Prefrontal chandelier cells encode stimulus salience to influence learning in male mice

Domain: neuroscience
Published: 2026-02-03
Editorial outcome: major_revision

Abstract
Detecting and utilizing salience information are essential for organisms to prioritize behaviorally relevant stimuli. While the prefrontal cortex is critical for cognition and learning, how it leverages salience information remains unclear. We report that chandelier cells (ChCs) in the medial prefrontal cortex (mPFC), a unique GABAergic interneuron, encode salience information and influence learning. Using cell-type-specific calcium signal recording in male mice, we demonstrate that ChCs respond robustly to stimuli across modalities, with responses dependent on stimulus salience, as determined by novelty and intensity. Circuit-specific manipulations reveal that ChC salience coding is attributable to synaptic inputs from the anterior insular cortex and paraventricular thalamus. Furthermore, ChCs acquire salience-coding property for behaviorally relevant stimuli through associative learning. Notably, bidirectional manipulations of ChC-mediated salience detection correspondingly led to improved or impaired associative learning. Together, these findings establish mPFC ChCs as a key circuit element for processing salience information to shape learning and behavior.

Introduction
The brain continuously processes streams of complex sensory inputs and must prioritize the most behaviorally relevant—or salient—stimuli to support effective attention, decision-making, and learning. Salience processing is a fundamental neural mechanism that allows organisms to detect and respond to critical environmental cues based on their physical properties, contextual relevance, and emotional or motivational significance1–5. Disruptions in salience processing are associated with several neuropsychiatric disorders, including schizophrenia and autism spectrum disorder6–9, where the abnormal assignment of salience disrupts perception, cognition, and behavior. Recent studies have identified key brain regions involved in salience processing, such as the anterior insular cortex, anterior cingulate cortex, and subcortical areas like the thalamus and striatum3,10–13. However, while the medial prefrontal cortex (mPFC) is central to higher-order cognitive functions—including attention, decision-making, and associative learning14,15—by integrating inputs from these regions, the cellular and circuit-level mechanisms by which the mPFC detects and utilizes salience information remain poorly understood.

Pyramidal neurons (PNs), the principal output cells of the cortex, are tightly regulated by GABAergic interneurons, which are critical for maintaining circuit balance and optimizing neural computation16–19. Among the diverse interneuron subtypes, chandelier cells (ChCs) standout due to their unique morphology and connectivity. ChCs, also known as axo-axonic cells, form highly specific “cartridges” of axonal boutons that exclusively synapse onto the axon initial segments of PNs20,21, a key site for action potential initiation. Remarkably, a single ChC can innervate hundreds of PNs22–24, positioning them to exert a powerful “veto” function over large populations of excitatory neurons25. Notably, ChCs are enriched in the mPFC26,27, and their vulnerability has been observed in neuropsychiatric conditions such as schizophrenia28 and autism spectrum disorder29, implicating their dysfunction in disrupted cognitive processes and behavioral impairments. Despite their hypothesized importance25,30, the physiological role of ChCs in the mPFC remains largely unexplored, particularly in the context of salience processing.

Here, we report that ChCs in the mPFC play a critical role in detecting and processing the salience of stimuli. Using advanced genetic and viral tools for reliable in vivo labeling and manipulation, we demonstrate that mPFC ChCs encode diverse forms of salience, including novelty, intensity, and learned associations. Importantly, we show that ChCs utilize salience information to modulate associative learning. These findings identify mPFC ChCs as the integral component of the salience processing circuitry, providing new insights into the neural mechanisms underlying salience processing and highlighting their implications for cognitive function and neuropsychiatric disorders.

Results
ChCs in the mPFC are robustly activated by multiple stimuli across modalities regardless of valence
To selectively target and manipulate ChCs in the mPFC, we utilized transgenic mouse strain Unc5b-2A-CreERT2 (hereafter referred to as Unc5b-CreER mice)27,31,32, in combination with Cre-dependent adeno-associated virus (AAVs). We injected AAV-DIO-GFP into mPFC of Ucn5b-CreER mice and then administered tamoxifen (TMX) to induce Cre-dependent GFP expression, enabling selective labelling of ChCs (Fig. 1a, b). Consistent with prior descriptions of ChCs in Nkx2.1-CreER mice21,22, GFP-expressing cells were predominantly located at the layer 1-2 (L1-2) border, with dendrites extending into L1 and axon plexus localized to L2/3. The characteristic axon “cartridges” of ChCs—strings of boutons terminating on the axon initial segments of PNs—were clearly observed in GFP-labeled cells (Fig. 1b). In sparsely labeled samples, ChCs were unambiguously identified based on their axon “cartridge” morphology (Supplementary Fig. 1a). Additionally, electrophysiological recordings confirmed that the labeled cells exhibited typical fast-spiking properties (Supplementary Fig. 1b). Together, these results demonstrate that our approach using Unc5b-CreER mice and viral tools enables efficient and specific labeling of mPFC ChCs.

To monitor the activities of ChCs in the mPFC, we expressed the genetically encoded Ca2+ sensor GCaMP7s using viral vectors and performed fiber photometry to record neuronal activity in real time. In freely moving animals, ChCs exhibited a high level of spontaneous activity (Fig. 1c). To investigate the factors driving these activities, we exposed the animals to a range of stimuli across modalities with varying emotional valences, including a pure tone, electric foot shocks, and water consumption (Fig. 1c). Interestingly, mPFC ChCs were robustly activated by all tested stimuli, regardless of their valence (Fig. 1d–f). For comparison, we also examined the responses of parvalbumin (PV)-positive cells and somatostatin (SST)-positive cells, which mediate perisomatic and dendritic inhibition, respectively16,17. Although ChCs and PV basket cells have been considered as two major subtypes of PV-expressing cells, our previous study32 has shown that the majority of ChCs labeled in Ucn5b-CreER mice in this area are not captured by the PV driver mouse line, indicating that ChCs recorded here did not belong to a subset of PV-positive cells. PV and SST cells in the mPFC exhibited valence-dependent activity: both cell types were activated by negatively valenced footshocks, inhibited during positively valenced water consumption, and unresponsive to neutral stimuli such as pure tones (Fig. 1g–l), which were consistent with previous reports33,34.

We further tested the responses of ChCs to additional olfactory and visual stimuli, including exposure to 2-methylthiazole (2-MT, a predator odor analog), visual flash presentations, and exploration of novel objects. All these stimuli elicited strong activation of mPFC ChCs (Supplementary Fig. 2a–c). Taken together, these results indicate that mPFC ChCs are highly responsive to external stimuli across modalities, regardless of emotional valence.

ChCs in the mPFC detect stimulus salience based on their novelty and intensity
Stimulus salience refers to both the physical distinctiveness and behavioral relevance of a stimulus. Novelty and intensity are critical properties that determine the salience of a stimulus1,2. To investigate whether the activity of ChCs in mPFC is influenced by stimulus novelty, we conducted experiments using repeated presentations of stimuli with varied valence in head-fixed mice. This approach enabled precise recording of ChC activity and accurate delivery of stimuli, categorized by valence as neutral and positive (e.g., tone and sugar water consumption) or negative (e.g., air puff and electric shock), with 10 repetitive presentations of each type of the stimulus in an approximately 5-min window per stimulus (Fig. 2a, b).

Recordings primarily captured ChC activity in the prelimbic area of the mPFC (Supplementary Fig. 3a, b). We defined novel stimuli as the first presentation of each type of stimulus, which robustly activated ChCs. Our results consistently showed a rapid decline in ChC responses across four types of repeated stimuli regardless of valence, although the habituation dynamics might vary slightly across stimuli (Fig. 2c–f). The 10th exposure of each type of stimulus was referred as familiar stimuli in this short-term habituation stage. These results suggested that mPFC ChCs could detect the novelty-based salience of the stimulus. Notably, no correlation was observed between licking behavior during sugar water consumption and ChC responses (Supplementary Fig. 4a, b), suggesting that the adaptation of ChC activity is not simply a reflection of changes in behavioral engagement. In contrast, PV and SST cells (Supplementary Fig. 3c–f), examined under the same conditions, showed no adaptive response to repeated stimuli (Fig. 2g–n and Supplementary Fig. 5a–h). Given the recent literatures indicating that ChCs’ activity in single-cell level may correlate with movement speed in hippocampus and visual cortex31,35,36, a more thorough analysis of movement-related effects on the Ca2+ signal of ChC, PV, and SST population in mPFC was performed in freely moving animals (Supplementary Fig. 6). We found that the population activity of ChCs, as well as PV and SST cells, was not significantly affected by movement speed, suggesting that the observed responses were indeed driven by sensory stimuli rather than movement-related factors. Furthermore, a control experiment within the auditory modality was conducted by sequentially presented two distinct tones (tone A followed by tone B) (Supplementary Fig. 7). After repeatedly exposing the animals to tone A, we observed that ChCs still exhibited robust responses to tone B, followed by a similar pattern of habituation to tone B as was seen with tone A, indicating that ChCs’ detection of novelty was stimulus-specific rather than modality-specific.

To further disentangle the contributions of novelty and intensity to ChC responses, we examined ChC activity across two exposure stages—an initial exposure stage (Session 1, S1) and a later exposure stage (Session 3, S3)—using water stimuli of varying volumes. We first assessed ChC responses under a blocked, fixed-volume structure, in which each water volume was delivered for 20 consecutive trials and the five blocks (100 trials total) were presented in a randomized block order (Supplementary Fig. 8a). Under this sequential structure, ChCs showed strong activation during the earliest exposures in both S1 and S3, but their responses progressively declined across blocks regardless of water volume (Supplementary Fig. 8b–m). These results indicate that when stimulus intensity remains constant within long trial epochs, ChC activity is dominated by exposure-driven attenuation, and differences in water volume exert minimal influence. Consistently, ChC activity converged to a similar pattern after approximately ten exposures across stimulus types (Supplementary Fig. 9), reinforcing that ChCs primarily encode novelty-driven salience under repetitive, fixed-intensity stimulation.

We next asked whether intensity encoding would emerge when stimulus magnitude varied on a trial-by-trial basis. Under this randomized structure (Fig. 3a)—where five water volumes were interleaved across 100 trials—ChCs in S1 showed no correlation between trial-by-trial Ca2+ changes (ΔCa2+ signal) and volume changes (Δvolume) (Fig. 3b), and sorting trials by absolute volume confirmed similarly sized responses across volumes (Fig. 3e–g). In contrast, during S3, ΔCa2+ signal and Δvolume exhibited a positive correlation (Fig. 3b–d), and when trials were sorted by volume, ChC responses increased systematically with larger water volumes (Fig. 3e–g). Thus, although ΔCa2+ signal and Δvolume covaried, this reflected the emergence of absolute volume encoding rather than perceived sensitivity to relative trial-to-trial changes. This shift in ChC response dynamics was not accompanied by changes in licking behavior (Supplementary Fig. 4c–f). ChCs also differentiated tone stimuli of different intensities during the later exposure stage (Supplementary Fig. 10), albeit with distinct temporal profiles compared to water consumption. Together, these findings indicate that intensity-dependent modulation of ChC activity emerges after extended experience under conditions of high trial-to-trial variability: ChCs initially responded in a novelty-driven manner during S1, but with continued exposure in a variable context, their responses began to scale with absolute stimulus intensity during S3.

Importantly, this shift from novelty-dominated responses in S1 to intensity-modulated responses in S3 was not observed in PV or SST interneurons (Fig. 3h–m, Supplementary Fig. 4g–n, and Supplementary Fig. 5i–n), indicating that this adaptive encoding property is specific to ChCs. Collectively, these results demonstrate that mPFC ChCs encode stimulus salience through both novelty and intensity, with novelty shaping responses during early exposures and intensity sensitivity emerging after prolonged experience in a context with substantial trial-to-trial variability.

AI and PVT inputs to the mPFC are essential for ChCs to detect the stimulus salience
Neuroimaging studies have revealed the cortical nodes of the salience network, including the dorsal anterior cingulate cortex (ACC) and anterior insular cortex (AI), along with subcortical structures such as the thalamus, striatum and midbrain dopamine nuclei3,37. Recent studies in rodents have demonstrated that neurons in the paraventricular thalamus (PVT) and ventral pallidum (VP) play a significant role in encoding stimulus salience11,12. Given the critical role of these regions in salience processing, we aimed to investigate how ChCs in the mPFC detect and process salience information from traditional “salience network” regions. Therefore, we specifically focused on the AI as a key cortical node and the PVT as a critical subcortical region within this network.

To examine how the salience-related AI and PVT regulate ChCs in the mPFC, we virally expressed tetanus toxin light chain (TetTox) in neurons within either region to inhibit synaptic transmission. The effect was confirmed by the effective suppression of VAMP2 expression in their axon terminals in the mPFC, which is the substrate of TetTox, and critical for the fusion of presynaptic vesicles to the presynaptic plasma membrane32,38 (Fig. 4a, b and Supplementary Fig. 11). We then assessed the TetTox treatment of AI and PVT on ChC responses during novelty-based salience detection (Fig. 4c–i). Blocking AI synaptic transmission significantly reduced ChC responses to auditory stimuli and rewards, abolishing the distinction between novel and familiar responses. For air puff stimuli, ChC responses were moderately diminished, and adaptive reductions between novel and familiar stimuli were eliminated (Fig. 4d–f). Similarly, blocking PVT synaptic transmission reduced ChC responses to auditory stimuli, again eliminating the distinction between novel and familiar responses. For reward stimuli, blocking PVT had no significant effect on novel responses, but adaptive reductions in familiar responses were completely abolished. In the case of air puff stimuli, blocking PVT synaptic transmission moderately reduced ChC responses and eliminated the adaptive reduction observed after repeated exposures (Fig. 4g–i).

Next, we investigated the role of AI and PVT in ChC detection of intensity-based salience (Fig. 4j). Consistent with the findings for novelty-based salience, blocking AI synaptic transmission reduced ChC responses to rewards in the initial exposure stage (S1) (Fig. 4k–m). Interestingly, in the later exposure stage (S3), ChC responses partially recovered (Fig. 4l), but responses to varying water volumes remained uniform, indicating impaired intensity discrimination (Fig. 4m, n). In contrast, blocking PVT synaptic transmission abolished differences in ChC responses between S1 and S3, and ChCs failed to adapt their responses to varying water volumes in S3 (Fig. 4o–r). Together, these results demonstrate that synaptic transmission of AI or PVT neurons are essential for ChCs in the mPFC to detect stimulus salience. Notably, AI and PVT lesion had distinct effects on ChC responses to varying water reward volumes from S1 to S3. In AI-lesioned mice, Ca2+ signals increased overall, from nearly undetectable in S1 to large signals in S3 (Fig. 4l), whereas in PVT-lesioned mice, Ca2+ responses remained stable and invariant across S1 and S3 (Fig. 4p). These results suggest that AI and PVT contributed differently to salience processing in mPFC ChCs. However, TetTox was to inhibit the synaptic transmission of AI or PVT, which included local synaptic transmission and long-range synaptic transmission to multiple brain regions. We then directly examined the synaptic inputs from AI or PVT to ChCs in the mPFC, using channelrhodopsin 2 (ChR2)-assisted circuit mapping39. We performed whole-cell patch recordings on mPFC ChCs, and did find the synaptic responses on ChCs by optic stimulation on the ChR2 expressed terminal in the mPFC, although with different circuitry mechanisms: AI sent monosynaptic excitatory inputs on mPFC ChCs; PVT sent synaptic excitation to mPFC without direct monosynaptic responses to ChCs (Supplementary Fig. 12).

To further dissect the influence of AI and PVT inputs on the mPFC, we combined fiber photometry with site-specific optogenetic manipulations. Using eJaws40, we optogenetically inhibited AI and PVT synaptic inputs to the mPFC while simultaneously recording ChC responses (Fig. 5a–c). Blocking AI and PVT inputs did not affect initial ChC responses to auditory, reward, or air puff stimuli during the initial exposure stage (Fig. 5d–i). However, adaptive reductions in ChC responses, a hallmark of novelty-based salience detection, were significantly impaired (Fig. 5d–i). Similarly, during intensity-based salience detection with varying water volumes, blocking AI and PVT inputs did not alter ChC responses to rewards in the initial exposure stage (Fig. 5j–r). However, in the later exposure stage (S3), ChCs failed to discriminate between different water volumes when AI or PVT inputs to the mPFC were inhibited (Fig. 5j–r).

These findings demonstrate that AI and PVT inputs to the mPFC are indispensable for adaptive reductions in ChC responses and for the ability of ChCs to discriminate stimulus intensity after continued exposure. AI and PVT thus serve as critical upstream regulators of ChC-mediated salience detection in the mPFC, enabling ChCs to dynamically encode both novelty and intensity of stimuli in an adaptive manner.

ChCs in the mPFC encode stimulus salience to facilitate associative learning
Learned associations imbue the predictive cue with acquired salience12,41. To investigate the role of ChCs in this process, the trace fear conditioning (TFC) was chosen because it is known to engage the mPFC42, which is critical for higher-order cognitive processes such as decision-making and associative learning43. We examined ChC response dynamics to conditioned stimuli (CS, predictive cues) and unconditioned stimuli (US, reinforcing stimuli) within a TFC paradigm, characterized by a trace interval between CS and US presentations (Fig. 6a). In this paradigm, mice were trained to associate a tone (CS), which had lost its novelty through habituation, with a foot shock (US), and the efficacy of learning was measured by the animals’ freezing behavior (Fig. 6b).

We first presented the tone on Day 0 (D0) to assess the habituation of ChC responses to the auditory stimulus prior to any learning (Fig. 6c, d). ChCs exhibited robust responses to the novel tone presentation, followed by a rapid reduction in activity as the tone became familiar through repeated exposure, in line with findings showed in Fig. 2. This habituation phase established a baseline of diminished ChC responsiveness to a non-reinforced, familiar cue.

During subsequent TFC session (in D1), ChCs initially exhibited subdued responses to the pre-exposed tone as the CS (Fig. 6e, f). However, their responses to the CS significantly increased following its association with the US (Fig. 6e–g), suggesting that associative learning enhanced ChC responses to detect the acquired salience of the CS. Notably, ChCs exhibited sustained responses to the US when it was part of the CS-US pairing during TFC, whereas their responses to unpaired US presentations diminished rapidly (Fig. 6e, h, and i). These findings indicate that learned associations enhance ChC responses to both predictive cues and reinforcing stimuli, enabling ChCs in the mPFC to encode acquired salience during associative learning.

To further investigate the contribution of AI-mPFC and PVT-mPFC inputs to ChC activity during associative learning, we combined fiber photometry recordings of ChCs with site-specific optogenetic inhibition of AI or PVT projections in the mPFC using eJaws (Fig. 7a–b and Supplementary Fig. 13). Mice underwent the same TFC paradigm as in Fig. 6, with conditioning performed under optogenetic inhibition (on D1) (Fig. 7b). During conditioning, red light (638nm) was delivered starting 3s before CS onset, continuing throughout the 5-s CS period, and resumed 3s before US onset lasting through the 2-s US. When light-induced inhibition was applied, suppression of AI or PVT projections (eJaws groups) prevented the expected increase in ChC responses to the CS observed in control groups (mRuby3), indicating impairment in the acquisition of acquired salience by ChCs through learned associations (Fig. 7c–h). Additionally, US-evoked responses, which are modulated by learned associations, were also significantly reduced in the inhibited groups (Fig. 7c–h). These results demonstrated that salience-related inputs from both AI and PVT are critical for the acquisition of acquired salience in ChCs during associative learning.

Furthermore, we found that optical inhibition of AI-mPFC and PVT-mPFC inputs impaired the learning efficacy of the mice behaviorally (Fig. 7i, j). However, this result could not distinguish whether impaired salience detection in ChCs affected learning efficacy or whether the reverse was true. To address this, we further explored the contribution of ChC-mediated salience detection to associative learning. We first disrupted ChC activity during TFC using an optogenetic approach. Natronomonas pharaonis halorhodopsin (NpHR) was virally expressed in bilateral mPFC ChCs, and dual-core fiber optic cannulas were implanted to enable optical inhibition of these cells (Fig. 8a, b and Supplementary Fig. 14a, b). Inhibition of ChC activity during the CS-US pairing phase significantly impaired learning efficacy, as evidenced by reduced freezing behavior, without affecting locomotor activity (Supplementary Fig. 14c–f). Furthermore, inhibition restricted to the CS and US stimulation period (Fig. 8c) was sufficient to suppress learning efficacy, confirming the essential role of ChC responses to stimuli in associative learning.

To directly bidirectionally manipulate the salience detection in ChCs, we employed chemogenetic modulation to alter their basal activity. Using designer receptors exclusively activated by designer drugs (DREADDs), hM3Dq (to enhance excitability) or hM4Di (to reduce excitability) was virally expressed in mPFC ChCs, along with co-expression of GCaMP7s to simultaneously monitor and regulate ChC activity (Fig. 8d, e and Supplementary Fig. 15a–m). Chemogenetic excitation of ChCs resulted in a significant reduction in tone-evoked Ca2+ signals (Fig. 8f and Supplementary Fig. 15n–s), while chemogenetic inhibition enhanced tone-evoked Ca2+ signals (Fig. 8g), suggesting that chronic ChC excitation suppresses stimulus salience detection, whereas inhibition enhances it. Then, we tested whether such manipulation of ChC salience detection during TFC modulated learning efficacy (Fig. 8h–j and Supplementary Fig. 16a, b). Bidirectional regulation of ChC basal activity during TFC resulted in opposing effects on associative learning. Chemogenetic excitation reduced freezing behavior during both learning and retention phases (Fig. 8i), while inhibition enhanced freezing behavior (Fig. 8j). Notably, these manipulations did not affect locomotor activity, ruling out general changes in behavior as a confounding factor (Supplementary Fig. 16c–g). Together, our results showed that bidirectional manipulations of ChC-mediated salience detection correspondingly led to improved or impaired learning efficacy in the trace fear conditioning.

Finally, to further evaluate the role of mPFC ChCs in associative learning beyond fear conditioning, we employed a reward-based associative learning task44, in which mice were trained to associate a tone (CS+) with a sugar water reward, while a control tone (CS−) was not rewarded. Learning efficacy was assessed by the discrimination between CS+ and CS−, as revealed by the animals’ licking behavior (Supplementary Fig. 17a–c). We first investigated the activity of mPFC ChCs during this reward conditioning task (Supplementary Fig. 17d–j). We found that ChCs maintained robust responses to the reward-predictive cue (CS+) across training sessions, while responses to the non-rewarded cue (CS−) gradually declined. This suggests that mPFC ChCs selectively sustain responses to behaviorally relevant stimuli while diminishing responses to less predictive cues, thereby supporting their role in encoding acquired salience during appetitive associative learning. Furthermore, bilateral optogenetic inhibition of ChC activity during reward conditioning significantly suppressed learning efficacy (Supplementary Fig. 18), further reinforcing the importance of ChC-mediated salience processing in associative learning.

Together, these findings demonstrate that mPFC ChCs encode acquired salience of external stimuli, facilitating associative learning. By dynamically modulating salience detection, ChCs in the mPFC play a critical role in integrating predictive and reinforcing stimuli to support learning and memory processes.

Methods
Animals
Animal care and the experimental protocols were approved by the Animal Ethics Committee of Shanghai Jiao Tong University School of Medicine and the Institutional Animal Care and Use Committee at the Department of Laboratory Animal Science under animal protocol number JUMC2023-099-A. The Unc5b-CreER mouse line (provided by Professor Z. Josh Huang, Duke University School of Medicine) was used to specifically label chandelier cells (ChCs). PV (parvalbumin)-Cre (Jackson stock no. 017320) mouse were obtained from the Dr. Siyu Zhang at Shanghai Jiao Tong University School. SST (somatostatin)-Cre (Jackson stock no. 013044) mouse were obtained from the Dr. Ju Huang at Shanghai Jiao Tong University School. All mice used in this study were on a C57BL/6J genetic background. Male mice aged 6 to 12 weeks were utilized. Mice were housed under standard conditions with temperatures of 18–23°C, humidity levels of 40–60%, and a 12-h light/dark cycle (lights on at 8:00 a.m. and off at 8:00 p.m.), with free access to food and water.

Viruses
AAV2/9-hSyn-DIO-jGCaMP7s-WPRE-pA (AAV-DIO-GCaMP7s, 3.35E+12v.g./mL), AAV2/9-hSyn-DIO-hM3Dq-GFP (AAV-DIO-hM3Dq-GFP, 1.69E+12v.g./mL), AAV2/9-EF1a-DIO-hM3Dq-mCherry (AAV-DIO-hM3Dq-mCherry, 5E+12v.g./mL), AAV2/9-hSyn-DIO-hM4Di-GFP (AAV-DIO-hM4Di-GFP, 6.4E+12v.g./mL), AAV2/9-EF1a-DIO-hM4Di-mCherry (AAV-DIO-hM4Di-mCherry, 5E+12v.g./mL), AAV2/9-EF1a-DIO-eNpHR-3.0-EYFP (AAV-DIO-NpHR-EYFP, 1.6E+12v.g./mL), AAV2/9-hEF1a-DIO-EYFP-WPRE-pA (AAV-DIO-EYFP, 2.5E+12v.g./mL), AAV2/9-hSyn-mCherry-P2A-TetTox-WPRE-pA (TetTox-mCherry, 1.25E+13v.g./mL), AAV2/9-hSyn-mCherry (5.0E+12v.g./mL), AAV2/9-mCaMKIIa-eJaws 3.0-mRuby3-WPRE-pA (eJaws-mRuby3, 1.72E+13v.g./mL), AAV2/9-mCaMKIIa-mRuby3-WPRE-pA (1.5E+13v.g./mL), AAV2/9-hSyn-hChR2(H134R)-EGFP-ER2-WPRE-pA (AAV-hSyn-ChR2-EGFP, 2E+12v.g./mL), all viruses were aliquoted and stored at −80°C until use.

Stereotaxic injection and fiber implantation
Adult mice were anesthetized with isoflurane (5% induction and 1.5% maintenance) and placed on a stereotaxic frame (RWD Life Science). The temperature was kept at 37°C throughout the procedure using a heating pad. After asepsis, the skin was incised to expose the skull, and the overlying connective tissue was removed. A craniotomy of approximately 0.5 mm in diameter was performed directly above the designated injection site.

The injection coordinates for the medial prefrontal cortex (mPFC) were as follows: antero-posterior (A/P): 2.0–2.3 mm to the bregma, medio-lateral (M/L): 0.25 mm, dorso-ventral (D/V): 1.25 mm from the pial surface. PVT: A/P −1.4 mm, M/L ±0.1 mm, D/V −3.0 mm; AI: A/P +1.0 mm, M/L ±3.0 mm, D/V −2.4 mm. Viruses were loaded in a sharp micropipette mounted on a Nanoject II attached to a micromanipulator and then injected at a speed of 60 nL/min. In fiber photometry recording experiments, an optic fiber (200 μm diameter, 0.37 numerical aperture, NA) was implanted in the designated locations. The fiber was then secured to the skull using dental cement. During the head-fixed experiments, both the head bar and the fiber were securely attached to the skull. Mice were allowed to recover for 3–4 weeks before the behavioral tests were administered. Post hoc verification was performed to confirm the accuracy of viral infection and optical fiber placement.

For optogenetic experiments, we bilaterally injected 300 nL of AAV-DIO-NpHR-EYFP or AAV-DIO-EYFP into the L2/3 mPFC of Unc5b-CreER mice. We then implanted dual-core fiber optic cannula (200 μm diameter, 0.37 NA, with 500 µm core spacing) 100 μm above the center of the viral injection site.

For chemogenetic activation or inhibition experiments, we bilaterally injected AAV-DIO-hM3Dq (or hM4Di)-GFP into the mPFC of Unc5b-CreER mice.

For dual-virus strategy in fiber photometry recording, the unilateral mPFC of Unc5b-CreER mice was simultaneously infected with AAV-DIO-GCaMP7s and AAV-DIO-hM3Dq (or hM4Di)-mCherry. Subsequently, an optic fiber (200 μm diameter, 0.37 NA) was implanted.

Tamoxifen induction
Mice were anesthetized with isoflurane and injected intraperitoneally (i.p.) with tamoxifen (TMX) dissolved in corn oil (Sigma-Aldrich) at 20 mg/mL. For almost all experiments, mice were injected with TMX (100 mg/kg to body weight) on days 1, 3, and 5 after virus injection. For the sparse labeling, mice were injected low dose (20 mg/kg to body weight).

Fiber photometry
Fiber photometry was used to monitor real-time Ca2+ dynamics from GCaMP7s-expressing neurons in head-fixed awake or freely moving mice. Before conducting behavioral tests, mice were gently handled by experimenters for 15 min and then accustomed to the fiber patch cord or experimental apparatus for at least 15 min per day over 3 days. The GCaMP signals were acquired using a fiber photometry system (Inper Ltd., China, or ThinkerTech., China). GCaMP signals were excited with a 470 nm LED (20–40 μW at the fiber tip), and a 410 nm LED at the same intensity was used to record Ca2+-independent reference signals. The LEDs operated in continuous-wave mode without frequency modulation or lock-in demodulation. A video camera (Logitech, C930c) was utilized for recording animal behaviors, coupled with a screen recorder to synchronize behavioral events with analog voltage signals of fluorescence for the further verification. The fluorescence signals and behaviors were recorded throughout the test session.

Photometry data processing and analysis
The raw data were first processed using the instrument’s software (Inper or ThinkerTech) for photobleaching and motion correction, followed by further analysis in MATLAB using custom scripts. (1) Photobleaching Correction: A second-order polynomial fitting was applied to correct slow baseline drifts caused by photobleaching. (2) Motion Correction: The 410 nm signal was linearly fitted to the 470 nm signal, and the fitted reference was subtracted to minimize motion-related fluctuations. (3) ΔF/F Calculation: The corrected 470 nm signal was normalized to a pre-stimulus baseline using the formula: ΔF/F=(Ft - F0)/F0, where Ft is the fluorescence at time t, and F0 is the baseline fluorescence, defined as the median value during a pre-defined window (1 s) prior to stimulus onset. (4) Z-score Calculation: The Z-scores were calculated using the formula (F - μ)/σ, where μ represents the mean of each trial period and σ is the standard deviation of the respective trial, with the trial period varying across different experimental tests (12–35 s). The Z-score quantified how far a data point deviated from the mean in terms of standard deviations. (5) Trial Averaging and Quantification: The quantification of neuronal response to the stimulus involved the integration of the area under the curve (AUC) of Z-scores within the initial 2 s following the stimulus onset. This pipeline was applied consistently across all experiments unless otherwise noted.

Administering multiple stimuli to freely moving mice
All animals reported here for water or 10% sucrose testing was subjected to a water restriction regimen: initially, water was withheld for 2–3 days until the mice’s body weight dropped to between 85% and 90%, followed by daily water supplementation in moderate amounts (1–1.5 mL) to maintain their weight within this range. In the event of a sustained decrease in the body weight of the mice, they were promptly granted unrestricted access to water.

Mice were allowed at least 5 min to habituate to the test cage before recording. In the novel object interaction experiment, mice were placed in a standard housing cage with a novel object (plastic clip) placed in the center of the cage. In the experiment involving exposure to 2-methyl-2-thiazoline (2-MT, a predator odor analog), a paper strip containing 5 µL of 2-MT (10%) was placed into the cage, with mice approaching or sniffing it marking the onset of the trial. Object interactions and odor sniffing were hand-scored with EthoVision software. For the free consumption of water experiment, a spout was inserted into a hole in the wall of the cage during the recording, allowing the mice to consume water. The trial onset was marked by the first lick and concluded when the mice ceased drinking. Neutral tone (8 kHz, 60 dB, 2 s) and electric foot shock (0.6 mA, 2 s) were delivered by the standard fear conditioning shock box (UGOBASILE SRL, Italy). Visual flash presentations were delivered by the monitor controlled by MATLAB. These tests were conducted on different days. Most experiments were performed while the animals were in a relatively quiescent state, with low running speeds during stimulus presentation.

Assessing Ca2+ dynamics during spontaneous locomotion in freely moving mice
To examine the relationship between Ca2+ activity and spontaneous locomotion, fiber photometry recordings were performed while mice freely explored an open-field arena (50×50 cm). Locomotion was tracked using an overhead camera and analyzed with ANY-maze software. Ca2+ signals were z-scored and aligned to movement onset. Movement speed was calculated frame-by-frame, and trials were defined as 3-s movement bouts separated by at least 10 s of immobility. For each trial, Ca2+ signals and locomotor speed were analyzed within a fixed 2-s window following movement onset. Area under the curve (AUC) was calculated for both z-scored Ca2+ signals and speed values within this window. Pearson’s correlation coefficients (R) were computed between speed and Ca2+ activity across trials. To further assess speed dependence, trials were grouped into low-, medium-, and high-speed categories based on tertile binning of speed AUC values. Specifically, the distribution of speed AUCs was divided using the 33rd and 66th percentiles, and trials were classified accordingly. Group comparisons of Ca2+ activity was then conducted using appropriate statistical tests depending on data distribution.

Administering multiple stimuli to head-fixed water-restricted mice
All experimental animals underwent initial handling and acclimatization to the soundproof chamber (dark) and the custom head-fixation apparatus for 3 consecutive days. After the mice had been positioned within the head-fixation apparatus, the fiber photometry recording was initiated. Following a 2 min baseline period, the testing phase commenced. The testing comprised four segments: tone, sucrose, air puff, and shock. A 3–5 min interval separated each segment. Within each segment, there were 10 trials, with the stimulus interval between each trial randomly fluctuated between 20 and 40 s, decided using a pseudorandom number generator. Behavioral tests were typically ordered from neutral or positive to negative valence. The stimuli, comprising tone (8 kHz, 60 dB, 2 s), sucrose (10%, 5 μL, 100 ms), air puff (40 psi, 100 ms), and shock (0.6 mA, 1 s), were precisely administered using an Arduino Uno board and a self-developed MATLAB program. Water or sucrose were supplied by a custom-made metal lickport placed in front of the mouse mouth. The lickport was connected to a capacitive-sensing board that detects the contact of the tongue during licking. The amount of water delivered was controlled by valves open time calibrated at least once a week. The fluid pressure was similar for each behavioral session.

To directly compare responses to familiar versus novel auditory cues, we conducted an independent experiment in which head-fixed mice were sequentially exposed to two distinct tones. Tone A (3 kHz, 2 s) was delivered 10 times, followed by tone B (12 kHz, 2 s) delivered 10 times, with a 3–5 min inter-block interval. Each trial was separated by a pseudorandomized inter-trial interval (20–40 s). Photometry recording procedures and the head-fixation setup were consistent with those described above. This design enabled within-session comparison between familiar and novel tone responses, addressing adaptation specificity.

Stimulus intensity tests under head-fixation
For reward-based intensity paradigms, fiber photometry recordings were performed in head-fixed, water-restricted mice. The water volume test spanned three consecutive days, each consisting of a single session. Fiber photometry recordings were conducted during session 1 (S1) and session 3 (S3). Mice were head-fixed in a custom apparatus, and recording began with a 2-min baseline. Each testing session included 100 trials, with 20 trials allocated to each of the following water volumes: 2, 4, 7, 10, and 15 μL. Trial order was randomized using MATLAB’s randperm function to ensure equal distribution and prevent repetition. Inter-trial intervals were pseudorandomly set between 8 and 15 s. Fluid delivery was controlled via calibrated solenoid valves, and licking behavior was monitored using a capacitive touch-sensing circuit. A variant of this paradigm involved presenting the five water volumes (2, 4, 7, 10, 15 μL) in blocks, each containing 20 consecutive trials of the same volume (100 trials per session in total). The block order was randomized across sessions, maintaining constant water volume within each block but varying it between blocks.

In a separate experiment, mice were presented with white noise bursts at varying sound intensities to assess auditory response scaling. A total of 60 trials were conducted in randomized order, consisting of 20 trials each at 60, 80, and 100 dB (duration: 60 ms). A 2-min baseline preceded stimulus delivery. Inter-trial intervals were set between 20 and 40 s in a pseudorandom fashion. All photometry procedures and hardware configuration were consistent with those described above. Data were later segmented into early (first 20 trials) and late (last 20 trials) stages for correlation and response amplitude analyses.

Trace fear conditioning
All trace fear conditioning (TFC) and retention tests were conducted using the Ugo fear conditioning system. The delivery of tones and foot shocks was automated using ANY-maze software (version 6.31, Stoelting Co., USA).

Two different contexts were used in TFC. Context A featured black-and-white checkered wallpapers, and the chambers were cleaned with 75% ethanol. Context B included dark gray wallpaper and a non-shocking plexiglass floor of gray coloration, and the chambers were cleaned with a 4% acetic acid solution. In the first day (D1), the test animal was placed in Context A, and after 3 min of habituation, they received six tones (conditioned stimulus, CS)—shock (unconditioned stimulus, US) pairings, distributed at variable intervals of 90 ± 30 s. The electrical foot shock (0.6 mA, 2 s) was delivered 13 s after the end of the pure tone (60 dB, 4 kHz, 5 s). The empty trace interval (13 s) interposed in each CS-US pairing. In the second day (D2), mice were placed in Context B for 3 min and subsequently given four pure tone presentations (as the same as those in D1) without any shocks.

For fiber photometry, there was a separate day (D0), CS habituation stage, where the mice were placed in Context A for 3 min and subsequently given six tone presentations without any shocks. To assess the specificity of ChC responses to different associative learning components, three independent groups of mice were used, each receiving only one of the following paradigms on Day 1: (1) CS-US paired, in which a 5-s tone (CS) was followed by a foot shock (US) after a 13-s trace interval; (2) CS only, in which the tone was presented without any foot shock; and (3) US only, in which mice received unpredicted foot shocks without any preceding tone. Each animal was assigned to only one paradigm. The order of stimuli within each group was pseudorandomized to avoid temporal expectation effects.

In TFC, animals exhibited conditional responses to the CS offset (trace interval) in addition to the CS itself. A fear response was operationally defined as measurable behavioral freezing (more than 1-s cessation of movement), which was automatically scored and analyzed by ANY-maze software. In our experiments, animals connected to an optical fiber to the head, light stimuli during test sessions might interfere the program’s freezing detection. To further validate the automatic software detection, the freezing behavior was scored independently for each video by an experienced experimenter in a double-blind manner. The time spent freezing during the CS and trace was measured for each trial.

Reward learning paradigm
Mice were trained in a Pavlovian appetitive conditioning task over 8 daily sessions. Each session included 100 trials: 50 CS+ trials (auditory tone paired with a 5 μL drop of 10% sucrose solution delivered 1 s after cue offset) and 50 CS− trials (auditory tone without reward). Each auditory cue was a 2-s pure tone (3 kHz or 12 kHz at 60 dB), with cue identities (CS+ vs. CS−) counterbalanced across animals. A 1-s trace interval followed the cue in both trial types. Trials were pseudorandomized within each session, with a variable inter-trial interval (10–20 s). To assess cue-evoked responses in the absence of reward, a no-reward (NR) session (day 9) was conducted following training. In the NR session, mice received 20 CS+ and 20 CS− trials, but no sucrose reward was delivered. Licking behavior was recorded using a capacitive lick detection system. To quantify behavioral discrimination between CS+ and CS− trials, we computed a cue discrimination score based on anticipatory licking. For each trial, the anticipatory licking rate during the cue period (0–3 s from CS onset) was baseline-corrected using the licking rate in the 1 s window prior to cue onset (–1 to 0 s). The corrected licking rates from CS+ and CS− trials were compared using receiver operating characteristic (ROC) analysis to calculate the area under the curve (auROC). The final discrimination score was defined as:

Discrimination score = 2 × auROC − 1

This value ranges from −1 to +1, with +1 indicating perfect discrimination (more licking in CS+), 0 indicating no discrimination, and −1 indicating reversed discrimination.

Optogenetic manipulations during behavior
For optogenetic inhibition, yellow light (594 nm, pure light, not pulsed) was bilaterally delivered to layer 2 of the mPFC in Unc5b-CreER mice during behavioral tasks. Light delivery was synchronized with specific experimental events via the intelligent optogenetic system, with final output power ranging from 8 to 10 mW depending on the light transmission efficacy of the implanted optical fiber. The ANY-maze software controlled light onset via a TTL cable to trigger the modulation generator.

During trace fear conditioning, photoinhibition was applied in multiple ways: (1) for a total of 20 s, starting at CS onset and continuing through the 2 s US presentation; (2) specifically for a 7 s window covering each CS-US pairing (5 s CS + 2 s US), to test for temporally precise inhibition effects.

During reward learning, optogenetic inhibition was performed using a 594 nm yellow light (8–10 mW, pure light, not pulsed) in animals expressing NpHR or EYFP. In both CS+ and CS− trials, light was delivered continuously for 5 s starting at cue (CS) onset, covering the 2-s auditory cue, the 1-s trace interval, and the 2 s following expected reward (US) onset (total 5 s per trial). Control animals underwent identical surgical procedures and received the same light delivery protocol, but expressed only the control virus. This light stimulation protocol was applied throughout all 8 training sessions. On the final day (day 9), a no-light (NL) session was conducted under the same behavioral conditions, including 50 CS+ and 50 CS− trials, but with the optogenetic inhibition omitted. This allowed for assessment of behavioral performance in the absence of ongoing light manipulation following the full course of training under inhibition.

During the open field test, mice underwent a 15-min session divided into three consecutive 5-min periods: an initial light-off phase, followed by yellow light (594 nm, pure light, not pulsed) displayed for 5 min to inhibit NpHR-expressing neurons, and concluding with a final light-off phase. In a separate cohort, an intermittent stimulation protocol consisting of 20 s light on and 20 s light off repeated over a 10-min period was applied. Specifically, yellow light (594 nm) was used to inhibit neurons expressing NpHR, while red light (638 nm, 5 mW, pure light, not pulsed) was used to inhibit neurons expressing eJaws. Both eJaws and mRuby3 control groups received the same light protocol. This intermittent protocol was employed to assess the effects of prolonged light exposure while minimizing potential nonspecific or phototoxic effects. All control animals were injected with control viruses and underwent identical surgical procedures and light stimulation protocols.

The fiber photometry system (ThinkerTech, Nanjing, China) allows for simultaneous recording and optogenetic manipulation of long-range input terminal. To simultaneously record mPFC ChC activity and optogenetically inhibit anterior insular cortex (AI) or paraventricular thalamus (PVT) neuron terminals in the mPFC, AAV-DIO-GCaMP7s was injected into the mPFC, while AAV-mCaMKIIa-eJaws-mRuby3 or a control virus was injected into the AI or PVT. Optical fibers (200 μm diameter, 0.37 NA) were then implanted in the mPFC for recording and manipulation. To inhibit AI or PVT terminals in the mPFC, red laser light (638 nm, 5 mW, pure light, not pulsed) was delivered through the same fiber used for photometry. An Arduino-based controller triggered laser onset 3 s before stimulus delivery and extended inhibition to 5 s after. For unpredicted water delivery trials, inhibition persisted for 5 s after the first detected lick, if any occurred within 10 s of delivery.

For photoinhibition during trace fear conditioning, laser stimulation was applied to suppress AI or PVT terminals in the mPFC in a temporally precise manner. During Day 1 of conditioning, laser illumination was precisely timed relative to stimulus events. Specifically, light was turned on 3 s before CS onset and maintained throughout the 5-s CS duration, and was turned off immediately at CS offset. Subsequently, light was turned on again 3 s prior to US onset and remained on during the 2-s US presentation, terminating at US offset. No photoinhibition was applied during habituation (Day 0) or retention testing (Day 2). Control animals received the same viral injection surgery (AAV-hSyn-mRuby3) and underwent identical light delivery procedures to control for nonspecific effects of illumination. To prevent crosstalk between photometry and optogenetic channels, the system employed an integrated hardware design combining frequency-division multiplexing, spectral separation, and dedicated detection pathways. The GCaMP excitation light (470 nm) was sinusoidally modulated at a unique frequency (~2963 Hz), and lock-in amplifiers selectively demodulated signals at this frequency, effectively rejecting contamination from the constant 638 nm laser used for optogenetic inhibition. High-performance multi-band dichroic mirrors and emission filters were used to physically separate the excitation and emission spectra, minimizing red light interference. Fluorescence signals were collected through the same optical fiber, spectrally split, and detected by photomultiplier tubes (PMTs) configured for green and red emission bands, with only the green channel analyzed in this study. This configuration enabled high signal-to-noise recordings of Ca2+ dynamics during temporally precise optogenetic perturbations.

Measuring ChC responses to spontaneous activity and tones under chemogenetic manipulation
To assess the effects of chemogenetic activation or inhibition on mPFC ChC activity, Unc5b-CreER mice were injected with AAV-DIO-GCaMP7s and either AAV-DIO-hM3Dq-mCherry or AAV-DIO-hM4Di-mCherry. After recovery and habituation to the head-fixation setup, experiments were conducted under two conditions: spontaneous activity and stimulus-evoked responses.

To evaluate changes in spontaneous ChC activity, a separate set of sessions was conducted in which no external stimuli were delivered. Mice received an intraperitoneal injection of CNO (5 mg/kg body weight) or saline, and Ca2+ signals were recorded continuously. Data from the baseline (0–3 min post-injection) and post (40–43 min) time windows were extracted for within-subject comparisons of baseline and post-injection activity in the absence of sensory stimulation. In addition, a control cohort was injected with AAV-DIO-GCaMP7s and AAV-DIO-mCherry (without hM3Dq/hM4Di) to evaluate potential off-target effects of CNO on ChC activity.

For tone-evoked responses, the experiment consisted of two stimulus blocks separated by a 40-min interval. Each block included six presentations of a pure tone (8 kHz, 60 dB, 2 s duration) delivered at pseudorandomized inter-trial intervals of 20–30 s. The first tone block began at 2–3 min after CNO injection, and the second block was delivered at 42–43 min post-injection. Fiber photometry signals were recorded continuously throughout the session to capture cue-evoked responses before and after chemogenetic manipulation.

Chemogenetic manipulations during behavior
Unc5b-CreER mice with bilateral infection of AAV-DIO-hM3Dq (or hM4Di)-GFP in the mPFC were employed. The behavioral tests were administered when mice recovered for 3–4 weeks after viral injection. Clozapine-N-Oxide (CNO, Sigma) was dissolved in saline (0.9% NaCl solution) to a stock solution of 20 mg/mL stored at −20°C. For the chemogenetic experiments, mice were divided into two groups: CNO and saline. For mice in the CNO group, CNO (5 mg/kg to body weight, i.p.) were injected 40 min before the test. In contrast, mice in the saline group were injected with an equivalent volume of saline (i.p.) 40 min before the test.

Measuring ChC responses to tone under chemogenetic manipulation of ChCs
The mPFC ChCs were simultaneously infected with AAV-DIO-GCaMP7s and AAV-DIO-hM3Dq-mCherry (or hM4Di) in Un5b-CreER mice. After the mice were positioned within the head-fixation apparatus and allowed to habituate, the experiment commenced. The experiment consists of two segments separated by a 40 min interval. The first segment of stimuli consisted of six pure tones (8 kHz, 60 dB, 2 s) with intervals of 20–30 s was administered at 2–3 min following injection of CNO (5 mg/kg body weight, i.p). Subsequently, the same tone presentation was applied as in the first segment at 42–43 min post-injection. Fiber photometry signals were recorded continuously throughout the session to capture cue-evoked responses before and after chemogenetic manipulation.

Locomotion test under manipulations of ChCs
A 50×50 cm plastic open field chamber was used. Mouse movements were tracked with ANY-maze software. Chemogenetic manipulation of ChCs involved injecting mice with CNO or saline. Forty minutes later, the mice were placed in the chamber for a 15-min test. For optogenetic manipulation of ChC cells, the mouse test was divided into three 5-min stages, with only the second stage involving yellow light stimulation.

Slice preparation and electrophysiological recording
Mice (P40-P60) were anesthetized with 5% isoflurane before decapitation. The dissected brain was rapidly immersed in ice-cold, oxygenated, artificial cerebrospinal fluid (section ACSF: 110 mM choline chloride, 2.5 mM KCl, 4 mM MgSO4, 1 mM CaCl2, 1.25 mM NaH2PO4, 26 mM NaHCO3, 11 mM D-glucose, 10 mM sodium ascorbate, 3.1 sodium pyruvate, pH 7.35, 300 mOsm) for 1 min. Coronal prefrontal cortical slices were sectioned at 300 µm thickness using a vibratome (VT1200S, Leica) at 1–2°C and incubated with oxygenated ACSF (working ACSF: 124 mM NaCl, 2.5 mM KCl, 2 mM MgSO4, 2 mM CaCl2, 1.25 mM NaH2PO4, 26 mM NaHCO3, 11 mM D-glucose, pH 7.35, 300 mOsm) at 34°C for 30 min, and then transferred to ACSF at room temperature (25°C) for >30 min before use. Patch pipettes were pulled from borosilicate glass capillaries with filament (1.2 mm outer diameter and 0.69 inner diameter; Warner Instruments) with a resistance of 3–6 MΩ. The pipette recording solution consisted of 147 mM potassium gluconate, 3 mM KCl, 10 mM sodium phosphocreatine, 10 mM HEPES, 4 mM ATP·Mg, 0.3 mM Na2GTP, and 0.3 mM EGTA (pH 7.25 adjusted with KOH, 292 mOsm). All recordings were performed at 30–32°C with the chamber perfused with oxygenated working ACSF. Data were processed and exported by Clampfit v10.7 (Molecular Devices). Dual whole-cell patch recordings were performed in mPFC with one fluorescent protein-positive ChC and one nearby pyramidal cell identified by its triangle cell body. After 5 min of baseline recording, 10 μM CNO was perfused into the recording chamber for chemogenetic-activating or inhibiting ChCs.

For circuit mapping, AAV-hSyn-ChR2-EGFP was injected into the anterior insular cortex (AI) or paraventricular thalamus (PVT) of Unc5b-CreER mice, and AAV-DIO-mCherry was injected into the mPFC to label ChCs. After 3–4 weeks, acute mPFC slices were prepared, and whole-cell patch-clamp recordings were obtained from mCherry-positive ChCs. ChR2-expressing axon terminals in the mPFC were stimulated with brief blue light pulses (1 ms, 473 nm). Postsynaptic responses were recorded sequentially under the following pharmacological conditions: ACSF (baseline), tetrodotoxin (TTX, 1 μM), TTX+4-aminopyridine (4-AP, 100 μM), and TTX+4-AP+NBQX (50 μM). Evoked EPSPs from AI terminals persisted in TTX+4-AP, and were blocked by NBQX, confirming monosynaptic AMPA receptor-mediated transmission. In contrast, PVT-evoked EPSPs showed longer onset latency and were abolished by TTX, suggesting polysynaptic transmission. EPSP amplitudes and latencies were quantified across conditions.

To validate the efficacy of optogenetic inhibition, whole-cell current-clamp recordings were obtained from pyramidal neurons expressing Jaws. Red laser light (638 nm) was delivered through the objective using a range of intensities (3%–96%, corresponding to a maximum of ~1 mW at the sample). Membrane hyperpolarization was quantified under increasing light intensities. To assess spike suppression, depolarizing current (~150 pA) was injected to evoke spiking, and firing rates were compared with and without light stimulation.

Histology and imaging
Animals were anesthetized with isoflurane and transcardially perfused with phosphate-buffered saline (PBS) followed by ice-cold 4% paraformaldehyde (PFA) in PBS. The brain was taken out and post-fixed overnight at 4°C in 4% PFA in PBS. Coronal brain slices (50 μm thickness) containing mPFC were cut with a vibratome (VT1000S, Leica). Brain sections were washed in PBS, soaked in 4% PFA for 10 min, washed again in PBS, and then blocked for 2 h at 37°C in PBS-T (0.2% Triton X-100 in PBS) containing 10% donkey serum. Tissue sections were incubated with primary antibodies (1:500, rabbit anti-green fluorescence protein (GFP), A11122, Invitrogen; goat anti-mCherry, AB0081-200, SICGEN; mouse anti-VAMP2, 104211, Synaptic Systems) for 2 h at room temperature, overnight at 4°C. After rinsing with PBS, the sections were incubated with secondary antibodies (1:500, donkey anti-rabbit Alexa 488-conjugated antibody, A32790, Invitrogen; donkey anti-goat Alexa 594-conjugated antibody, A11058, Invitrogen; donkey anti-mouse Alexa 647-conjugated antibody, A-31571, Invitrogen). Incubate with secondary antibody for 1 h at room temperature and wash with PBS. Finally, the brain slice was attached to the slide and sealed with anti-fluorescence quenching sealing liquid (including DAPI; Beyotime Bio-tech).

Whole brain slice images were acquired with a slide scanner (VS200, Olympus). The detail images were captured using a confocal microscope (FV3000, Olympus). Anatomical regions were identified according to the Mouse Brain in Stereotaxic Coordinates by Franklin and Paxinos and the Allen Institute Mouse Brain Atlas.

Quantification of VAMP2 fluorescence intensity
To assess the relative level of presynaptic protein VAMP2 in AI or PVT terminals within the mPFC, we performed quantitative fluorescence analysis on immunostained brain sections. Raw confocal images were first background-subtracted using ImageJ. Pixel intensities were normalized across the image using the formula:

Normalized intensity = (pixel intensity − minimum intensity) / (maximum intensity − minimum intensity)

ROIs corresponding to axonal terminals were manually defined based on mCherry fluorescence. The mean normalized fluorescence intensity of VAMP2 within each ROI was measured. For cross-condition comparison, we calculated the relative mean fluorescence intensity by dividing each ROI’s mean intensity (TetTox or Control) by the average intensity of all Control ROIs from the same experiment.

Statistics and reproducibility
All statistical analyses were conducted using OriginPro 2023 Learning Edition (OriginLab) and custom-written MATLAB R2023b (MathWorks) scripts. Statistical tests were selected based on the normality of data distribution (assessed via the Shapiro–Wilk test) and the experimental design (paired or unpaired). For normally distributed data, two-sided Student’s t-tests—either paired or two-sample (unpaired)—were applied as appropriate; for non-normally distributed data, the Wilcoxon signed-rank test (paired, two-sided) or Mann–Whitney test (unpaired, two-sided) was used. Repeated-measures data were analyzed using one-way or two-way repeated-measures ANOVA when assumptions of normality and sphericity were met. Sidak’s post hoc test was used to correct for multiple comparisons (two-sided), and all reported p-values reflect adjusted significance levels. All tests were two-tailed unless otherwise stated. Effect sizes were reported as Cohen’s d for parametric tests and as a median-based effect size (median difference divided by pooled MAD) for non-parametric tests. Statistical power for parametric tests was estimated using MATLAB’s sampsizepwr function. Significance thresholds were defined as *P<0.05, **P<0.01, ***P<0.001, and ns for not significant. Asterisk notation (*, **, ***) is used to indicate significance in group comparisons, whereas Pearson’s R and P values are reported directly for correlation analyses. Data are presented as mean ± SEM unless otherwise noted. Open symbols represent data from individual mice, and filled symbols represent group means. Each line linking two open circles represented the data from an individual sample. Box plots indicate the first and third quartiles (box), the median (horizontal line within the box), the mean (black horizontal line), and the data range (whiskers). Sample sizes were based on prior literature, and the number of replicates (n) is specified in the text or figure legends. As all statistics were carried out at 95% confidence intervals, a significant threshold of P<0.05 was used in all analyses. Comprehensive statistical details for all quantified experiments, including the experiment identifier, statistical test used, test statistic, degrees of freedom (df), exact P value, effect size, statistical power, and normality assessment, are provided in Supplementary Data 1.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Zhang, Shao, Kong et al. present an interesting study analyzing the role of chandelier cells (ChCs) in the medial prefrontal cortex (mPFC) in encoding salience information and influencing associative learning. Using Unc5b-CreER mice with tamoxifen administration, the authors selectively target and manipulate ChCs in the mPFC. Through a combination of a fiber photometry recording of ChCs’ activity, optogenetics, chemogenetics, and circuit-specific manipulation in this transgenic mouse, the authors demonstrate the following key findings:\n1) Compared to PV and SST interneurons, ChCs are selectively responsive to stimulus salience.\n2) ChCs were activated by novel stimulus.\n3) ChC responses scale with the intensity of familiar stimuli (volume of water reward).\n4) Abolishing synaptic outputs from AI or PVT neurons disrupts salience detection by ChCs.\n5) Inactivation of AI or PVT synaptic outputs similarly abolishes ChC salience detection.\n6) ChCs acquire salience-coding property for behaviorally relevant stimuli through associative learning.\n7) Bidirectional manipulation of ChCs’ activity leads to improvement and impairment of fear conditioning.\nThe manuscript thoroughly examines ChC activity across different contexts—novelty, intensity, and associative learning—and links these dynamics to behavioral outcomes. The findings position mPFC ChCs as key circuit elements in the salience processing for detection and utilization of salience information in learning and behavior. In its revised form, this manuscript could provide meaningful insights into salience processing and the role of ChCs in cognitive functions.\nOverall, this study addresses an important role of the ChCs in salience processing and learning. Despite its strengths, some of the key assumptions are not sufficiently validated experimentally, and some results lack critical details. These limitations hinder the clear interpretation of the authors’ main claims. With major revisions to improve clarity and data presentation, this manuscript could make a valuable contribution to the field of neuroscience, with implications for both basic research and translational studies in neuropsychiatric disorders.\nBelow, I offer detailed comments for the authors to consider.\nMajor Concerns:\n- Inconsistent Use and Definition of Salience-Related Terms:\nThe authors frequently use terms related to salience (e.g., stimulus salience, salience processing, novelty-based salience detection, Familiarity-based salience detection, intensity-based salience detection, task-driven salience, acquired salience, the perceived salience) without clear definitions or distinctions. The lack of clarity undermines the interpretation of ChC contributions to salience processing. Salience generally refers to the physical distinctiveness of an object or a stimulus and behavioral relevance, yet the manuscript does not clearly define how distinctness is operationalized. For example, the definition of novelty is unclear and not consistent. In Fig 2, novelty is based on the first stimulus presentation (description of familiar responses is missing), whereas in Fig 3, it is determined by experimental stage rather than trial.\nInstead of using one stimulus for each modality, the author should consider adding control stimuli to allow comparisons between two stimuli (e.g., familiar tone A vs. novel tone B, or familiar odor A vs. novel odor B) to strengthen their conclusions.\n- Comparison between PV and ChC Responses:\nIn Fig 1c and EDF3, optic fiber placements appear to precisely target L1 and L2 of the mPFC. Since ChCs are a subset of PV cells, I expected that responses of PV cells in these layers somewhat resemble those of ChCs. However, they do not for Tone and Water stimuli. A clear explanation of response differences between PV and ChC is necessary to support the claim that ChCs play unique role in salience processing.\n- Movement-Related Confounds in ChC Activity:\nIn recent ChC literature (Schneider-Mizell et al., 2021; Dudok et al., 2021; Jung et al., 2023), ChCs’ activity is often correlated with movement speed. Although the authors mentioned that ChCs exhibited a high level of spontaneous activity in freely moving animals and ChCs were robustly activated by all tested stimuli, they do not address potential confounds from movement-related activity. It would be valuable to analyze the relationship between movement speed and ChC, PV, and SST activity to disentangle sensory-driven responses from movement-related inputs.\n- Distinction Between Novelty and Intensity Coding:\nThe manuscript presents compelling data showing that novelty drives ChC responses in S1, while stimulus intensity modulates responses in S3, suggesting evidence of neuronal adaptation of ChC activity. However, several factors may confound this interpretation:\n1) The authors used only water reward for their conclusion. It is possible that intensity of tone or shock does not reliably drive scaled responses in S3. To claim that stimulus intensity is a key factor to drive the adaptation in ChCs, additional testes across modalities (e.g., tones, shocks) that are less influenced by motivational factors would strengthen the conclusions.\n2) Just 2-3 days of water deprivation may differentially affect motivation level for water reward between S1 and S3, influencing ChC responses.\n- Circuit-Specific Manipulations of AI and PVT outputs:\nRegarding circuit-specific dissection focusing on AI and PVT, background and rationale for focusing on AI and PVT circuits is not well-articulated. The author should provide more detailed background, rationale on the unique properties and connectivity of these circuits with ChCs in mPFC to understand their interplay.\nThe authors used TetTox to block AI or PVT outputs. However, this approach raises two concerns: 1) The hSyn promotor targets not only excitatory neurons but potentially other types of neurons. 2) Blocking AI or PVT outputs are not limited to inputs to ChCs in mPFC. It may block AI or PVT outputs to other brain regions rather than L1-2 mPFC. To support the author’s claim precisely, evidence of monosynaptic connectivity between excitatory neurons in AI or PVT and ChCs is needed, along with manipulation of AI or PVT activity limited to these connections. While optogenetic inhibition of AI or PVT axons in mPFC partially addresses this in Fig 5, as shown in Fig 5, their axonal projections extend beyond L1. Thus, showing monosynaptic connectivity between excitatory neurons in AI or PVT and ChC and the effect of optogenetic inhibition of AI or PVT inputs to ChCs (similar to EDF7) would be needed.\n- Trace Fear Conditioning (TFC) Paradigm: I have several comments regarding Trace Fear Conditioning paradigms and results.\n1) It would be helpful for readers if authors can provide rationale why TFC paradigm was chosen instead of classical fear conditioning.\n2) Why was the tone present on D0 for fiber photometry recording? It would be informative to compare response to tone between D0 and D1 in a trial-by-trial manner.\n3) How CS-US, CS only, and US only were presented on D1 is not clear. The authors need to describe clearly how each condition was conducted. Were all three CS-US, CS only, and US only conditions tested within subjects on D1? If so, how was the order of conditions determined?\n4) The increasing responses of ChCs to CS in CS-US condition are very compelling to show the involvement of ChCs in learning. To precisely support the role of ChCs in encoding acquired salience, comparing responses to a second, distinct CS (e.g., a different tone) would be critical.\n5) Furthermore, similar to Fig 6, manipulating AI or PVT inputs to mPFC during TFC and measuring their effects on ChCs responses to CS could elucidate the role of AI or PVT circuit in acquired salience.\n- Discussion: While the discussion effectively integrates the findings with existing literature, it tends to overstate the role of ChCs as central hubs for salience processing. The authors should adopt a more cautious tone, acknowledging the complex interplay between ChCs, other interneuron types, and mPFC circuits. Moreover, potential compensatory mechanisms in ChC-deficient conditions and their broader behavioral relevance are not sufficiently addressed. Including more nuanced perspectives on how ChCs interact with local and long-range circuits would provide a balanced interpretation. Additionally, the discussion would benefit from highlighting limitations such as the focus on specific brain regions without considering global network dynamics.\nMinor Concerns:\n● While the data presentation is comprehensive, more explicit details on methods would strengthen the rigor of the analyses.\n● In line 52, consider using em dashes instead of commas for “most behaviorally relevant—or salient—stimuli” to enhance readability.\n● Extended Data Fig 4c-d. Should the legend for S1 or S3 reference Figure 2 instead of Figure 3?\n● Fig 3m. missing symbol for the S1 legend.\n● Extended Data Fig 6. Clarify the difference between the left and right panels.\n● Extended Data Fig 6. \"Tettox\" should be corrected to \"TetTox.\"\n● Extended Data Fig. 6. Clarify how normalized mean fluorescence intensity was calculated.\n● In line 303, Remove unnecessary spaces after citations to maintain formatting consistency.\n● Fiber Photometry:\n- Since only GCaMP was used, revise terms like “two-color” or “dual-color” fiber photometry to avoid confusion.\n- As fiber photometry data represent the primary neural readout, provide detailed information about the photometry setup – particularly how photometry signals were effectively separated from 638 nm light during optogenetic inhibition experiments. Additionally, include detailed data analysis procedures such as signal preprocessing, motion correction, photobleaching correction, and any relevant information.\n● For figure legends, ensure that the legend for “ns” (not significant) is included and remove descriptions of asterisks (*) that are not used in the figures.\n● In Fig 7d-f, include a comparison between CNO and saline conditions to support the findings\n• In Fig 7h-j, it is unclear whether AAV-DIO-GFP was used as the control. Given relatively large variance in the hM4Di-saline group in Fig 7j, a comparison between the appropriate control group (AAV-DIO-GFP) and AAV-DIO-hM3Dq(hM4Di) group is necessary to strengthen the conclusions.", "reviewer_2": "Zhang and colleagues use fiber photometry and opto/chemogenetic manipulation strategies to examine the encoding properties and function of PFC chandelier cells on salience detection and associative fear learning. The experiments are thorough, with the exceptions of not including an appetitive associative learning task and only including male subjects. The conclusions are interesting but are quite overstated, as the authors did not have single-cell resolution during imaging and the effects of the presynaptic manipulations on salience detection are questionable at best (other interpretations are just as fitting).\nWhile I do have significant concerns, I also think that the findings are novel and will be very useful for the field. I recommend that the authors significantly dial back their conclusions, better integrate past literature on PVT, AI, and most importantly PFC single-cell dynamics in the context of salience detection, associative learning, behavioral inhibition (in regards to input circuitry) and circuits. The authors should provide better alternative interpretations including those I mention below throughout the manuscript.\nMajor Concerns:\n1. The authors are using fiber photometry to infer how bulk populations of interneurons respond to various stimuli. They make the conclusion that ChCs respond to novelty/salience, but there are significant caveat interpretations to this idea. Most notably, others in the field have shown that PFC excitatory neurons in general can encode many types of distinct stimuli, yet single-cell resolution imaging demonstrates that such effects are attributed to unique populations of neurons (e.g., 34184635) Thus, there is divergent encoding by ensembles that overall could seem like a salience signal if visualized through a bulk signal. The same could be true here. Results could simply be due to distinct ChCs that respond to aversion vs reward and so on, and that resolution would be completely lost with the current approach.\nThis limitation needs to be adequately addressed within the manuscript. First, the authors need to cite relevant literature discussing the encoding dynamics of prefrontal (including prelimbic, studied here) neurons at a single cell level, which has been shown to be highly heterogeneous at the level of excitatory neurons. The authors also need to expand discussion of how this circuitry influences learning, including through its interactions with the AI and PVT at the level of computationally diverse neuronal ensembles (e.g., PMIDs: 34913438; 25600268; 38747563; 34637750; 34184635; 28225752, 31196673, 31160741; 39415234).\nSecond, the authors need to thoroughly discuss the potential that diverse populations of ChCs could have unique encoding dynamics, and thus it is possible that one ensemble of ChCs is not encoding salience per se, but rather the bulk population could be an amalgamation of diverse signals that overall is a representation of salience. This would fit with the citations provided above that are performed at the single-cell level.\n2. There are important differences in the results of Figures 4 and 5 that are not discussed. Most notably, while lesions of AIC and PVT cells reduces PFC ChC population activation to a novel stimulus, inhibition of AIC and PVT terminals in PFC do not. This suggests that the lesion effects are likely due to polysynaptic effects, perhaps on salience detection, potentially involving many structures not under study here. Furthermore, the JAWS results actually suggest that these inputs do NOT relay information about novelty per se but rather enable PFC ChCs to habituate to novel stimuli (while control animals show habituation in ChC responsivity, JAWS animals show perseverative activation). Similarly, the value scaling experiment clearly demonstrates that AI and PVT lesions or inhibition amplify the response of PFC neurons to low value reward. This suggests that these neurons may actually mitigate responsivity to stimuli that have low salience, rather than amplify responsivity to stimuli that have high salience. This framework is highly consistent with data showing that PVT is critical for suppressing information under certain conditions, such suppressing behavior when reward is omitted (28426970) or when reward is paired with behavioral suppressors including stress, fear, and extinction which is highly relevant to the current manuscript (36369508; 38141605).\n3. Optogenetic inhibition of ChCs for 5-minutes is likely to have many effects on neurophysiology, as has been recently reported in the literature. Thus, the value and interpretability of data in Ext. Data Fig. 7 is of significant concern. Related to this, the authors indicate that optogenetic inhibition was given in a “continuous pattern”, which seems misleading. If it was 20s or 5min of pure light, that needs to be clear. Pure light is not really a “pattern”.\n4. If authors wish to conclude that PFC ChCs are critical for associative learning via generalized salience detection, the functional experiments should be performed not only in the context of Pavlovian fear conditioning, but also for appetitive conditioning which has been shown to rely on distinct PFC excitatory neuronal ensembles including those that interact with the PVT.\n5. Use of male mice only is of significant concern.\nMinor Concerns:\n1. Authors suggest that the TetTox experiment is an inactivation of synaptic output experiment, which is a very poor descriptor. The experiment lesions AI and PVT cells, and suggesting it is specific to synaptic output is not warranted. The manuscript should be rephrased.\n2. Fig 2 Caption: Rephrase the title. The stimulus is novel, rather than naïve. The title also does not clearly indicate that the response habituated, which is the main point.", "reviewer_3": "Zhang et al. investigate how a specific interneuron subtype, chandelier cells (ChCs), in the medial prefrontal cortex (mPFC) contribute to detecting stimulus salience and guiding associative learning. The authors employed a combination of advanced techniques, including cell-type-specific genetic targeting (using Unc5b-CreER mice to selectively label ChCs), in vivo calcium imaging via two-color fiber photometry, optogenetics, and chemogenetics. Using these tools, they monitored ChC activity in freely behaving mice while presenting a range of stimuli (auditory tones, sucrose rewards, air puffs, foot shocks, and others) and manipulated inputs and ChC excitability. Key comparisons were made with other interneuron types (parvalbumin-positive [PV] and somatostatin-positive [SST] cells) to determine if salience encoding is unique to ChCs. The study also examined circuit mechanisms by functionally disconnecting upstream regions of the known “salience network” – specifically the anterior insula (AI) and paraventricular thalamus (PVT) – to see how this affects ChC responses. Finally, the authors tested the causal role of ChCs in learning by observing their activity during trace fear conditioning and by bidirectionally manipulating ChC activity during learning.\nThe major findings are novel and compelling. First, ChCs in mPFC responded robustly to stimuli across multiple modalities and valences, firing strongly to both appetitive and aversive cues, which suggests they encode the salience or significance of stimuli rather than their specific pleasant or unpleasant nature. Second, ChC responses were greatest to novel stimuli and diminished upon repeated exposures (habituation), indicating that ChCs dynamically track stimulus novelty. In contrast, the other interneurons (PV and SST cells) did not show such adaptation between first and later presentations, highlighting that this novelty-driven response pattern is unique to ChCs. Third, ChCs also encoded stimulus intensity: for example, they differentiated between larger versus smaller reward volumes, especially once the stimulus was familiar, thereby signaling relative intensity when novelty was no longer a factor. The authors demonstrate that inputs from AI and PVT are essential for these salience signals – blocking synaptic output from either region greatly reduced ChC responses and abolished their ability to distinguish novel vs. familiar or high vs. low intensity stimuli. Fourth, in an associative learning paradigm (trace fear conditioning), ChC activity reflected acquired salience: a tone that was initially mundane (habituated) elicited only weak ChC activity, but once that tone was paired with a foot shock, ChC responses to the tone increased significantly. Notably, ChCs maintained strong responses to the shock only when the shock was part of a learned cue-shock association, whereas if shocks were given randomly (unpaired), ChC responses to those shocks rapidly declined. This indicates that ChC firing tracks the learned significance of both predictive cues and reinforcement, not just the physical stimulus. Finally, the manipulation experiments establish a causal link to behavior: optogenetic silencing of ChCs during the critical cue/shock pairing period markedly impaired fear learning (reduced freezing to the tone), demonstrating that ChC activity is necessary for normal associative learning of that stimulus. Complementary chemogenetic experiments showed that enhancing ChC excitability (with a DREADD agonist) dampened ChCs’ calcium responses to stimuli and led to poorer learning (less freezing), whereas suppressing ChC excitability (making them less active at baseline, which paradoxically led to larger responses to stimuli) enhanced learning (greater freezing). Importantly, these manipulations did not produce general performance deficits or hyperactivity, suggesting the effects were specific to learning and salience processing rather than due to nonspecific behavioral changes.\nIn summary, this study presents strong evidence that mPFC chandelier cells serve as a dedicated cortical module for detecting stimulus salience (through novelty, intensity, and learned relevance) and that their activity can bias learning outcomes. This represents a significant contribution to our understanding of how prefrontal circuits filter important stimuli and influence cognitive processes, adding cellular-level insight to the broader concept of the brain’s salience network.\nMajor Concerns\n1. Experimental Design and Controls\nWhile the scope of experiments is impressive, there are some concerns about the experimental design and control conditions used. A primary issue is the adequacy of control groups for the optogenetic and chemogenetic manipulations. The authors inhibited ChCs optogenetically (NpHR) during conditioning and observed a learning deficit, but it is not stated whether control mice (e.g. animals expressing a control fluorophore but no opsin) were also subjected to the same light delivery. Such a control is important to ensure that the effect on learning is truly due to ChC inhibition and not any unforeseen consequence of light or fiber implant (e.g. heating, tissue damage, or behavioral distraction). If no “light only” control was included, the authors should either provide justification or consider this in a revision, as it strengthens the causal claim. Similarly, for chemogenetic modulation, it’s unclear if the study included vehicle injections or control-virus mice to control for any off-target effects of the DREADD actuator (CNO or equivalent). Given known issues with CNO, it would be reassuring to know that CNO injection in mice lacking the DREADD did not alter learning or ChC activity. The manuscript mentions that locomotor activity was unchanged by these manipulations, which is a good control for general effects; however, controls for the learning context specifically would further ensure interpretational clarity. These additional control experiments (or clarifications if they were done) would bolster confidence in the conclusions.\nAnother aspect of experimental design to consider is the sample size and statistical power. Many of the key experiments (e.g. fiber photometry comparisons, optogenetic and DREADD groups) used around n ≈ 6–8 mice per group (as indicated in the figure legends). While these sample sizes are not uncommon in in vivo neuroscience studies, they are relatively modest and might limit the robustness of detecting effects, especially given between-subject variability. The fact that many comparisons reached statistical significance suggests the effects are reasonably strong; however, some results could be underpowered. The authors should clarify how sample sizes were determined (was a power analysis conducted?) and whether each “n” refers to an individual mouse’s averaged data. Additionally, it would help to report the number of trials or cells contributing to each mouse’s data point for fiber photometry. If certain groups had any exclusions, that should be reported. Overall, slightly larger cohorts would increase confidence that the findings generalize, though the consistency across multiple experiments already suggests the phenomena are reliable.\nPotential confounding variables were generally well-addressed, but a few points merit discussion. One could argue that factors like slight differences in arousal or attention between the start and end of a session might contribute to the decline in response, though the authors did control for certain behavioral measures such as licking behavior. It would be useful to know if the stimuli were presented in pseudorandom order or fixed sequence; randomizing or counterbalancing stimulus sequences would prevent any systematic order or time-of-session confound. Another possible confound is the specificity of the fiber photometry signal to ChCs, but the authors used Unc5b-CreERT2 mice and performed morphological/electrophysiological validation. Lastly, for the fear conditioning paradigm, the use of trace fear conditioning is appropriate to engage the prefrontal cortex, though clarifying any context counterbalancing or exclusions would be helpful. The control groups (CS-only or US-only) are an excellent demonstration that freezing specifically requires cue-shock pairing. Overall, the experimental design is innovative and multi-faceted, but providing a bit more detail on controls and addressing the above points would strengthen the rigor.\n2. Statistical Analysis and Reporting\nThe statistical approaches used in the manuscript raise some consistency and justification concerns. Throughout the figures, the authors employ a mix of parametric tests (e.g. two-tailed paired Student’s t-tests) and non-parametric tests (e.g. Wilcoxon signed-rank tests), as well as ANOVAs for multi-factor comparisons, but it is not always clear why a particular test was chosen. It’s possible that the authors decided on a case-by-case basis depending on normality of the data distribution, but this rationale needs to be stated explicitly. The manuscript would benefit from a clear statement in the Methods about the criteria for choosing parametric vs. non-parametric tests. If normality tests were not done, it would be safer to use non-parametric tests throughout for consistency. Repeated-measures ANOVA (with Sidak’s multiple comparisons) for multi-level intensity experiments is appropriate if assumptions were satisfied; the authors should confirm that. In terms of reporting, the figures and legends do present p-values and mention the tests used, which is commendable. However, ensuring every figure uses a consistent notation for significance (whether asterisks or explicit p-values) would be helpful. For multiple comparisons, clarifying whether the reported p-values were thresholded with corrections is also important. Finally, the authors should consider reporting effect sizes. Overall, the analysis approach is sound, but a more uniform application of statistical tests and thorough reporting of assumptions would improve rigor and reproducibility.\n3. Interpretational Robustness and Data Presentation\nThe authors conclude that ChCs are specialized for salience detection and that this drives their influence on learning. The data largely support this interpretation, but there are some points that should be addressed. One concern is whether ChC responses could be explained by more general factors such as global arousal or neuromodulatory states. The authors partially address circuit inputs by examining AI and PVT, which indeed are part of the salience/relevance network. It would strengthen the interpretation if the authors briefly discuss how neuromodulators or other inputs might be involved. Another alternative explanation to rule out is that ChC responses might correlate with behavioral reactions to stimuli, but the authors did well to check for licking and locomotion confounds. Highlighting these controls more in the main text would assure readers that behavioral confounds were examined.\nAnother interpretational point is the causal link to learning. The impairment of fear conditioning with ChC inhibition (and enhancement with disinhibition) shows that ChCs contribute to associative learning. Yet, we should consider the precise mechanism. ChC control of pyramidal neuron axon initial segments is powerful but not fully examined here (the authors did not directly record pyramidal activity). A brief discussion of how reduced or enhanced ChC activity translates to changes in excitatory circuits would be valuable, especially given the seemingly paradoxical result that silencing ChCs might disinhibit local circuits. For the DREADD experiments, the authors suggest that increasing ChC excitability leads to overall reduced stimulus-evoked Ca²⁺ signals and impaired learning, presumably because the baseline inhibition is higher. A short mechanistic discussion tying these results to known ChC function would clarify the significance. Finally, data presentation is generally good, but a few minor suggestions (e.g. marking the analysis window on photometry plots, providing raw ΔF/F traces) would improve clarity. This can be done in figures or supplemental materials.\nMinor Concerns and Suggestions\n• Indicate the specific time window used for analysis of calcium signals on the photometry traces.\n• Consider including raw photometry ΔF/F traces for a single session or animal to show signal-to-noise and response time course in more detail.\n• Expand figure legends to define abbreviations like S1, S3, etc., and ensure they are self-contained.\n• Provide numerical source data for main summary graphs if possible and note the analysis pipeline (subtraction of isosbestic or motion channel).\n• Clarify if baseline changes in ChC activity were observed with hM3Dq or hM4Di even before CNO, and whether the same injection protocol was applied to both experimental and control mice.\n• State whether both sexes were used or only males/females.\n• Consider briefly discussing related studies on ChC roles in other regions and how the present results align with or extend those findings.\nTo summarize my review, Zhang et al. have presented a substantive and insightful study that identifies chandelier cells of the mPFC as key players in salience detection and learning. The experiments are ambitious in scope, combining observational and interventional approaches to make a strong case that ChCs not only encode salient features of stimuli (novelty, intensity, predictive value) but also causally contribute to how an animal learns about those stimuli. The manuscript’s strengths lie in its innovative cell-type-specific approach, the clear demonstration of both necessity (via optogenetic inhibition) and sufficiency/modulation (via chemogenetic tuning) of ChC activity for learning, and the linkage of circuit-level mechanisms (input from AI/PVT) to behavior. The data are largely convincing and novel, and they significantly advance our understanding of cortical interneuron function in cognitive processes.\nThat said, there are important revisions needed to ensure the work meets the highest standards of rigor and clarity. The major concerns outlined regarding controls, statistical consistency, and interpretational clarity should be addressed before publication. Most of these issues can be dealt with through additional analysis, explanation, or minor experimental validation, rather than requiring entirely new experimental paradigms. The minor concerns are straightforward improvements that will enhance readability and transparency. None of the concerns undermine the fundamental conclusions of the paper, but resolving them is critical."}, "author_response": "Prefrontal chandelier cells encode stimulus salience to influence learning\nCorresponding Author: Professor Jiangteng Lu, Yilin Tai\nOur responses and comments are written in black while the original reviewer comments are in blue.\nSummary of response to reviewers:\nWe thank the reviewers for their insightful comments on our manuscript. We have responded point-by-point to all the\nconcerns of the 3 reviewers. Here are the simple summaries:\n1. New Figures Added\nWe generated several new main and extended data figures, including:\nMain Figure. 7 and Extended Data Figs. 6, 7, 8, 9, 11, 12, 16, 17,\nto address reviewer suggestions on:\nMovement control data, associative learning in reward paradigms, manipulation of AI/PVT inputs in TFC, additional\ncontrol experiments and clarified the existing control data, and stimulus salience comparisons across modality.\n2. New Panels and Analyses Added to Existing Figures\nWe also expanded existing figures with new experimental panels and re-analysis, including:\nTrial-aligned traces for individual animals (Fig. 1, Extended Data Fig. 2)\nBaseline tone responses on D0 (Fig. 6c–d)\nadditional control experiments for optogenetic manipulations (Extended Data Fig. 13),\nAdditional controls for ChC under saline or CNO (Extended Data Fig. 14, 15)\n3. Methods and Statistics Improved\nWe added detailed descriptions of fiber photometry preprocessing, signal separation from 638 nm light, and response\nquantification.\nWe clarified statistical rationale, specifying use of Shapiro-Wilk for normality, appropriate use of parametric/non-\nparametric tests, and ANOVA assumptions.\nA complete statistical summary table was included, reporting exact p-values, normality, effect sizes, and power.\nFigure legends and statistical notations have been standardized.\nFurthermore, the updated information has been labeled in red in the text, figure legends, methods."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have carefully reviewed the authors’ detailed and comprehensive responses to my previous comments, and I would like to express my appreciation for their diligence in addressing each point. Their revisions have substantially strengthened the manuscript. I believe the study has strong potential for publication, pending the authors’ attention to the following concerns and clarifications.\nMajor Concerns:\n-Movement-related activity (lines 145–152):\nThe authors state that the activity of ChCs, as well as PV and SST cells, was not significantly affected by movement speed, suggesting that responses were driven by sensory stimuli rather than motor factors. This conclusion seems overstated. Prior studies (refs 31, 35, 36) report that cortical and hippocampal ChCs show correlations with movement speed at single-cell resolution, and PV and SST activity is known to be modulated by running speed (Adler et al., 2019 Neuron; Khan et al., 2018 Nat Neurosci). Because the present study measured bulk Ca²⁺ signals, it is difficult to rule out movement effects based on these signals alone. It would be more informative to present both speed traces and activity traces, and to clarify whether most experiments were conducted under relatively low running speeds.\n-Encoding of water consumption intensity (Fig. 3):\nRegarding ChCs’ encoding of intensity of water consumption, in Fig 3, a random sequence of water volume was delivered to mice and the authors showed correlation between volume change and Ca2+ signal change. It makes me wonder whether ChCs encode relative changes in water volume in S3 rather than absolute water volume. To strengthen their claim that ChCs encode consumption intensity, additional experiments without randomized sequences or analysis would be necessary to rule out this alternative explanation.\n-AI synaptic transmission effects (Fig. 4l):\nIt is interesting that blocking AI synaptic transmission reduced ChC responses to water reward in S1 across different reward volumes. Responses to varying water volumes remained uniform, but overall signal level increased in S3 compared to S1. It appeared to me that AI:TetTox impaired inhibition of ChC activity to low water volumes (2-7ul). Authors should clarify why overall signal level significantly increased in S3 in AI: TetTox condition. ChCs may encode intensity or relative changes in water volume through other mechanisms that inhibit ChC activity on low intensity.\n-PVT effects (Fig. 4p):\nThe authors should explain why overall signal level slightly increased in S3 in PVT:TetTox condition. Authors pointed out differences of synaptic connectivity between mPFC and AI or PVT. The difference in its impact in failure of adaptive response to varying water volumes in S3 should be noted in the result and discussed.\nMinor Points:\n-In Figs. 4a and 4b, the orientation of representative images should be described in the legends.\n-Light intensity used in eJaws optogenetic manipulations (EDF12) should be specified in the legend or figure.\n-Light intensity used in NpHR manipulations should also be provided.\n-For each experiment, the number of mice and the statistical tests used for reported p-values should be clearly noted.\n-Correct the reference error at line 386.", "reviewer_2": "THe authors were very thorough in the responses to my review. I approve of publication at this time.", "reviewer_3": "The authors have satisfactorily addressed the main concerns raised in the initial review. Specifically, they have clarified the control groups for both optogenetic and chemogenetic experiments, expanded the statistical reporting with appropriate rationale for test choices, effect sizes, and power estimates, specified sample sizes and exclusion criteria, described stimulus randomization and additional behavioral controls, extended the discussion on neuromodulation and circuit mechanisms, and improved data presentation with clearer analysis windows, raw traces, and figure legends.\nWith these revisions, the manuscript has substantially improved in rigor and clarity. I am satisfied with the changes."}, "author_response": "Prefrontal chandelier cells encode stimulus salience to influence learning\nCorresponding Author: Professor Jiangteng Lu, Yilin Tai\nOur responses and comments are written in black while the original reviewer comments are in blue. The updated information has been highlighted in red in the text, figure legends, and\nMethods.\nResponses to comments:\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nI have carefully reviewed the authors’ detailed and comprehensive responses to my previous\ncomments, and I would like to express my appreciation for their diligence in addressing each\npoint. Their revisions have substantially strengthened the manuscript. I believe the study has\nstrong potential for publication, pending the authors’ attention to the following concerns and\nclarifications.\nMajor Concerns:\n-Movement-related activity (lines 145–152):\nThe authors state that the activity of ChCs, as well as PV and SST cells, was not significantly\naffected by movement speed, suggesting that responses were driven by sensory stimuli rather than\nmotor factors.\nThis conclusion seems overstated. Prior studies (refs 31, 35, 36) report that cortical and\nhippocampal ChCs show correlations with movement speed at single-cell resolution, and PV and\nSST activity is known to be modulated by running speed (Adler et al., 2019 Neuron; Khan et al.,\nBecause the present study measured bulk Ca²⁺ signals, it is difficult to rule out movement effects\nbased on these signals alone. It would be more informative to present both speed traces and\nactivity traces, and to clarify whether most experiments were conducted under relatively low\nrunning speeds.\nResponse:\nWe thank the reviewer for this important comment. We acknowledge that our recordings represent\nbulk Ca²⁺ signals from the ChC population in the mPFC, which cannot resolve single-cell\ncorrelations with movement. To clarify, we have revised the manuscript (Lines 145–150) to\nspecify that the reported activity reflects population-level signals.\nFollowing the reviewer’s suggestion, we have now included representative speed traces alongside\nCa²⁺ activity traces in the revised Extended Data Fig. 6. Importantly, most experiments were\nconducted under head-fixed conditions. In freely moving experiments, stimuli were delivered\nduring periods of relatively quiet state (in low running speeds). We now clarified this in the\nrevised Methods (Line 104-106).\n-Encoding of water consumption intensity (Fig. 3):\nRegarding ChCs’ encoding of intensity of water consumption, in Fig 3, a random sequence of\nwater volume was delivered to mice and the authors showed correlation between volume change\nand Ca2+ signal change. It makes me wonder whether ChCs encode relative changes in water\nvolume in S3 rather than absolute water volume. To strengthen their claim that ChCs encode\nconsumption intensity, additional experiments without randomized sequences or analysis would\nbe necessary to rule out this alternative explanation.\nResponse:\nWe thank the reviewer for raising this important point regarding the interpretation of ChC responses\nto water consumption. In Fig. 3b, we show the correlation between trial-by-trial differences in water\nvolume (ΔVolume) and corresponding Ca²⁺ signal changes (ΔZ-score), and in Fig. 3e, we further\nanalyze the relationship between absolute water volume and Ca²⁺ responses, indicating that ChCs\nin S3 can encode absolute volume.\nThe reviewer’s comment raises an important question regarding whether randomized delivery of\nwater volumes (i.e., relative changes) influences intensity encoding. To address this, we performed\nan additional block-design experiment in which each water volume was delivered in 20\nconsecutive trials, with block order randomized across volumes. Under this condition, ChCs did\nnot show a consistent correlation between Ca²⁺ responses and water volume in either S1 or S3,\nand responses generally declined across trials regardless of volume, similar to Fig. 2. These\nfindings suggest that ChCs’ ability to discriminate water volumes emerges specifically in\nrandomized contexts, supporting the notion that stimulus intensity is encoded in a context-\ndependent manner. Investigating the underlying mechanism is intriguing but beyond the current\nscope. We present this finding solely to address the reviewer’s concern; it is not included in the\nrevised manuscript.\nFigure: Responses of mPFC ChCs to water volumes delivered in randomized blocks.\na, Schematic of experimental paradigm in which five water volumes (2, 4, 7, 10, 15 µL) were\ndelivered in randomized blocks (20 consecutive trials per volume, total 100 trials per session). b-e,\nRepresentative case of ChC Ca²⁺ responses in Session 1 (S1, b-c) and Session 3 (S3, d-e). b and d,\nheatmaps of Ca²⁺ responses (Z-scores) to water consumption, with blocks presented in actual order\n(top to bottom). c and e, averaged Ca2+ trace (line) ± SEM (shaded area, 20 trials/volume block)\naligned to the onset of water consumption. f, Quantification of ChC responses in S1, shown as\naverage response amplitudes plotted according to block sequence (dark line) or volume-sorted\norder (2-15 µL, light line) (n = 9 mice). g-h, Comparison of ChC activity between 2 µL and 15 µL\ntrials (g) and between block 1 and block 5 (h) in S1 (block 1 vs block 5, P < 0.05, two-tailed\npaired Student’s t-test, n = 9 mice). i-k, Same analyses as in (f-h) for S3 (k, block 1 vs block 5, P\n< 0.01, two-tailed paired Student’s t-test). Each line linking two open symbols represents data\nfrom an individual mouse. Data are presented as mean ± SEM. Statistics as indicated, **P < 0.01,\n*P < 0.05, ns is not significant.\n-AI synaptic transmission effects (Fig. 4l):\nIt is interesting that blocking AI synaptic transmission reduced ChC responses to water reward in\nS1 across different reward volumes. Responses to varying water volumes remained uniform, but\noverall signal level increased in S3 compared to S1. It appeared to me that AI:TetTox impaired\ninhibition of ChC activity to low water volumes (2-7ul). Authors should clarify why overall signal\nlevel significantly increased in S3 in AI: TetTox condition. ChCs may encode intensity or relative\nchanges in water volume through other mechanisms that inhibit ChC activity on low intensity.\n-PVT effects (Fig. 4p):\nThe authors should explain why overall signal level slightly increased in S3 in PVT:TetTox\ncondition. Authors pointed out differences of synaptic connectivity between mPFC and AI or\nPVT. The difference in its impact in failure of adaptive response to varying water volumes in S3\nshould be noted in the result and discussed.\nResponse:\nWe thank the reviewer for highlighting the differential contributions of AI and PVT lesions to\nmPFC ChC responses to water reward in S1 and S3. In the PVT:TetTox condition, although the\nCa²⁺ signal visually appears slightly higher in S3 compared to S1, there was no statistically\nsignificant difference (Fig. 4p). Thus, PVT lesions produced stable and invariant responses to\nvarying water volumes, indicating a failure of adaptive suppression to low-salience stimuli.\nIn contrast, AI lesions disrupted ChC responses in S1, leading to previously undetectable activity,\nand subsequently produced overall large, invariant responses in S3 (Fig. 4l). This suggests that AI\ncontributes to salience processing in a manner distinct from PVT, potentially beyond merely\nsuppressing ChC responses to low-salience stimuli in familiar contexts. In both lesion conditions,\ninhibition of ChC activity to low-volume water rewards was impaired in S3, but the additional\ndisruption in S1 caused by AI lesions highlights a unique role of AI inputs in regulating ChC\nresponsiveness to novel or low-salience stimuli.\nWe have clarified these points in the revised Results (Line 220–224) and Discussion (Line 371–\n379) sections.\nMinor Points:\n-In Figs. 4a and 4b, the orientation of representative images should be described in the legends.\nResponse: We have updated the legends for Figs. 4a and 4b to indicate the orientation of the\nrepresentative brain slices. Specifically, all images are coronal sections, and anatomical directions\n(dorsal/ventral) are indicated where appropriate.\n-Light intensity used in eJaws optogenetic manipulations (EDF12) should be specified in the\nlegend or figure.\nResponse: We have now specified the light intensities used for eJaws optogenetic manipulations\nin the figure legends for Fig. 5 and Extended Data Figs. 12.\n-Light intensity used in NpHR manipulations should also be provided.\nResponse: We have now specified the light intensities used for optogenetic manipulations in the\nfigure legends for Fig. 8, Extended Data Figs. 11, 13 and 17, including both NpHR and ChR2\nexperiments.\n-For each experiment, the number of mice and the statistical tests used for reported p-values\nshould be clearly noted.\nResponse: We have carefully reviewed all experiments and verified the number of mice used. All\nfigures and figure legends now clearly indicate the corresponding n values (Fig. 8 and Extended\nData Fig. 7, 8). In addition, our supplementary table provides a complete summary of the\nstatistical tests applied and the exact p-values for all major comparisons.\n-Correct the reference error at line 386.\nResponse: We have corrected the reference error at line 386.\nReviewer #2 (Remarks to the Author):\nThe authors were very thorough in the responses to my review. I approve of publication at this\ntime.\nResponse: We sincerely thank the reviewer for the positive feedback and support for publication.\nReviewer #3 (Remarks to the Author):\nThe authors have satisfactorily addressed the main concerns raised in the initial review.\nSpecifically, they have clarified the control groups for both optogenetic and chemogenetic\nexperiments, expanded the statistical reporting with appropriate rationale for test choices, effect\nsizes, and power estimates, specified sample sizes and exclusion criteria, described stimulus\nrandomization and additional behavioral controls, extended the discussion on neuromodulation\nand circuit mechanisms, and improved data presentation with clearer analysis windows, raw\ntraces, and figure legends.\nWith these revisions, the manuscript has substantially improved in rigor and clarity. I am satisfied\nwith the changes.\nResponse: We thank the reviewer for the constructive comments and are pleased that the\nrevisions have satisfactorily addressed the concerns. We greatly appreciate the positive evaluation\nand support for our manuscript."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily addressed the minor concerns raised in my previous review. However, the main issue regarding the encoding of water consumption intensity remains insufficiently resolved.\nIn Fig. 3, the authors reported that Ca²⁺ responses of ChCs correlate with relative changes in water volume (ΔVolume)(Fig. 3b) and also increase with absolute water volume (Volume)(Fig. 3e). This dual correlation is puzzling, as it raises the question of how ChCs can simultaneously encode both absolute and relative quantities. My earlier comment aimed to clarify this apparent inconsistency.\nTo address this, the authors performed an additional experiment to test whether ChCs encode relative changes in water volume (ΔVolume) rather than absolute water volume (Volume) in S3. The results showed that ChCs did not show a consistent correlation between Ca²⁺ responses and water volume in either S1 or S3. These findings appear inconsistent with the authors’ original claim that ChCs in S3 encode absolute volume. Instead, the authors now suggest that ChCs’ ability to discriminate water volumes emerges only under randomized trial contexts; however, this result and the new figure were not included in the revised manuscript.\nIt would be helpful if the authors could further examine whether Ca²⁺ responses of ChCs correlate with relative changes in water volume (ΔVolume) occurring upon block changes, as initially shown in Fig.3b. Furthermore, if the encoding of intensity indeed depends on the randomized trial design, the authors should explicitly discuss the potential mechanisms underlying this context dependence and revise their interpretations accordingly. In particular, the claims of intensity encoding related to the correlation between Ca²⁺ responses and water volume (as shown in Fig. 3, Fig. 4I, and Fig. 4P) should be clarified in light of these findings.\nOverall, this is an interesting and valuable study that provides novel insights into ChC function and the manuscript could be suitable for publication once the authors address this remaining issue clearly and consistently."}, "author_response": "Prefrontal chandelier cells encode stimulus salience to influence learning\nCorresponding Author: Professor Jiangteng Lu, Yilin Tai\nOur responses and comments are written in black while the original reviewer comments are in blue. The updated information has been highlighted in red in the text, figure legends, and\nMethods.\nResponses to comments:\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have satisfactorily addressed the minor concerns raised in my previous review.\nHowever, the main issue regarding the encoding of water consumption intensity remains\ninsufficiently resolved.\nIn Fig. 3, the authors reported that Ca²⁺ responses of ChCs correlate with relative changes in water\nvolume (ΔVolume) (Fig. 3b) and also increase with absolute water volume (Volume) (Fig. 3e).\nThis dual correlation is puzzling, as it raises the question of how ChCs can simultaneously encode\nboth absolute and relative quantities. My earlier comment aimed to clarify this apparent\ninconsistency.\nTo address this, the authors performed an additional experiment to test whether ChCs encode\nrelative changes in water volume (ΔVolume) rather than absolute water volume (Volume) in S3.\nThe results showed that ChCs did not show a consistent correlation between Ca²⁺ responses and\nwater volume in either S1 or S3. These findings appear inconsistent with the authors’ original\nclaim that ChCs in S3 encode absolute volume. Instead, the authors now suggest that ChCs’\nability to discriminate water volumes emerges only under randomized trial contexts; however, this\nresult and the new figure were not included in the revised manuscript.\nIt would be helpful if the authors could further examine whether Ca²⁺ responses of ChCs correlate\nwith relative changes in water volume (ΔVolume) occurring upon block changes, as initially\nshown in Fig.3b. Furthermore, if the encoding of intensity indeed depends on the randomized trial\ndesign, the authors should explicitly discuss the potential mechanisms underlying this context\ndependence and revise their interpretations accordingly. In particular, the claims of intensity\nencoding related to the correlation between Ca²⁺ responses and water volume (as shown in Fig. 3,\nFig. 4I, and Fig. 4P) should be clarified in light of these findings.\nOverall, this is an interesting and valuable study that provides novel insights into ChC function\nand the manuscript could be suitable for publication once the authors address this remaining issue\nclearly and consistently.\nResponse to Reviewer #1:\nWe sincerely thank the reviewer for the insightful comments, which have helped us clarify the\ninterpretation of ChC intensity encoding. Below we address the remaining concern point-by-point.\n1. Clarifying the perceived inconsistency between Δvolume and absolute volume correlations.\nWe appreciate the reviewer’s concern that ΔVolume (Fig. 3b) and absolute Volume (Fig. 3e)\ncorrelations might suggest dual encoding. We have now clarified explicitly that both analyses reflect\nthe same underlying phenomenon (“during S3, ChCs discriminate absolute water volume under\nrandomized trial structures”).\nThe ΔCa²⁺ signal–Δvolume relationship arises because responses differ across volumes; it does not\nindicate explicit encoding of relative differences. We now state this clearly in the revised manuscript\n(Results, Lines 173–178).\n2. Block-design recordings reveal no intensity encoding.\nAs recommended by the reviewer, we performed and fully analyzed a blocked-presentation\nexperiment (20 consecutive trials per volume). We now explicitly report these findings in the\nResults (Lines 159–167). These new results (Extended Data Fig. 8) show that:\n(1) ChC responses decline across blocks, independent of volume, in both S1 and S3.\n(2) This indicates strong exposure-driven attenuation with minimal influence from stimulus\nmagnitude.\nFollowing the reviewer’s suggestion, we additionally examined whether ΔCa²⁺ signal correlated\nwith block-transition Δvolume. Because each session contains only four transitions, statistical\npower is limited; nonetheless, no correlation was observed (Extended Data Fig. 8l–m), which\nindicated that ChCs do not track relative volume changes.\n3. Trial-randomized presentation reveals emergence of intensity encoding.\nFollowing the reviewer’s suggestion, we have now integrated a coherent explanation in the\nResults (Lines 182–192) based on the combined randomized-trial and block-design findings:\n(1) In the trial-randomized condition, water volume varies substantially from trial to trial.\nDuring S1, ChCs respond uniformly to all volumes → novelty-driven salience.\nDuring S3, after repeated exposure to a variable environment, ChCs begin to scale their\nresponses with absolute water volume → intensity-based salience encoding.\n(2) In the blocked condition, stimulus intensity remains constant for long epochs.\nChCs show strong early responses but quickly habituate, with no emergence of\nintensity discrimination.\nThus, intensity encoding by ChCs depends on extended experience under conditions with\nhigh trial-to-trial variability, rather than on relative trial-to-trial changes per se. We now\nemphasize this point throughout the revised manuscript.\nWe have also clarified that Figures 3, 4l, and 4p reflect randomized-trial conditions (under which\nintensity encoding emerges), whereas Extended Data Fig. 8 demonstrates the absence of intensity\nencoding under blocked conditions.\n4. Consistent revisions throughout the manuscript\nWe revised all relevant portions of the Results, and figure legends to ensure clarity and consistency.\nKey points now clearly stated include:\n(1) ChCs encode novelty-based salience in repetitive stimuli with fixed intensity.\n(2) Intensity encoding develops only after extended experience in highly variable trial\ncontexts, and reflects absolute stimulus magnitude, not ΔVolume.\n(3) PV and SST interneurons do not show this adaptive shift, indicating this property is\nspecific to ChCs.\nWe thank the reviewer for prompting this clarification, which substantially strengthened the\nmechanistic interpretation of ChC salience encoding. Our revised manuscript now clearly\ndistinguishes novelty-driven and intensity-driven components, explains the context dependence of\nintensity encoding, and resolves the perceived Δvolume–volume inconsistency."}, {"round": 4, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily addressed the concerns raised in my previous review. The revision substantially strengthened the mechanistic interpretation of ChC encoding for both novelty-driven and intensity-driven components. I am satisfied with the thoroughness and clarity of the changes presented."}, "author_response": ""}]

Decision letter
[Editorial Note: See attachment]