern of mostly inhibition mixed with sparse excitation has been hypothesized to increase the signal to noise ratio to more efficiently propagate the signal of Trace-Up cells to downstream areas  Yet, it is also conceivable that Trace-down cells participate in an independent form of coding. Inhibition in CA1 might for example play an active role in suppressing well expected incoming stimuli, i.e. reward delivery .In PFC, responses during appetitive trace-conditioning are also similar to what has previously been found during aversive trace-conditioning. Specifically, higher numbers and stronger excitation of Trace-Up cells have also been found in rat PFC and parts of rabbit PFC during aversive trace-conditioning . A learning dependent reduction in responses to CS- like pseudo conditioning stimuli have also previously been described in PFC .In combination, this suggests that sparse excitation with strong surrounding inhibition in CA1 and mostly excitation in PFC are two general coding principles employed to bridge the temporal gap between a salient cue and a behaviorally relevant event, independently of the appetitive or aversive nature of the event and the specific anticipatory action that it requires.“7. In Figures 3and4, the authors used population vector distance to track the separation between the representations of two different sounds versus time. They all showed a sustained increase from the baseline. First, the distance was expressed as a Z-score. What distribution was the Z-score calculated against? Second, did the distance between the two sounds ever come back to the baseline level, e.g., close to 0, meaning not separable anymore? This could be a way to validate the method or check the stability of the recording.We thank the reviewers for this thoughtful comment which motivated us to include an additional supplementary Figure 3.1 that highlights the changes in firing rate and population vector distance over a longer time period (25s) after the conditioning trials.In our main data set, we found that firing rates and population rate vector distance 20 s after reward delivery are back at baseline levels. This closely matches the time course within which average single cell activity settles back to baseline levels and is well within the inter trial interval of 30-45 s.The Z-score of the population rate vector distance was calculated using the mean and standard deviation of the averaged population rate vector distances at baseline (-5s – 0) before sound onset.We furthermore noticed that the comparison between CS+ (a) and CS+(b) did not reach significance anymore when using raw (not Z-scored) single cell PSTHs to calculate the population rate vector distances. We therefore excluded previous Figure 4 from the paper.8. As the authors wrote, traditionally sustained firing patterns are considered a neural correlate for the association of temporally discontinuous stimuli. However, recent studies using trace eyeblink conditioning  show that CA1 and the prelimbic region also contain cells transiently increase firing rates during a specific time segment and form a sequential firing pattern that bridges the trace period. These cells will not pass the authors' criterion for "trace-responsive" cells, which is based on the averaged firing rate in the entire 1-second delay period. Given the scarcity of the large-scale neural activity recording during explicitly non-spatial tasks, it is worth investigating whether or not the authors observe similar sequential patterns in either region.Indeed, neural ensemble patterns in this task are extremely complex, as highlighted by the literature cited by the reviewer. We are now analyzing our data looking for sequential activations and other patterns with advanced statistics developed with our collaborators. We believe that this work falls outside the scope of the present paper, which concentrates more on firing rate modulations, and will be the subject of a dedicated additional manuscript that is currently in preparation.9. The medial prefrontal cortex consists of several sub-regions, such as rostral anterior cingulate, prelimbic, and infralimbic cortex. In particular, Hattori et al. (2014) reported substantial differences in firing selectivity between the rostral cingulate and prelimbic area in trace eyeblink conditioning. Therefore, the authors need to show histology and report which sub-region was the primary source of recordings..The specific recording position within the frontal cortex is indeed a crucial factor for the interpretation of our data and we thank the reviewer for pointing this out. We included a representative histological image and a schematic of the estimated electrode position in 6 animals in PFC and 4 animals in CA1 as a Supplementary Figure 1We set out to record from deep layers of prelimbic cortex at 1.8mm anterior and 2.4mm ventral from Bregma. Our recordings were performed with two prototype “Neuroseeker” 128 channel silicon probes that did not allow us to verify the exact recording position with electrolytic lesions. However, we managed to reconstruct the electrode position in 6 out of 17 animals in which the electrode track was visible under the light microscope. In all 6 animals, the reconstructed electrode position was found slightly more anterior than planned at 1.9mm – 2.1mm anterior to Bregma at the correct depth of 2.4mm at the intersection between Prelimbic, Infralimbic and Medial Orbitofrontal cortex. The electrophysiological signature of CA1 allowed us to guide our recording electrodes to the correct position in dorsal CA1.10. Another concern is how the authors ensure the stability of their recordings. Acute recording is less table than chronic recording. Is there any analysis or method for the authors to only include the stably recorded cells in the analyses?We thank the reviewers for bringing up this important point. We did take great care to only including stably recorded cells in the analysis and we will shortly describe our approach below. However, we do want to point out that most of the analysis in this manuscript does not describe changes in neural activity over the course of an individual session but rather relies on averaging across randomly interleaved CS+ or CS- trials. We assume that for this analysis, cells fading in or out, could only “water down” any effect because these cells would be unresponsive in some trials .In general, to identify single unit activity, the raw voltage signal was automatically spike sorted with Kilosort  (https://github.com/cortex-lab/Kilosort) and then manually inspected and curated with the ‘phy’ gui (https://github.com/kwikteam/phy).During this step we took great care to only include stable units for further analysis.To further verify the stability of our recordings, we split each recording session into 3 blocks and compared the average firing rates as well as the change in firing rate of all cells from the first to the last third of each session See  .We found no significant difference in firing rates between early and late periods in each session (Wilcoxon Ranksum p=.958). In addition, we found that 97% of all 3514 cells changed their firing rate by less than 10% and less than 0.5% of cells changed their firing rate by more than 30%.Based on these results, we feel confident that our recordings were stable and did not significantly affect our results.11. In several sections , the authors first used ANOVA and then follow-up with rank-sum tests. Please check the normality of the data and use either parametric or non-parametric tests.We thank the reviewers for pointing this inconsistency out to us. Peak sound evoked responses in our data set were not normally distributed in CA1 (Kolmogorov-Smirnov Test p<0.001) and PFC (Kolmogorov-Smirnov Test p<0.001). We therefore resorted to performing a Kruskal-Valis nonparametric ANOVA and found significant group differences for peak sound evoked responses in CA1 =23.64, p<0.001) and PFC =21.49, p<0.001). Post-hoc Wilcoxon Rank Sum Tests revealed that peak CS+ and CS- sound evoked responses increased from Pre to Post learning in CA1 . In PFC peak evoked responses to CS+ sounds did not change significantly but peak evoked responses to CS- stimuli decreased significantly .We updated the respective paragraph in the manuscript.Essential revisions:All reviewers appreciated the authors' effort in conducting additional analyses to strengthen their claims further. In particular, the new results successfully tighten the link between learning and detected neural activity patterns. Although Reviewers 1 and 2 thought their concerns were fully addressed, Reviewer 3 raised several concerns. Among them, we agree that the third comment is critical and invite the authors to include additional discussion on the following points:1) The role of aSWR reactivation in non-spatial associative learning.2) The uncorrelated reactivation between CA1 and mPFC in light of contradicting findings by other groups.We thank the reviewers for raising these two important points. We decided to address them together in a new section of the discussion:“Surprisingly, we did not observe that assemblies, derived from activity during conditioning trials in CA1 and PFC, significantly co-reactivated during aSWR.Several studies previously reported single cells in CA1 and PFC with similar spatial firing fields to also be strongly correlated during aSWRs and that synaptic inputs to individual mPFC cells increased if CA1 replay was more coordinated . However, on the population level, CA1 and mPFC reactivation of specific spatial trajectories has been found to occur independently . Moreover, reactivation of spatial sequences in CA1 and other cortical areas, specifically the entorhinal cortex has been shown to occur independently as well .One way to reconcile these findings is that the coordination of aSWRs reactivation within the CA1-PFC circuit might depend on task structure. If animals have to follow rules that are based on specific sequences of behavioral events, e.g., in spatial alternation tasks , replay of sequences of events in CA1 might drive the activation of cells or cell assemblies in PFC that encode the appropriate behavioral response to those sequences . If the task structure instead “only” requires stimulus response mappings, as in our experiment and visually guided spatial navigation experiments , PFC might not rely on additional information from CA1 and reactivation remains independent. However, coordinated aSWRs reactivation in CA1 and PFC might happen robustly in non-spatial tasks if the tasks require the animals to learn sequences .Lastly, it would be intriguing to know how SWRs reactivation of task relevant information during classical conditioning depends on the current state of the animal. A previous study reported significant differences in coordinated CA1-PFC reactivation between wakefulness and sleep . Yet evidence for sleep SWR reactivation of nonspatial information is still lacking. This further highlights the importance to study SWR reactivation with a battery of different behavioral task and across behavioral states that can help to disentangle the exact content and relevance of replay events for learning and behavior in the future. "In addition, please address editorial suggestions/concerns raised by all reviewers.We addressed the additional comments.Reviewer #3 (Recommendations for the authors):The authors have addressed several concerns in the revised manuscript. In particular, they have clarified key analyses, and added new analyses examining coordination of CA1-PFC ensembles during the trace conditioning task. The revised manuscript does a better job of addressing of how physiological phenomena contribute to learning of the task. There are still a few areas that remain unaddressed and additional clarification can be added.We thank reviewer 3 for this positive feedback and the helpful suggestions for further improvement.1) The authors add LFP coherence and reduced rank regression (RRR) analyses to examine CA1-PFC interactions during trace conditioning . Here, it will be useful to show examples of raw LFP traces in CA1 and PFC from which the coherence is derived.We fully agree that examples of raw data are useful for the interpretation of our data. We therefore revised Figure 4 and added a new panel A which shows simultaneously recorded LFP and single unit activity from CA1 and PFC during one CS+ conditioning trial.Furthermore, we decided to add example traces of simultaneously recorded LFP and single unit activity during aSWR to Figure 5—figure supplement 2 A.The RRR analyses is informative about directionality of interactions, and similar CA1-PFC interactions are observed across task periods, suggesting generality of interactions unrelated to the task. The question of whether the different cell type responses, Trace-up and Trace-down neurons, across CA1 and PFC regions are correlated or not is not addressed. Since these response types are shown to be task-specific in Figure 2, examining correlations for specific neuronal response types across regions has the potential to reveal an interaction, and is a missed opportunity.We thank the reviewer for pointing out this interesting avenue for further analysis. We completely agree with this reasoning but decided that as a first approach to find coordinated activity between both areas, we wanted to use a measure that is unbiased and does not rely on a previous classification into trace-up and trace-down cells. However, as pointed out below, we are currently working on a second manuscript and are approaching these questions with new analysis methods which we feel like fall beyond the scope of this manuscript.2) The authors also choose not to examine possibility of transient increases in neuronal activity and sequential patterns, which can also drive the population vector trajectories shown in Figure 3.We thank the reviewer for this insight full comment and we absolutely share the interest in sequential firing patterns during conditioning in CA1 and PFC. In fact, we are now analyzing our data looking for sequential activations and other patterns with advanced statistics developed with our collaborators. We believe that this work falls outside the scope of the present paper, which concentrates more on firing rate modulations, and will be the subject of a dedicated additional manuscript that is currently in preparation.3) The authors have added to the reactivation analyses to examine what kinds of information is reactivated in CA1 and PFC during aSWRs in the inter-trial periods. They report reactivation of CS+ specific information in CA1, and non-specific reactivation of all assemblies in PFC that increases over learning. The authors also report that reactivation across regions remains uncorrelated.In the light of these new findings, the authors can provide additional interpretation regarding the possible role of aSWR reactivation in the learning of this task. It seems that aSWR reactivation is only beneficial for within-region changes during learning? Although role of intermediate regions such as entorhinal cortex is speculated, reactivation in these regions can also be independent.We thank the reviewer for raising these important points. We decided to address them together with the reviewers finally comment on our view on how to reconcile our findings with previous studies that reported coordinated activity in CA1 and PFC during aSWRs. We added this paragraph to our original discussion:“Surprisingly, we did not observe that assemblies, derived from activity during conditioning trials in CA1 and PFC, significantly co-reactivated during aSWR.Several studies previously reported single cells in CA1 and PFC with similar spatial firing fields to also be strongly correlated during aSWRs and that synaptic inputs to individual mPFC cells increased if CA1 replay was more coordinated . However, on the population level, CA1 and mPFC reactivation of specific spatial trajectories has been found to occur independently . Moreover, reactivation of spatial sequences in CA1 and other cortical areas, specifically the entorhinal cortex has been shown to occur independently as well .One way to reconcile these findings is that the coordination of aSWRs reactivation within the CA1-PFC circuit might depend on task structure. If animals have to follow rules that are based on specific sequences of behavioral events, e.g., in spatial alternation tasks , replay of sequences of events in CA1 might drive the activation of cells or cell assemblies in PFC that encode the appropriate behavioral response to those sequences . If the task structure instead “only” requires stimulus response mappings, as in our experiment and visually guided spatial navigation experiments , PFC might not rely on additional information from CA1 and reactivation remains independent. However, coordinated aSWRs reactivation in CA1 and PFC might happen robustly in non-spatial tasks if the tasks require the animals to learn sequences .Lastly, it would be intriguing to know how SWRs reactivation of task relevant information during classical conditioning depends on the current state of the animal. A previous study reported significant differences in coordinated CA1-PFC reactivation between wakefulness and sleep . Yet evidence for sleep SWR reactivation of nonspatial information is still lacking. This further highlights the importance to study SWR reactivation with a battery of different behavioral task and across behavioral states that can help to disentangle the exact content and relevance of replay events for learning and behavior in the future."The finding of uncorrelated reactivation and associated analyses can also use additional explanation. As reported in Figure 6D, the proportion of reactivation in each region was compared against the rate of joint reactivation across CA1-PFC assemblies, with the finding that joint reactivation was no more likely than independent reactivation. Here, it is important to report the probability of significant assembly reactivation (reactivation rate per aSWR) in CA1 and PFC. If these probabilities are low, then it is possible that data limitations constrain the statistical probability of observing joint reactivation across assemblies. Although Figure 5 shows the strength of assembly reactivation, the rate of this reactivation per aSWR is not clear.We thank the reviewer for pointing this issue and we now address the aSWRs joint reactivations in more detail. First. we computed the rate of significant aSWRs reactivation as suggested by the reviewer. To do so, we used a threshold of 2sd above the median (s.d. was estimated using the median absolute deviation to minimize the effects of outliers). We found that ~5% of the reactivations were above this threshold. This is now shown in Figure 6—figure supplement 1.We then recomputed the proportion of joint reactivations , but using the quadrants defined now by the 5% of highest and lowest reactivations. We found that the percentage of joint CA1/PFC reactivations falling in the 5% highest on both areas was not different than the other three quadrants .We show that in the figure above (right), but choose not to include it in the revised manuscript to avoid redundancy with Figure 6 D.Finally, given that PFC reactivation is detected during hippocampal aSWRs, which drive synchronous reactivation of prefrontal neurons , it will be useful to hear the authors' interpretation of why prefrontal reactivation may be uncorrelated, the possible function of independent reactivation in task learning, and if perhaps post-task sleep SWRs can have a role in driving synchronous CA1-cortical reactivation for learning.We appreciate the reviewer’s comment and addressed this in our revised discussion posted above.