Catecholamines have a well-established role in hippocampal function. Both dopamine and norepinephrine have been shown to impact hippocampal-dependent learning and memory (de Silva et al., 2012; Retailleau and Morris, 2018; Tsetsenis et al., 2019; Hansen and Manahan-Vaughan, 2014; Gibbs and Summers, 2002; Thomas, 2015; André et al., 2015), alter synaptic plasticity (Hagena and Manahan-Vaughan, 2013; Hansen and Manahan-Vaughan, 2014; Chu et al., 2011; Edison and Harley, 2012; Hagena and Manahan-Vaughan, 2012; Goh and Manahan-Vaughan, 2013), modulate cell excitability (Edelmann and Lessmann, 2018; Segal et al., 1991), and influence the formation and stability of place cells (Kentros et al., 2004; Retailleau and Morris, 2018), hippocampal neurons that selectively fire at specific locations in an environment (O’Keefe and Dostrovsky, 1971). Traditionally, the main source of dopamine to the dorsal hippocampus was thought to be sparse inputs from the ventral tegmental area (VTA), while locus coerulues (LC) inputs provided the main source of norepinphrine.

VTA Dopaminergic (DA) inputs to dorsal CA1 of the hippocampus mainly innervate stratum oriens (Takeuchi et al., 2016; Adeniyi et al., 2020; Adeyelu and Ogundele, 2023), and their activity bidirectionally modulates Schaffer Collateral (CA3–CA1) synapses (Rosen et al., 2015), enhances persistence of reward-location associations (McNamara et al., 2014), and drives place preference (Mamad et al., 2017). VTA-hippocampus input activity can also bias place field location (Mamad et al., 2017), improve place field stability across days (McNamara et al., 2014), and drive reward expectation dependent enhancement of place field quality (Krishnan et al., 2022). However, many of the effects of DA modulation of the hippocampus have now been attributed to LC inputs as their activity enhances the strength of Schaffer Collateral synapses (Takeuchi et al., 2016), improves memory retention (Kempadoo et al., 2016), improves place field stability across days (Wagatsuma et al., 2018), and can bias place fields to a location when paired with a reward (Kaufman et al., 2020) through DA mechanisms. Although many of the effects of LC and VTA are overlapping, potentially indicating shared mechanisms of action, they are believed to play different roles in spatial learning and memory (Duszkiewicz et al., 2019). LC inputs influence the encoding of novel environments (Kempadoo et al., 2016; Wagatsuma et al., 2018), while VTA DA inputs increase persistence of reward context associations (McNamara et al., 2014), alter hippocampal neurons firing rate (Adeniyi et al., 2020), and their suppression can evoke place avoidance (Mamad et al., 2017). It is possible that these differences arise because of the differences in activity observed between LC and VTA DA neurons. Therefore, characterizing the encoding properties of LC and VTA inputs directly in the hippocampus during navigation and spatial learning would provide important insights into the specific roles of these distinct neuromodulatory pathways.

Additionally, recent findings indicate considerable heterogeneity in the activity of VTA (Engelhard et al., 2019) and LC (Uematsu et al., 2017; Noei et al., 2022; Chandler et al., 2014) neurons, highlighting the need for projection specific recordings. Therefore, we functionally imaged VTA DA and LC axons with single-axon resolution in dCA1 of mice as they navigated familiar and novel virtual reality (VR) environments for rewards. We observed distinct encoding properties between these sets of inputs during navigation and in response to environmental novelty. During the approach to a previously rewarded location, the activity of most VTA DA axons ramped up. In contrast, most LC axons did not show this ramping activity and instead predicted the start of motion. Additionally, a majority of LC axons and some VTA axons showed activity associated with the animal’s velocity. Following exposure to a novel environment, VTA axon ramping-to-reward signals greatly reduced but LC axon activity sharply increased. These findings support distinct roles for VTA and LC inputs to the hippocampus in spatial navigation of rewarded and novel environments.

To record the activity of dopaminergic inputs to the dorsal hippocampus, we expressed axon-GCaMP6s or axon-GCaMP7b in LC or VTA neurons of different mice. We utilized the NET-Cre mouse line (Wagatsuma et al., 2018) to restrict expression to noradrenergic LC neurons, and the DAT-Cre line (Zhuang et al., 2005) to restrict expression to dopaminergic VTA neurons (Figure 1b). Mice were then head-fixed and trained to run a linear VR track for water rewards delivered through a stationary waterspout when they reached the end of the virtual track (Figure 1a). Following reward delivery, mice were teleported to the beginning of the track and allowed to complete another lap. On experiment day mice navigated the familiar, rewarded VR environment for 10 min while two-photon microscopy was used to image the calcium activity of LC (87 regions of interest [ROIs] from 22 imaging sites in 16 mice) or VTA (9 ROIs from 8 imaging sites in 8 mice) axons in the dorsal CA1 (Figure 1c). Based on the z-axis depth of the recording planes, and the presence of increased autofluorescence in stratum pyramidal, we determined all 9 VTA axons were in Stratum Oriens, while for LC recordings, 18 sessions (78 axons in 11 mice) occurred in Stratum Oriens and 5 sessions (9 axons in 5 mice) in Stratum Pyramidalis. Example VTA (left, orange) and LC (right, blue) axon calcium activity aligned to the animal’s behavior are shown in Figure 1d. Axons from both brain regions showed periodic activity linked to the animals’ exploration of the VR environment.

Figure 1 with 3 supplements see all

Download asset Open asset

Figure 1—source data 1

Fluorescence data of ventral tegmental area (VTA) and locus coeruleus (LC) axons in familiar virtual reality (VR) environments.

https://cdn.elifesciences.org/articles/95213/elife-95213-fig1-data1-v1.xlsx

Download elife-95213-fig1-data1-v1.xlsx

Distinct activity dynamics in VTA and LC inputs during rewarded navigation of a familiar environment

To examine axon activity further, we first looked at the mean activity across all axons as a function of normalized track position (Figure 1e). As previously reported (Krishnan et al., 2022), VTA DA axons increase activity along the track, peaking at the reward location at the end of the track. In contrast, LC input activity remains relatively constant across all positions along the track (Figure 1e,iii). To examine if this difference could be due to the lower sample size of VTA axons compared to LC axons, the LC axons were down-sampled to match the VTA sample size (n = 9 regions of interest [ROIs] in 8 mice) and the slope and intercepts of the down-sampled data were found. This was repeated 1000 times and did not generate any LC data points that overlap with VTA data demonstrating the difference in relationship between position and activity was not due to the different sample sizes (Figure 1e,iii). We also examined the position related activity of individual VTA and LC axons and observed a positive relationship between position and activity in 88.9% of VTA ROIs (8/9 in 7 mice) but only 28.7% of LC ROIs (25/87 from 12 sessions in 12 mice) while 42.5% of LC ROIs (37/87 from 16 sessions in 9 mice) had a negative relationship between position and activity (Figure 1e,iv). To account for the different track lengths between VTA and LC recordings, we also looked at the virtual distance from the rewarded end of the track as well as time from reward (Figure 1—figure supplement 1). The same trends were seen for VTA and LC axons, with 8 VTA ROIs showing positive relationships with both distance and time from reward, and 21 LC ROIs showing a positive relationship with distance from reward and 31 LC ROIs showing a positive relationship with time to reward. This suggests that track length does not influence the encoding properties of VTA and LC axons.

For a subset of VTA (n = 6) and LC ROIs (n = 26), the reward at the end of the track was removed, and the activity of these axons in the unrewarded condition was examined (Figure 2a). While the slope of the VTA population activity across positions significantly decreased (Figure 2b,i) as expected and previously reported (Krishnan et al., 2022), the LC population activity across positions did not significantly change (Figure 2c,i). This confirmed that the ramping activity in VTA axons was due to the animal’s proximity to an expected reward and this signal was not present in the average activity of LC axons .

Figure 2

Download asset Open asset

Figure 2—source data 1

Fluorescence data of ventral tegmental area (VTA) and locus coeruleus (LC) axons in rewarded and unrewarded virtual reality (VR) environments.

https://cdn.elifesciences.org/articles/95213/elife-95213-fig2-data1-v1.xlsx

Download elife-95213-fig2-data1-v1.xlsx

Next, we investigated the mean activity of these axons as a function of velocity. The population mean of both VTA and LC axons increased as velocity increased (Figure 1f). This is consistent with the finding that LC inputs to dCA1 encode velocity (Kaufman et al., 2020) and the finding that some DA VTA neurons encode kinematics (Engelhard et al., 2019). Again, to account for differences in sample size, we down-sampled the LC axons 1000 times and found the slope and y-intercept of each sampling. The overlap of the VTA and LC slopes and intercepts confirms we cannot conclude any differences in velocity-related activity in the VTA and LC axon populations (Figure 1f,iii). However, analyzing individual ROIs, we observed a statistically significant positive relationship between velocity and activity in the majority, 72.4%, of LC axons (63/87 ROIs from 21 sessions in 13 mice), while only 28.6% (2/9 ROIs in 2 mice) of VTA axons showed a positive relationship with velocity (Figure 1f,iv). The strong velocity correlated activity in a small subset of VTA DA axons indicates heterogeneity in the activity of these inputs similar to what is observed in VTA DA somas (Engelhard et al., 2019). To determine if the velocity correlated activity in VTA DA axons is confounded by the ramping-to-reward activity, we examined this activity in the unrewarded condition where the ramping-to-reward activity is absent. Here, the VTA population activity across all velocities is decreased but the slope, or the relationship between velocity and activity, remains unchanged (Figure 2b,ii). This is consistent with a subset of VTA axons encoding velocity information, while the overall decrease in activity is explained by the decrease in reward related activity demonstrated in Figure 2b,i. However, the relationship between velocity and LC activity is unchanged in the unrewarded environment (Figure 2c,ii), indicating condition invariant velocity encoding in LC axons.

Finally, we examined the activity of LC and VTA axons during rest and the transition to movement. The population of LC axons ramped up in activity during the 2 s leading up to motion onset (Figure 1g). This is consistent with reports of activity of LC axons in cortical areas (Reimer et al., 2016) showing LC activity prior to motion onset. In contrast, VTA axons show decreasing activity during the 2 s leading up to motion onset (Figure 1g). This ramping down in VTA axon activity is likely due to most periods of immobility occurring between reward delivery and the start of the next lap, during which we previously demonstrated reward related activity decays in VTA axons (Krishnan et al., 2022). Indeed, in the unrewarded condition the negative slope of the VTA population activity leading up to motion onset disappears (Figure 2b,iii), indicating this relationship is largely driven by the presence of reward rather than motion onset. However, the LC population activity remains unchanged in the unrewarded environment (Figure 2c,iii), suggesting this activity is related to the lead up to motion initiation. These differences in activity are not an artifact of lower sample size of VTA ROIs as shown by down-sampling the LC ROIs activity 1000 times and measuring the slopes and intercepts of the down-sampled data did not generate any data points that overlapped with the VTA slope and intercept (Figure 1g,iii). In further support of distinct activity profiles leading up to motion onset, we found that the majority (6/9 ROIs in 6 mice) of VTA ROIs decreased in activity leading up to motion onset but only 2/87 LC ROIs in one session decreased in activity, while the majority (56/87 ROIs in 17 sessions in 9 mice), of LC ROIs increased in activity leading up to motion onset (Figure 1g,iv). Together, this analysis demonstrates overlapping but distinct activity in VTA and LC axons during spatial navigation with VTA axons showing strong activity correlated with distance to reward and some velocity correlated activity, while LC axons demonstrate activity correlated to velocity and time to motion onset.

Because the DAT-Cre mice were injected with a virus for expression of either axon-GCaMP6s (four mice) or axon-GCaMP7b (four mice), we asked whether these two GCaMP variants led to different activity dynamics (Figure 1—figure supplement 2). The axon-GCaMP6s (five ROIs) and axon-GCaMP7b (four ROIs) both had a positive relationship with position, negative relationship with time to motion initiation, and a neutral relationship with velocity, although the GCaMP6s relationships were stronger (Figure 1—figure supplement 2a, c). Importantly, when we compared only axon-GCaMP6s expressing VTA ROIs with the axon-GCaMP6s expressing LC ROIs (Figure 1—figure supplement 3), we saw similar relationships as the comparisons using all VTA ROIs (Figure 1).

Environmental novelty induces activity in LC but not VTA inputs

Both LC and VTA neurons have been shown to respond to novel environments (Takeuchi et al., 2016). Therefore, we aimed to examine the activity of VTA DA and LC inputs to the hippocampus during exposure to novel VR environments. Following 10 min in the familiar environment, mice were teleported to a novel VR environment of the same track length, with a reward at the same position at the end of the track. Following teleportation, we found the running speed of both DAT-Cre and NET-Cre mice transiently decreased (Figure 3a) and they spend less time immobile (Figure 3a), demonstrating mice recognize they are navigating a novel environment. While the velocity quickly recovered for both groups of mice, the freezing ratio, or amount of time spent immobile, never recovered in the first 10 laps in the novel environment for the NET-Cre mice indicating some novelty-induced changes in behavior persist.

Figure 3 with 2 supplements see all

Download asset Open asset

Figure 3—source data 1

Fluorescence data of ventral tegmental area (VTA) and locus coeruleus (LC) axons in novel virtual reality (VR) environments.

https://cdn.elifesciences.org/articles/95213/elife-95213-fig3-data1-v1.xlsx

Download elife-95213-fig3-data1-v1.xlsx

We aligned VTA and LC axon activity to the switch to the novel environment and investigated changes in activity due to exposure to novelty. To test whether the mean axon activity is significantly elevated or lowered, we defined a baseline by generating 1000 shuffles of the axon traces across the entire recording sessions, down-sampling the shuffled data 1000 times to match the VTA (n = 7) and LC (n = 87) sample sizes, and calculating the mean and 95% CI of the shuffled data. After teleportation, the periodic activity observed in the mean of VTA axons, likely reflecting ramping-to-reward signals in each individual axon, disappeared (Figure 3b). This is evident in the traces of most of the individual VTA ROIs showing a loss of the ramping-to-reward signal (Figure 3c), and in the VTA population position binned activity showing a significant reduction in ramping activity (Supp. Fig. 1). However, one VTA ROI showed an increase in activity immediately following exposure to novelty (Figure 3c), indicating heterogeneity across VTA axons in CA1 and the lack of a novelty signal on average may be due to a small sample size.

Strikingly, LC axons show a dramatic increase in mean activity that remained elevated for >1 min following exposure to the novel environment (Figure 3b) similar with findings that LC cell body activity is elevated for minutes following exposure to environmental novelty (Takeuchi et al., 2016). A significant increase in activity above baseline activity following the switch to a novel environment can be seen in 36 LC axon ROIs (from 15/22 sessions in 10/16 mice) (Figure 3c). To further characterize this activity, we found the mean population activity for each lap and separately for 10 s time bins leading up to and following exposure to the novel environment. This analysis shows that LC activity is significantly elevated above baseline for six consecutive laps and approximately 90 s following exposure to the novel environment (Figure 3d). These findings demonstrate that LC inputs signal environmental novelty, supporting a role for these inputs in novelty encoding in the hippocampus.

Novelty-induced changes in behavior explain the late but not early increases in LC activity

It is possible that the change in the amount of time the animals spend running versus immobile in the novel environment could explain the increase in LC activity in the novel environment, as LC activity is related to behavior (Figure 1). For instance, LC axons show elevated activity during motion versus rest (Figure 1g). Therefore, an increase in the time spent in motion upon exposure to the novel environment could lead to an increase in LC activity. To account for the differences in time spent running vs immobile between the two environments, we removed any periods where the mice were immobile to isolate the effects of novelty from changes in behavior (Figure 4b). When only looking at activity during running in both environments, we found that LC axon activity is elevated for two laps, or 40 s, in the novel environment (Figure 4b–e). Additionally, when we look only at activity when the mice were immobile, we see that activity is elevated for 30 s in the novel environment (Figure 3—figure supplement 2b). Together, this indicates that there are two separate components that drive LC axon activity during the initial exposure to the novel environment. One, a shorter purely novelty-induced increase in activity which occurs during the first two laps, or about 40 s, in the novel environment. Two, a behavior-induced increase in LC activity caused by an increase in the percentage of time spent running that extends beyond the increase in the novelty-induced activity for six laps or 90 s.

Figure 4

Download asset Open asset

Figure 4—source data 1

Fluorescence data of locus coeruleus (LC) axons during good behavior in novel virtual reality (VR) environments.

https://cdn.elifesciences.org/articles/95213/elife-95213-fig4-data1-v1.xlsx

Download elife-95213-fig4-data1-v1.xlsx

If the short, novelty-induced signal in LC axons is an additional signal riding on top of the behavior correlated signals – position, velocity, and motion onset – we would expect a disruption of these behavioral correlations during the initial lap in a novel environment. To test this we examined these behavioral correlations lap-by-lap following exposure to the novel environment. Indeed, the slopes of the position binned, velocity binned, and motion onset aligned data are all significantly more negative in the first lap in the novel environment than the final laps of the familiar environment (Figure 4f–h). This is consistent with a decaying novelty signal that peaks at the start of the first lap and rides on top of these behaviorally correlated signals. This produces an elevation in activity at positions near the start of the first lap that is lower at positions near the end of the first lap, causing a negative slope relationship between position and LC axon activity on the first lap (Figure 4f; light green line). Furthermore, low velocities occur at the start of each lap compared to the end of the lap. Because the novelty signal is highest when animals are running slowest, the novelty signal flattens the velocity–LC activity relationship (Figure 4g; light green line). Lastly, rest periods typically occur at the start of the track. Therefore, motion onset encoding on the first lap in the novel environment occurs when the novelty signal is highest, again, flattening the relationship (Figure 4h; light green line). By the third lap in the novel environment, where the novelty-induced signal is no longer observable, the relationships between LC activity and position is no longer different than the relationship in the familiar environment (Figure 4f, green). The relationship between LC activity and motion onset is also no longer different by the third lap in the novel environment (Figure 4h). Although the relationship between velocity and LC activity is different in the third novel lap than that of the final familiar laps, by the final lap in the novel environment it is no longer different than the familiar relationship (Figure 4g). Together this demonstrates that the relationships between LC activity and behavior in the novel environment quickly return to those in the familiar environment. Interestingly, the slope is significantly increased in the final lap of the novel environment (Figure 4f), potentially indicating the development of activity at the novel reward location as has been previously reported (Kaufman et al., 2020). Altogether, examining the lap-by-lap dynamics of the position, velocity, and motion onset activity indicates that environmental novelty induces a sharp increase in LC input activity during the first two laps in the novel environment while also inducing a change in behavior that leads to increased LC input activity in subsequent laps.

We also examined the activity of a subset of LC axons (50 ROIs from 11 sessions in 9 mice) during the transition from darkness to the familiar VR environment. These axons showed elevated activity for 20 s following initial exposure to the familiar environment (Figure 3—figure supplement 2a). While this activity did not persist as long as the activity following exposure to the novel environment, it indicates these axons may be generally responsive to abrupt and unpredicted changes to the animal’s environment. This fits with the proposed role of the LC in arousal, with novelty driving greater arousal than abrupt exposure to a familiar environment.

During spatial navigation in a familiar environment, activity of VTA DA inputs to dCA1 was strongly modulated by position relative to reward, ramping up as mice approached the end of the track where reward was located. This activity could be related to the animal’s distance or time from the rewarded location. Further experiments should be conducted to distinguish between time and distance such as those conducted in VTA DA soma recordings (Kim et al., 2020). We have previously shown that this activity is dependent on the history of reward delivery and reflects the animals’ reward expectation (Krishnan et al., 2022). VTA axons in dCA1 also showed decreasing activity during rest prior to motion onset. However, after removing reward, VTA axons showed no activity prior to motion onset indicating this relationship was driven by reward related activity. This ramping activity cannot be explained by changes in the animals’ sensory experience, as the VR environment and waterspout position remained unchanged in the unrewarded condition. Additionally, on the initial laps with no reward, the ramping activity is still present (Krishnan et al., 2022) indicating this activity is not directly related to the delivery of water but is instead caused by the animal’s internal state of reward expectation.

On average, VTA axons showed activity modulated by velocity in a familiar rewarded environment. This relationship was largely due to the activity of two VTA axons that were strongly modulated by velocity, suggesting that there is heterogeneity in the population of VTA axons in dCA1. A positive relationship between average VTA axon activity and velocity persisted in the unrewarded condition and in the novel environment, indicating velocity encoding in these inputs that is not a result of reward related activity. This heterogeneity in the encoding across individual VTA axons is consistent with studies demonstrating heterogeneous encoding of behavioral variables in VTA DA cell bodies, including activity related to rewards and kinematics (Engelhard et al., 2019).

Our findings that VTA DA axons show no novelty-induced activity and instead show reduced activity following exposure to a novel environment, is in contrast with several studies showing novelty-induced activity in VTA DA cell bodies (Takeuchi et al., 2016; Duszkiewicz et al., 2019; Lisman and Grace, 2005), indicating potential heterogeneity in VTA neurons in response to novelty. It is possible that some VTA DA inputs to dCA1 respond to novel environments, and the small number of axons recorded here are not representative of the whole population. Another possibility is that the lack of a novelty response we observe is due to differences in experimental design. Here, mice learned to approach a location for reward which has been shown to lead to ramping activity in dopaminergic VTA neurons (Howe et al., 2013; Krishnan et al., 2022; Kim et al., 2020; London et al., 2018; Jeong et al., 2022). Following exposure to novelty, the disappearance of reward related activity could obscure any novelty-induced increases in activity. In addition, as noted above, on average we did observe a velocity-associated signal in VTA axons. When mice were exposed to the novel environment their velocity initially decreased. This would be expected to reduce the average signal across the VTA axon population relative to the higher velocity in the familiar environment. It is possible that this decrease could somewhat mask a subtle novelty-induced signal in VTA axons. Therefore, additional experiments should be conducted to investigate the heterogeneity of these axons and their activity under different experimental conditions during tightly controlled behavior.

LC axons showed no position encoding. Instead, they were modulated by velocity and ramped up in activity prior to motion initiation, consistent with recordings of LC axons by others in dCA1 (Kaufman et al., 2020) and in the cortex (Reimer et al., 2016), respectively. An important question is how is this LC axon activity impacting hippocampal neurons during navigation in a familiar environment? It is possible that LC axons during navigation provide increases in excitability that promotes place cell activity as both dopamine and norepinephrine in the hippocampus can impact cell excitability (Segal et al., 1991; Edelmann and Lessmann, 2011; Edelmann and Lessmann, 2018). Additionally, place cells are flexible during spatial navigation with new place fields forming in familiar environments (Sheffield et al., 2017; Dong et al., 2021) and shifting position with time/experience (Dong et al., 2021). Place fields can also shift to follow changing reward (Gauthier and Tank, 2018) and object locations (Bourboulou et al., 2019). Dopamine and norepinephrine have also been shown to impact hippocampal synaptic plasticity (Zhang et al., 2009; Edelmann and Lessmann, 2011; Hagena and Manahan-Vaughan, 2012; Goh and Manahan-Vaughan, 2013). Therefore, LC inputs may promote the plasticity necessary for place cells to flexibly adapt to changes in a familiar environment (Kaufman et al., 2020; Redondo and Morris, 2011). In other words, LC inputs could allow the hippocampus to be flexible during navigation through their impacts on synaptic plasticity (Takeuchi et al., 2016; Yamasaki and Takeuchi, 2017; Duszkiewicz et al., 2019).

Exposure to environmental novelty leads to an increase in dopamine in the dorsal hippocampus (Ihalainen et al., 1999) and promotes synaptic plasticity (Li et al., 2003; Hagena and Manahan-Vaughan, 2012), hippocampal replay (McNamara et al., 2014; Dupret et al., 2010), and memory persistence (Li et al., 2003; Cohen et al., 2017). In our experiment, exposure to a novel environment caused an increase in LC axon activity but not in VTA DA axon activity, supporting findings that novel experiences induce activity of LC neurons (Takeuchi et al., 2016). As discussed above, the slowing down of animal behavior in the novel environment could have decreased LC axon activity and reduced the magnitude of the novelty signal we detected during running. The novelty signal we report here may therefore be an under estimate of its magnitude under matched behavioral settings. The increased activity of LC neurons in the novel environment could increase hippocampal neuron activity (Wagatsuma et al., 2018), increase efficacy of Schaffer Colateral synapses (Takeuchi et al., 2016), stabilize place cells across days (Wagatsuma et al., 2018), and enhance memory persistence (Wagatsuma et al., 2018; Takeuchi et al., 2016; Chowdhury et al., 2022) through dopamine receptor-dependent mechanisms. Importantly, while LC inputs to CA1 have been shown to cause an increase in activity (Wagatsuma et al., 2018; Chowdhury et al., 2022), shape over-representation of novel reward locations (Kaufman et al., 2020), and modulate memory linking (Chowdhury et al., 2022), they have not been shown to play a role in the formation of contextual memories (Chowdhury et al., 2022) or stabilization of place cell maps across days (Wagatsuma et al., 2018) in dCA1. However, it is possible this novelty-induced activity in LC inputs to dCA1 impacts the formation of instant place fields observed in novel environments (Sheffield et al., 2017; Dong et al., 2021). Instant place fields form on the first lap of a novel environment, right when the LC novelty signal is highest in dCA1, suggesting LC inputs may play a role in their formation or stabilization. Therefore, further experiments should investigate the role of LC axons on dCA1 place fields on a trial-by-trial basis.

While LC neurons have been shown to impact novelty encoding through dopaminergic mechanisms (Wagatsuma et al., 2018; Takeuchi et al., 2016; Chowdhury et al., 2022), this does not exclude the possibility that they also release norepinephrine during exposure to novelty and exploration of a familiar environment. Indeed, hippocampal levels of norepinephrine also increase during exposure to environmental novelty (Lima et al., 2019; Moreno-Castilla et al., 2017), but how this norepinephrine release effects hippocampal function is not well understood. Additionally, it is not known whether norepinephrine and dopamine are released from the same LC inputs or from distinct sets of LC inputs. Dopamine is in the synthesis pathway of norepinephrine and in LC neurons it is loaded into vesicles where it is then converted to norepinephrine by dopamine $\beta$-hydroxylase (Cimarusti et al., 1979). It is possible that high levels of activity of LC inputs, like those occurring during exposure to novelty, lead to release of vesicles before dopamine can be converted to norepinephrine thus leading to the release of dopamine under these conditions. However, low levels of LC activation, like those observed during familiar environment exploration, may provide time for dopamine to be converted to norepinephrine and thus lead to the release of norepinephrine from LC terminals under these conditions. Further experiments investigating the dynamics of dopamine conversion and release from LC terminals in the hippocampus should be conducted to test this hypothesis.

Here, we show that LC input activity is modulated by velocity, time to motion onset, abrupt exposure to a familiar environment and abrupt exposure to novel environments. Each of these conditions is associated with an increase in arousal, and LC activity has been strongly linked to arousal levels (Aston-Jones and Bloom, 1981; Berridge and Waterhouse, 2003; Carter et al., 2010). Therefore, rather than encoding each of these variables independently, LC inputs are likely encoding the animals arousal level during spatial navigation. It has been shown that attention and arousal levels impact tuning properties in many cortical areas and this is thought to be mediated through LC activity (Shulman et al., 1979; Bouret and Sara, 2002; Martins and Froemke, 2015; Waterhouse and Navarra, 2019). Similarly, changes in the animals’ brain state, including changes in attention (Kentros et al., 2004) and engagement (Pettit et al., 2022), alter the tuning properties of place cells. This indicates arousal could impact the function of hippocampal neurons through these LC inputs, either directly or through astrocytes (Rupprecht et al., 2023).

The distinct activity dynamics exhibited by LC and VTA DA axons during spatial navigation of familiar and novel environments underscore their distinct contributions to hippocampal-dependent learning and memory processes. Notably, these findings reinforce the notion that VTA DA inputs play a pivotal role in the ongoing maintenance and updating of associations between expected rewards and the locations that lead to them, while LC axons appear to be integral to the process of encoding memories of entirely new environments and stimuli.

Each figure in the manuscript has an associated source data file that contains the numerical data used to generate each figure panel. Time-series behavioral data and imaging data shown in figures can be found here: https://doi.org/10.5061/dryad.ffbg79d4h. Scripts used for data analysis are available on Github (https://github.com/chadheer/LC_VTA_paper; copy archived at Heer, 2024).

1. 1. Heer CM
   2. Sheffield MEJ

   (2024) Dryad Digital Repository

   Distinct catecholaminergic pathways projecting to hippocampal CA1 transmit contrasting signals during navigation in familiar and novel environments.

   https://doi.org/10.5061/dryad.ffbg79d4h

1. 1. Adeniyi PA
   2. Shrestha A
   3. Ogundele OM

   (2020) Distribution of VTA glutamate and dopamine terminals, and their significance in CA1 neural network activity

   Neuroscience 446:171–198.

   https://doi.org/10.1016/j.neuroscience.2020.06.045

   • PubMed
   • Google Scholar
2. 1. Adeyelu T
   2. Ogundele OM

   (2023) VTA multifaceted modulation of CA1 local circuits

   Neurobiology of Learning and Memory 202:107760.

   https://doi.org/10.1016/j.nlm.2023.107760

   • PubMed
   • Google Scholar
3. 1. André MAE
   2. Wolf OT
   3. Manahan-Vaughan D

   (2015) Beta-adrenergic receptors support attention to extinction learning that occurs in the absence, but not the presence, of a context change

   Frontiers in Behavioral Neuroscience 9:125.

   https://doi.org/10.3389/fnbeh.2015.00125

   • PubMed
   • Google Scholar
4. 1. Aronov D
   2. Tank DW

   (2014) Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system

   Neuron 84:442–456.

   https://doi.org/10.1016/j.neuron.2014.08.042

   • PubMed
   • Google Scholar
5. 1. Aston-Jones G
   2. Bloom FE

   (1981) Norepinephrine-containing locus coeruleus neurons in behaving rats exhibit pronounced responses to non-noxious environmental stimuli

   The Journal of Neuroscience 1:887–900.

   https://doi.org/10.1523/JNEUROSCI.01-08-00887.1981

   • PubMed
   • Google Scholar
6. 1. Berridge CW
   2. Waterhouse BD

   (2003) The locus coeruleus-noradrenergic system: modulation of behavioral state and state-dependent cognitive processes

   Brain Research. Brain Research Reviews 42:33–84.

   https://doi.org/10.1016/s0165-0173(03)00143-7

   • PubMed
   • Google Scholar
7. 1. Bourboulou R
   2. Marti G
   3. Michon FX
   4. El Feghaly E
   5. Nouguier M
   6. Robbe D
   7. Koenig J
   8. Epsztein J

   (2019) Dynamic control of hippocampal spatial coding resolution by local visual cues

   eLife 8:e44487.

   https://doi.org/10.7554/eLife.44487

   • PubMed
   • Google Scholar
8. 1. Bouret S
   2. Sara SJ

   (2002) Locus coeruleus activation modulates firing rate and temporal organization of odour-induced single-cell responses in rat piriform cortex

   The European Journal of Neuroscience 16:2371–2382.

   https://doi.org/10.1046/j.1460-9568.2002.02413.x

   • PubMed
   • Google Scholar
9. 1. Carter ME
   2. Yizhar O
   3. Chikahisa S
   4. Nguyen H
   5. Adamantidis A
   6. Nishino S
   7. Deisseroth K
   8. de Lecea L

   (2010) Tuning arousal with optogenetic modulation of locus coeruleus neurons

   Nature Neuroscience 13:1526–1533.

   https://doi.org/10.1038/nn.2682

   • PubMed
   • Google Scholar
10. 1. Chandler DJ
    2. Gao WJ
    3. Waterhouse BD

    (2014) Heterogeneous organization of the locus coeruleus projections to prefrontal and motor cortices

    PNAS 111:6816–6821.

    https://doi.org/10.1073/pnas.1320827111

    • PubMed
    • Google Scholar
11. 1. Chowdhury A
    2. Luchetti A
    3. Fernandes G
    4. Filho DA
    5. Kastellakis G
    6. Tzilivaki A
    7. Ramirez EM
    8. Tran MY
    9. Poirazi P
    10. Silva AJ

    (2022) A locus coeruleus-dorsal CA1 dopaminergic circuit modulates memory linking

    Neuron 110:3374–3388.

    https://doi.org/10.1016/j.neuron.2022.08.001

    • PubMed
    • Google Scholar
12. 1. Chu HY
    2. Wu Q
    3. Zhou S
    4. Cao X
    5. Zhang A
    6. Jin GZ
    7. Hu GY
    8. Zhen X

    (2011) SKF83959 suppresses excitatory synaptic transmission in rat hippocampus via a dopamine receptor-independent mechanism

    Journal of Neuroscience Research 89:1259–1266.

    https://doi.org/10.1002/jnr.22653

    • PubMed
    • Google Scholar
13. 1. Cimarusti DL
    2. Saito K
    3. Vaughn JE
    4. Barber R
    5. Roberts E
    6. Thomas PE

    (1979) Immunocytochemical localization of dopamine-beta-hydroxylase in rat locus coeruleus and hypothalamus

    Brain Research 162:55–67.

    https://doi.org/10.1016/0006-8993(79)90755-8

    • PubMed
    • Google Scholar
14. 1. Cohen JD
    2. Bolstad M
    3. Lee AK

    (2017) Experience-dependent shaping of hippocampal CA1 intracellular activity in novel and familiar environments

    eLife 6:e23040.

    https://doi.org/10.7554/eLife.23040

    • PubMed
    • Google Scholar
15. 1. de Silva WCN
    2. Köhler CC
    3. Radiske A
    4. Cammarota M

    (2012) D1/D5 dopamine receptors modulate spatial memory formation

    Neurobiology of Learning and Memory 97:271–275.

    https://doi.org/10.1016/j.nlm.2012.01.005

    • Google Scholar
16. 1. Dombeck DA
    2. Harvey CD
    3. Tian L
    4. Looger LL
    5. Tank DW

    (2010) Functional imaging of hippocampal place cells at cellular resolution during virtual navigation

    Nature Neuroscience 13:1433–1440.

    https://doi.org/10.1038/nn.2648

    • PubMed
    • Google Scholar
17. 1. Dong C
    2. Madar AD
    3. Sheffield MEJ

    (2021) Distinct place cell dynamics in CA1 and CA3 encode experience in new environments

    Nature Communications 12:2977.

    https://doi.org/10.1038/s41467-021-23260-3

    • PubMed
    • Google Scholar
18. 1. Dupret D
    2. O’Neill J
    3. Pleydell-Bouverie B
    4. Csicsvari J

    (2010) The reorganization and reactivation of hippocampal maps predict spatial memory performance

    Nature Neuroscience 13:995–1002.

    https://doi.org/10.1038/nn.2599

    • PubMed
    • Google Scholar
19. 1. Duszkiewicz AJ
    2. McNamara CG
    3. Takeuchi T
    4. Genzel L

    (2019) Novelty and dopaminergic modulation of memory persistence: A tale of two systems

    Trends in Neurosciences 42:102–114.

    https://doi.org/10.1016/j.tins.2018.10.002

    • PubMed
    • Google Scholar
20. 1. Edelmann E
    2. Lessmann V

    (2011) Dopamine modulates spike timing-dependent plasticity and action potential properties in ca1 pyramidal neurons of acute rat hippocampal slices

    Frontiers in Synaptic Neuroscience 3:6.

    https://doi.org/10.3389/fnsyn.2011.00006

    • PubMed
    • Google Scholar
21. 1. Edelmann E
    2. Lessmann V

    (2018) Dopaminergic innervation and modulation of hippocampal networks

    Cell and Tissue Research 373:711–727.

    https://doi.org/10.1007/s00441-018-2800-7

    • PubMed
    • Google Scholar
22. 1. Edison HT
    2. Harley CW

    (2012) Medial and lateral perforant path evoked potentials are selectively modulated by pairing with glutamatergic activation of locus coeruleus in the dentate gyrus of the anesthetized rat

    Hippocampus 22:501–509.

    https://doi.org/10.1002/hipo.20916

    • PubMed
    • Google Scholar
23. 1. Engelhard B
    2. Finkelstein J
    3. Cox J
    4. Fleming W
    5. Jang HJ
    6. Ornelas S
    7. Koay SA
    8. Thiberge SY
    9. Daw ND
    10. Tank DW
    11. Witten IB

    (2019) Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons

    Nature 570:509–513.

    https://doi.org/10.1038/s41586-019-1261-9

    • PubMed
    • Google Scholar
24. 1. Gauthier JL
    2. Tank DW

    (2018) A dedicated population for reward coding in the hippocampus

    Neuron 99:179–193.

    https://doi.org/10.1016/j.neuron.2018.06.008

    • PubMed
    • Google Scholar
25. 1. Gibbs ME
    2. Summers RJ

    (2002) Role of adrenoceptor subtypes in memory consolidation

    Progress in Neurobiology 67:345–391.

    https://doi.org/10.1016/s0301-0082(02)00023-0

    • PubMed
    • Google Scholar
26. 1. Goh JJ
    2. Manahan-Vaughan D

    (2013) Hippocampal long-term depression in freely behaving mice requires the activation of beta-adrenergic receptors

    Hippocampus 23:1299–1308.

    https://doi.org/10.1002/hipo.22168

    • PubMed
    • Google Scholar
27. 1. Hagena H
    2. Manahan-Vaughan D

    (2012) Learning-facilitated long-term depression and long-term potentiation at mossy fiber-CA3 synapses requires activation of β-adrenergic receptors

    Frontiers in Integrative Neuroscience 6:23.

    https://doi.org/10.3389/fnint.2012.00023

    • PubMed
    • Google Scholar
28. 1. Hagena H
    2. Manahan-Vaughan D

    (2013) Differentiation in the protein synthesis-dependency of persistent synaptic plasticity in mossy fiber and associational/commissural CA3 synapses in vivo

    Frontiers in Integrative Neuroscience 7:10.

    https://doi.org/10.3389/fnint.2013.00010

    • PubMed
    • Google Scholar
29. 1. Hansen N
    2. Manahan-Vaughan D

    (2014) Dopamine D1/D5 receptors mediate informational saliency that promotes persistent hippocampal long-term plasticity

    Cerebral Cortex 24:845–858.

    https://doi.org/10.1093/cercor/bhs362

    • PubMed
    • Google Scholar
30. Software

    1. Heer C

    (2024) LC_VTA_paper, version swh:1:rev:14cfb2bd57569d7aa7be69dff5a99c36d33bfba5

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:a3b1648245eee21a6a2d28e9ffe6460184e5c648;origin=https://github.com/chadheer/LC_VTA_paper;visit=swh:1:snp:33e2577502bb5527f7986221a9c29e5eeabd7597;anchor=swh:1:rev:14cfb2bd57569d7aa7be69dff5a99c36d33bfba5
31. 1. Heys JG
    2. Rangarajan KV
    3. Dombeck DA

    (2014) The functional micro-organization of grid cells revealed by cellular-resolution imaging

    Neuron 84:1079–1090.

    https://doi.org/10.1016/j.neuron.2014.10.048

    • PubMed
    • Google Scholar
32. 1. Howe MW
    2. Tierney PL
    3. Sandberg SG
    4. Phillips PEM
    5. Graybiel AM

    (2013) Prolonged dopamine signalling in striatum signals proximity and value of distant rewards

    Nature 500:575–579.

    https://doi.org/10.1038/nature12475

    • PubMed
    • Google Scholar
33. 1. Ihalainen JA
    2. Riekkinen P Jr
    3. Feenstra MG

    (1999) Comparison of dopamine and noradrenaline release in mouse prefrontal cortex, striatum and hippocampus using microdialysis

    Neuroscience Letters 277:71–74.

    https://doi.org/10.1016/s0304-3940(99)00840-x

    • PubMed
    • Google Scholar
34. 1. Jeong H
    2. Taylor A
    3. Floeder JR
    4. Lohmann M
    5. Mihalas S
    6. Wu B
    7. Zhou M
    8. Burke DA
    9. Namboodiri VMK

    (2022) Mesolimbic dopamine release conveys causal associations

    Science 378:eabq6740.

    https://doi.org/10.1126/science.abq6740

    • PubMed
    • Google Scholar
35. 1. Kaufman AM
    2. Geiller T
    3. Losonczy A

    (2020) A role for the locus coeruleus in hippocampal CA1 place cell reorganization during spatial reward learning

    Neuron 105:1018–1026.

    https://doi.org/10.1016/j.neuron.2019.12.029

    • PubMed
    • Google Scholar
36. 1. Kempadoo KA
    2. Mosharov EV
    3. Choi SJ
    4. Sulzer D
    5. Kandel ER

    (2016) Dopamine release from the locus coeruleus to the dorsal hippocampus promotes spatial learning and memory

    PNAS 113:14835–14840.

    https://doi.org/10.1073/pnas.1616515114

    • Google Scholar
37. 1. Kentros CG
    2. Agnihotri NT
    3. Streater S
    4. Hawkins RD
    5. Kandel ER

    (2004) Increased attention to spatial context increases both place field stability and spatial memory

    Neuron 42:283–295.

    https://doi.org/10.1016/s0896-6273(04)00192-8

    • PubMed
    • Google Scholar
38. 1. Kim HR
    2. Malik AN
    3. Mikhael JG
    4. Bech P
    5. Tsutsui-Kimura I
    6. Sun F
    7. Zhang Y
    8. Li Y
    9. Watabe-Uchida M
    10. Gershman SJ
    11. Uchida N

    (2020) A unified framework for dopamine signals across timescales

    Cell 183:1600–1616.

    https://doi.org/10.1016/j.cell.2020.11.013

    • PubMed
    • Google Scholar
39. 1. Krishnan S
    2. Heer C
    3. Cherian C
    4. Sheffield MEJ

    (2022) Reward expectation extinction restructures and degrades CA1 spatial maps through loss of a dopaminergic reward proximity signal

    Nature Communications 13:6662.

    https://doi.org/10.1038/s41467-022-34465-5

    • PubMed
    • Google Scholar
40. 1. Li S
    2. Cullen WK
    3. Anwyl R
    4. Rowan MJ

    (2003) Dopamine-dependent facilitation of LTP induction in hippocampal CA1 by exposure to spatial novelty

    Nature Neuroscience 6:526–531.

    https://doi.org/10.1038/nn1049

    • PubMed
    • Google Scholar
41. 1. Lima KR
    2. da Silva de Vargas L
    3. Ramborger B
    4. Roehrs R
    5. Sevenster D
    6. Izquierdo I
    7. D’Hooge R
    8. Mello-Carpes PB

    (2019) Noradrenergic and dopaminergic involvement in novelty modulation of aversive memory generalization of adult rats

    Behavioural Brain Research 371:111991.

    https://doi.org/10.1016/j.bbr.2019.111991

    • PubMed
    • Google Scholar
42. 1. Lisman JE
    2. Grace AA

    (2005) The hippocampal-VTA loop: controlling the entry of information into long-term memory

    Neuron 46:703–713.

    https://doi.org/10.1016/j.neuron.2005.05.002

    • PubMed
    • Google Scholar
43. 1. London TD
    2. Licholai JA
    3. Szczot I
    4. Ali MA
    5. LeBlanc KH
    6. Fobbs WC
    7. Kravitz AV

    (2018) Coordinated ramping of dorsal striatal pathways preceding food approach and consumption

    The Journal of Neuroscience 38:3547–3558.

    https://doi.org/10.1523/JNEUROSCI.2693-17.2018

    • PubMed
    • Google Scholar
44. 1. Mamad O
    2. Stumpp L
    3. McNamara HM
    4. Ramakrishnan C
    5. Deisseroth K
    6. Reilly RB
    7. Tsanov M

    (2017) Place field assembly distribution encodes preferred locations

    PLOS Biology 15:e2002365.

    https://doi.org/10.1371/journal.pbio.2002365

    • PubMed
    • Google Scholar
45. 1. Martins ARO
    2. Froemke RC

    (2015) Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex

    Nature Neuroscience 18:1483–1492.

    https://doi.org/10.1038/nn.4090

    • PubMed
    • Google Scholar
46. 1. McNamara CG
    2. Tejero-Cantero Á
    3. Trouche S
    4. Campo-Urriza N
    5. Dupret D

    (2014) Dopaminergic neurons promote hippocampal reactivation and spatial memory persistence

    Nature Neuroscience 17:1658–1660.

    https://doi.org/10.1038/nn.3843

    • PubMed
    • Google Scholar
47. 1. Moreno-Castilla P
    2. Pérez-Ortega R
    3. Violante-Soria V
    4. Balderas I
    5. Bermúdez-Rattoni F

    (2017) Hippocampal release of dopamine and norepinephrine encodes novel contextual information

    Hippocampus 27:547–557.

    https://doi.org/10.1002/hipo.22711

    • PubMed
    • Google Scholar
48. 1. Noei S
    2. Zouridis IS
    3. Logothetis NK
    4. Panzeri S
    5. Totah NK

    (2022) Distinct ensembles in the noradrenergic locus coeruleus are associated with diverse cortical states

    PNAS 119:e2116507119.

    https://doi.org/10.1073/pnas.2116507119

    • PubMed
    • Google Scholar
49. 1. O’Keefe J
    2. Dostrovsky J

    (1971) The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat

    Brain Research 34:171–175.

    https://doi.org/10.1016/0006-8993(71)90358-1

    • PubMed
    • Google Scholar
50. 1. Pettit NL
    2. Yuan XC
    3. Harvey CD

    (2022) Hippocampal place codes are gated by behavioral engagement

    Nature Neuroscience 25:561–566.

    https://doi.org/10.1038/s41593-022-01050-4

    • PubMed
    • Google Scholar
51. 1. Redondo RL
    2. Morris RGM

    (2011) Making memories last: the synaptic tagging and capture hypothesis

    Nature Reviews. Neuroscience 12:17–30.

    https://doi.org/10.1038/nrn2963

    • PubMed
    • Google Scholar
52. 1. Reimer J
    2. McGinley MJ
    3. Liu Y
    4. Rodenkirch C
    5. Wang Q
    6. McCormick DA
    7. Tolias AS

    (2016) Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex

    Nature Communications 7:13289.

    https://doi.org/10.1038/ncomms13289

    • PubMed
    • Google Scholar
53. 1. Retailleau A
    2. Morris G

    (2018) Spatial rule learning and corresponding CA1 place cell reorientation depend on local dopamine release

    Current Biology 28:836–846.

    https://doi.org/10.1016/j.cub.2018.01.081

    • PubMed
    • Google Scholar
54. 1. Rosen ZB
    2. Cheung S
    3. Siegelbaum SA

    (2015) Midbrain dopamine neurons bidirectionally regulate CA3-CA1 synaptic drive

    Nature Neuroscience 18:1763–1771.

    https://doi.org/10.1038/nn.4152

    • PubMed
    • Google Scholar
55. Preprint

    1. Rupprecht P
    2. Duss SN
    3. Becker D
    4. Lewis CM
    5. Bohacek J
    6. Helmchen F

    (2023) Centripetal integration of past events by hippocampal astrocytes and its regulation by the locus coeruleus

    bioRxiv.

    https://doi.org/10.1101/2022.08.16.504030

    • Google Scholar
56. 1. Segal M
    2. Markram H
    3. Richter-Levin G

    (1991) Actions of norepinephrine in the rat hippocampus

    Progress in Brain Research 88:323–330.

    https://doi.org/10.1016/s0079-6123(08)63819-4

    • PubMed
    • Google Scholar
57. 1. Sheffield MEJ
    2. Adoff MD
    3. Dombeck DA

    (2017) Increased prevalence of calcium transients across the dendritic arbor during place field formation

    Neuron 96:490–504.

    https://doi.org/10.1016/j.neuron.2017.09.029

    • PubMed
    • Google Scholar
58. 1. Shulman GL
    2. Remington RW
    3. McLean JP

    (1979) Moving attention through visual space

    Journal of Experimental Psychology. Human Perception and Performance 5:522–526.

    https://doi.org/10.1037//0096-1523.5.3.522

    • PubMed
    • Google Scholar
59. 1. Takeuchi T
    2. Duszkiewicz AJ
    3. Sonneborn A
    4. Spooner PA
    5. Yamasaki M
    6. Watanabe M
    7. Smith CC
    8. Fernández G
    9. Deisseroth K
    10. Greene RW
    11. Morris RGM

    (2016) Locus coeruleus and dopaminergic consolidation of everyday memory

    Nature 537:357–362.

    https://doi.org/10.1038/nature19325

    • PubMed
    • Google Scholar
60. 1. Thomas SA

    (2015) Neuromodulatory signaling in hippocampus-dependent memory retrieval

    Hippocampus 25:415–431.

    https://doi.org/10.1002/hipo.22394

    • PubMed
    • Google Scholar
61. 1. Tsetsenis T
    2. Badyna JK
    3. Subramaniyan M
    4. Li R
    5. Yang K
    6. Dani JA

    (2019) Dopamine and norepinephrine modulate the formation of aversive memories in the hippocampus

    SSRN Electronic Journal 1:3509875.

    https://doi.org/10.2139/ssrn.3509875

    • Google Scholar
62. 1. Uematsu A
    2. Tan BZ
    3. Ycu EA
    4. Cuevas JS
    5. Koivumaa J
    6. Junyent F
    7. Kremer EJ
    8. Witten IB
    9. Deisseroth K
    10. Johansen JP

    (2017) Modular organization of the brainstem noradrenaline system coordinates opposing learning states

    Nature Neuroscience 20:1602–1611.

    https://doi.org/10.1038/nn.4642

    • PubMed
    • Google Scholar
63. 1. Wagatsuma A
    2. Okuyama T
    3. Sun C
    4. Smith LM
    5. Abe K
    6. Tonegawa S

    (2018) Locus coeruleus input to hippocampal CA3 drives single-trial learning of a novel context

    PNAS 115:E310–E316.

    https://doi.org/10.1073/pnas.1714082115

    • PubMed
    • Google Scholar
64. 1. Waterhouse BD
    2. Navarra RL

    (2019) The locus coeruleus-norepinephrine system and sensory signal processing: A historical review and current perspectives

    Brain Research 1709:1–15.

    https://doi.org/10.1016/j.brainres.2018.08.032

    • Google Scholar
65. 1. Yamasaki M
    2. Takeuchi T

    (2017) Locus coeruleus and dopamine-dependent memory consolidation

    Neural Plasticity 2017:8602690.

    https://doi.org/10.1155/2017/8602690

    • PubMed
    • Google Scholar
66. 1. Zhang JC
    2. Lau PM
    3. Bi GQ

    (2009) Gain in sensitivity and loss in temporal contrast of STDP by dopaminergic modulation at hippocampal synapses

    PNAS 106:13028–13033.

    https://doi.org/10.1073/pnas.0900546106

    • Google Scholar
67. 1. Zhuang X
    2. Masson J
    3. Gingrich JA
    4. Rayport S
    5. Hen R

    (2005) Targeted gene expression in dopamine and serotonin neurons of the mouse brain

    Journal of Neuroscience Methods 143:27–32.

    https://doi.org/10.1016/j.jneumeth.2004.09.020

    • PubMed
    • Google Scholar

Author details

1. Chad Heer

   The Department of Neurobiology, The University of Chicago, Chicago, United States

   Contribution

   Data curation, Formal analysis, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4527-0405

2. Mark Sheffield

   The Department of Neurobiology, The University of Chicago, Chicago, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   sheffield@uchicago.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0969-7820

• 1,456

  views

• 78

  downloads

• 1

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.