However, the nuanced relationship between leaf height, sunlight exposure and the resulting variation in isotope ratios and leaf mass per area (LMA) has not been documented for an African rainforest. Here, we present δ13C, δ18O and δ15N values of leaves (n = 321) systematically collected from 58 primate food plants throughout the canopy (0.3 to 42 m) in Côte d’Ivoire, West Africa. Besides leaf sample height and light availability, we measured leaf nitrogen and carbon content , as well as LMA (n = 214) to address the plants’ vertical resource allocations. We found significant variation in δ13C, δ18O and δ15N, as well as LMA in response to height in combination with light availability and tree species, with low canopy leaves depleted in 13C, 18O and 15N and slightly higher in %N compared to higher canopy strata. While this vertical isotopic variation was not well reflected in the δ13C and δ15N of arboreal primates from this forest, it did correspond well to primate δ18O values.The canopy effect describes vertical variation in the isotope ratios of carbon (δ This phenomenon has been widely described8 and is commonly referred to as the “canopy effect”9. Isotopic fractionation related to this effect is known to be the result of vertical gradients in sunlight, humidity, availability of atmospheric isotope values, source water and photosynthetic rates. Rather than height from the forest floor, the more ecologically meaningful way of thinking about the canopy effect is probably in terms of depth from the top canopy, as it is the dense structure of intertwining tree crowns that is largely causing these gradients rather than soil-mediated processes permeating upwards into the canopy10. However, by reason of feasibility, canopy effect studies have always considered height from the ground rather than estimating or measuring the depth from the top of the canopy. While most studies focused on δ13C variation within plants, some also included δ18O, δ15N and δ2H17. In this study we investigate plant δ13C, δ18O and δ15N in their relationship to the canopy effect and to stable isotope data of arboreal primate feeding at different canopy heights.There is a remarkable amount of variation in the stable isotope ratios within even one organism. In plants growing in closed canopy forests, leaves measured at varying heights from one individual tree can have markedly different stable isotope ratios of carbon  as the upper canopy can block between 95 and 99.5% of all light from ever reaching the forest floor20. In the lower canopy where lower light and temperature reduce transpiration rates, leaves can afford maximum stomatal opening. Here, diffusion of CO2 is high and the enzyme rubisco will preferentially fix 12CO2 while discriminating the heavier 13CO2. These leaves will have more negative δ13C values. However, in the upper canopy, where potentially high evaporative flux can lead to stomatal closure to conserve water, rubisco will fix 13CO2 at a higher rate than in the understory. Hence, upper canopy leaves will have higher δ13C values21 (and references therein). The canopy effect is often measured in forests with very large trees, such as in large conifers, which will compensate for hydraulic constraints due to gravity by modifying various traits associated with water transport and photosynthesis24.Several processes in the forest canopy cause fractionation and variation in δ18O within the forest canopy is mainly driven by atmospheric humidity levels. Low relative humidity in the upper canopy tends to lead to higher rates of transpiration14. Preferential loss of lighter H216O during evaporation will leave leaf tissue enriched in remaining 18O. Besides rates of evaporation, the δ18O composition of plant water is primarily determined by the isotope composition of source water in the soil, which vary geographically and is affected by local surface air temperature, altitude, amount of precipitation, etc.26. The canopy effect in δ18O, with δ18O values varying with canopy height, has been reported for tropical forest ecosystems with closed canopy cover14, yet not all studies could find a clear difference between understory and canopy leaves δ18O11.Variation in δ15N values in non-leguminous plants are determined by the isotopic composition of the source soil27. δ15N is predicted to fluctuate between ecological zones based on variation in temperature and precipitation as well as mycorrhizal activity27. While epiphytes are seen to experience different rates of δ15N fractionation with canopy height16, fractionation of δ15N within individual leaves has not been seen to be affected by height in trees11. In this study, we explore whether leaf δ15N values vary with canopy height, which has only been documented in a few cases8.δ28. In shade leaves, nitrogen is preferentially allocated to components of light-harvesting processes, whereas sun leaves invest nitrogen into rubisco. However, with increasing height, hydraulic limitations due to gravity, and water stress imposed by greater vapor pressure deficit, leaves will increasingly invest in water saving strategies to optimize water use efficiency29. These high canopy leaves tend to be smaller than their shade-adapted counterparts, with more numerous and larger cells to increase leaf capacitance, altogether making the leaf thicker (higher LMA) and thus more resilient to water loss21. This variation in LMA within the forest canopy should have consequences for the material properties and nutritional value of leaves as well as herbivore food preference31.The considerable gradient in light intensity within the canopy does not only affect isotope fractionation, but also the chemical and structural makeup of the leaves, such as leaf mass per area (LMA). Similar to the canopy effect in isotopes, leaf structure varies with canopy height to maximize radiation-use efficiency and photosynthetic rate with varying levels of light intensity within the canopy. Understory leaves experiencing shade conditions will maximize their radiation-use efficiency by compensating with larger, thinner leaves (lower LMA) to increase photon capture33. Isotopic variation in leaves and fruits due to height differences in the canopy can be expected to be reflected in the hair, bones, and teeth of primates which feed on these various plant food items35. In tropical forests around the world, arboreal primate species occupy specific spatial niches within the vertical strata of the canopy36(and references therein). Long term field studies have observed primate species of the genera Colobus, Cercopithecus, and Cercocebus displaying habitat preference within the canopy of the rainforest of Taï National Park in Côte d’Ivoire, with different sympatric primates occupying different strata within the rainforest canopy. Body size constraints and dietary niches seem to drive spatial differentiation for Taï’s arboreal primates, and this is most pronounced during inter-specific associations37. Variations in branch size and stability as well as plant material properties and possibly uneven nutrient availability within the canopy are assumed to correlate with a vertical stratification of feeding niches occupied by different sympatric arboreal primate species34. An isotope study carried out in Taï National Park, measured the δ13C and δ18O values in primate bone collagen and apatite to determine the extent to which isotopes reflected this stratification in the canopy34. While variation in δ13C values was in large part a reflection of mixed diets rather than height in the canopy38, the study found strong correlation between δ18O values and canopy strata use. This is interesting as while δ13C and δ15N mainly relate to the isotopic characteristics of ingested foods, δ18O in animal bodies can be determined by not only the food, but much more so by drinking water and air39. Arboreal primates are commonly assumed to mainly rely on water deriving directly from plant foods40. This would suggest that leaf δ18O values will considerably affect the δ18O values of these primates, which we will test in this study.The predictability of the canopy effect in the stable isotope ratios of forest plants allows for the study of feeding and foraging behavior in elusive or extinct species of primates and arboreal animals by means of stable isotopes13C, δ18O and δ15N baseline values in 321 leaves of 58 individual plants collected at a range of heights between the understory and emergent trees (0.3 to 42 m) of Taï National Park (Table Pan troglodytes versus) and the different arboreal primates of Taï forest41 . We here aim at establishing a multi-isotope baseline for future research on canopy stratigraphy and arboreal feeding niches in this and other rainforest ecosystem, which has the potential to also inform paleoecological reconstructions of forest species communities in the fossil record42.In this study, we explore the canopy effect in plant foods which is underlying the isotopic variation found in these arboreal primates, by reporting bulk organic δ2), leaf δ13C and δ18O values will strongly correlate with sampling height in interaction with light (PAR), producing the lowest isotope values in the forest understory and the highest values in leaves of emergent trees6. We had no such clear predictions for leaf δ15N values, as few studies found any significant variation in δ15N with height in the canopy16. Further, we investigated how leaf height and light availability affect leaf structure in an effort to better understand and describe the underlying mechanisms associated with isotopic fractionation. We predicted that LMA measurements (n = 214) will be lowest in understory plants and highest in emergent trees. We further anticipated that nitrogen content (%N) of leaves may be negatively correlate with LMA as smaller more shaded leaves should invest into higher photosynthetic capacity. Finally, we considered how leaf mass and %N may have implications for the material properties of leaves and thus relevance for primate feeding behavior.We predicted that the leaves of the tallest trees, exposed to higher proportions of radiation, will modify their structure to accommodate for high rates of photosynthesis and evaporation. As a result of this strategy and other factors summarized under the canopy effect , δ18O  and δ15N values . LMA values (mean = 90.8 g/m2) varied considerably between samples, ranging from 28.6 to 247.1 g/m2  and documented considerable variation in PAR , tree species, %N and %C and the random effect of plant individual on the response variables δ13C, δ18O, δ15N (‰) and LMA (g/m2). All models were highly significant, suggesting that height and PAR, or the interaction of both, as well as %N, %C and tree species have significant effects on δ13C, δ18O and δ15N values and LMA  and showed that while %C was not significantly affecting δ13C values, the effect of height interacting with PAR was significant and tree species was highly significant, with higher and more light exposed leaves revealing significantly higher δ13C values . Here the main driving effects were again sample height interacting with PAR and also tree species, with increasing height and PAR resulting in significantly higher δ18O values . Here, tree species was highly significant, whereas %N and the effect of PAR were significant, with lower light exposure resulting in lower leaf δ15N values . Here the main driving effects were identified to be particularly %N, tree species and the interacting covariates height and PAR. Increasing height and PAR consistently lead to higher LMA and reductions in %N across plant species  in interaction with PAR μmol m− s−1, tre13C, δ18O and even δ15N.For plants growing in a competitive regime, strategies which maximize light efficiency or water efficiency will result in very different structural and nutrient partitioning strategies which will likely have consequences for isotopic fractionation within plant parts. This study documents the gradual changes in leaf isotope ratios in response to absolute height and the sunlight available for photosynthesis and responsible for leaf water evaporation, and thus provides further evidence on the so-called canopy effect in δ43. Consequently, upper canopy leaves will reduce their stomatal conductance in response to the perceived lower water availability23. Furthermore, leaves will also reduce stomatal conductance under increased vapor pressure deficit, brought about by the lower humidity levels typically experienced in the higher strata of the forest canopy44.First, height and evapotranspirative demand are known to impact carbon and oxygen discrimination in leaves along the canopy height gradient. Leaf water potentials decrease with increasing elevation due to longer water transport path lengths, as well as the downward pull of gravity on the water column45. Solar radiation is a driver of photosynthetic rates, relative humidity levels and evaporation rates in the forest interior. Indeed, δ13C and δ18O values as well as measures of LMA, all of which are highly influenced by photosynthetic rate and evaporation respectively, were highly correlated with PAR .We also found a highly significant negative correlation between LMA and %N Table  in our lCercocebus atys and Cercopithecus campelli), mid  and high  canopy, which differ clearly in the stable isotope ratios of leaves  and heterotrophic  plant parts, with leaves always being several ‰ lower in δ13C compared to other plant organs59. We can expect that fruit and seed eating primates will always have higher δ13C values than their folivorous counterparts, even if they forage in the forest understory such as sooty mangabeys (Cercocebus atys). And indeed, the most folivorous primate at Taï, the olive colobus monkey (Procolobus verus) had the lowest δ13C values among all taxa34. In sum, the degree of folivory versus frugivory should be accounted for when using δ13C in reconstructing canopy strata use60.Primates living in Taï National Park, grossly group into the feeding niches low , mid (10–20 m), and high (20–42 m) as shown in Fig. 15N, but low canopy leaves are on average ~ 1‰ lower in δ15N (mean 3.6 ± 1.4‰ 1σ) than leaves higher than 10 m (4.7 ± 1.2 δ15N‰ 1σ). However, this effect is barely visible, e.g. in the δ15N values of low canopy foraging primates at Taï 34. Nevertheless, that canopy height may affect δ15N values of plant foods may find some consideration in future isotopic research as an effect which may interfere with interpretations of faunivory and trophic level62.Our data suggest that δ high 20–2 m as sh high 20–2 m as sh63. A previous study on the canopy effect suggested that young and mature leaves do not substantially differ in δ13C or δ18O and mature foliage and fruits did not differ in δ18O11. On the other hand, isotopic fractionation between specific plant organs are well described for δ13C and allows us to correct plant δ13C data for plant part59. In sum, it is important to understand that both plant part and canopy height can be expected to significantly affect the δ13C ratios of plant foods, whereas canopy height and water source will be the stronger predictor for δ18O. This suggests that sampling of various primate plant food species remains crucial for research in primate isotope ecology when focusing on nuanced effects of canopy niche and dietary adaptations.This isotope study on the canopy effect in primate foods is limited to mature leaves out of logistical reasons as the timing of flowering, fruiting and young leaf flushing can be unpredictable and stretch over many months. Canopy research however, is extremely risky and should involve the presence of experienced certified arborists, which rarely are available for fieldwork months on end. Nevertheless, we are confident that similar isotopic effects we describe for leaves will also likely be found in other forest plant foods. Non-photosynthetically active plant parts such as fruits, flowers and seeds can be expected to source their water and energy from proximate sources within the plant, e.g. from adjacent leaves via phloem translocationMinistre de l'Enseignement supérieur et de la Recherche scientifique, Côte d’Ivoire. We collected mature leaves at ~ 1 m increments for 58 individuals of 12 different plant species  consumed by primates in the research area of the Taï Chimpanzee Project, located in the primary rainforest of Taï National Park, Côte d’Ivoire, West Africa  with a measuring tape lowered to the forest floor and PAR with a handheld quantum light sensor  coupled to a Delta Plus XP isotope ratio mass-spectrometer . For the analysis of bulk leaf material δ18O we used a Thermo-Chemical Elemental Analyzer  also coupled to a Delta Plus XP isotope ratio mass-spectrometer. The resulting isotope data is reported here in ‰ (or mUr). Analytical precision was better than 0.2‰ for δ13C and δ15N, and better than 0.3‰ for δ18O.This study was conducted in compliance with all relevant institutional, national, and international guidelines and legislation. None of the plant species selected for this study are listed by the IUCN as endangered species. Voucher specimens .We measured LMA in initially dried intact leaves, which were later rehydrated at the University of California, Santa Cruz. Dry leaves were weighed prior to rehydration and leaf area was measured using a LI-3100 Area Meter (cm13C, δ18O, δ15N and LMA (please see the Using four linear mixed effects models we statistically analyzed the effects of height in interaction with PAR, tree species, %N and %C and the random effect of plant individual on the response variables δSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.


In this study, we conducted laboratory incubations with soils from boreal and temperate peatlands across Canada. Peat soils were pretreated for different soil moisture levels, and CO2 production rates were measured at 12 sequential temperatures, covering a range from − 10 to + 35 °C including one freeze–thaw event. On average, the CO2 production rates in the boreal peat samples increased more sharply with temperature than in the temperate peat samples. For same temperature, optimum soil moisture levels for CO2 production were higher in the peat samples from more flooded sites. However, standard reaction kinetics  failed to account for the apparent lack of temperature dependence of CO2 production rates measured below 0 °C, and a sudden increase after a freezing event. Thus, we caution against using the simple kinetic expressions to represent the CO2 emissions from northern peatlands, especially regarding the long NGS period with multiple soil freeze and thaw events.Peat accumulation in high latitude wetlands represents a natural long-term carbon sink, resulting from the cumulative excess of growing season net ecosystem production over non-growing season (NGS) net mineralization in soils. With high latitudes experiencing warming at a faster pace than the global average, especially during the NGS, a major concern is that enhanced mineralization of soil organic carbon will steadily increase CO Climate models project that this rate of warming will continue through the twenty-first century, with the greatest warming occurring during the fall, winter and spring period, which is generally non-growing season (hereafter ‘NGS’)3. Given that microbially mediated soil organic carbon (SOC) mineralization (or heterotrophic soil respiration) responds positively to increasing temperatures, warming during the NGS could substantially alter the carbon balance of high latitude soils. For example, arctic tundra ecosystems might already be shifting from annual carbon sinks to sources due to increasing NGS carbon emissions7. The extent to which such shifts apply to a wider spectrum of cold region soil carbon pools remains uncertain, however9.Surface air temperatures in northern high latitude regions are increasing two to six times faster than the global average2) and the availability of liquid water, even if there are sufficient organic substrates remaining from the growing season. Especially where the soil is poorly drained, a portion of the growing-season plant CO2 fixation is therefore not mineralized10. This leads to the accumulation of organic peat layers that can be preserved for up to several millennia11. The resulting northern peatlands have played a substantial role in storing atmospheric CO2 since the last deglaciation13. Nevertheless, recent rapid climate warming, as well as direct human encroachment of peatlands, threaten to release this globally important carbon pool into the atmosphere. While Canada has the second largest areal coverage of peatland14, much of its southern peatlands have already been lost to agriculture and impacted by other anthropogenic disturbances15. In Canada’s north , ongoing ecological and biogeochemical changes driven by climate warming are affecting the carbon storage potential of remaining natural peatlands.In addition to the temperature-dependent slowing down of microbial metabolic activity, soil organic matter decomposition during the NGS can also be inhibited by soil freezing, which limits the supply of molecular oxygen  has been developed for inclusion into the national carbon budget estimations17. However, the model integrates soil carbon emissions over annual time steps and, hence, does not account for seasonal variations in peat decomposition kinetics, which in turn produces unaccountable uncertainties in model predictions under changing climate conditions20.Recent advances in data synthesis and modeling have highlighted the sensitivity of global soil respiration to climate change21. For example, the temperature sensitivity coefficient (Q10) has been widely used as a simple numerical parameter to correct rates of soil carbon mineralization and other microbially-controlled transformation processes25. The Q10 approach assumes an exponential increase in the reaction rate with increasing temperature, and a typical Q10 value of 2 implies that the rate doubles with every 10 °C increase in the ambient temperature24. Most land surface and dynamic vegetation models use the constant Q10 values, but recent findings suggest that the Q10 required to temperature-correct CO2 emissions may be higher  in cold climate regions29, likely because of the interplay of multiple factors affecting soil physiochemical properties  during the NGS30.One way to improve the representation of NGS soil carbon dynamics, is to incorporate variable reaction parameters that reflect the seasonal changes of key soil environmental variables, in particular soil temperature and moistureQ10 values have been reported from field and laboratory studies, thus enabling global syntheses31. However, a relatively limited number of studies have focused on the warming response of organic-rich wetland and peatland soils despite their disproportionately large share of the global SOC stock32, and considering the rapid NGS changes in high latitudes8. In addition to the effect on the temperature regime of northern peatland soils, rapid climate warming is also driving changes in landscape hydrology and, thus, in the moisture status of the soils33. Thus, latitudinal, and seasonal variations in soil temperature and moisture should be considered when estimating SOC mineralization rates. Without consideration of such variations, projections of future annual peatland CO2 emissions will be fraught with uncertainty, especially at higher latitudes where the impacts of climate change are expected to be most pronounced in the winter and shoulder seasons  compared to summer2.To date, a considerable amount of soil carbon Q10 values as discussed above). The lab environment allows one to perform measurements of CO2 production rates under simulated NGS conditions by controlling key environmental factors, such as temperature, moisture content and freezing-induced changes in the accessibility of organic substrates37.Because it is challenging to have regular or continuous access to multiple remote peatland locations in wintertime to perform in situ flux measurements, laboratory experiments with field samples can help with the calibration of model parameters  delineate systematic differences in the Q10 of CO2 production between sites, and relate them to the ecoclimate, sample depth and moisture content, (2) identify the optimum moisture levels for CO2 production, and (3) assess the impact of a soil freezing event on the CO2 production during the event and following thaw. We also explored the use of the Macromolecular Rate Theory  as a general framework to describe the measured CO2 production rates.In this study, we conducted laboratory incubations with peat samples of different depth intervals retrieved at seven Canadian peatland locations in two climate zones . We measured CO39  and four sites in “Boreal Forest & Woodland”  biogeoclimatic vegetation zones (‘ecoclimate’ hereafter), based on the classification of Baldwin et al.After the two weeks of chamber-drying, the peat samples were homogenized and divided into five subsamples of approximately equal mass and volume. These subsamples were placed in five 500-mL glass mason jars, filling less than half the space. The initial dry masses of peat varied by site and depth, from ~ 120 to ~ 600 g (or ~ 24 to ~ 120 g per jar), with lower masses for the drier and lower density peat samples . One in each of the five-jar series was incubated at the gravimetric moisture content of the peat remaining after the two weeks of chamber-drying. The moisture contents of the peat in the other four jars were adjusted by adding an artificial water solution prepared based on the chemical composition of the soil water collected at the corresponding site  which was cycled through a series of 12 sequential temperature settings of 25, 35, 25, 15, 10, 1, − 2, − 5, − 10, − 2, 5 and 25 °C. Note that the incubation temperature range was not restricted for a given sample location/depth to the site-specific in-situ soil temperatures. Rather, we imposed the same temperature range and trajectory (including one freezing event) for all the samples to assess the temperature effect, separate from other site- and soil-specific variables. Specifically, the incubation started at room temperature 25 °C, that is, the same temperature under which the peat samples were dried in the anoxic chamber. Thus, the peat samples adjusted to the aerobic and variable moisture conditions without experiencing a sudden temperature change when transferred out of the anoxic chamber. At the high temperature end, 35 °C is usually near the optimum temperature for soil microbial activity. By extending the temperature to the optimum value, we expected a more robust curve-fitting analysis than for a smaller temperature range2 production rates were determined after 48 h of incubation at a given temperature using an automated multiplexer CO2-flux system . The rates of headspace CO2 concentration increase with time  were measured following the method of Davidson et al.44, considering the variable headspace volume of the jars . To compare CO2 production among the different peat subsamples, the rates were normalized to the dry weight of the peat in each jar and expressed in units of µmol g−1 h−1. Overall, our incubation experiment was not aimed at quantifying the in-situ CO2 emissions from northern peatlands, but to delineate the response of CO2 production rates to variable temperature and moisture, as well as to a freeze–thaw event.At each setting, the temperature fluctuations were within ± 0.5 °C. The CO2 production rates measured across a range of temperatures (RT) were fitted to the following exponential equation 46 with temperature  as the independent variable:RT are the CO2 production rates normalized to the dry weight of the peat. Equation , the CO2 production rate) always increases with increasing temperature. Microbially-mediated reaction processes, however, often exhibit a temperature optimum at which the rate reaches its maximum value. This feature is reproduced by the Macromolecular Rate Theory (MMRT)  according to which the temperature dependence of enzyme-catalyzed reactions is given by:R is the universal gas constant, The Arrhenius equation predicts that a reaction rate  and temperature (T) using Eq. (Equation  has threentclass1pt{minimasing Eq.  with the2 production rates (51)2 production.Using the measurements at 25 °C across the range of experimental moistures, the COQ10 values. The Q10 values were then summarized for each site and ecoclimate in box-and-whisker plots (function ‘ggplot’). One-way ANOVA (function ‘aov’) was performed to examine whether the Q10 values significantly varied from site to site, as well as between sites in the boreal versus temperate ecoclimate zone. Statistical differences between any two groups of samples were evaluated by performing t-tests (function ‘t-test’).The statistical analyses were performed in R version 4.0.3 using the ‘rpart,’ ‘ggplot2,’ and ‘stats’ packages. A decision tree learning approach (function ‘rpart’) was used to identify the relative importance of ecoclimate, peat depth, and soil moisture content in predicting the Q10 variance using the mean site Q10 values and the mean air temperatures recorded during the 1981–2010 Climate Normals at the nearest weather station  was conducted to explore the apparent influence of local site climate conditions on the 2 production rates increased exponentially with temperature  = 14.18, p < 0.001]  = 2.01, p = 0.126]. While the differences between the Cartier Lawn (temperate) and James Bay Bog  rates were not statistically significant , the mean James Bay Bog Q10 value was distinctly higher than that of Cartier Lawn  = 7.89, p < 0.001], with the boreal peat samples having a higher average Q10 value  than the temperate peat samples .For all sites, depth intervals and moisture contents, the COure Fig. . The Q10 sites . In addition, the site mean Q10 values correlated positively with the annual temperature range, expressed as the difference between the coldest and the warmest month   = 82.13, p < 0.001].The mean Q10 values was related in descending order to the variations in the following variables: ecoclimate zone > soil moisture > depth interval .The rates in the 25 °C incubations systematically exhibited downward concave trends with respect to moisture content (%WFPS) Fig. . The opt2 production rate data from all sites in each of the ecoclimate zones were binned together. The two resulting ecoclimate datasets are shown in Fig. 2 production rates at 25 °C measured during the first and third temperature steps , as well as the 25 °C rates measured during the last temperature step  were not significantly different from one another according to a one-way ANOVA analysis of temperate  and boreal  rates.Because the ecoclimate site location emerged as the variable with the highest explanatory power, the CO2 production rates were detectable even during the sub-zero conditions. The mean rates were higher for the temperate than boreal locations, apart from the − 10 °C rates. Interestingly, the expected decreasing trend of the rates with decreasing temperature was not observed. In fact, for each ecoclimate zone, the mean rates remained relatively constant over the − 2 to − 10 °C range, with values comprised between 0.047 and 0.053 µmol g−1 h−1 for the temperate and between 0.024 and 0.040 µmol g−1 h−1 for the boreal peat samples. Furthermore, following thaw, the mean CO2 production rates observed at 5 °C were similar (temperate samples) or even higher  than those measured at 10 °C before freezing.The inset of Fig. 2 production rates grouped according to ecoclimate zones were fitted to both the Arrhenius equation and the MMRT models. Fits were obtained separately for rates measured before freezing  and after the start of freezing . The fitted curves and the resulting model parameter values can be found on Fig. 2 production rates. Note that the model fitting was done using all the available rate data, not just the mean rates.The CO2 production rates, both before and after the freezing event. For both ecoclimate groups, the Arrhenius fits yielded activation energies (Ea) after freezing that were 10–12% lower than before freezing. At temperatures ≤ 10 °C, the Arrhenius equation did not reproduce the temperature trends of the mean CO2 rates. In particular, the Arrhenius equation did not account for the near-constant rates in the + 1 to − 10 °C range, or for the differences between rates measured before and after the onset of freezing.For data above 10 °C, the Arrhenius fitted curves closely matched the mean CO2 production rates measured at the lower temperatures (≤ 10 °C), but only if the before and after freezing data were fitted separately. For both ecoclimates, the fits yielded lower optimum temperatures and more negative The MMRT model yielded better fits to the average COQ10 values for CO2 production than their temperate counterparts  explains most of the spread in the mean tes Fig. c. PossibQ10 values (~ 1.3–1.8)55 that generally fall below the mean Q10 values observed here, thus suggesting a broad latitudinal trend that could help attenuate the response of global peatland CO2 emissions to ongoing and future climate warming29. According to climate model projections, by the end of the century most peatlands may have transitioned into a warmer climate zone40 , peat decomposition rates will still increase but the slope of the increase would be less steep than anticipated without thermal adaptation56. Further research is required, however, to determine how long it would take for thermal adaptation to readjust latitudinal gradients of temperature sensitivities under future climate conditions and, thus, to more realistically account for the feedback to climate of peat soil carbon cycling59.Tropical peat soils have 2 production kinetics, remains elusive61. Nonetheless, empirical model parameter values obtained under relevant environmental conditions can still significantly improve regionalized models of carbon emissions from peatlands62. For example, the most recent version of the Canadian peatland model (CaMP v2.0) incorporates variable SOC decay rate coefficients for different peatland ecological types but does not explicitly consider differences due to ecoclimate17. Our results show that the temperature sensitivity of CO2 production may vary significantly and, possibly, systematically, depending on ecoclimate location. This finding therefore implies that including ecoclimate-specific temperature sensitivities of peat mineralization in models such as CaMP could significantly reduce parameter uncertainties when assessing the contribution of peat decomposition to continental-scale CO2 budgets.A complete mechanistic understanding of the thermal adaptation of soil microbes, and hence CO2 production rates at 25 °C for which we have the most complete set of replicate measurements. The concave down shapes of the moisture response curves .The effect of soil moisture was evaluated using the COves Fig. d tend to68) and biological factors . The general shift to higher optimum moisture levels in peat soils is likely, at least in part, due to their high organic matter content and complex (multi-domain) pore structure compared to non-peat soils70. Still, the organic carbon concentrations of our peat samples fall in a fairly narrow range  covers are characterized by shallow water tables and wet conditions73. While speculative, the correlation between the water table depth and the measured optimum WFPS may reflect an adaptation of peatland microbial communities to the prevailing in situ moisture conditions59.The variations in the optimum WFPS for COon Table . For exa75). The experimental results presented here support a systematic decline of the CO2 production rates at full saturation . However, the complete evaluation of the effect of peatland rewetting on carbon emissions must consider the concomitant increases in CH4 release due to the decreased availability of oxygen76. Our results further indicate that CO2 production rates also decrease when the moisture level drops below the optimum WFPS. In the field, drier conditions due to a lowering of the water table would shift the zone of optimum moisture level downwards and, therefore, not necessarily translate in a reduction of the CO2 emissions at the soil surface.Rewetting of peatlands has been proposed as a management strategy to restore their carbon sink function  incubations the simulated drainage of wetland soils resulted in cumulatively more organic carbon being mineralized. Recent work has also been exploring the role of moisture on the temperature sensitivity of soil CO2 production . Together, the results of these studies call for more systematic and data-driven approaches to represent the regulating role of soil moisture in soil carbon process models77 that, in turn, would improve the reliability of predicted responses of peatland carbon budgets to changes in soil moisture regime due to climate and land use changes.In addition, there is evidence that, given enough time, the microbial decomposer communities may adapt to drier conditions. Arnold et al.2 at sub-zero temperatures in both ecoclimate groups exhibits small but measurable rates through the − 2 to − 10 °C temperature range . Most importantly, the observed trends do not follow the exponential drop of the rates with cooling below 0 °C that is often assumed8. On average, the sub-zero CO2 production rates are remarkably constant and of comparable magnitude for both ecoclimates . These changes are in line with the “Enzyme Rigidity Hypothesis” , according to which cold-adapted enzymes are more sensitive, or “less rigid,” than enzymes adapted to warmer temperatures. Thus, in addition to releasing more labile organic substrates, the cooling to sub-zero temperatures could also have been accompanied by the selective activation of “cold-adapted enzymes” by the microbial communities.The divergence of the temperature trends of the COues Fig. . By contTopt)37. However, the predicted post-freezing Topt values, 23.4 and 29.6 °C, would need to be verified by Increasing the experimental temperature range well above 25 °C , and including more frequent measurement intervals49. Nonetheless, the MMRT provides a more flexible approach that could be useful in modeling the in-field observed seasonal hysteresis in the temperature trends of CO2 emissions , as well as CH4 emissions84.When dynamic enzyme responses modulate the rates of organic matter degradation, the temperature dependence is expected to depart from the Arrhenius equation and exhibit an optimum biological temperature  are decreasing88.It will be important to further determine whether the post-thaw CO2 production rates between the cold-temperate and boreal ecoclimate zones. Given the higher Q10 values, in combination with spatial and seasonal patterns of global warming, boreal peatlands may increase future NGS CO2 losses to a larger degree than temperate peatlands. The variable temperature sensitivities under different climate conditions need to be accounted for when assessing future global trajectories of peatland carbon pool stability.This study highlights the statistically significant variations in the temperature sensitivity of peat soil CO2 production is a function of peatland type  and microtopography , which, in turn, are related to the relative position of the water table. The variations in optimum moisture content may thus reflect an adaptation of the resident microbial communities to the prevailing moisture conditions.Peatland soils tend to support high organic matter mineralization activity at higher moisture levels compared to well-drained mineral soils. Our findings suggest that the optimum moisture level for maximum CO2 production rates. This shift is captured by the MMRT model, but not by the conventional Arrhenius equation. Furthermore, the reaction rate models are unable to account for the near-constant CO2 production rates measured across the sub-zero temperature range (− 2 to − 10 °C). More research is needed to unravel the various processes and structural properties controlling CO2 production in frozen peat soils and their response to the changes in temperature and hydrology under climate warming. Only with this improved mechanistic understanding supported by precise rate data will process models be able to grapple with the implications of climate and land use changes for NGS carbon budgets of northern peatlands.Freezing of the peat samples causes a shift in the temperature response curve for the COSupplementary Information.


Heterobilayers of transition metal dichalcogenides (TMDCs) can form a moiré superlattice with flat minibands, which enables strong electron interaction and leads to various fascinating correlated states. These heterobilayers also host interlayer excitons in a type-II band alignment, in which optically excited electrons and holes reside on different layers but remain bound by the Coulomb interaction. Here we explore the unique setting of interlayer excitons interacting with strongly correlated electrons, and we show that the photoluminescence (PL) of interlayer excitons sensitively signals the onset of various correlated insulating states as the band filling is varied. When the system is in one of such states, the PL of interlayer excitons is relatively amplified at increased optical excitation power due to reduced mobility, and the valley polarization of interlayer excitons is enhanced. The moiré superlattice of the TMDC heterobilayer presents an exciting platform to engineer interlayer excitons through the periodic correlated electron states. 2/WSe2 heterobilayer to reveal the onset of various correlated insulating states.Heterobilayers of transition metal dichalcogenides host moiré superlattices that give rise to strong electron interactions. Here, the authors study the photoluminescence from interlayer excitons in a WS Twisted bilayer graphene at the magic angle possess flat minibands that result in strong electron interactions, enabling rich, correlated states, such as Mott insulators, unconventional superconductivity, and Chern insulators9. The moiré superlattice of the twisted bilayer transitional metal dichalcogenides (TMDCs) can form flat minibands with less stringent requirements, with no magic angle required. In addition, due to the large effective mass12, the kinetic energy is further reduced in twisted bilayer TMDCs13. The resulting enhanced Coulomb interaction with respect to the kinetic energy leads to strong interactions that give rise to various exotic correlated insulating states21 with high transition temperatures. Very recently, Mott insulator states at fillings n = ±1 (one electron or hole per Moiré cell), generalized Wigner crystal states at filings n = ±1/3, ±2/3, and even more exotic insulating states at other fractional fillings have been reported21. The TMDCs moiré superlattice provides an exciting platform to investigate strongly correlated physics.Bilayers of two-dimensional materials with a small twist angle or a lattice mismatch can form moiré superlattices23. For example, a type-II alignment in the heterobilayer allows the formation of interlayer excitons, in which the optically excited electrons reside in one layer, while the holes reside in the other layer25. Due to their long lifetime, valley degree of freedom, and tunability, interlayer excitons are a promising candidate for quantum emitters3. Both interlayer excitons and correlated electrons are results of strong interactions, and it is natural to raise the intriguing question of how they interact with each other, which, to our best knowledge, remains an uncharted territory.Meanwhile, stacking two different TMDC monolayers also enables the possibility for band engineering in the heterobilayer2/WSe2 moiré superlattice. We find that this interaction significantly modifies the photoluminescence (PL) of the interlayer exciton. Sensitive to the change in dielectric constant and the gap opening at the correlated insulating states, the interlayer exciton PL peak energy unravels the Mott insulator state at n = 1, the generalized Wigner crystal states at n = 1/3 and 2/3, as well as at n = 1/4 and 3/4 with an even longer range interaction, and a stripe phase state at n = 1/2. Interestingly, the presence of the correlated states also significantly modifies the PL intensity. At increased optical excitation power, the PL intensity of interlayer excitons brightens up at the correlated electron states, suggesting reduced mobility of interlayer excitons in the moiré cell due to their attractive interaction with the localized correlated electrons. Furthermore, the valley polarization of interlayer excitons is strongly enhanced at certain fillings. Our results inspire engineering interlayer excitons as quantum emitter arrays through moiré potential and long-range interactions, utilizing their interplay with the localized correlated electrons.In this work, we explore the interaction of interlayer excitons with the correlated states in angle-aligned WS2/WSe2 heterobilayer device is schematically shown in Fig. 2/WSe2 bilayer would form a triangular moiré superlattice with a period of ~8 nm. The strong moiré coupling5 will result in mini flatbands26, which could host correlated insulating state at half band filling with one carrier localized in one moiré cell (n = ±1)15, or even at various fractional fillings of the moiré unit cell that have been interpreted as generalized Wigner crystal states19 or stripe phases20. The filling patterns for n = 1 and 1/3 are shown schematically in Fig. 2 and WSe2 have a type-II alignment in their heterobilayer  is formed, as confirmed by gate-dependent reflectance spectra  compared with other fractional fillings (~2–3 meV) according to previous reports19.The sensitive readout of the correlated insulating states through the interlayer exciton PL energy can be understood by the larger spatial extent of the interlayer exciton, which renders it more sensitive to the dielectric environment than the intralayer exciton. Similar ideas has been utilized to use the larger 2n = −1/3 and −2/3, which are previously missing in the lower excitation power data. Detailed analysis of the integrated PL intensity as a function of the gate voltage shown in Fig. n = −1/4. We perform even more drastic excitation power dependence on another device D2, in which we increase the excitation power from 0.5 µW  under the low optical excitation power  and 5.1 ns for the n = 0 state (PL peak at ~1.45 eV) . As a result, the chance of nonradiative recombination is reduced, consistent with the TRPL data. It has been shown that the moiré potential can trap interlayer excitons29. Our results naturally raise the question of possible localization of interlayer excitons through the additional attraction by the periodic correlated electron solid, which we will explore in future studies.One other striking observation is that, in contrast to the intense PL intensity at the charge neutral region  and detect the interlayer exciton PL of the same (σ+) or opposite (σ−) helicity. The valley polarization 31. It is noticeable that the correlated states at both integer and fractional fillings are often associated with an enhanced valley polarization, and the enhancement is most drastic at the fillings of n = 1 and 2 for device D2. This valley polarization enhancement might originate from the decreased valley scatterings due to the interaction between interlayer excitons and correlated electrons. One major valley depolarization mechanism is through Maialle–Silva–Sham mechanism33, which annihilates one exciton in one valley and creates another in the opposite valley. However, when interlayer excitons are attracted by the localized correlated electrons, this probability will likely be significantly suppressed. The correlated electrons, therefore, also provide a tunable platform to manipulate the valleyspin of the interlayer excitons.Interlayer excitons also possess valley degree of freedomResearchers have been inspired to utilize the moiré potential to control interlayer excitons to form an array of quantum emitters. Here we show that the long-range interaction of the electrons in the moiré superlattice can be further exploited for this purpose. The interaction between correlated electrons and interlayer excitons not only allows us to reveal the correlated states at different fillings through the PL spectra, more excitingly, it can be further utilized to control the mobility and valley polarization of the interlayer excitons. Our results, therefore, shed light on how to engineer the interlayer excitons in the TMDCs heterobilayer moiré superlattice for quantum optics and optoelectronics.2/WSe2 heterobilayer device34. We first exfoliate monolayer WS2, monolayer WSe2, few-layer graphene and thin hBN flakes on Si substrate with 285 nm thermal oxide, respectively. We further confirm the crystal orientation of monolayer WSe2 and WS2 through second harmonic generation (SHG) measurements  under the microscope, ensuring a near-zero twist angle between the two flakes. The final device is annealed at 140 °C for 6 h in a vacuum chamber. The pre-patterned Au contact electrodes are fabricated through standard electron-beam lithography followed by ebeam evaporation and lift-off processes.We use a layer-by-layer pickup method to fabricate the WSThe optical measurements are taken with a home-built confocal microscope with spectroscopy capabilities in a liquid helium-controlled optical cryostat. The PL spectra are taken with a CW laser excitation. The reflectance spectra are taken with a supercontinuum white laser source (Fianium). The SHG measurements are taken with a pulsed laser with the width ~160 fs .35. The reflected signal is analyzed to extract the demodulated output channels, MIM-Im and MIM-Re, which are proportional to the imaginary and real parts of the admittance between the tip and the sample, respectively. The MIM-Im part characterizes the screening of the tip electric field by the sample and changes monotonically with the sample’s local conductivity. Therefore, the dips in the MIM-Im curve plotted in Fig. The MIM measurement is performed on a home-built cryogenic scanning probe microscope platform. A small microwave excitation of ~0.1 µW at a fixed frequency around 10 GHz is delivered to a chemically etched tungsten tip mounted on a quartz tuning forkSupplementary Information


Tobacco use disorder continues to be a leading public health issue and cause of premature death in the United States. Nicotine is considered as the major tobacco alkaloid causing addiction through its actions on nicotinic acetylcholine receptors (nAChRs). Current pharmacotherapies targeting nicotine’s effects produce only modest effectiveness in promoting cessation, highlighting the critical need for a better understanding of mechanisms of nicotine addiction to inform future treatments. There is growing interest in identifying potential contributions of non-nicotine components to tobacco reinforcement. Cotinine is a minor alkaloid, but the major metabolite of nicotine that can act as a weak agonist of nAChRs. Accumulating evidence indicates that cotinine produces diverse effects and may contribute to effects of nicotine. In this review, we summarize findings implicating cotinine as a neuroactive metabolite of nicotine and discuss available evidence regarding potential mechanisms underlying its effects. Preclinical findings reveal that cotinine crosses the blood brain barrier and interacts with both nAChRs and non-nAChRs in the nervous system, and produces neuropharmacological and behavioral effects. Clinical studies suggest that cotinine is psychoactive in humans. However, reviewing evidence regarding mechanisms underlying effects of cotinine provides a mixed picture with a lack of consensus. Therefore, more research is warranted in order to provide better insight into the actions of cotinine and its contribution to tobacco addiction. Cigarette smoking remains to be a leading public health issue. Despite a steady decline over the past decades, smoking rate remained at 17.2 percent in people aged 12 or older in 2018 in the United States . In addiThere are growing efforts investigating the potential involvement of minor tobacco alkaloids and nicotine metabolites in nicotine’s effects and tobacco use . CotininNicotiana Glauca plant with negligible conversion of nicotine to cotinine -iminium ion, which was catalyzed from nicotine by a cytochrome P450 (CYP)-linked oxidase, and was in rapid equilibrium with 5′-hydroxynicotine (1′ (5′)-iminium ion (1′ (5′)-iminium ion  and enter the brain. Early studies, using whole-body radiography, reported that intravenous injection of radiolabeled nicotine resulted in uniform and diffuse cotinine-related radioactivity in the brain of mice and cats, and that cotinine could be isolated from brain tissue in mice . A laterin situ metabolism in the brain, thus contributing to cotinine accumulation in the brain following peripheral nicotine administration  were over 200 μM for cotinine and 0.6 nM for nicotine , but cotinine was ∼100 fold less potent than nicotine, with IC50 values at 1 mM and 10 μM, respectively  binding in the caudate with an IC50 value of ∼3.5 μM, which was ∼600-fold less potent than nicotine at 5.7 nM. Cotinine also inhibited [125I]A-85380 (a ligand for both α3/α6β2∗ and α4β2∗ nAChRs) binding with an IC50 value of 65–80 μM, ∼10,000-fold less potent than nicotine at 7.53 nM. Complete inhibition of 125I-a-conotoxinMII or [125I]A-85380 binding by cotinine occurred at ∼ 1 mM. This study suggested that cotinine might be more potent at α3/α6β2∗ than α4β2∗ receptors  cells expressing human α7 nAChRs, cotinine at concentrations up to 1 mM did not elicit appreciable activation of these receptors , second messengers , prostaglandins , brain/gut peptides , and enzymes   .50 values at 0.19 μM, 1.7 μM, 110 μM, and 160 mM, respectively. Amino acid sequence analysis of this protein showed no identity to then known proteins except for the homology to the human p205 synovial fluid protein  isomers more effective than the (+) isomers. In these effects, cotinine showed similar potency and efficacy to nicotine  and efficacy to nicotine  and a fixed-interval (FI) schedule, whereas nicotine decreased response rates during the FR schedule, but produced biphasic effects during the FI schedule . In squi50 value of cotinine was approximately 30 mg/kg and cotinine was 1000-2000 fold less potent than nicotine  is an anxiety disorder triggered by exposure to life-threatening traumatic events. Epidemiological studies indicate a high prevalence of tobacco use in PTSD patients, and tobacco use has been proposed as a form of self-medication to improve neuropsychiatric symptoms in PTSD . In rodel cortex . In addil cortex , calcinel cortex , and GFAl cortex  in hippoCotinine reduced depressive-like behaviors induced by chronic stress , fear coIt is well known that nicotine can enhance cognitive functions , and recRepeated cotinine improved spatial recognition memory in a novel location recognition test in rats receiving chemotherapy . ChronicCotinine attenuated age- and neurodegeneration-related cognitive impairments. In senescent mice, chronic cotinine treatment reversed impairments in spatial and recognition learning and memories in the Morris water maze and novel object recognition tasks in a α7 nAChR-dependent manner . In both–/– mice, a murine model of Fragile X syndrome. Cotinine rescued deficits in spatial memory in the coordinate and categorical spatial processing tests, increased the performance toward a novel object in the novel object recognition test, and reversed memory impairment in the temporal order memory test. This study also established the causal role of the AKT-GSK3β signaling pathway in mediating cotinine’s effects by demonstrating that cotinine failed to enhance cognition in GSK3β knockin mice that exhibited impaired phosphorylation of GSK3β  appeared to be safe and well-tolerated in humans, with no major side effects other than dizziness and headache , 1998a. There is evidence that cotinine alters withdrawal-related psychological and physiological signs and symptoms. Acute cotinine infusion in abstinent smokers reduced self-reported desire to smoke, irritability, low energy, anxiety and tension. A tendency to think less about smoking was also observed. These effects were small and subtle, and there was no placebo control group included for a comparison . One ranCotinine was shown to cause cognitive deficit in healthy non-smokers. Cotinine significantly impaired memory on the long list of a verbal recall task, and slowed serial information processing in a visual choice reaction time task . On the In these studies, cotinine was administered either acutely or repeatedly over a short period of time, usually fewer than 14 days. Given the chronic relapsing nature of habitual smoking, it would be worth examining effects of cotinine over a longer-term administration period to better understand the chronic effects of cotinine in humans.2+ concentrations in cultured bovine adrenal chromaffin cells, and these effects were antagonized by nAChR antagonists, hexamethonium, chlorisondamine, and dihydro-β-erythroidine 3 stoichiometry on plasma membrane of cultured undifferentiated mouse neuroblastoma 2a cells  of α7 nAChRs to explain its behavioral effects in various animal models involving cognitive impairments . Currentproposal . It is nproposal . An earl ∼175 μM . In addi oocytes . Therefo∗ and α7 nAChRs, and few studies have examined potential involvement of other nAChR subtypes. For example, cotinine was shown to be more potent toward α3/α6β2∗ than α4β2∗ nAChRs in monkey striatum (∗ receptor density in mouse neuroblastoma 2a cells (Most studies have focused on the interactions between cotinine and α4β2striatum . Moreove2a cells . TherefoSince cotinine is a weak agonist at nAChRs, many studies employed relatively high concentrations of cotinine to study its effects . AlthougAnother remaining question is whether cotinine can contribute to the development of nicotine use, abuse and addiction. Our recent study  indicateZ-MD conceptualized and drafted the manuscript. KV and XT contributed to the draft and review of the manuscript. All authors approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Biomolecular systems show how host–guest binding can induce changes in molecular behavior, which in turn impact the functions of the system. Here we report an artificial host–guest system where dynamic adaptation during guest binding alters both host conformation and guest dynamics. The self-assembled cage host employed here possesses concave walls and a chirotopic cavity. Complementarity between the curved surfaces of fullerenes and the inner surface of the host cavity leads the host to reconfigure stereochemically in order to bind these guests optimally. The curved molecule corannulene undergoes rapid bowl-to-bowl inversion at room temperature. Its inversion barrier is increased upon binding, however, and increased further upon formation of a ternary complex, where corannulene and a cycloalkane are both bound together. The chiral nature of the host also leads to clear differences in the NMR spectra of ternary complexes involving corannulene and one or the other enantiomer of a chiral guest, which enables the determination of enantiomeric excess by NMR. The preparation of artificial host–guest systems that display dynamic adaptation during guest binding is challenging. Here the authors report a chiral self-assembled tetrahedral cage featuring curved walls that reconfigures stereochemically to fit fullerene guests, regulates corannulene inversion, and enables the determination of co-guest enantiomeric excess by NMR spectroscopy. Artificial molecular systems that mimic the sophisticated processes of induced fit and property regulation via ternary complex formation are challenging to design, but important in producing complex and functional systems of molecules5, and in understanding the molecular basis of binding in relevant biological processes.Recognition and binding processes involving biomolecules, such as enzyme–substrate, antibody–antigen, and protein–protein, are integral to living systems. In many cases, the conformations and shapes of biomacromolecules can change to fit the target substrate, in a process known as induced fit6, which have promising applications as light-harvesting materials7, and corannulene, a bowl-shaped hydrocarbon that can be considered a sub-unit of a fullerene. Corannulene undergoes rapid bowl inversion at room temperature, with an energy barrier calculated to be 11.5 kcal mol−1 9. Stabilization of the planar transition state to corannulene inversion has been achieved by threading this molecule through a cationic macrocycle10, and through compression within an anthracene-walled host11. Manipulation of corannulene to slow its inversion rate, in contrast, is a harder goal, having only been achieved through chemical modifications12.Curved aromatic molecules are well-suited to investigations into complex host–guest phenomena, as their interactions principally involve dispersion forces. These include fullerenes13. These cavities can be used for the stabilization of labile species15, separations18, and catalysis24. Principles have been developed to enable the selective binding of specific guests, or classes of guests, from among many chemically similar prospective guests in solution30. These principles guide the design of artificial hosts to maximize host–guest interactions, with greater shape complementarity often leading to additional selectivity and affinity32.Metal-organic cages provide confined inner phases that are usefully distinct from the outside environmentIn this work, we employ these ideas to prepare a host with curved walls, designed to interact with the curved π-conjugated surfaces of fullerenes and corannulene. Fullerenes induce a configurational change in the host, highlighting the impact of the guest on the host. The dynamic inversion of corannulene is restricted inside the host, demonstrating host impact on the guest. Further, co-guests form ternary complexes, resulting in further modulation of the behavior of both guests inside the cage. The corannulene guest also reports on the stereochemistry of a chiral co-guest.A was designed around a phosphangulene core  bis(trifluoromethylsulfonyl)imide (Zn(NTf2)2) in acetonitrile.Subcomponent ore Fig. , which w1 from racemic A gives rise to a large set of stereochemical possibilities, as each metal center may adopt Δ or Λ handedness, each ligand may possess M or P helicity. The observation of only one set of ligand proton signals in the 1H NMR spectrum of 1 allows us to infer that self-assembly occurred stereoselectively in two ways. First, the two ligand enantiomers narcissistically self-sorted  and T2 36, only one was observed. The X-ray crystal structure of 1 showed that 1 adopted exclusively the T1 configuration 1 and (M4−Δ4)1. The transformation of 1 from its T1 configuration to T2 following guest uptake was confirmed by CD studies of enantiopure 1 and its corresponding fullerene host–guest complexes  × 103 M−1 2⊂1 were observed by 1H NMR. Competitive binding experiments indicated a higher affinity of 1 for corannulene over pyrene, which may result from the better fit of the bowl-shaped corannulene to the concave ligands  was determined to be 17.9 ± 0.3 kcal mol−1, which is notably higher than the experimentally-determined value for a monosubstituted corannulene 8 and the extrapolated value for free corannulene 9.The chirotopic cavity of Hq′ Fig.  are diasres Fig. . The exc4+ was found to accelerate the bowl-to-bowl inversion of corannulene bound within it, by stabilizing the planar transition state10. We infer that cage 1, in contrast, acted to stabilize the ground state of curved corannulene through binding to the curved inner surface of the cage. Thus, encapsulation inside 1 raises the barrier to corannulene inversion, provided the transition state is not also stabilized.Macrocyclic ExBox1, we infer that space remains to accommodate a second guest. Cycloalkanes (CnH2n) with five to eight carbons were found to be suitable second guests, forming ternary complexes. The encapsulation of a second guest was signaled by shifts in the 1H NMR spectra  and Ka2  are shown in Supplementary Table Ternary complexes were observed to form for guests that did not bind in the absence of corannulene, such as cyclohexane. Upon addition of excess cyclohexane to a solution of est Fig. . The encsts Fig. . A 1H NO6H12•corannulene⊂1  to corannulene inversion at least 3.7 kcal mol−1 greater for C8H16•corannulene⊂1 than for corannulene⊂1 at 348 K, cumulative with the 6 kcal mol−1 barrier increase due to encapsulation within 1  also bound within 1 together with corannulene, requiring this co-guest to bind (Supplementary Section R-MB•corannulene⊂(M4−Δ4)1 and R-MB•corannulene⊂(P4−Λ4)1 1 and corannulene⊂(P4−Λ4)1 do not discriminate when binding the enantiomers of MB.Enantiopure P4−Λ4)1 together with corannulene and racemic MB produced similar 1H NMR spectra, corroborating non-enantioselective guest binding  of chiral MB.Enantiopure cage P−Λ41 togeMB mixtures with different degrees of ee were combined with (P4−Λ4)1 and corannulene (Supplementary Section MB reflected well the ratios between the R and S enantiomers of the guest (Supplementary Table R/S ratios were also reflected in the integrals of the signals of the encapsulated corannulene (Fig. To support this hypothesis, ed with P−Λ41 and 1 enabled it to bind fullerenes well, reconfiguring stereochemically in order to optimize host–guest contact. The chirotopic space within 1 enabled the exploration of its stabilization of the bowl-shaped ground state of corannulene, which in turn served to “pad” the cavity in order to optimize the binding of cycloalkanes in ternary complexes. Ternary complexes involving chiral guests can report upon the guest’s stereochemistry. The ability of the guests to influence each other’s dynamics suggests novel applications where guest motions might be geared together—for example, the ring-flipping of corannulene might compress a second guest, thus accelerating a reaction with an unfavorable volume of activation, such as an intramolecular cycloaddition.The concave panels of host Supplementary Information


GammaproteobacteriaEscherichia coli and Salmonella, the regulatory RNA chaperone Hfq appears to play a global role in gene regulation, directly controlling ∼20 to 25% of the entire transcriptome. While the model FirmicutesBacillus subtilis and Staphylococcus aureus encode a Hfq homologue, its role has been significantly depreciated. These bacteria also have marked differences in RNA turnover. E. coli and Salmonella degrade RNA through internal endonucleolytic and 3′→5′ exonucleolytic cleavage that appears to allow transient accumulation of mRNA 3′ UTR cleavage fragments that contain stabilizing 3′ structures. In contrast, B. subtilis and S. aureus are able to exonucleolytically attack internally cleaved RNA from both the 5′ and 3′ ends, efficiently degrading mRNA 3′ UTR fragments. Here, we propose that the lack of 5′→3′ exoribonuclease activity in Gammaproteobacteria has allowed the accumulation of mRNA 3′ UTR ends as the “default” setting. This in turn may have provided a larger pool of unconstrained RNA sequences that has fueled the expansion of Hfq function and small RNA (sRNA) regulation in E. coli and Salmonella. Conversely, the exoribonuclease RNase J may be a significant barrier to the evolution of 3′ UTR sRNAs in B. subtilis and S. aureus that has limited the pool of RNA ligands available to Hfq and other sRNA chaperones, depreciating their function in these model Firmicutes.Regulatory RNAs have emerged as ubiquitous gene regulators in all bacterial species studied to date. The combination of sequence-specific RNA interactions and malleable RNA structure has allowed regulatory RNA to adopt different mechanisms of gene regulation in a diversity of genetic backgrounds. In the model  Escherichia coli and Salmonella enterica serovar Typhimurium. Recently, it has become clear that regulatory sRNAs can be generated from a variety of sources in Gammaproteobacteria, including mRNA 5′ untranslated regions (UTRs) (rne) , which litter the transcriptome and have been extensively characterized in the model Gram-negative enteric bacteria s (UTRs) , 2, intes (UTRs) , 3, and s) (rne) –9. TheseGammaproteobacteria like E. coli and Salmonella and the model Gram-positive FirmicutesB. subtilis and S. aureus. Using examples, we propose that 3′ UTR sRNAs represent the latter stages of a stepwise continuum of increasing regulatory independence for mRNA 3′ UTRs. Finally, we propose that 5′→3′ exoribonuclease activity may be a barrier to this stepwise progression of regulatory 3′ UTR evolution in Gram-positive Firmicutes, which has contributed to the depreciation of sRNA chaperone Hfq and ProQ function.Here, we highlight some of the key differences in RNA metabolism between Gammaproteobacteria and the FirmicutesB. subtilis and S. aureus, bulk RNA turnover is carried out by a large multiprotein complex termed the RNA degradosome  that contains the conserved catalytic N-terminal region of the protein and can functionally substitute for rne in E. coli  that is commonly associated with the RNA degradosome complex of Gram-positive Firmicutes. There has recently been evidence of a 5′→3′ exoribonuclease within E. coli , the 3′-terminal RNA fragments (containing the transcription terminator) of more than 50% of mRNAs were increased, highlighting the importance of RNase J1 in degradation of 3′-terminal RNA decay intermediates  and is expected to have 5′→3′ exoribonuclease activity.Chaperones play critical roles in facilitating sRNA regulation by increasing the association rate of sRNA-mRNA interactions to physiologically useful rates. The sRNA chaperone Hfq binds many sRNAs through their poly(U) tail and acts as a matchmaker within Gram-negative bacteria to anneal sRNAs with complementary mRNAs, while ProQ binds to the 3′ UTR of mRNAs to protect the free mRNA 3′ end against 3′→5′ exoribonucleolytic degradation. Some Gram-positive organisms, such as  or ProQ . In factrmicutes , 22. In nditions ; however changes . In S. a mutants , 26. Thehimurium ) and hasifficile , 29, andry sRNAs , and theB. subtilis and S. aureus may be a barrier to evolution of independent regulatory 3′ UTR sRNAs along this continuum.In the section below, we propose that there exists a continuum of regulatory 3′ UTR independence and that the 5′→3′ exoribonuclease activity of RNase J in E. coli largely appear in ancestral genomes before their cognate target mRNA binding sites, suggesting that sRNAs are first produced and then drive evolution of target mRNAs . The simplest arrangement of a regulatory UTR and target mRNA is found in S. aureus where the 3′ UTR of icaR mRNA loops on itself (or between icaR mRNAs) and base pairs to the ribosome-binding site (RBS) of its own 5′ UTR to block translation and promote RNase III-dependent degradation  (E. coli [trans-acting regulatory 3′ UTRs could be a widespread mechanism of regulation in Firmicutes. An advantage is that trans-acting regulatory 3′ UTRs like the hly 3′ UTR are protected from the 5′→3′ exoribonucleolytic processing of RNase J (to the extent that the mRNA is protected).A slightly more complex variation where mRNA 3′ UTRs act in NA prsA2 . This mR protein . The hlydes (nt)  (compare(E. coli ), suggesS. aureus, the regulatory 3′ UTR-derived sRNA RsaC is generated by endoribonucleolytic cleavage of the polycistronic mntABC-rsaC transcript by RNase III (mntABC mRNA (encoding a manganese transporter) and while they share a transcriptional activation signal (repressed in the presence of Mn2+ by MntR), RNase III cleavage separates these transcripts so that they have independent fates within the cell. RsaC retains mRNA targets that are functionally coherent with the mntABC-rsaC operon, repressing the Mn2+-dependent superoxide dismutase (SodA), and other metal-dependent pathways. RNase III cleavage of mntABC-rsaC generates a free 5′ end that should render RsaC highly susceptible to RNase J exonucleolytic attack. However, a notable feature of RsaC is the 25-nt stem-loop that sequesters the 5′ end in a duplex preventing exoribonuclease attack and stabilizes the 3′ UTR-derived sRNA  allowing coordination between these functionally related genes . RNase E cleavage after the oppF stop codon releases OppZ from the parent transcript. In a regulatory circuit that parallels the 5′ UTR-3′ UTR looping autoregulation of icaR in S. aureus, OppZ binds the upstream RBS of oppB to silence expression and control the cellular levels of the precursor oppBCDF transcript through a feedback loop  transporter in Salmonella. The narK mRNA is expressed during anaerobic respiration (2−) transporter nirC, which is located within the nirBDC-cysG operon and controls cytoplasmic nitrate levels during anaerobic growth , and further experiments confirmed that DapZ negatively regulates the synthesis of major ABC transporters through the repression of both dpp and opp mRNAs , suggesting that independently transcribed or processed 3′ UTRs are less abundant in these Gram-positive organisms (Many Gram-negative organisms lack a 5′→3′ exoribonuclease and rely on endoribonucleolytic cleavage (such as RNase E) and 3′→5′ exoribonucleases to degrade RNA within the cell. In the model Gram-negative bacteria y 175 nt , generaty 175 nt . It seemsequence . In contdy state . This isp codons , likely himurium . In S. arganisms  to D.E. coli and Salmonella, it is plausible that this pool of 3′ mRNA fragments provides a source of transiently stable, unconstrained RNA sequence space for the selection and evolution of regulatory RNA features. It follows that RNase J exoribonucleolytic activity in many Gram-positive organisms may pose a significant barrier to evolution of new 3′ UTR regulatory RNAs. Without first acquiring stabilizing 5′ structures, these 3′ RNA fragments are rapidly degraded and would have limited opportunity to gain a foothold on the ladder to positive selection.In The functional importance of Hfq appears to be significantly expanded in many Gram-negative organisms, and we propose that this may be linked to the availability of “stable” 3′ UTR degradation intermediates . Hfq bin62–Firmicutes where the function of Rho terminator may have been depreciated because the pioneering ribosome lags behind the elongating RNA polymerase, exposing the nascent transcript to premature Rho interactions and potentially toxic transcription termination .An analogous scenario has occurred in many Gram-positive mination . In contE. coli and Salmonella allows transient stabilization of cleaved mRNA 3′ UTRs, which has provided abundant raw materials for the evolution of 3′ UTR sRNAs and the expansion of sRNA regulatory networks. This in turn has centralized the function of sRNA chaperones like Hfq in E. coli and Salmonella as the sRNA network has expanded. In Gram-positive Firmicutes that encode a 5′→3′ exoribonuclease (RNase J), cleaved 3′ UTRs are rapidly degraded. In these bacteria, 3′ UTR sRNAs must first acquire 5′ stems or internal transcription start sites before they are stabilized. We propose that RNase J represents a major evolutionary barrier to the expansion of the 3′ UTR sRNA network and has depreciated the function of the sRNA chaperones Hfq and ProQ.All bacteria appear to use regulatory RNA to control gene expression posttranscriptionally; however, it is clear that differences exist in the distribution and importance of RNA chaperones that facilitate sRNA-mRNA interactions. We propose that mRNA 3′ UTRs are a major evolutionary source for regulatory sRNAs and highlight some of the potential intermediate stages of 3′ UTR sRNA evolution. In addition, we propose that the lack of 5′→3′ exoribonucleases in hly mRNA in Listeria) that are not cleaved to protect the 5′ end of the UTR. Evolution of internal transcription start sites should be the preferred mechanism of 3′ UTR release, as this provides a protective 5′ triphosphate.Some testable predictions arise from the above. (i) As the relative importance of Hfq for global gene regulation is uncovered in more bacteria, the size of the Hfq regulon should be negatively correlated with the presence of a functional RNase J. (ii) Endoribonuclease cleavage of 3′ UTR sRNAs should be less prevalent in RNase J-encoding bacteria. These bacteria may make more widespread use of regulatory mRNA 3′ UTRs (like


We propose a Double EXponential Adaptive Threshold (DEXAT) neuron model that improves the performance of neuromorphic Recurrent Spiking Neural Networks (RSNNs) by providing faster convergence, higher accuracy and a flexible long short-term memory. We present a hardware efficient methodology to realize the DEXAT neurons using tightly coupled circuit-device interactions and experimentally demonstrate the DEXAT neuron block using oxide based non-filamentary resistive switching devices. Using experimentally extracted parameters we simulate a full RSNN that achieves a classification accuracy of 96.1% on SMNIST dataset and 91% on Google Speech Commands (GSC) dataset. We also demonstrate full end-to-end real-time inference for speech recognition using real fabricated resistive memory circuit based DEXAT neurons. Finally, we investigate the impact of nanodevice variability and endurance illustrating the robustness of DEXAT based RSNNs. Recurrent spiking neural networks have garnered interest due to their energy efficiency; however, they suffer from lower accuracy compared to conventional neural networks. Here, the authors present an alternative neuron model and its efficient hardware implementation, demonstrating high classification accuracy across a range of datasets. However, SNNs are relevant as they hold the promise for energy-efficient hardware realization owing to their bio-inspired nature3. Activation functions or neuron models play a pivotal role in the overall learning accuracy and energy efficiency of both ANN and SNN implementations. If RSNNs with spike- based temporal computation are to perform better on sequential tasks, it is essential that they get the capability of Long Short-Term Memory (LSTM) cells as stable working memory/memory states. In this regard, in a recent theoretical work4 authors have shown that inclusion of Adaptive-Leaky Integrate and Fire (ALIF) neurons in RSNN can improve their computational capabilities. Such neurons are also used in implementing RSNN that can learn through hardware friendly algorithms like e-prop5. Adaptive neuron models generally incorporate biological property of neuronal adaptation6 by either removing a part of membrane current after each spike event or by increasing the leak current9. However, in4 authors demonstrate the use of an adaptive threshold model in which the firing threshold varies dynamically on spike event. The threshold voltage in such model increases on neuron firing and decays back exponentially to a baseline.Neuromorphic Spiking Neural Networks (SNNs) are promising computational paradigms that take deep inspiration from the working of mammalian brains, firing neurons, and synaptic plasticity. SNN algorithms and specialized SNN hardware have been shown to be useful for addressing a wide variety of real-world data-centric applications. While conventional artificial neural networks (ANNs) primarily depend on continuous valued functions and supervised gradient descent based learning rules, SNNs also exploit sparse neuron spikes and unsupervised learning rules. ANNs have been shown to surpass SNNs and recurrent-SNNs (RSNNs) in terms of accuracy11 have been widely reported in literature both with complementary metal oxide semiconductor (CMOS) and emerging resistive memory devices like resistive random access memory (RRAM), phase change memory, conductive bridge random access memory etc.20 However, there are limited studies that show hardware implementation of adaptive/dynamically adaptive neuron functions. Mixed signal silicon implementation of adaptive neuron models exploiting membrane dynamics like Mihalas-Niebur model9 and adaptive exponential decay model (AdeX)8 have been shown in22. Wang et al.23 have recently simulated a RRAM device to realize an adaptive neuron, in which the adaptation behavior is achieved by increasing the neuron membrane resistance at each spike event.Hardware implementation of neuron models such as Integrate and Fire (IF) and Leaky Integrate and Fire (LIF)In this work our contribution is twofold; first, we propose a new dynamically varying Double EXponential Adaptive Threshold (DEXAT) neuron model for RSNNs, and second we show an efficient hardware implementation of the new neuron model. In the proposed DEXAT neuron model the threshold voltage decays by two exponential rates (slow and fast). We observe that the proposed model has several benefits while realizing RSNNs such as faster convergence, higher accuracy, re-configurability, and ease of hardware implementation. For hardware realization, we demonstrate the capability of non-filamentary OxRAM (oxide-based resistive memory) devices to realize double exponentials, using extracted data from multiple OxRAM material stacks. We also show experimental demonstration of the DEXAT neuron that exploits unique and tightly coupled circuit–device properties of non-filamentary OxRAM devices. Further, we perform system-level RSNN simulations on classification of sequential MNIST (SMNIST) handwritten digits and speech commands from GSC dataset based on the neuron parameters extracted from experiments. We demonstrate full end to end RSNN using fabricated resistive memory based DEXAT neurons for live speech recognition application on GSC dataset.4 propose an architecture of RSNN called Long Short-Term Spiking Neural Network (LSNN) to enhance their computational performance. This involves providing RSNN with a LSTM by including ALIF neurons in the network. Hence, LSNNs consist of a network of LIF and ALIF neurons and can achieve similar performances as LSTM on sequential tasks. Threshold voltage adaptation in the ALIF neuron model is described by Eqs. (Bj(t) is the adaptive firing threshold of the neuron, bj0 is the baseline threshold voltage, β is a constant scaling factor which scales the deviation bj(t) from the baseline bj0, ρj = exp(−δt/τa) and governs the exponential decay of threshold voltage, τa is the adaptation time constant of the threshold decay and zj(t) denotes the output spike generated by neuron “j” having value 1/δt for a spike event and zero otherwise. The equations are described in discrete time with a minimum time step of δt. In this model the threshold voltage is increased by a fixed amount β/τa when the neuron fires and then decays exponentially back to a baseline value bj0 with a time constant τa ..4 propossee Fig. .Fig. 1Quρj1 = exp(−δt/τa1), ρj2 = exp(−δt/τa2). Equation (δt. First terms in Eqs. (bj1(t), bj2(t) define the magnitude of threshold voltage decay governed by the two exponentially decaying factors ρj1, ρj2, and second terms govern threshold voltage increment when an output spike zj(t) occurs. The two equations get independently updated at each time step of δt. Values from both the equations are individually scaled by factors β1, β2 and added in , ,  encoded by a spike sequence. The network has to store a character in its working memory on receiving a STORE instruction and has to recall the character on a RECALL instruction. The time duration between STORE and RECALL instruction is the working memory of the network. A LSNN network with 10 LIF and 10 ALIF neurons as shown in Fig. δt is taken to be 1 ms in simulations. The network is trained for 200 iterations with a minimum desired decision error of 0.05. Figure τa of 1200 ms in 200 iterations while for smaller τa values of 600 and 300 ms LSNN network fails to converge to the desired decision error. On the other hand, LSNN with DEXAT neurons reaches the decision error requirement successfully even with adaptation time constants τa1 = 30 ms, τa2 = 300 ms . A case for a working memory of 600 ms is also presented for different time-constant values of ALIF and DEXAT neurons  of ALIF neuron to observe the ranges of working memory it can support. We conclude from Fig. τa is greater than or equal to the value of required working memory. In Fig. τa1 to 30 ms and vary larger time constant τa2 of DEXAT neuron. We observe that for a case of τa2 = 200 ms and working memory requirement 2000 ms  also, LSNN converges successfully within 200 iterations using DEXAT neurons; not possible with ALIF neurons as shown in Fig. τa2) and vary smaller time constant (τa1) for different working memories. We observe from Fig. τa2/τa1 is greater than six. However, with a τa2/τa1 ratio of three the network fails to converge for larger working memories . We also test the effect of scaling parameters  RESET pulse(s) has been commonly observed. The relative change in conductance on application of subsequent RESET pulses  is more gradual and tends to saturate. This nonlinear behavior is considered undesirable while emulating ideal linear synaptic characteristics, and efforts have been made to overcome the nonlinearity through use of complex pulse schemes34. However, we exploit this undesirable combination of initial abrupt conductance jump followed by subsequent gradual conductance jumps, to realize the dual time constants  in our proposed DEXAT neuron model.Asymmetric conductance change: Non-filamentary OxRAM devices in which resistance change is determined by modulation of the interfacial Schottky/tunneling barrier by the electron trapping/detrapping or ion migration have potential for MLC characteristicsCoupling of OxRAM initial resistance state with programmed resistance state: We exploit the coupled nature of initial and final programmed states of OxRAM to realize the adaptive behavior of the threshold with consecutive firing events. The programmed conductance state of an OxRAM device would depend on two factors: (i) the programming condition used  and (ii) the initial conductance state prior to application of programming pulse. In proposed DEXAT hardware realization the programming SET pulse on each firing event is identical, however the initial high resistance state (HRS) prior to the application of the SET pulse is variable. Our programming methodology (detailed in next section) ensures that the initial HRS is a function of the time between two consecutive firing events. If the time between consecutive firing events is less the RESET pulse is shorter and thus the corresponding initial HRS is weak. Similarly, if the time between two consecutive firing events is larger the devices ends up in a stronger HRS state owing to the application of a longer RESET pulse. Based on the desired biological adaptation of firing threshold, if two consecutive firing events occur within a short time duration the subsequent firing threshold of the neuron must increase and should make the second firing event harder to achieve. This same effect is emulated in our implementation as when HRS is weaker the SET pulse leads the OxRAM device in a higher conductance state compared to the case when the HRS state was stronger. Using this phenomena and the proposed programming methodology we are able to ensure progressively increasing firing threshold after every spike event without modifying the intermediate SET condition (see below).Emerging nonvolatile memory (NVM) devices have been used to realize multiple synaptic and neuronal functions. Most neuromorphic NVM implementations in literature primarily rely on conductance modulation of the devices. For instance, analog multilevel cell (MLC) characteristics are exploited for realizing synaptic weight update characteristics like Long Term Potentiation (LTP) and Long Term Depression (LTD) by increasing or decreasing the device conductance respectively. For efficient hardware realization of the proposed DEXAT neuron, multiple nonlinear/analog functions need to be harnessed. In particular: (i) we need a circuit–device property that can help realize the double exponential with varying time constants, during the decaying phase of the threshold and (ii) another property that helps to increase (adapt) the peak threshold value after each consecutive firing event. We propose to extract both of these analog properties from the circuit–device interactions of OxRAM devices in the following manner:29 . Our main focus in this section is efficient realization of the proposed novel threshold adaptation block with capability to realize double exponential decay behavior inside the DEXAT neuron. The neuron fires when output of integrator i.e membrane potential (Vmem) crosses the voltage on threshold terminal (Vth) of comparator. Firing triggers the pulse-generator block to generate a sequence of control pulse signals (and their complements) which activate the SET and RESET paths in the threshold modulator circuit as shown in Fig. VR is low, its effect on SET process is negated by the OFF MP3 transistor. The RESET path of the device is then activated through the ON MP1, MP3 and MN2 transistors as shown in Fig. Figure i + 1)th spike-event depends on the final HRS reached due to the preceding RESET pulse in the ith event. A weak/shorter RESET pulse due to shorter inter-spike delay ensures that the final high resistance reached prior to (i + 1)th firing event is lesser compared to the state achieved prior to the ith spike event. Thus the SET pulse in (i + 1)th spike event drives the device to a stronger set state  compared to the SET pulse in ith event. Thus, the desired threshold adaptation behavior for a sequence of spike events is realized, as shown in Fig. Figure Figure We also realize a fully digital implementation of the proposed threshold modulator circuit using FPGA and simulated digital ASIC hardware blocks as detailed in Supplementary Note 37 to produce the label of the sequentially presented handwritten digit. This is shown in Fig. 4 and only 2.4% less than LSTMs.We use parameters extracted from proposed hardware neuron to simulate a standard benchmark test of classifying SMNIST requiring LSTM. LSNN network used for this task is shown in Fig. 29 was obtained, respectively, as shown in Fig. ηr values (0 to 40%) as shown in Fig. ηr = 40%, the accuracy drop was found to be only ~2.1% in case of SMINIST application shown in Fig. We also benchmark through simulations the performance of LSNN networks based on DEXAT neurons realized using different OxRAM technology stacks Fig. . Baselinues 0 to 0% as sho38 while significantly outperforming literature in terms of total network resources . DEXAT based LSNNs were also found to beat their ALIF based counterparts in lesser number of iterations . System-level simulations of LSNN network with DEXAT neurons achieved a test accuracy of 96.1% for classification on SMNIST dataset and 91% on GSC dataset. In case of GSC dataset, DEXAT based LSNNs were found to match state of the art accuracy with significantly less number of hidden layer neurons. Further, we experimentally demonstrate that the proposed DEXAT neuron model can be realized in hardware using tightly coupled circuit–device interaction of different bilayer OxRAM devices (through hybrid CMOS-OxRAM circuits). Effects of OxRAM device variation and endurance on network performance are also investigated. LSNN networks with DEXAT neurons are found to be robust to OxRAM device variability and are able to achieve an accuracy of 94% with even 40% resultant device variability. We demonstrate real-time speech inference using full end-to-end experimental setup consisting of real hardware CMOS-OxRAM DEXAT neurons. Further, we explore digital implementations of the adaptive threshold modulator block of the proposed DEXAT neuron for performance benchmarking. Proposed new DEXAT neuron model and its different hardware implementations have the potential to realize efficient large-scale recurrent/temporal neuromorphic networks. Among various realizations, hybrid CMOS-OxRAM circuits offer clear area-benefit. Future efforts should be in the direction of reducing individual OxRAM device switching energy to sub pico-joules i.e., through lower programming voltages, switching currents (~nA or lower), and ultra-fast switching speeds (sub ns)).Characterization of the OxRAM devices is performed using Keithley 4200 SCS parameter analyzer. Keithley 4210 high power SMU (Source Measure Unit) was used to perform the DC sweep measurements. For DC characterization a dual voltage sweep is applied on TE using SMU with SET voltage from 0 to 3 V and a RESET voltage from 0 to −5 V. Pulse characterization of our device is performed by applying multiple SET and RESET pulses Keithley 4225 PMU and reading the device conductance after every pulse. We built our proposed neuron circuit on a General Purpose Board  having various blocks like microcontroller, analog to digital converter (ADC), digital to analog converter (DAC), and a separate daughter PCB consisting of DEXAT threshold modulator circuit components. Daughter PCB is interfaced with the parent PCB as shown in Supplementary Fig.Ni/3 nm HfO2/7 nm Al-doped-TiO2(ATO)/TiN (top to bottom) structure were fabricated by following a CMOS compatible process in Prof. Tuo’s Lab (NCTU). The active device area was 50 μm × 50 μm, and the ATO as well as HfO2 were deposited using plasma-enhanced atomic layer deposition (PE-ALD). The device fabrication flow is as follows: first, 100 nm thick TiN BE flm was deposited on thermal-SiO2 (500 nm)/Si wafer by physical vapor deposition, RF magnetron sputtering. The BEs were then patterned by optical photolithography (first mask) and dry etching using inductively-coupled plasma (ICP). The bottom, 7 nm thick ATO dielectric, was then deposited by interchanging varying amount of TiO2 and Al2O3 PE-ALD cycles, using TDMATi (Tetrakis(dimethylamido)titanium) and TMA  as metal-organic precursors and O2 plasma as a reactant. Upper, 3 nm thick dielectric HfO2 flm, was deposited using TDMAHf (Tetrakis(dimethylamido)hafnium) and O2 plasma. All depositions were carried out at 250 °C using Veeco-CNT Fiji F202 remote plasma hot-wall reactor PE-ALD system. Top Electrode (TE) pattern (similar to the BE pattern but rotated 90°) was defined using second optical photolithography mask and 100 nm thick Ni TE film was deposited by DC sputtering and patterned using lift-off technique. Final photolithography (third mask) and ICP dry etching step was performed to open the contact windows (etch the dielectrics) to the BE contact pads. Wire bonding and packaging were the final steps for the OxRAM encapsulation.Analog resistive switching OxRAM stacks of ηr). We use the methodology used in40 for variability analysis. In order to experimentally characterize variability, we performed both stand-alone variability  and combined variability  experiments as shown in Supplementary Fig. ηr) is defined such that it helps to analyze the statistical impact of variations for any generic OxRAM device . First, several devices are cycled multiple times for implementing a given threshold sequence. Next, for each time-step (i) the corresponding mean μi, standard deviation (σi), and coefficient of variation (ηi = σi/μi) of the threshold voltage values  are calculated. Finally, median of all ηi values is defined as the resultant variability (ηr) parameter. From Supplementary Fig. ηr = 30% for the Ni/HfO2/Al-doped TiO2/TiN stack device, for sequence S1. In order to generate the simulated neuron thresholding traces, each Y-axis value  for a corresponding X-axis value  is drawn from a random gaussian distribution . Supplementary Fig. ηr.In all the full network analysis we incorporate variability on the adaptive threshold voltage of each neuron in the network based on experimental data and a combined variability (C2C + D2D) parameter defined as resultant variability . We have defined the desired minimum decision error to be <0.05. Learning rate, learning rate decay and dampening factor are taken to be 0.01, 0.8, and 0.3, respectively, in the simulations. Training and testing batch size is set to 256 for simulations. All the 60,000 images in the train set of MNIST dataset are used for training. For inference, input test image is presented from 10,000 test images in a sequential manner where each input pixel is presented in 1 time step “δt” (here 1 ms). Hence, inference of one MNIST image takes 784 time steps (δt). It is important to note that the simulation time step “δt” can be chosen to satisfy the number of time steps required for the temporal task based on the extracted values of time constants from the experimental device. Each pixel of 28 × 28 MNIST image is presented to the 80 input neurons and is encoded by the firing of the input neurons with threshold crossing method. Network architecture details are specified in Supplementary Table System-level simulations for STORE and RECALL tasks and classifying SMNIST dataset are performed using a three layer recurrently connected network of spiking neurons. Hidden layer consists of LIF and adaptive neurons (ALIF/DEXAT). Our proposed DEXAT neuron model equations are integrated using Tensorflow library in python code based onThis application involves initial speech data sample preprocessing step. First, an input speech sample of 1 second duration is taken from GSC dataset at a sampling rate of 16 kHz. Next, Mel Frequency Cepstral Coefficient (MFCC) with a 30 ms window and 1 ms stride is applied to the sample. This leads to a feature vector consisting of 40 output features, extracted for all 100,503 speech samples, spanning across the 30 classes of the GSC dataset. We trained multiple LSNN networks of varying dimensions (Supplementary Fig. Supplementary Information FileSupplementary Movie 1Supplementary Movie 2Description of Additional Supplementary FilesPeer Review File