It is increasingly recognized that plastic reorganization of brain circuits needed for learning and other cognitive functions involves not only principal cells but also local inhibitory interneurons (Buzsáki, 2010). A large body of work has demonstrated that excitatory connections onto inhibitory interneurons, as well as inhibitory connections on principal cells, are often plastic (Kullmann and Lamsa, 2007; McBain and Kauer, 2009). However, the rules governing the plasticity of excitatory synapses on inhibitory interneurons are not always similar to that targeting other principal cells (Bartos et al., 2011; Lamsa et al., 2007; Pelkey et al., 2017). Even in the CA1 region of the hippocampus, plasticity rules can be different, depending on the experimental conditions and fibers stimulated. Although in the majority of cases some pyramidal-interneuron cell connections show anti-Hebbian non-associative plasticity, others show weight changes that are governed by Hebbian rules (Le Roux et al., 2013; Nissen et al., 2010; Topolnik et al., 2009). In addition, some inhibitory interneuron types in the hippocampus do not seem to possess plastic synapses with their excitatory inputs, such as the CCK cells (Nissen et al., 2010).

In vivo work, primarily during anesthesia, has also demonstrated that plastic alterations can occur between afferent excitatory fibers and CA1 interneurons (Buzsáki and Eidelberg, 1982; Lau et al., 2017). This work showed that stimulation of these fibers could either up- or downregulate evoked spike responses, depending on the interneuron subtype. However, our knowledge about the precise reorganization of pyramidal-interneuron connections during behavior is limited because of the technical challenge of directly performing patch-clamp recordings from monosynaptically connected pyramidal cell-interneuron pairs during such conditions. It is, however, possible to study these connections indirectly by identifying monosynaptically connected pyramidal cell-interneuron pairs by using cross-correlation analysis of the spike timing and measuring spike transmission probability between them (Csicsvari et al., 1998; Csicsvari et al., 2003; Marshall et al., 2002). Early work demonstrated the behavioral state-dependent modulation of such connections (Csicsvari et al., 1998). Moreover, changes in spike transmission probability have been seen in the prefrontal cortex during behavioral tasks (Fujisawa et al., 2008). In the hippocampus, spatial learning can cause lasting changes in these connections (Dupret et al., 2013). While such studies provide strong evidence of plasticity at excitatory-interneuron synapses, so far, no data has established a causal link between pre- and postsynaptic firing to changes in connection strength during behavior.

In this study, we optogenetically interfered with the circuit function by activating Halorhodopsin or Archaerhodopsin in a subpopulation of pyramidal cells and interneurons. This manipulation led to the inhibition of a subset of pyramidal cells and interneurons and also light-triggered disinhibition of many pyramidal cells (Gridchyn et al., 2020; Schoenenberger et al., 2016). Here, we examined whether these light-induced rate changes and the associated network effects could lead to the lasting reorganization of pyramidal-interneuron connection weights, as assessed by monosynaptic spike transmission probabilities.

We recorded multiple unit and field potential activities from the dorsal hippocampus in five rats, during exploration and quiet immobility sessions. In these rats, Halorhodopsin (NpHR-YFP, n = 4 rats) or Archaerhodopsin (ArchT, n = 1 rat) was expressed in the dorsal CA1 region of the hippocampus under the control of the CaMKIIα promoter using an adeno-associated virus (AAV2/1). In four rats, fifteen independently-movable tetrodes and one 200 µm optic fiber centered in the middle of the tetrode bundle targeted the dorsal CA1 region while, in the remaining animal, 24 tetrodes and four optic fibers were used (see Materials and methods). We recorded during four 25 min exploration sessions in which first a familiar environment (FAM1) was explored followed by a novel environment (NOV), and finally, the animals were returned to the familiar environment for the remaining two sessions. During the second familiar exploration session laser stimulation was applied (FAML) in a fixed part of the environment but not in the last exploration (FAM2, see Figure 1A). We tested whether the light application affected the behavior of the animals (Figure 1—figure supplement 1). In all sessions, neither the average speed nor the occupancy within the light stimulation sector were significantly different, compared to the part of the environment where no light was triggered (all p>0.5607). We identified monosynaptically-connected pyramidal cell-interneuron pairs by calculating the cross-correlation of their spike firing times and testing for the presence of a short-latency (1–2 ms) sharp (1–2 ms wide) peak. This peak indicates the presence of a monosynaptic connection in which the presynaptic pyramidal cell can discharge the postsynaptic interneuron within a short latency. In turn, the magnitude of the peak, that is, its transmission probability, reflects the connection weight between a given cell pair. Changes in firing rate across sessions by either or both cells in the pair would result in an alteration of the magnitude of the peak that does not reflect a change in this connection weight, but rather a general change in the probability of joint firing. To account for this, throughout all the analysis, we measured the chance probability that the pair fires together, by averaging the correlation probabilities over the 10–50 ms time bins and subtracting this from the peak. Altogether, we identified 78 pyramidal cell-interneuron pairs in these recordings (see Materials and methods).

Figure 1 with 1 supplement see all

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The light stimulation inhibited not only a selected population of pyramidal cells but also many interneurons, while a further group of pyramidal cells increased their firing due to disinhibition, as assessed directly by their light responses to brief light pulses in the rest session at the end of the recordings (Figure 1B). In our previous work, we showed that these disinhibited pyramidal cells only increased their firing after the maximum light-mediated suppression on interneurons (Gridchyn et al., 2020; Schoenenberger et al., 2016). We also showed before (Schoenenberger et al., 2016) that both somatostatin- and parvalbumin immunopositive interneurons can express transgenes following AAV-mediated transduction in agreement with earlier work (Nathanson et al., 2009). It is possible, however, that other adeno-associated virus serotypes or the usage of the same virus in other brain regions may yield principal cell-specific expression. As a result, many pyramidal cell - interneuron pairs with monosynaptic connections showed changes in firing rate during the FAML session, when compared to FAM1 (Figure 1C). The altered network activity during the light session is demonstrated by significantly lower correlation of the FAM1 vs FAML rates as compared to rates measured in alternating 5 s time windows within FAM1, both for pyramidal cells and interneurons (all p<0.0001, Z-test). However, no significant differences were found in the median of FAM1 and FAML firing rates (interneuron p=0.3213 pyramidal cell p=0.1448, Mann-Whitney test). The spike transmission probability of these cell pairs was also changed (Figure 1D). As for firing rates, the correlation FAM1 vs FAML spike transmission values was lower than that measured in alternating 5 s time windows within FAM1 (p<0.0001, Z-test). Moreover, there was a significant reduction in the median of the spike transmission probabilities from FAM1 to FAML (p<0.01. Mann-Whitney test). Because in the spike transmission measurements the chance probability that cells randomly fire together was compensated for, light-induced network modifications altered the ability of the pyramidal cell to drive the postsynaptic interneuron, beyond that of the firing rate alterations-mediated changes. Changes in connection weight between cell pairs during the FAML session could be either transient or reflect longer-term plasticity that outlasts optogenetic stimulation. Moreover, connection strength could change as place cells remap their place fields during exploration of a different environment (Wilson and McNaughton, 1993). We, therefore, tested whether significant changes in the spike transmission of monosynaptic pairs can be seen across sessions, relative to the baseline identified in FAM1 (Figure 2A–B). To do this, we generated a score that represented the absolute value of normalized spike transmission differences between sessions (difference divided by the sum, see Materials and methods). Overall, this score was the largest between NOV-FAM1 and FAML-FAM1 sessions. However, while the changes between FAM2-FAM1 sessions were about 30% weaker relative to the others, they were still significantly larger than zero (all p<0.001; ANOVA). In addition, changes between FAM2-FAM1 sessions were significantly larger than changes within FAM1 sessions as assessed by correlations measured in alternating 5 s time windows (p<0.0268, F-test), independent of the variability across animals (p=0. 0562, Likelihood-ratio test).

Figure 2

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These population changes suggest that pyramidal cell-interneuron synaptic weights were, to some degree, reorganized by both exposure to a new environment and artificially by light stimulation. However, connection strengths were different between the first and last exposure to the same familiar environment (FAM1 and FAM2), indicating that a more lasting change had also occurred. We set out to determine the factors that accounted for the connection strength observed in FAM2 by testing at the population level whether spike transmission in FAM2 was predicted by that observed in the previous sessions. In addition, we controlled for the possible variability across animals by including animal identity as a variable into the analysis (Figure 2C–D). Spike transmissions in FAM2 were strongly predicted by the observed connection weights in all the previous sessions (all p<0.0001, F-test, Figure 2C), indicating that connection strength between the pairs was only partially reorganized. The variability across animals did not significantly account for variability in the spike transmission (all p>0.5656, Likelihood-ratio test). We then used a linear model comparison to reveal which behavioral sessions best explained the monosynaptic connection strengths in FAM2 (Figure 2D). We found that both FAM1 and FAML predicted FAM2 spike transmission, independent of the other (all p<0.0105, F-test). However, the model comparison showed that the NOV no longer predicted FAM2 when the effect of FAM1 was taken into consideration (p=0.4453, F-test) nor the variation across animals contributed (all p>0.5656, Likelihood-ratio test). Thus, the changes in connection weights during NOV did not influence the subsequent weights observed in FAM2, which instead was explained by weights in both FAM1 and FAML, independently. Therefore, while spike transmission values were more similar within the same environmental context, light application significantly biased spike transmission and caused lasting changes in FAM2 relative to FAM1.

The significant influence of the FAML session on FAM2 suggests that the light-induced alterations of the network activity led to lasting changes in the spike transmission probabilities even in the absence of light in the same environmental context. Direct optogenetic inhibition of some cells and the indirect firing increase of others led to either an increase or a decrease of spike transmission probabilities during the light application. Therefore, next, we tested whether the direction of change in spike transmission in FAML relative to FAM1 predicted similar change from FAM1 to FAM2 (Figure 2E–F). Indeed, those cell pairs in which light application led to a decrease in spike transmission relative to FAM1 also maintained weaker spike transmission values in FAM2, while cell pairs with light-enhanced transmission showed a persistent increase in transmission probability in FAM2 (all p<0.0001, F-test). Moreover, the relative change of spike transmission (difference divided by the sum) between FAML-FAM1 predicted the score change from FAM1 to FAM2 (p<0.0001, F-test), indicating that larger relative changes in spike transmission between FAML-FAM1 were accompanied by similarly larger changes between FAM2-FAM1. Thus, light-induced changes in neuronal firing during FAML may lead to lasting modifications of spike transmission. The variability across animals did not significantly account for variability in the transmission probability change (p>0.3046, Likelihood-ratio test).

The light application can change the firing rate of the presynaptic pyramidal cell or the postsynaptic interneuron, which may, in turn, account for the observed plastic changes in transmission probability. Therefore, to address whether our effects reflected a pre- or postsynaptic mechanism, we assessed the relationship between firing rate changes within connected pairs and the modification of their monosynaptic connection. To do this, we calculated the relative firing rate changes between FAML-FAM1 for both pyramidal cells and interneurons, a measure that reflects the influence of light on the baseline firing of these cells. We then analyzed whether this measure predicted changes in transmission probabilities between FAM1 and FAM2, which reflects the longer-lasting change in synaptic strength. As a control, we also measured relative firing rate changes between FAM2-FAM1 because rate alterations reflecting the alterations in the average excitatory inputs cells received. Such changes in excitability may have influenced spike transmission beyond the rate alteration-mediated changes of the chance joint firing probability, which later were already compensated for by normalizing the histograms (Figure 3A–B). We found that interneuron rate changes of both FAML-FAM1 and FAM2-FAM1 influenced FAM2-FAM1 spike transmission changes, even when each other’s influence was taken into account (all p<0.0064, Likelihood-ratio test). However, pyramidal cell rate changes during FAML no longer significantly influenced FAM2-FAM1 spike transmission changes when FAM2-FAM1 rate change was taken into account (p=0.0999, Likelihood-ratio test). This suggests that changes in the excitability of interneurons during the light application, as assessed by rate changes, influenced the spike transmission strength subsequently in FAM2 even when the FAM2-FAM1 rate alterations of the interneuron were compensated for. In these models, the variability across animals did not significantly account for variability in the transmission probability change (all p>0.0907, Likelihood-ratio test). In addition to the changing firing rate, the light application can cause remapping in a subpopulation of cells (Schoenenberger et al., 2016). However, a change in spike transmission in FAM2 did not predict the degree of pyramidal place field remapping (p=0.1612, F-test).

Figure 3

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The finding that interneuron rate change between FAM2-FAM1 itself independently influenced FAM2-FAM1 spike transmission changes suggests that the excitability of the postsynaptic interneuron has a strong influence on the strength of spike transmission. Spike transmission changes we detected in the NOV session may, in part, have been caused by the excitability change of the interneurons. To test whether rate changes of pyramidal cells or interneurons in NOV influenced spike transmission in FAM2, we calculated normalized (difference divided by the sum) spike transmission and rate changes, in order to predict weight change separately for both cell types (Figure 4). As before, only interneuron rate changes predicted spike transmission changes, even when the pyramidal rate change was taken into consideration (interneuron: p<0.0001; pyramidal: p=0.1672, Likelihood-ratio test). A similar analysis was performed for the FAML session itself, where again, we found that only interneuron rate alterations predicted spike transmission alterations during the light application, even when each-others’ contribution was considered (interneuron: p<0.0001; pyramidal: p=0.5885, Likelihood-ratio test). In these models, the variability across animals did not significantly account for variability in the transmission probability changes in NOV-FAM1 and FAML-FAM1 (all p>0.382, Likelihood-ratio test).

Figure 4

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Next, we examined whether the direction of firing rate change during light application influenced the spike transmission change between FAM1 and FAM2 (Figure 3C–D). As before, we considered FAM1 as a baseline and generated a normalized score for the rate and weight change. Interneurons that exhibited elevated or reduced firing rate during FAML session exhibited significantly different changes in monosynaptic spike transmission probability across FAM2 and FAM1 (p<0.0001, F-test) independent of the variability across animals (p=0.3372, Likelihood-ratio test). Pyramidal cells did not show such a relationship (p=0.4499, F-test) and the variability across animals did not influence this result (p=0.1359, Likelihood-ratio test). Therefore, suppressed interneurons tended to weaken their monosynaptic weights with presynaptic pyramidal cells, whereas those that increased their rate exhibited increased weights. These effects lasted after the light application in the same environmental context. To confirm that the observed changes in monosynaptic weight were indeed mediated by a postsynaptic change in the interneuron firing rate, we differentiated monosynaptic cell pairs into four groups, according to whether the pair exhibited a rate increase or decrease, of both pyramidal cells and interneurons (Figure 5). A two-way ANOVA analysis showed that the interneuron increase and decrease groups were significantly different, independent of the pyramidal increase and decrease as a factor (p<0.0004, F-test), but not the pyramidal group (p=0.4067, F-test), and the variability across animals did not influence the result (p=0.2749, Likelihood-ratio test) and no significant interactions were seen between cell pair groups (p=0.6109, F-test).

Figure 5

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We found that the modulation of interneuron activity during light stimulation or exposure to a novel environment directly influenced changes in transmission probabilities between putative monosynaptic connections of pyramidal-interneuron cell pairs. This raises the possibility that such changes are activity-dependent. To examine this, we measured the number of instances in which pyramidal cell firing occurred in 10 ms, 20 ms, 50 ms, and 100 ms time windows before or after the interneuron spike as well as their sum for all spike-pairing events (Figure 6). The relative change (difference/sum) of spike pairing events was calculated between FAML and FAM1 and these spike pairing changes predicted significantly FAM2-FAM1 spike transmission change in all three cases for 50 ms window (p<0.00099, F-test) even when multiple testing correction was performed while the variability across animals did not influence the result (all p>0.3913, Likelihood-ratio test). In the other tested time intervals, the correlations were not significant (all p>0.0517, F-test). Moreover, spike pairing event number no longer predicted the FAM2-FAM1 spike transmission changes when interneuron and pyramidal changes were together taken into account, (all p>0.417, F-test). However, spike pairing itself was strongly predicted (all R²>0.859) by pyramidal and interneuron firing rates, explaining why spike transmission changes could not be predicted independently from the combined interneuron and pyramidal rates by spike pairing.

Figure 6

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Here we showed that light-induced, optogenetic alterations of the CA1 network activity can trigger lasting alterations of the monosynaptic spike transmission probability of pyramidal-cell interneuron pairs. During the light session, both changes in the postsynaptic interneuron rates and the pairing probability of cell firing predicted the changes in monosynaptic spike transmission within the same familiar environment, when sessions before and after the light interference were compared. In addition, we observed spike transmission changes in the novel environment relative to the familiar environment, which took place before the light application. These changes were specific to the novel environment, however, and were not maintained during the subsequent familiar sessions. This suggests that altered interneuron firing rate and the activity-dependent alterations of the pyramidal-interneuron spike pairing can modify pyramidal-interneuron connection weights during exploratory behavior. Our study did not use control animals in which only YFP was expressed. Therefore, we cannot exclude the possibility that optogenetic channel expression, or, perhaps, light application enhanced the plasticity on pyramidal-interneuron synapses. Yet, we observed similar activity-dependent changes during spatial learning before (Dupret et al., 2013). So, it is likely that the optogenetic, light-mediated rate alteration was a primary driver of the activity-dependent, lasting connection weight changes.

We used a measure of spike transmission probability that compensated for the changes in the chance probability that the two cells fire together as a result of firing rate alterations. However, the average depolarization level of the cell, as reflected by its mean firing rate, can lead to more efficient spike transmission, even without changes in the synaptic weight. Indeed, the spike transmission changes from FAM1 to FAM2 were influenced by the rate changes of the postsynaptic interneuron between these sessions. In turn, this suggests that the postsynaptic interneuron’s general level of depolarization can influence spike transmission. However, in a similar manner, changes in FAML-FAM1 interneuron rate also predicted FAM2-FAM1 spike transmission changes. Indeed, when FAML interneuron rate increased, a stronger spike transmission was seen subsequently in FAM2, while reduced spike transmission was associated with reduced interneuron rate. Considering that the interneuron rate increase in FAML was not directly mediated by the light, we cannot exclude the possibility that stronger pyramidal inputs caused the interneuron rate increase in FAML that is caused by the plastic strengthening of these connections. Nevertheless, the excitability of interneurons in FAM2 alone, which may be indicative of plasticity-mediated input to interneurons, did not explain the spike transmission changes. Indeed, rate changes in FAML could predict spike transmission changes in FAM2-FAM1 independent of FAM2-FAM1 rate changes. That is, FAML-FAM1 rate changes predicted FAM2-FAM1 spike transmission changes even when the FAM2-FAM1 interneuron rate changes were accounted for. Therefore, FAML-FAM1 interneuron rate changes had further predictive value beyond those observed by FAM2-FAM1 rates changes and consequently excitability/depolarization alterations in FAML that were no longer present in FAM2 still predicted FAM2-FAM1 spike transmission changes. This finding indicates that interneuronal excitability changes during light application session caused lasting changes of the pyramidal interneuron connections, beyond any lasting nonspecific excitability changes that occurred between FAM2-FAM1.

We observed changes in spike transmission between FAM1 and the novel environment, which were larger in amplitude than those across the familiar environment before and after the light application. Exposure to the novel environment nevertheless did not influence changes in the familiar environment. Can we expect that pyramidal-interneuron weights change from one environment to another but they revert to the previous configuration when the animal is returned to the first environment? Although we cannot exclude this possibility, it is more likely that the average depolarization level of each interneuron is different across different environments, which in turn reveals different monosynaptic connections and connection strengths. Interneuron rates reorganize across the familiar and novel environment, which may reflect the influence of different nonspecific neurotransmitters (Nitz and McNaughton, 2004; Wilson and McNaughton, 1993). Moreover, spike transmission changes across the familiar and novel environments were predicted by the rate changes of interneurons but not pyramidal cells. This suggests that postsynaptic effects such as differences in interneuron depolarization levels contributed to the changes in spike transmission between different environments. In addition to nonspecific neuromodulator transmitters, presynaptic place cells that were specifically active in the novel environment may have caused changes in the interneuron depolarization as well. Although novel environments may not entirely reorganize pyramidal connections, spatial learning is able to do so (Dupret et al., 2013). During the course of spatial learning, some interneurons increase their rates while others decrease, which are accompanied by changes in spike transmission probabilities of monosynaptic pairs. However, these spike transmission changes depended on both pyramidal and interneuron rate changes. One common aspect of the light and the spatial learning-mediated monosynaptic spike transmission changes is that, in both cases, it took place in a familiar environment in which some of the place cells remapped their place fields. In Dupret et al., 2013 paradigm, some cells altered their place fields to represent the changed goal locations while in our paradigm, some of the place cells whose in-field firing was inhibited by the light remapped their place fields (Schoenenberger et al., 2016).

Can rules derived from in vitro observations, or those seen in vivo during anesthesia through afferent stimulations explain our findings during behavior? Our interneurons were recorded in the CA1 pyramidal layer where Ca⁺⁺ permeable AMPA receptors mediate the primary form of LTP and LTD. This form of plasticity requires the stimulation of their synaptic inputs and the hyperpolarization or a non-depolarized state of the interneuron (Le Roux et al., 2013; Nissen et al., 2010). In addition, metabotropic glutamate receptors can further regulate dendritic Ca⁺⁺ levels and the direction of synaptic plasticity (Camiré and Topolnik, 2014). In our case, the firing rate alteration of the interneurons was the strongest predictor of spike transmission change. The firing rate of the interneuron during FAML may reflect both the afferent excitation level of the cell as well as the light-induced inhibition; both of which could contribute to plastic changes. Excitability changes caused by the light stimulation are also expected to contribute to our observed results because both FAML-FAM1 and FAM2-FAM1 rate changes independently predicted FAM2-FAM1 spike transmission changes. It has been shown that Schaffer collateral stimulation enhances the excitability of CA1 PV cells following the stimulation, mediated via mGluR5 receptors (Campanac et al., 2013). In our dataset, the majority of interneurons showed a reduction in firing rate. Therefore, interneurons may be able to undergo both up- and downregulation of their excitation levels, which are not exclusively controlled by Schaffer collateral inputs.

We also saw that spike pairing of the pyramidal cells and interneuron in 50 ms time windows weakly influenced spike transmission, independent of the light-induced rate changes of these cells. Indeed, parvalbumin-positive CA1 interneurons exhibit NMDA-dependent associative plasticity as well (Le Roux et al., 2013) on their feedback connections from CA1 pyramidal cells. This may explain our spike pairing results. In vivo work suggested that theta-frequency afferent stimulation was optimal to induce LTP or LTD-like changes (Lau et al., 2017). Our light application occurred during theta oscillations, where such afferents would indeed provide theta-rhythmic stimulation of the CA1 interneurons. However, such a pairing relationship was observed only for a 50 ms time window. Moreover, it was no longer significant when pyramidal and interneuron rate changes were together taken into account. Because our optogenetic manipulation altered rates of individual cells without specifically influencing pyramidal-interneuron spike pairings, the combination of pyramidal and interneuron rates strongly predicted spike pairing probability. This can explain why spike pairing no longer predicted spike transmission changes when these rates were taken into account. After all, this result shows that rate-predicted spike pairing numbers are as good as the real ones to predict spike transmission changes. Nevertheless, we cannot exclude the possibility that the independent rate alterations of pyramidal cells and interneurons in FAML governed spike transmission probability changes, without spike pairing itself directly influencing it. Future work in which interneuron (or a certain genetic type) firing rate is selectively altered by optogenetics may provide further evidence for the independent contribution of postsynaptic interneuron depolarization in plasticity. Nevertheless, even in such manipulations, indirect alteration of pyramidal rates (e.g., because of disinhibition) is expected to occur.

Overall, our data indicate that during active behavior, changes in interneuron excitability that is coupled with spike pairing or altered presynaptic pyramidal spiking during theta epochs may trigger plasticity at the excitatory inputs to CA1 interneurons. Learning and the associated reorganization of the CA1 network may be a condition where such changes occur naturally.

This study used previously published electrophysiological spike data (Schoenenberger et al., 2016). Accordingly, the experimental and spike clustering work has been described in this previous work in detail. Data from one additional rat recorded in the same paradigm and analyzed using the same methods was included in the data set.

Original data and programs are available in the scientific repository of the Institute of Science and Technology Austria (https://doi.org/10.15479/AT:ISTA:8563).

1. 1. Csicsvari JL
   2. Gridchyn I
   3. Schönenberger P

   (2020) IST Austria

   Optogenetic Alteration of Hippocampal Network Activity.

   https://doi.org/10.15479/AT:ISTA:8563

1. 1. Abeles M

   (1982)

   Role of the cortical neuron: integrator or coincidence detector?

   Israel Journal of Medical Sciences 18:83–92.

   • PubMed
   • Google Scholar
2. 1. Bartos M
   2. Alle H
   3. Vida I

   (2011) Role of microcircuit structure and input integration in hippocampal interneuron recruitment and plasticity

   Neuropharmacology 60:730–739.

   https://doi.org/10.1016/j.neuropharm.2010.12.017

   • PubMed
   • Google Scholar
3. 1. Boyden ES
   2. Zhang F
   3. Bamberg E
   4. Nagel G
   5. Deisseroth K

   (2005) Millisecond-timescale, genetically targeted optical control of neural activity

   Nature Neuroscience 8:1263–1268.

   https://doi.org/10.1038/nn1525

   • Google Scholar
4. 1. Buzsáki G

   (2010) Neural syntax: cell assemblies, synapsembles, and readers

   Neuron 68:362–385.

   https://doi.org/10.1016/j.neuron.2010.09.023

   • PubMed
   • Google Scholar
5. 1. Buzsáki G
   2. Eidelberg E

   (1982) Direct afferent excitation and long-term potentiation of hippocampal interneurons

   Journal of Neurophysiology 48:597–607.

   https://doi.org/10.1152/jn.1982.48.3.597

   • PubMed
   • Google Scholar
6. 1. Camiré O
   2. Topolnik L

   (2014) Dendritic calcium nonlinearities switch the direction of synaptic plasticity in fast-spiking interneurons

   Journal of Neuroscience 34:3864–3877.

   https://doi.org/10.1523/JNEUROSCI.2253-13.2014

   • PubMed
   • Google Scholar
7. 1. Campanac E
   2. Gasselin C
   3. Baude A
   4. Rama S
   5. Ankri N
   6. Debanne D

   (2013) Enhanced intrinsic excitability in basket cells maintains excitatory-inhibitory balance in hippocampal circuits

   Neuron 77:712–722.

   https://doi.org/10.1016/j.neuron.2012.12.020

   • PubMed
   • Google Scholar
8. 1. Csicsvari J
   2. Hirase H
   3. Czurko A
   4. Buzsáki G

   (1998) Reliability and state dependence of pyramidal cell-interneuron synapses in the Hippocampus: an ensemble approach in the behaving rat

   Neuron 21:179–189.

   https://doi.org/10.1016/s0896-6273(00)80525-5

   • PubMed
   • Google Scholar
9. 1. Csicsvari J
   2. Hirase H
   3. Czurkó A
   4. Mamiya A
   5. Buzsáki G

   (1999) Oscillatory coupling of hippocampal pyramidal cells and interneurons in the behaving rat

   The Journal of Neuroscience 19:274–287.

   https://doi.org/10.1523/JNEUROSCI.19-01-00274.1999

   • PubMed
   • Google Scholar
10. 1. Csicsvari J
    2. Jamieson B
    3. Wise KD
    4. Buzsáki G

    (2003) Mechanisms of gamma oscillations in the Hippocampus of the behaving rat

    Neuron 37:311–322.

    https://doi.org/10.1016/S0896-6273(02)01169-8

    • Google Scholar
11. 1. Dupret D
    2. O'Neill J
    3. Csicsvari J

    (2013) Dynamic reconfiguration of hippocampal interneuron circuits during spatial learning

    Neuron 78:166–180.

    https://doi.org/10.1016/j.neuron.2013.01.033

    • PubMed
    • Google Scholar
12. 1. Fujisawa S
    2. Amarasingham A
    3. Harrison MT
    4. Buzsáki G

    (2008) Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex

    Nature Neuroscience 11:823–833.

    https://doi.org/10.1038/nn.2134

    • PubMed
    • Google Scholar
13. 1. Gridchyn I
    2. Schoenenberger P
    3. O'Neill J
    4. Csicsvari J

    (2020) Assembly-Specific disruption of hippocampal replay leads to selective memory deficit

    Neuron 106:291–300.

    https://doi.org/10.1016/j.neuron.2020.01.021

    • PubMed
    • Google Scholar
14. 1. Harris KD
    2. Henze DA
    3. Csicsvari J
    4. Hirase H
    5. Buzsáki G

    (2000) Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements

    Journal of Neurophysiology 84:401–414.

    https://doi.org/10.1152/jn.2000.84.1.401

    • PubMed
    • Google Scholar
15. 1. Henze DA
    2. Borhegyi Z
    3. Csicsvari J
    4. Mamiya A
    5. Harris KD
    6. Buzsáki G

    (2000) Intracellular features predicted by extracellular recordings in the Hippocampus in vivo

    Journal of Neurophysiology 84:390–400.

    https://doi.org/10.1152/jn.2000.84.1.390

    • PubMed
    • Google Scholar
16. 1. Kullmann DM
    2. Lamsa KP

    (2007) Long-term synaptic plasticity in hippocampal interneurons

    Nature Reviews Neuroscience 8:687–699.

    https://doi.org/10.1038/nrn2207

    • PubMed
    • Google Scholar
17. 1. Lamsa KP
    2. Heeroma JH
    3. Somogyi P
    4. Rusakov DA
    5. Kullmann DM

    (2007) Anti-Hebbian Long-Term potentiation in the hippocampal feedback inhibitory circuit

    Science 315:1262–1266.

    https://doi.org/10.1126/science.1137450

    • Google Scholar
18. 1. Lau PY
    2. Katona L
    3. Saghy P
    4. Newton K
    5. Somogyi P
    6. Lamsa KP

    (2017) Long-term plasticity in identified hippocampal GABAergic interneurons in the CA1 area in vivo

    Brain Structure & Function 222:1809–1827.

    https://doi.org/10.1007/s00429-016-1309-7

    • PubMed
    • Google Scholar
19. 1. Le Roux N
    2. Cabezas C
    3. Böhm UL
    4. Poncer JC

    (2013) Input-specific learning rules at excitatory synapses onto hippocampal parvalbumin-expressing interneurons

    The Journal of Physiology 591:1809–1822.

    https://doi.org/10.1113/jphysiol.2012.245852

    • Google Scholar
20. 1. Leutgeb S
    2. Leutgeb JK
    3. Treves A
    4. Moser MB
    5. Moser EI

    (2004) Distinct ensemble codes in hippocampal Areas CA3 and CA1

    Science 305:1295–1298.

    https://doi.org/10.1126/science.1100265

    • PubMed
    • Google Scholar
21. 1. Leutgeb S
    2. Leutgeb JK
    3. Barnes CA
    4. Moser EI
    5. McNaughton BL
    6. Moser MB

    (2005) Independent codes for spatial and episodic memory in hippocampal neuronal ensembles

    Science 309:619–623.

    https://doi.org/10.1126/science.1114037

    • PubMed
    • Google Scholar
22. 1. Marshall L
    2. Henze DA
    3. Hirase H
    4. Leinekugel X
    5. Dragoi G
    6. Buzsáki G

    (2002) Hippocampal pyramidal cell-interneuron spike transmission is frequency dependent and responsible for place modulation of interneuron discharge

    The Journal of Neuroscience 22:RC197.

    https://doi.org/10.1523/JNEUROSCI.22-02-j0001.2002

    • PubMed
    • Google Scholar
23. 1. McBain CJ
    2. Kauer JA

    (2009) Presynaptic plasticity: targeted control of inhibitory networks

    Current Opinion in Neurobiology 19:254–262.

    https://doi.org/10.1016/j.conb.2009.05.008

    • PubMed
    • Google Scholar
24. 1. Nathanson JL
    2. Yanagawa Y
    3. Obata K
    4. Callaway EM

    (2009) Preferential labeling of inhibitory and excitatory cortical neurons by endogenous tropism of adeno-associated virus and lentivirus vectors

    Neuroscience 161:441–450.

    https://doi.org/10.1016/j.neuroscience.2009.03.032

    • Google Scholar
25. 1. Nissen W
    2. Szabo A
    3. Somogyi J
    4. Somogyi P
    5. Lamsa KP

    (2010) Cell type-specific long-term plasticity at Glutamatergic synapses onto hippocampal interneurons expressing either parvalbumin or CB1 cannabinoid receptor

    Journal of Neuroscience 30:1337–1347.

    https://doi.org/10.1523/JNEUROSCI.3481-09.2010

    • PubMed
    • Google Scholar
26. 1. Nitz D
    2. McNaughton B

    (2004) Differential modulation of CA1 and dentate gyrus interneurons during exploration of novel environments

    Journal of Neurophysiology 91:863–872.

    https://doi.org/10.1152/jn.00614.2003

    • PubMed
    • Google Scholar
27. 1. Pelkey KA
    2. Chittajallu R
    3. Craig MT
    4. Tricoire L
    5. Wester JC
    6. McBain CJ

    (2017) Hippocampal GABAergic inhibitory interneurons

    Physiological Reviews 97:1619–1747.

    https://doi.org/10.1152/physrev.00007.2017

    • PubMed
    • Google Scholar
28. 1. Schoenenberger P
    2. O'Neill J
    3. Csicsvari J

    (2016) Activity-dependent plasticity of hippocampal place maps

    Nature Communications 7:11824.

    https://doi.org/10.1038/ncomms11824

    • PubMed
    • Google Scholar
29. 1. Topolnik L
    2. Chamberland S
    3. Pelletier JG
    4. Ran I
    5. Lacaille JC

    (2009) Activity-dependent compartmentalized regulation of dendritic Ca2+ signaling in hippocampal interneurons

    Journal of Neuroscience 29:4658–4663.

    https://doi.org/10.1523/JNEUROSCI.0493-09.2009

    • PubMed
    • Google Scholar
30. 1. Wilson MA
    2. McNaughton BL

    (1993) Dynamics of the hippocampal ensemble code for space

    Science 261:1055–1058.

    https://doi.org/10.1126/science.8351520

    • PubMed
    • Google Scholar
31. 1. Zhang F
    2. Wang LP
    3. Brauner M
    4. Liewald JF
    5. Kay K
    6. Watzke N
    7. Wood PG
    8. Bamberg E
    9. Nagel G
    10. Gottschalk A
    11. Deisseroth K

    (2007) Multimodal fast optical interrogation of neural circuitry

    Nature 446:633–639.

    https://doi.org/10.1038/nature05744

    • PubMed
    • Google Scholar

Author details

1. Igor Gridchyn

   Institute of Science and Technology Austria (IST Austria), Klosterneuburg, Austria

   Contribution

   Software, Formal analysis, Validation, Investigation, Writing - review and editing

   Contributed equally with

   Philipp Schoenenberger

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1807-1929

2. Philipp Schoenenberger

   Institute of Science and Technology Austria (IST Austria), Klosterneuburg, Austria

   Present address

   School of Psychology, Cardiff University, Cardiff, United Kingdom

   Contribution

   Conceptualization, Software, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing - review and editing

   Contributed equally with

   Igor Gridchyn

   Competing interests

   No competing interests declared

3. Joseph O'Neill

   Institute of Science and Technology Austria (IST Austria), Klosterneuburg, Austria

   Present address

   F. Hoffmann-La Roche Ltd, Roche Innovation Center Basel, Grenzacherstrasse, Switzerland

   Contribution

   Conceptualization, Writing - review and editing

   Competing interests

   No competing interests declared

4. Jozsef Csicsvari

   Institute of Science and Technology Austria (IST Austria), Klosterneuburg, Austria

   Contribution

   Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   jozsef.csicsvari@ist.ac.at

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5193-4036

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