Conditional learning increases the inhibition of layer 4 excitatory neurons by somatostatin- and parvalbumin-expressing interneurons in the barrel cortex

doi:10.21203/rs.3.rs-5071377/v1

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Conditional learning increases the inhibition of layer 4 excitatory neurons by somatostatin- and parvalbumin-expressing interneurons in the barrel cortex

https://doi.org/10.21203/rs.3.rs-5071377/v1

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Synaptic strength underlies information processing, learning, and memory storage, yet little is known about how learning impacts synaptic inputs and outputs of specific GABAergic interneurons, particularly in the somatosensory cortex. Using a simple conditional model of learning in mice, where whisker stimulation was paired with a tail shock, we investigated plastic changes of inhibition mediated by somatostatin- (SST-), parvalbumin- (PV-), and vasoactive intestinal polypeptide-expressing interneurons (VIP-INs) in the barrel cortex. In vitro patch-clamp recordings and optogenetics revealed that conditional learning increases SST-IN and PV-IN synaptic outputs onto layer 4 (L4) excitatory neurons. A small fraction of L4 excitatory neurons in the barrel cortex receives inhibition from local VIP-INs, but learning does not affect this inhibition. Additionally, learning does not alter excitatory inputs to all three interneuron types. These findings suggest that enhanced inhibition by SST-INs and PV-INs may improve information processing and memory coding in L4 of the barrel cortex.

associative learning

conditioning

pseudoconditioning

somatosensory cortex

synaptic plasticity

VIP interneurons

Long-lasting modifications of synaptic transmission are considered as a primary mechanism of learning and memory formation. Many studies on synaptic changes related to memory and learning have primarily focused on synaptic inputs and outputs of excitatory neurons. Less attention has been paid to synaptic plasticity of specific types of GABAergic interneurons. Relatively recently, our knowledge regarding the involvement of GABAergic interneurons in information processing, learning, memory consolidation, and retrieval has expanded (Sachidhanandam et al. 2016; Krabbe et al. 2019; Cummings et al. 2022). However, mechanisms and a role of specific GABAergic interneuron types in these processes are still under intense investigation. It is especially essential to dissect learning-related changes in the strength of synaptic input and outputs of different types of GABAergic interneurons in specific brain areas and forms of learning.

Among these interneurons, SST-, PV-, and VIP-INs compromise almost 100% of all the inhibitory interneurons in the mouse neocortex (Rudy et al. 2011; Wamsley and Fishell 2017). In general, PV-INs target mainly soma, proximal parts of dendrites, or the axon initial segment (Kawaguchi and Kubota 1997), whereas SST-INs innervate primarily (but not exclusively) distal parts of excitatory neurons (Wang et al. 2004). VIP-INs preferentially inhibit other classes of interneurons, creating disinhibitory circuits (Caputi et al. 2009; Lee et al. 2013; Pfeffer et al. 2013; Jiang et al. 2015; Walker et al. 2016; Kullander and Topolnik 2021). All three classes of interneurons differ in terms of cell morphology, gene expression, electrophysiological properties, distribution across the cortical layers, connectivity and function in the local circuit (Hu et al. 2014; Liguz-Lecznar et al. 2016; Tremblay et al. 2016; Feldmeyer et al. 2018; Apicella and Marchionni 2022).

Previous reports have shown specific learning-related plastic changes of excitatory and inhibitory synaptic transmission in the amygdala (Lucas et al. 2016) and medial prefrontal cortex (mPFC) (Cummings and Clem 2020) in mice. It has been found that fear conditional learning decreases excitatory drive to PV-INs and reduces inhibition mediated by these interneurons in the lateral but not basal amygdala (Lucas et al. 2016). In the medial prefrontal cortex, the strength of excitatory inputs increases in L2/3 SST-INs, whereas it decreases in L5/6 PV-INs after conditional learning (Cummings and Clem 2020). Further experiments have shown that in mPFC, fear learning specifically enhances excitatory transmission to SST-INs, which were active during learning (Cummings et al. 2022). Moreover, it has been demonstrated that this subpopulation of learning-activated SST-INs provides proportionally more potent inhibition to PV-INs than to learning-activated excitatory cells in mPFC (Cummings et al. 2022).

Also, previous studies have shown an enhancement of GABAergic inhibition of L4 excitatory neurons (Tokarski et al. 2007) after conditional learning where tactile stimulation of whiskers was paired with a tail shock (Siucinska and Kossut 1996, 2006; Siucinska et al. 1999; Lech et al. 2001; Cybulska-Klosowicz and Kossut 2006; Cybulska-Klosowicz et al. 2009, 2013; Jasinska et al. 2010, 2013, 2016; Urban-Ciecko et al. 2010; Bekisz et al. 2010; Urban-Ciecko and Mozrzymas 2011; Posluszny et al. 2015; Dobrzanski et al. 2022; Kanigowski and Urban-Ciecko 2024). However, it is unknown which GABAergic interneuron types are responsible for this elevated inhibition and how conditioning affects both excitatory inputs to various molecular classes of L4 GABAergic interneurons and specific inhibitory outputs in the barrel cortex. Thus, we decided to examine how the conditioning paradigm in mice influences the excitatory inputs and outputs of three main molecularly distinctive classes of GABAergic interneurons (SST-, PV- and VIP-INs) in L4 of the barrel cortex using in vitro electrophysiological recordings and optogenetic tools. Our previous experiments have shown that conditioning paradigm changes intrinsic excitability of three classes of GABAergic interneurons in a cell type-specific manner (Kanigowski and Urban-Ciecko 2024) suggesting that learning-evoked synaptic changes might also be synapse-specific.

Our present experiments revealed that conditioning enhances the inhibition of L4 excitatory cells by SSTINs and PV-INs but not VIP-INs and has no effect on global excitatory inputs to these three types of GABAergic interneurons in L4 of the barrel cortex. In conclusion, in this case, conditional learning specifically controls the outputs but not the inputs of L4 GABAergic interneurons. Our findings suggest that the increased inhibition of L4 excitatory neurons by SST-INs and PV-INs could enhance the precision of information processing and coding by L4, which is considered the main input layer of the barrel cortex. Increased inhibition of L4 excitatory neurons and the resulting changes in network activity might create favorable conditions for the association process.

Experimental Animals

Mice were bed in the Animal Facility at the Nencki Institute of Experimental Biology PAS (Warsaw, Poland), being under the constant veterinary care. Animals with unlimited access to water and food were kept at 20–23°C and relative air humidity of 40–50%. The day/night cycle in which the animals lived consisted of alternating 12-hour light and dark phases.

The experiments involved the progeny of animals of homozygous lines imported from The Jackson Laboratory (The United States): SST-Cre (line number: #013044), PV-Cre (#012358), VIPCre (#010908), Ai14 (#007908) and Ai32 (#024109). Double transgenic animals were obtained from crossing SST-Cre, PV-Cre, or VIP-Cre with the Ai14 or Ai32 line to obtain tdTomato or ChR2 expression following Cre-mediated recombination, respectively. Experiments were done on P21-50 mice of both sexes.

Learning paradigms

Habituation

Before the learning session, mice were habituated to the immobilization of the neck in a special immobilization device. Each immobilization session lasted 10 min a day for 5 consecutive days starting at P21. Habituation and subsequent learning paradigms were performed at a fixed time in the first two hours of the light phase.

Conditioned group (CS + UCS)

One day after the last habituation session, mice were subjected to aversive conditioning (Siucinska and Kossut 1996). In this procedure, a mouse was immobilized as in the habituation session, and the animal's tail was attached with a clip to the ACS100 electrical stimulator (Circlelabs, Poland). Then, as a conditioned stimulus (CS), row B of vibrissae on the left side of the animal’s snout was stroked manually with a fine brush from the back of the snout to its front. Each stimulation of vibrissae lasted for 3 s and was repeated 3 times. In the final second of the last stroke, an unconditioned stimulus (UCS) was applied to the mouse's tail in the form of an electrical shock (0.5 mA, 0.5 s). The sequence was repeated after a 6-s interval. The pairing of CS + UCS stimuli was repeated 40 times during a 10-min session. Each mouse in the CS + UCS group was subjected to three sessions over 3 consecutive days, resulting in a cumulative total of 120 CS + UCS pairings.

Pseudoconditioned group (Pseudo)

In the Pseudo group, the administration of the CS was performed in the same manner as in the group of CS + UCS animals. The number of the CS was the same in both groups. However, the UCS (electric shock to the tail) was delivered randomly (not paired in time with the CS, Fig. S1). Every mouse in the Pseudo group was subjected to the same number of sessions as the CS + UCS group.

Naïve group

In the Naïve group (Naïve), animals did not experience any handling by the experimenter.

Behavioral assessment

A subset of the CS + UCS or Pseudo mice was video recorded during the training sessions to evaluate behavioral outcomes. The number of trials in which a mouse responded to vibrissae stimulation by moving its head was counted (Cybulska-Klosowicz et al. 2009; Dobrzanski et al. 2022; Kanigowski and Urban-Ciecko 2024). Only trials in which the mouse moved its head toward the CS were counted; head movements during UCS application and intertrial intervals were excluded.

Electrophysiological experiments

Brain sectioning

Roughly 24 h following the third CS + UCS or Pseudo session, the mice underwent anesthesia with isoflurane (approximately 5% concentration in inhaled air; Iso-Vet) to extract their brains. Exclusively, the right hemisphere was used for subsequent experiments, also in the case of the Naïve group. The frontal part of the hemisphere was cut off at an angle of 45° to the sagittal plane and was attached with its front side to the metal base of the vibratome (VT1000S Leica Microsystems, Germany). The unique cutting procedure ensures the hemisphere is sliced across five rows of barrels (A-E) to obtain sections encompassing one barrel from each row (Finnerty et al. 1999). The slice thickness was 350 µm. Brain was cut in a cooled (0–2°C) artificial cerebrospinal fluid (ACSF) composed of (in mM): 113 NaCl, 2.5 KCl, 2 MgSO₄, 2 CaCl₂, 1 NaH₂PO₄, 26.2 NaHCO₃ and 11 glucose, equilibrated with carbogen in a volume ratio of 95% O₂/5% CO₂. The slices were kept in a recovery chamber in warmed (30°C) carbogen-equilibrated ACSF solution. After 5 min, the chamber containing the slices was transferred to room temperature for an additional recovery period.

Whole-cell patch-clam recordings

A single slice was moved to the recording chamber mounted under a Zeiss microscope. Barrels were visualized at 4x magnification, and sections containing 5 barrels were taken for further analysis. A fluorescence microscope with IR-DIC and an objective of 40x magnification was used to visualize neurons. Whole-cell patch-clamp recordings were done in neurons located in L4 of barrel B, the cortical representation of one of the whiskers manipulated during learning procedures. All recordings were performed at room temperature.

Interneurons were recognized based on the presence of fluorescent protein and characteristic spiking patterns (Kawaguchi 1995; Ma et al. 2006). A modified ACSF solution (mACSF) was used to record spontaneous action potential (sAP) firing and spontaneous excitatory postsynaptic currents (sEPSCs) in interneurons. mACSF contained (in mM): 113 NaCl, 3.5 KCl, 0.5 MgSO₄, 1 CaCl₂, 1 NaH₂PO₄, 26.2 NaHCO₃, and 11 glucose, and was equilibrated with carbogen (95% O₂/5% CO₂).

Excitatory neurons were distinguished based on their regular spiking pattern (Beierlein et al. 2003; Tokarski et al. 2007) and the absence of fluorescent marker. Regular ACSF (rACSF) was used to record evoked inhibitory postsynaptic currents (eIPSCs) in excitatory neurons. rACSF contained (in mM): 113 NaCl, 2.5 KCl, 2 MgSO₄, 2 CaCl₂, 1 NaH₂PO₄, 26.2 NaHCO_3, and 11 glucose and was equilibrated with carbogen (95% O₂/5% CO₂).

Patch pipettes (5–8 MΩ) were filled with the internal solution containing (in mM): 125 K-gluconate, 10 HEPES, 0.5 EGTA, 2 KCl, 4 ATP-Mg, and 0.3 GTP-Na. Data were acquired using Clampex 10.6.2.2 software, a Multiclamp 700B amplifier, and an Axon Digidata 1550B analog-to-digital converter (Molecular Devices, United States). The analog signal was filtered at 3kHz and sampled at 20kHz.

Resting potential, access resistance and input resistance were monitored online. Resistance was measured in the current-clamp mode as a membrane response to a -10 pA pulse of 100 ms duration or in the voltage-clamp mode in response to a + 10 mV pulse (10–50 ms). Recordings in which input or access resistance alterations exceeded 30% of their initial values were disqualified from subsequent analysis.

eIPSCs in excitatory cells were recorded in the voltage-clamp mode at 0 mV, the reversal potential for AMPAR and NMDAR currents. eIPSCs were evoked by the optogenetic activation of ChR2 expressed in a selected population of GABAergic interneurons. ChR2 was activated by blue light (470 nm) delivered by an LED controller (UHPLCC-T, Prizmatix, Israel) and lens with 40x magnification. Three short light pulses of 0.5 ms were administered at 50-ms intervals at the maximal power intensity (3.1 mW). Twelve repetitions of the stimulation were performed, with a 15-second gap between each. We have also tested longer light pulses (3 and 10 ms) with various power intensities. However, we noticed that a short 0.5 ms light pulse with maximal power intensity was sufficient to evoke reliable spikes in all three types of GABAergic interneurons. The strength of inhibition originating from a given type of interneurons was analyzed in Clampfit and expressed as a charge transfer (Brickley et al. 1996; Moldavan et al. 2021). Charge transfer was calculated as the area under the curve of the neuronal response in a time interval ranging from 200 to 500 ms, depending on the current duration. Charge transfer was used in this case because a single light stimulation usually evokes several overlapping eIPSCs coming from all the interneurons of a given type connected to a single excitatory neuron. For this reason, this parameter better expresses the strength of inhibition than eIPSC amplitude.

The fraction of L4 excitatory neurons of the barrel cortex innervated by VIP-INs was calculated based on the presence of eIPSCs evoked by 3 pulses of blue light, each lasting 10 ms. The presence of eIPSCs was assessed by averaging 12 repetitions recorded from a single cell. The fraction of innervated excitatory neurons was presented as the percentage of the population of recorded regular spiking cells that were inhibited by VIP-INs.

sEPSCs and miniature (mEPSCs) were recorded in the voltage-clamp mode at -70 mV potential. sEPSCs were recorded in the mACSF, and mEPSCs were recorded in the rACSF with 1 µM tetrodotoxin (TTX). The recording time for a single neuron was approximately 3 min. EPSC analysis was performed using Clampfit 10.6.2.2. software (Molecular Devices, The United States). The rising time of sEPSCs and mEPSCs was calculated between 10 and 90% points of the current amplitude. In turn, the decay time was measured between 90 and 10% points of the current amplitude.

Spontaneous firing was recorded in the current-clamp mode at the resting membrane potential for approximately 3 min.

Statistical Analysis

The optimal sample size of the experiments was determined based on „resource equation method” (Charan and Biswas 2013), previous studies (Tokarski et al. 2007; Urban-Ciecko et al. 2010; Bekisz et al. 2010) and our experience. Animals were randomly assigned to 3 groups. Due to the nature of procedures experiments were not performed in a blinded manner. Statistical analyses were performed with GraphPad Prism 8.0.2 (GraphPad Software, United States) or R 4.1.2. software (R Foundation for Statistical Computing, Austria). The normality distribution was tested using the D'Agostino-Pearson test, and the equality of variance was verified using Bartlett's test. Parametric tests were used when data sets had normal distributions, and tested groups did not differ in the variances. Alternatively, non-parametric tests were used. Data were tested using following tests where appropriate: one-way ANOVA followed by Tukey’s post-hoc test or Šídák's multiple comparisons test (when testing relevant relations between groups), Kruskal–Wallis test followed by Dunn’s post-hoc test, Kolmogorov–Smirnov test with Bonferroni correction, Unpaired t-test, Mann-Whitney test, or 2-sample test for equality of proportions with continuity correction. Population data are shown as the mean ± S.E.M and were considered statistically significant when p < 0.05. The legends specify the number of neurons and animals in the following scheme: number of cells (number of mice).

We raised a question of whether a simple model of learning in mice influences the synaptic inputs and outputs of three molecularly distinctive classes of GABAergic cortical interneurons: SST-INs, PV-INs, and VIP-INs. To address this question, we subjected mice to a conditioning paradigm in which tactile stimulation of whiskers was paired in time with electrical tail shocks or to pseudoconditioning, where UCS was applied randomly (Fig. S1). A part of conditioned and pseudoconditioned mice was filmed during the training sessions to assess behavioral outcomes (Fig. S2). One day after the conditioning or pseudoconditioning, mice were sacrificed to obtain acute brain slices for whole-cell patch-clamp recordings.

Learning-related changes of inhibitory inputs to L4 excitatory neurons

In the first part of the experiments, we checked whether learning induces changes in the inhibition of L4 excitatory neurons by three classes of GABAergic interneurons. Specific, Cre-mediated expression of ChR2 in selected classes of GABAergic interneurons allowed us to measure the strength of optogenetically evoked IPSCs originating from distinct classes of interneurons. Initially, we verified that optogenetic stimulation was effective in each tested interneuron class (Fig. 1b, 2b, 3b). A single blue light stimulation (0.5 ms duration) evoked at least 1 spike (occasionally 2–3 spikes) in all interneuron classes. In general, a train of three pulses evoked at least 3 spikes in interneurons of a given class. In L4 VIP-INs, such a stimulation also evoked at least 1 spike (data not shown). However, to ensure effective stimulation of these interneurons, we applied a 10 ms-long blue light pulse (Fig. 3b), which evoked 2–4 spikes. Then, using transgenic mice with ChR2 expressed in SST-INs, we recorded optogenetically evoked IPSCs originating from all SST-INs connected to a given L4 excitatory neuron (Fig. 1a-c). These experiments showed higher charge transfer of eIPSCs originating from SST-INs in the CS + UCS group compared to the Naïve and Pseudo mice (Fig. 1c, d). Next, using transgenic mice with ChR2 expressed in PV-INs (Fig. 2a), we also observed higher charge transfer of eIPSCs originating from PV-INs in CS + UCS mice in contrast to the Naïve and Pseudo animals (Fig. 2c, d). Both results suggest that conditioning increases inhibition of L4 excitatory cells by SST-INs and PV-INs.

Finally, we wanted to test whether L4 excitatory neurons of the barrel cortex are innervated by VIP-INs and how this connectivity changes after learning. It has been wildly recognized that SST- and PV-INs are highly interconnected with neighboring excitatory neurons in the neocortex (Beierlein et al. 2003; Gabernet et al. 2005; Inoue and Imoto 2006; Ma et al. 2012; Xu et al. 2013; Koelbl et al. 2015; Scala et al. 2019), whereas, there is no data in the literature showing whether L4 excitatory neurons of the barrel cortex are innervated by VIP-INs. VIP-INs usually inhibit SST-INs and PV-INs, providing disinhibition of excitatory neurons (Caputi et al. 2009; Lee et al. 2013; Pfeffer et al. 2013; Jiang et al. 2015; Walker et al. 2016; Kullander and Topolnik 2021). However, it is also known that L2/3 VIP-INs of mouse barrel cortex, as well as those in the frontal and visual cortex, inhibit excitatory neurons (Caputi et al. 2009; Pfeffer et al. 2013; Garcia-Junco-Clemente et al. 2017). Nevertheless, the probability and strength of synaptic connections from VIP-INs onto excitatory cells are much lower than VIP-IN connections onto SST-INs and PV-INs (Caputi et al. 2009; Pfeffer et al. 2013). Using a train of 3 blue light pulses (each 10 ms-long) in transgenic mice with ChR2 in VIP-INs (Fig. 3a), we, indeed, observed that a small fraction of L4 excitatory neurons was inhibited by VIP-INs (Fig. 3d). Since the amplitude of eIPSCs mediated by VIP-INs was very small (roughly 5–15 pA; Fig. 3c), we were unable to measure the charge transfer of eIPSCs originating from VIP-INs. For this reason, only the fraction of L4 glutamatergic neurons innervated by VIP-INs was examined. In general, 33% and 34% of tested L4 excitatory neurons were inhibited by VIP-INs in Naïve and CS + UCS mice, respectively. At the same time, about 18% of tested L4 excitatory cells were innervated by VIP-INs in the Pseudo group (Fig. 3d). However, statistical analysis did not reveal any changes between groups of animals in the fraction of L4 excitatory neurons inhibited by VIP-INs, suggesting that conditioning or pseudoconditioning did not influence the innervation of L4 excitatory cells by VIP-INs in the barrel cortex.

Learning-related changes of excitatory inputs to L4 GABAergic interneurons

In the second part of the experiments, we checked whether learning induces changes in the excitation of L4 SST-, PV-, and VIP-INs using transgenic mice with fluorescently labeled interneurons of one of these three classes of interneurons. Previous studies using the learning model we employed here, have shown that conditioning leads to increased intrinsic excitability of L4 excitatory neurons and their higher spiking frequency recorded at the threshold potential (Bekisz et al. 2010). These experiments (Bekisz et al. 2010) have been performed in mACSF, which allows for studying spontaneous neuronal firing (Maffei et al. 2004; Reig et al. 2006; Maffei and Turrigiano 2008; Urban-Ciecko et al. 2015, 2018; Kanigowski et al. 2023). Thus, we suspected that conditioning may also influence the excitatory drive to interneurons because changes in intrinsic excitability are often accompanied by synapse alterations (Sehgal et al. 2013). To study this, we recorded sEPSCs in GABAergic interneurons in mACSF.

Since SST-INs constitute a heterogeneous group of cells in terms of anatomical, molecular, and electrophysiological properties, as well as connectivity patterns (Wang et al. 2004; Ma et al. 2006; Naka et al. 2019), we focused on a specific electrophysiological subtype of SST-INs characterized by low-threshold spiking (LTS) pattern and presenting rebound spikes (Fig. S3a). This SST-LTS subtype is most prevalent in the rodent neocortex (Wang et al. 2004; Ma et al. 2006; Kanigowski and Urban-Ciecko 2024). Recordings of sEPSCs in L4 SST-LTS did not reveal any differences in the amplitude and frequency of these currents between groups of tested mice (Fig. 4a-c). Also, there were no differences in the rise time and decay time between groups (Fig. 4d-f). This result suggests a lack of changes in the excitatory drive to SST-LTS after learning. Surprisingly, sEPSCs were similar to mEPSCs in L4 SST-LTS (Fig. S4) suggesting that spontaneous firing of excitatory neurons in mACSF had no effect on sEPSCs in SST-INs. In fact, among 8 recorded L4 excitatory neurons from 4 Naïve mice, we did not observe any spontaneous firing at the resting potential in mACSF (Fig S5). A semi-spontaneous firing was observed when the membrane potential was depolarized by somatic current injections to the threshold value (Fig S5), as it has been done in the previous study (Bekisz et al. 2010).

Moreover, mEPSCs did not differ between groups of animals (Fig. S6), further suggesting that learning does not influence the excitatory drive to SST-INs.

Figure 4 Learning does not influence the excitatory drive to L4 SST-LTS in the barrel cortex. a Example recordings of sEPSCs in three groups of mice. No changes in the b amplitude (Kruskal-Wallis test, p = 0.3022) or c frequency (Kruskal-Wallis test, p = 0.1278) of sEPSCs were observed. d Averaged sEPSCs normalized in terms of the amplitude. No differences in the e rise time (one-way ANOVA, F_{(2, 52)} = 0.02236, p = 0.9779) or f decay time (Kruskal-Wallis test, p = 0.8120) of sEPSCs were noted. b, c, e, f Naïve = 22(9), CS + UCS = 16(11), Pseudo = 17(9).

Subsequently, we analyzed how learning influences the excitatory inputs to L4 PV-INs. These interneurons are characterized by fast-spiking (FS) firing patterns with the presence (Fig. S3c) or absence (Fig. S3b) of rebound spikes. Our previous experiments have shown that pseudoconditioning decreases intrinsic excitability of L4 PV-INs (Kanigowski and Urban-Ciecko 2024). However, the analysis of sEPSCs recorded in PV-INs did not reveal any changes in the amplitude or frequency of these currents after conditioning or pseudoconditioning (Fig. 5a-c). The analysis of the current kinetics did not show any differences in the rise time between groups (Fig. 5d, e); however, it revealed an extended decay time of sEPSCs in Pseudo animals compared to the other two groups (Fig. 5d, f). The increased decay time of sEPSCs might suggest a deterioration in the precision of the PV-IN excitation. Eventually, the prolonged decay time of sEPSCs may also indicate the existence of certain mechanisms of compensatory homeostatic plasticity. In this case, extended postsynaptic excitation might partially counterbalance the decrease of PV-IN excitability after psudoconditioning.

Finally, we investigated the excitatory drive to L4 VIP-INs, a class of interneurons that is more diverse than SST-INs or PV-INs (Tremblay et al. 2016). VIP-INs present various morphological, biochemical, wiring, and electrophysiological features (Bayraktar et al. 2000; Caputi et al. 2009; Prönneke et al. 2015; He et al. 2016; Jiang et al. 2023). For this reason, we focused on L4 VIP-INs presenting accommodating (VIP-AC, Fig. S3d) or LTS (VIP-LTS, Fig. S3e) patterns of discharges as it has been done in the previous study (Kanigowski and Urban-Ciecko 2024). Our previous experiments have shown that VIP-AC and VIP-LTS constitute two dominant electrophysiological subtypes of L4 VIP-INs in the barrel cortex (Kanigowski and Urban-Ciecko 2024). The analysis of sEPSCs in VIP-AC did not reveal any changes between groups in the amplitude or frequency of recorded currents (Fig. 6a-c). We also did not observe any differences between groups of animals in the kinetics of sEPSCs (Fig. 6d-f). Similarly, the analysis of sEPSCs in VIP-LTS did not show any changes in the parameters of recorded currents after learning (Fig. 7). These experiments indicate that conditioning or pseudoconditioning did not change excitatory drive to L4 VIP-AC and VIP-LTS.

In the last part of experiments, we also recorded spontaneous activity at the membrane potential of all electrophysiological subtypes of GABAergic interneurons in L4. In contrast to L4 excitatory neurons (Fig. S5), GABAergic interneurons usually fire spontaneously in mACSF. Since learning changes intrinsic excitability of GABAergic interneurons (Kanigowski and Urban-Ciecko 2024), here we wanted to check whether learning-related changes in intrinsic excitability lead to changes in spontaneous firing. However, these experiments did not show any alterations in the frequency of spontaneously appearing APs in L4 SST-, PV- or VIP-INs after conditioning or pseudoconditioning (Fig. S7a, b; Fig. S8a, b; Fig. S9a, b; Fig. S10a, b). We also analyzed a cumulative distribution of AP frequencies. However, this analysis also did not reveal any significant differences in the cumulative distribution between groups in all electrophysiological subtypes of GABAergic interneurons (Fig. S7c; Fig. S8c; Fig. S9c; Fig. S10c). These results suggest that conditioning and pseudoconditioning do not influence the spontaneous activity of these three interneuron types in L4.

In the present study, we showed how a simple paradigm of conditional learning influences the excitatory inputs and inhibitory outputs of three molecularly distinct GABAergic interneurons in the barrel cortex. Our key findings reveal that the pairing of tactile stimulation of whiskers with a tail shock enhances inhibitory outputs of SST-INs and PV-INs but not VIP-INs onto L4 excitatory neurons in the cortical representations of one row of whiskers stimulated during the conditioning protocol. On the other hand, this form of learning has no effect on global excitatory inputs to these three types of GABAergic interneurons in L4 of the barrel cortex. In conclusion, in this case, conditional learning controls the outputs but not the inputs of L4 GABAergic interneurons. Surprisingly, after pseudoconditioning, we observed an increased decay time of sEPSCs in PVINs, indicating that this form of learning may alter the excitatory drive to PVINs.

Previous studies based on the learning model used in our work have shown that conditioning increases the density of double positive cells for SST and glutamate decarboxylase 67 (GAD67) in barrels corresponding to the manipulated whiskers (Cybulska-Klosowicz et al. 2013), suggesting increased activity of SST-INs after conditioning. Recent studies have revealed that L4 SST-IN activity in the barrel cortex is crucial for the learning process and formation of association between CS and UCS in mice (Dobrzanski et al. 2022). Also, our recent study has shown specific learning-induced plastic changes in intrinsic excitability of all three molecular classes of GABAergic interneurons in the barrel cortex (Kanigowski and Urban-Ciecko 2024). We have shown that conditioning increases intrinsic excitability of SST-LTS, whereas pseudoconditioning decreases intrinsic excitability of PV-INs and VIP-AC (Kanigowski and Urban-Ciecko 2024).

Our current optogenetic experiments showed that conditioning increases the inhibition of L4 excitatory neurons by SST-INs and PV-INs in the barrel cortex. The increased inhibition of L4 excitatory cells by SST-INs may be caused by the strengthening of synapses between these cells. However, our previous experiments have shown that conditioning increases intrinsic excitability of SST-LTS (Kanigowski and Urban-Ciecko 2024). Thus, elevated inhibition of L4 excitatory neurons by SST-INs may also be related to increased intrinsic excitability of SST-LTS.

In terms of PV-INs, previous studies have not found any plastic changes in L4 PV-INs of the barrel cortex in the conditional learning model used in this study (Siucinska and Kossut 2006; Tokarski et al. 2007; Bekisz et al. 2010; Kanigowski and Urban-Ciecko 2024). However, our present optogenetic experiments revealed an increase in the inhibition of L4 excitatory neurons by PV-INs following conditioning. Since our previous study has not shown any changes in intrinsic excitability of L4 PV-INs after conditioning (Kanigowski and Urban-Ciecko, 2024), changes in intrinsic excitability of PV-INs are unlikely responsible for stronger inhibition of L4 excitatory cells by PV-INs. So, it seems that increased inhibition of L4 excitatory neurons by PV-INs might depend on changes in the strength of inhibitory synapses between PV-INs and excitatory cells.

For the first time, we demonstrated that L4 excitatory neurons in the barrel cortex are innervated by VIP-INs. Consistent with data obtained from L2/3 and L5 of the medial prefrontal cortex, auditory cortex, and visual cortex (Pfeffer et al. 2013; Pi et al. 2013), we showed that also in the barrel cortex, L4 excitatory neurons are sparsely innervated by VIP-INs and the strength of these connections is weak. For these reasons, only the percentage of L4 excitatory cells of the barrel cortex innervated by VIP-INs was analyzed. However, this analysis did not show any statistically significant differences between the groups of animals in the fraction of L4 excitatory cells innervated by VIP-INs. Considering that VIP-INs primarily establish disinhibitory circuits and connect with SST-INs and PV-INs in various brain regions (Caputi et al. 2009; Lee et al. 2013; Pfeffer et al. 2013; Jiang et al. 2015; Walker et al. 2016; Kullander and Topolnik 2021), it would be valuable to investigate whether similar disinhibitory connection patterns are preserved in L4 of the barrel cortex and whether learning influences the strength of these connections. However, it would require a double transgenic mouse line with a possibility for the manipulation or visualization of two district classes of GABAergic interneurons simultaneously.

Moreover, our current experiments did not reveal any changes in the amplitude, frequency, or kinetic of sEPSCs in L4 SST-LTS or both electrophysiological types of VIP-INs as a result of conditioning or pseudoconditioning. We observed an increase in the decay time of sEPSCs in PV-INs after pseudoconditioning, with no changes in the amplitude or frequency of these currents. The increased decay time of sEPSCs in PV-INs after pseudoconditioning might indicate changes in the kinetics of AMPA receptors on PV-IN synapses, more specifically, their prolonged deactivation time (Wall et al. 2002). Prolonged decay time of sEPSCs may lead to reduced firing precision and information transmission between cells (Rodriguez-Molina et al. 2007). This would be consistent with the assumption that pseudoconditioning might be the opposition to associative learning and may lead to a deterioration in the functioning of the local network (Kanigowski and Urban-Ciecko 2024). At the same time, we did not observe any changes in the parameters of sEPSCs in PV-INs after conditioning, consistently as it has been observed for FS interneurons (presumably PV-INs) in the previous study (Tokarski et al. 2007).

Our results suggest that associative conditioning does not influence the excitatory connections of any class of L4 GABAergic interneurons in the barrel cortex. However, it is also possible that potential changes in excitatory inputs are unique and specific to a given connection type and that these changes are invisible in the global spontaneous activity.

Finally, our research did not reveal any changes in the spontaneous firing of all studied types of L4 GABAergic interneurons after conditioning or pseudoconditioning.

What is the impact of observed plastic changes on the local network? It has been shown that conditioning leads to increased intrinsic excitability of L4 excitatory neurons and elevated frequency of spikes recorded at the threshold potential (Bekisz et al. 2010). Likely, the increase in the inhibition of L4 excitatory cells by SST-INs and PV-INs after conditioning observed in our study is a response to elevated activity of glutamatergic cells. Thus, changes in synaptic inputs following conditioning would be a form of homeostatic plasticity maintaining a balance between excitation and inhibition (E/I balance) in the L4 local network of the barrel cortex and preventing excitatory neurons from excessive activity, which would otherwise result in epileptic activity (Le Roux et al. 2008; Turrigiano 2011). Increased inhibition of excitatory neurons by SST-INs and PV-INs may regulate glutamatergic neuron inputs and outputs and provide a balance that is necessary for synaptic plasticity and proper learning (Gandal et al. 2012; Campanac et al. 2013; Ntim et al. 2020; Toader et al. 2020). Many studies on mouse models of nervous system disorders have shown a disturbed E/I balance leading to learning disabilities of these animals (Costa et al. 2002; Souchet et al. 2014; Haji et al. 2020). Furthermore, transcranial magnetic stimulation in rats can modulate the ratio between excitation and inhibition in the cortex, which influences the responses of L4 neurons in the barrel cortex to vibrissae manipulation (Thimm and Funke 2015). Such magnetic stimulation of the cortex can also positively influence associative learning related to tactile stimuli recognized by the vibrissae (Mix et al. 2010). The abovementioned examples indicate that the E/I balance in L4 of the barrel cortex is fundamental for associative learning. Considering the ratio of SSTINs to PV-INs in the cortex, especially in L4, and the frequency and strength of connections formed between these interneurons and glutamatergic cells, the maintenance of the balance between excitation and inhibition would be primarily provided by PV-INs (Beierlein et al. 2003; Pfeffer et al. 2013; Tremblay et al. 2016). Also, stronger inhibition of L4 excitatory neurons by PV-INs than SST-INs was observed in our study (Fig. 1, 2).

The observed enhanced inhibition mediated by SST-INs and PV-INs may also increase the accuracy of information transfer between L4 glutamatergic cells in the barrel cortex cells by increasing the time accuracy of their discharges (Wlodarczyk et al. 2013). Moreover, increased inhibition from GABAergic interneurons may theoretically influence the accuracy of encoding information about the tactile stimulus sensed by the whiskers (Yu et al. 2016). It has been shown that the complexity of a somatosensory stimulus is encoded by the firing pattern of the cell population (Arabzadeh et al. 2006). Other analyses also have revealed that the timing of individual AP discharges potentially has a high information encoding capacity (Petersen et al. 2002). Thus, AP timing carries more information about texture of the surface than spike rates do. Also, AP timing better predicts animal behavior in a texture discrimination task (Zuo et al. 2015). Increased temporal precision of excitatory neuron discharges may positively impact on sensory information processing and encoding. Interestingly, as a result of conditioning, a simultaneous increase in inhibition from both SST-INs and PV-INs was observed. Due to their location in the network, both classes of interneurons are involved at different levels of information encoding. The increase in IPSCs originating from PV-INs may have dual effects, affecting both the initial inhibition of signals reaching L4 from the thalamus (due to the feed-forward inhibition) and the subsequent stages of sensory information processing in this layer by the feed-back inhibition (Beierlein et al. 2003; Gabernet et al. 2005; Inoue and Imoto 2006; Cruikshank et al. 2010; Koelbl et al. 2015; Sermet et al. 2019). Elevated inhibition of L4 excitatory neurons by PVINs increases the precision of the L4 glutamatergic cell responses to excitation coming from the thalamus. In a subsequent stage, SST-INs which receive only negligible excitation from thalamocortical axons and are activated by L4 glutamatergic neurons provide delayed feedback inhibition (Beierlein et al. 2003; Cruikshank et al. 2010; Ma et al. 2012; Sermet et al. 2019) and thus minimizing recurrent activity (M. L. Beierlein et al., 2002; Kapfer et al., 2007). Considering that SST-INs innervate distal dendrites and PV-INs innervate cell bodies and proximal dendrites of glutamatergic neurons, a simultaneous increase in inhibition from both classes of interneurons should enhance the control of information both at the input and output of excitatory cells. This two-step control should contribute to greater accuracy of correlated discharges of excitatory cells and lead to higher precision of information exiting from L4 to layers 2/3.

Competing Interests

The authors declares that they have no competing interests.

Ethics approval

All procedures were done in accordance with Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes and were approved by the first Local Ethical Committee for Animal Experiments in Warsaw (consent numbers: 172/2016 and 841/2019). All efforts were made to minimize the number of animals used and their suffering.

Funding

This work was supported by the National Science Centre, Poland (2015/18/E/NZ4/00721 to JUC).

Author Contribution

D.K.: conceptualization, investigation, formal analysis, writing – original draft.J.U.C.: conceptualization, investigation, formal analysis, supervision, project administration, writing – review & editing, funding acquisition.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Conditional learning increases the inhibition of layer 4 excitatory neurons by somatostatin- and parvalbumin-expressing interneurons in the barrel cortex

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Introduction

Materials and methods

Experimental Animals

Learning paradigms

Habituation

Conditioned group (CS + UCS)

Pseudoconditioned group (Pseudo)

Naïve group

Behavioral assessment

Electrophysiological experiments

Brain sectioning

Whole-cell patch-clam recordings

Statistical Analysis

Results

Learning-related changes of inhibitory inputs to L4 excitatory neurons

Learning-related changes of excitatory inputs to L4 GABAergic interneurons

Discussion

Declarations

Competing Interests

Ethics approval

Funding

Author Contribution

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1