The representation of omitted sounds in the mouse auditory cortex

Domain: neuroscience
Published: 2026-01-28
Editorial outcome: minor_revision

Abstract
Humans and animals use predictions to optimize their behavior, however, the underlying neuronal implementation remains elusive. We address this using omitted sounds on the macro- and microscale of the auditory cortex of female, normal hearing mice using high-speed imaging. Neuronal responses to the omission of expected sounds were time-locked to expected stimulus onset, localized to layer 1-4 of higher auditory area (Temporal Association Area, TeA), and continued to rise until the following stimulus. The omission responses differed from offset and deviant responses in their temporal shape, size and spatial localization. Omissions and sequence statistics correlated with behavioral changes by timed pupil dilation and rapid facial motions. While stimulus responses showed partial entrainment, omission responses maintained a distinct, unentrained shape. The localized omission response in TeA is consistent with a hierarchical organization of predictive processing. However, the continued rise suggests an integrated, absolute prediction error, instead of a direct representation of prediction or prediction error, which would terminate with the omission.

Introduction
The sensory world is full of predictable relationships in time and space. Previous research has convincingly demonstrated that humans and animals exploit this predictability to optimize their behavior, i.e., in generating faster and more accurate responses in complex decision tasks1. It has been suggested that predictable information and its use are so ubiquitous that this maybe one of the central principles of brain organization2,3. The underlying neural representation forms an entire field of study in neuroscience that has remained, however, without a clear consensus4–7.

In humans, a well-studied correlate of prediction-related processes is the mismatch negativity (MMN8), which can be reliably detected in EEG (electroencephalography) and MEG (magnetoencephalography) recordings and whose origin can be localized partly to the auditory cortex9–11. The MMN is reliably elicited when there is an unexpected change in a predictable sequence of sounds. Tracing the origin of the MMN in animal studies has provided a plethora of insights and has relied largely on deviants in predictable sequences12–17. In multiple species, neuronal responses to such deviant stimuli, in particular an unexpected change in pure tone frequency, have been shown to be elevated and organized hierarchically, with more prediction-related responses found in higher levels of the neuraxis14,18,19. Relating these results to the corresponding theories of predictive processing has been difficult, in part because these theories distinguish between predictions and prediction errors, which are hard to disentangle in classical deviant stimulus paradigms.

A promising alternative is to instead unexpectedly omit a predictable stimulus. It has been argued that this provides a clearer perspective on the putative top-down, predictive processes, as no bottom-up stimulus information arrives from the periphery20–23. Recent studies from our lab and others have demonstrated the existence of well-timed, positive neural responses to omitted sounds12,24–26, while another study demonstrated such responses only in LFPs in the AC27. Responses to end-of-sequence omissions in very slow sequences (0.5 Hz) have been described as echo responses for slow, repeating sequences28,29, however, without clear spatial localization in the auditory cortex. We recently demonstrated a substantial increase in omission responses in the awake state compared with anesthesia25. This renders omission responses a possible correlate of predictive processes of the neural representation.

Previous studies used electrophysiology, whose temporal resolution could resolve the fast stimulus sequences in the typically used paradigms16,30–32. However, electrophysiology provides only limited insight into spatial arrangement on the macro- and micro level. Imaging methods can provide complete spatial coverage and superior spatial resolution; however, have previously been limited in speed and signal-to-noise when using the GCaMP6-family of calcium indicators33,34. Here, we leveraged the substantial improvements of more recent Ca2+-indicators, using jGCaMP8m, which provides temporal fidelity up to 100 Hz (~2.5 ms rise time and 50% decay time ~40 ms see Extended Data Table 635) and manifold improved brightness. This enables the study of spatial organization of omission responses from the whole auditory cortex to the cellular level, including the dependence on cortical depth for fast acoustic sequences using aligned 2-photon and widefield imaging36.

We identify a specific subarea of the auditory cortex of mice, the posterior and medial part of the temporal association area (TeA), that dominantly shows responses to omitted tones, while sound responses are much smaller than in the primary areas, where the converse is observed. Omission responses are most prominent in L2-4 and decrease below. TeA has recently been noted for its late responses and top-down-like responses37. The omission responses show a time-locked increase at the beginning of the omitted sound, which continues to increase until the next stimulus. If a second omission follows the first omission, an additional, time-locked increase in response occurs. The omission-responsive region does not coincide with areas that show substantial repetition suppression or prediction error in traditional oddball sequences. The stimulus response increases in size upon repeated stimulus presentations (entrainment) in the omission-responsive region. The omission response also increases, however, much more slowly, and maintains a very distinct shape from the stimulus response, and can thus not be explained by entrainment. Omissions elicit a timed pupil dilation during passive listening, indicating that the animal noticed the change in stimulus sequence. The strength of rapid facial motions adapts to repeated acoustic stimulation and increases after an omission, further indicating that the mouse monitors recent stimulus statistics. Together, this provides challenging insights for improving our understanding and theories of predictive processing, highlighting the promise of fast and bright, large-scale imaging.

Results
We investigated the neural representation of unexpected stimulus omissions in extended sound sequences across multiple spatial scales and cortical depths of the auditory cortex (AC) in mice (CBA/JRj, female, age: 2–3 months, n = 10, Fig. 1A). We locally transfected these normal-hearing mice with the fast and bright Ca2+-indicator jGCaMP8m (Fig. 1B) and chronically imaged the whole auditory cortex (1p widefield, 100 Hz) and multiple fields of view (FOVs, 500 × 500 µm) at cellular resolution across different depths (2p resonant scanning, 30 Hz). Imaging was performed through a cranial window (ø = 3.0–4.2 mm) centered above the AC starting after 2–3 weeks of viral expression. Facial motion and pupil diameter were recorded and served as a fast behavioral readout of the acoustic stimulation and the omitted stimuli. Details on statistical testing are collected in Supplementary Data 1.

Omission responses originate from the temporal association area posterior to the auditory cortex
We have previously demonstrated the existence of neurons in the AC that respond to the omission of an expected sound using electrophysiological recordings25. However, many crucial aspects remained uncertain, in particular, whether these neurons are locally grouped and whether they reside in particular cortical layers. Calcium imaging with jGCaMP8m35 provides a highly suitable method to address these questions, as it fuses fast dynamics (Fig. 1C top), high signal-to-noise (Fig. 1C bottom; single-trial responses to sounds), with a direct visualization of cellular locations.

We first mapped the size of omission responses across the AC using widefield imaging. Division of the AC into classically described subareas36,38–40 was performed using a combination of response latency and stimulus modulation to demarcate core areas, and tonotopy to arrive at subareas (Fig. 1D, E, see “Methods” section and Fig. S3 for details). The low-latency core area was further divided into areas A1, A2, and AAF, while the surrounding longer-latency areas were divided into fields DP, DM, and TeA (temporal association area)41. TeA has recently been highlighted as a secondary area with suspected top-down influences37, and we divided it into three anatomical parts (ventral, medial, and posterior TeA).

The acoustic stimulus was a long sequence of regularly spaced (200 ms between stimulus onsets, i.e., Stimulus Onset Asynchrony; SOA), identical sounds, of which a random subset was omitted (Fig. 1F, see “Methods” section for details). As before, the Omission Response (OR) was defined as the mean activity from omission onset to the subsequent stimulus onset (Fig. 1G left, shaded areas25). The area that exhibited the largest OR was located postero-lateral to the primary areas (Fig. 1G right), and here the neural activity exhibited a substantial increase at the beginning of the omission and increased further from there (Fig. 1G left, red), sometimes plateauing, similar to what we observed previously using electrophysiology25, even though magnitude estimates depend on baseline choice during continuous stimulation (See Supplementary Fig. 7D, F). In contrast, the primary area (Fig. 1G right, blue circle) showed a much smaller or absent omission response (Fig. 1G right, blue). The dynamics of the omission response are markedly different from those of offset responses, which exhibited a faster decay (~100 ms, compare to Fig. 1C top). The posterior and medial parts of TeA exhibited the strongest OR, which was significantly greater than in other areas of AC (Fig. 1H, p < 0.001, Kruskal–Wallis with Bonferroni correction for multiple testing, n = 10, see Fig. S1 for omission and stimulus responses in all animals). Omission responses were observed for different frequencies and also noise bursts and showed overlapping but non-identical spatial profiles, without clear tonotopic organization across animals (Supplementary Fig. 2).

Omission and stimulus response sizes differ strongly in neurons from different areas
Next, we addressed the relation between responses to (predictable) sounds and an omitted sound for individual neurons in different locations of the AC. Using 2-photon imaging (Fig. 2A), we acquired multiple FOVs targeted to particular locations of interest determined from widefield imaging in the same animal (Fig. 2B), specifically the omission-responsive region (ORR, Fig. 2B red) and the low-latency region with primary-like responses (PR, Fig. 2B blue).

Cells in the ORR FOVs showed strongly elevated activity in the omission period (Fig. 2C top), corresponding to the widefield recordings (Fig. 1G), rising at or around the start of the omission period. Cells in the low-latency, primary region further anterior exhibited much reduced responses in the omission period, and typically larger evoked responses to the stimulus (Fig. 2C bottom).

The distribution of individual cells with dominant responses to omissions (Fig. 2D, dots) mirrored the spatial pattern of widefield responses (color-coded background); however, the transition was steeper in 2p (Fig. 2E, orange vs red), as expected based on light-spread in the tissue42. The OR was also substantially larger (~3×) than the stimulus response in the ORR (Fig. 2F left, p < 10−10, linear mixed-effects model, group effect, animal as random factor, n = 5 mice). In the PR, the stimulus response was significantly larger than the omission response (Fig. 2F right, p < 10−10, as above). As the stimulus response is within a long sequence of sounds, it was already strongly adapted in the primary region. A much greater fraction of cells showed a significantly positive OR in the ORR than in the PR (96% vs. 27%, Wilcoxon signed-rank test at p < 0.05). Cells in PR showed largely no significant change in activity (71%), while only a small fraction showed a significant decrease (2%). Comparing the two responses between the regions shows a significant separation (Fig. 2H; ORR: red vs PR: blue, group differences were tested separately for OR and SR, both p < 10−10, method as above). This difference can be summarized with the Omission Index OMI ≔ (OR-SR)/(OR+SR), which showed a substantial and significant difference between ORR and PR (Fig. 2I, p < 10−10, as above).

Taken together, these results indicate that the OR constitutes a strong, population-wide modulation of activity in TeA, while cells in primary areas do not strongly respond to the omissions.

Omission responses are most prominent in granular and supragranular layers
Theories of predictive coding typically include a hierarchical organization of prediction-related activity at multiple spatial scales2,4,6. On the local level, cortical circuits have been demonstrated to include hierarchical differentiation, e.g., receiving top-down input via L1 and bottom-up input via L4, while sending interregional output via L543. Recent research in the visual system has provided evidence for a separation of prediction-error related signals in L2/3 in the case of deviant stimuli44. Here, we investigated whether the more prediction-related omission responses are also more prominent in superficial compared with deeper layers (Fig. 3).

We acquired 2p depth stacks in FOVs located in either primary or omission-responsive region ranging from the brain surface (~50 µm) down to the middle of L5 (~500 µm45,) (Fig. 3A). In the omission-responsive region (ORR), omission responses were more prominent in superficial layers (Fig. 3B, top) and substantially reduced with depth (p < 10−50, Kruskal–Wallis test). There appeared to be two separate steps, one between 100 µm and 200 µm (L2–L3) and one between 300 µm and 400 µm (L4–L5). Stimulus responses also reduced significantly with depth; however, only slightly in absolute terms, given their small overall size. By contrast in the primary area, omission and stimulus responses exhibited a very different depth dependence, with the stimulus response increasing strongly from the top 100 µm to 200 µm (Fig. 3B, bottom, Fig. 3D). While overall much lower than in the ORR, the omission response here decreased first significantly from L1/2, reaching negative values in L3 (200 µm), and then increased again in L4/5 (p < 0.001, Kruskal–Wallis test, with Bonferroni-corrected post-hoc testing, see Fig. 3C for details). The stimulus response was instead rather low in L1/2 and increased significantly to L3 and deeper layers.

Overall, omission and standard responses showed substantial dependency across depth, which further depended on the region of the auditory cortex. The layer profile of ORs in TeA appears consistent with top-down inputs as proposed previously4.

Pupil dilation and brief facial movements are indicative of sequence statistics
While the animals were not performing a task, we found their innate behavioral responses to be indicative of monitoring the sequence structure. We analyzed the pupil diameter and facial movements from video recordings (Fig. 4A, B). During omissions, the pupil diameter increased significantly (Fig. 4C, p = 0.002, Kruskal–Wallis ANOVA, n = 8 mice), indicating heightened arousal46. Although pupil responses are generally slow47,48, the dilation of the pupil becomes significant 110 ms after omission onset (p = 0.027, Wilcoxon test), already before the presentation of the next stimulus. Facial movements have recently been demonstrated as a basic readout of an animal’s reception of visual and acoustic stimuli49, with subcortical origin but under cortical modulation47. Facial movements, in particular of the whisker pad (Fig. 4A, right), occurred closely timed after stimulus onset (Δt ~ 60–70 ms) and lasted only briefly (~100 ms, Fig. 4D). This induced facial motion was significantly larger after an omission than before (Fig. 4E, p < 0.01, Wilcoxon rank sum test). Subsequently, the response adapted within 5 stimulus repetitions (Fig. 4F, p < 0.001, Kruskal–Wallis test). Conversely, the post-omission stimulus response depended significantly on the number of consecutive sounds that preceded the omission (Fig. 4G, p < 0.001, Kruskal–Wallis test). Adaptation of stimulus response magnitude was similar in the neural data (see also ref. 25), the reduction being stronger in the ORR (57%) as opposed to PR (23%). Additionally, neural responses and facial movements were positively correlated across stimulus representations within 4 stimuli after the omission, with the ORR showing a higher degree of correlation than the PR (Pearson r = 0.229 and 0.116, respectively, both p < 0.001). Together, these results are indicative of the mouse monitoring acoustic sequence statistics beyond a basic reflex on the neural and behavioral level, even without training or reward.

Omission responses cannot be explained as offset responses
Neurons in the auditory cortex respond not only to the onset and spectrotemporal features of ongoing sounds, but also to the offset of sounds (see, e.g., Fig. 1C50,51). While offset responses had a much faster time-course than the omission responses (compare Fig. 1C/G and Supplementary Fig. 10) and did not share the same map across the auditory cortex (compare Fig. S3D, E and Fig. 1H), we ran multiple controls to rule out the possibility of mistaking offset responses or refractory characteristics for omission responses (Fig. 5). We reasoned that true omission responses must only be present when a sound is expected, exhibit a time-course that starts at the expected stimulus onset and be robust to SOA and frequency content.

The first control was a comparison of the neural activity in the omission period in 200 ms SOA sequences with the interstimulus (non-omission) period in 400 ms SOA (Fig. 5A). In the latter sequence, a long pause is expected between the stimuli, while in the former, a pause of this length between the same stimuli is an unexpected deviation. In both cases, stimulus-driven offset responses should be similar, and we can therefore attribute differences between the neural activity in these cases to the response to the omission. In the Omission-Responsive Region (ORR), the omission response in the SOA 200 ms sequence (OR 200, Fig. 5B, red & light gray) was much more pronounced than the interstimulus response in the 400 ms SOA sequence (ISR 400, orange & dark gray). The difference between the two localized again to the ORR (Fig. 5C), and this difference was highly significant within an animal across within-sequence repetitions (Fig. 5D, p < 10−10, Wilcoxon Rank Sum test, n = 162, n = 1428) and across animals (Fig. 5E, p = 0.0079, Wilcoxon Rank Sum test, n = 5), as well as in single cells recorded in the ORR in 2p in the same animals (Fig. 5F, p < 10−100, Wilcoxon Rank Sum test, n = 765).

The second control was the presentation of two subsequent omissions. These formed only a small subset of the total number of omissions (10/162), in order to not accustom the animal to this length of omission or strongly interfere with the predictability of the standard sequence. In addition to the first OR, we also observed a second, time-locked increase in the neural activity after the beginning of the second omission (Fig. 5H), measured by the difference between the second and first OR times the first OR size (double Omission index, dOM, see “Methods” section). The cortical region where this second omission occurred was qualitatively similar to the one in response to the first omission (Fig. 5I, J), while further extending to more posterior regions. The further increase for the second OR was consistent across all five animals in which this condition was run (Fig. 5K). To our knowledge, no human study exists which included double omissions and collected neural recordings. We are therefore not sure whether this would also be observed in humans in addition to the first omission response (see “Discussion” section).

The third control was the use of multiple SOAs to investigate the timing of the resulting omission responses, ranging from 100 to 400 ms (Fig. 5L). From human20 and animal studies25, we expected to find reduced neural responses to unexpected omission as the SOA increased. Consistently, we found that the size of the OR depended significantly on SOA (Fig. 5O, p = 0.009, Kruskal–Wallis test), and the slope reduced significantly with SOA (Fig. 5P, p = 0.0011, Kruskal–Wallis test) while the minimum value of the SR decreased significantly from SOA = 100 ms to SOA = 200 ms and longer (Fig. 5Q, p = 0.01, Kruskal–Wallis test; p = 0.001, Wilcoxon rank sum test between 100 ms and all longer SOAs grouped).

Altogether, the omission responses appear to be genuinely related to an expectation of the omitted sound, as they differ in their dynamics from offset responses (see also Fig. 1C, S3D, E, and Supplementary Fig. 10), occur also for a second omission, and change in time and size depending on the sequence structure.

Omission responses partially scale with the level of regularity in the stimulus sequence
If the OR reflects a prediction in a regular sequence of stimuli, then its size would be expected to scale with the degree of regularity in the sequence52–54. We assessed this question using two different controls, a temporally jittered version of the paradigm and an analysis of subsections of the stimulus sequence.

The jittered sequence was designed to be on many levels comparable to the standard SOA 200 sequence (Fig. 6A), i.e., it had the same average SOA of 200 ms, same standard sound (white noise), same order of sounds and omissions, same sequence length, while only the precise timing of the sounds was modified randomly in a balanced way, i.e., the SOA following a sound was randomly increased or decreased by up to 100 ms (in steps of 50 ms). The pseudo-random sequence of temporal shifts was identical after omissions, which made it completely random before the omissions (due to the random number of standards in between omissions). In this way, we could also inspect the adaptation of the aligned sound responses after the omission (Fig. 6B).

We expected a reduced OR, due to the reduced temporal predictability; however, not a complete abolishment of the OR, as there remained a predictability in sound identity and an average predictability in time. This hypothesis was confirmed by the data in both widefield (Fig. 6B–E, maroon vs. ocher) and cell responses (Fig. 6F), which showed significant reductions for JIT 200 compared with SOA 200 both within an animal (Fig. 6D, p = 0.0014, Wilcoxon signed-rank test) and across animals (Fig. 6E, F, WF: p = 0.03, Wilcoxon signed-rank test, n = 5 mice; 2P: p < 10−100, Wilcoxon signed-rank test, n = 736 cells). Spatially, the difference between the two measures was strongest in the omission area (Fig. 6C).

Secondly, we analyzed whether the number of stimuli in the sequence had an effect on the OR, both on shorter and longer time-scales. To analyze shorter time-scales, we binned the omissions by the number of preceding standards, N (Fig. 6G), with each N occurring equally often. We find only a weak dependence on N, showing an increase in OR for both WF and 2P (Fig. 6I, J). However, in both cases, referencing the post-stimulus trough preceding the omission period made this dependence insignificant, which could point to a baseline shift as a function of N.

Next, we analyzed the longer time dependence on sequence exposure. We find that as a function of the position of the omission in the overall sequence, the OR first increased and then slightly decayed again (Fig. 6K, early: light blue; late: dark blue; p < 0.008, Wilcoxon rank sum test, n = 5 mice). While the division point for this early/late trend analysis was chosen manually (here: 60 trials, where trial refers to an omission), the result was not strongly dependent on the choice, with divisions in the range of 40–70 trials giving similar results.

We also wondered whether the omission response could be a consequence of entrainment to the repeating stimulus (Fig. S955–57). We first analyzed whether the stimulus response was entrained to the sequence, i.e., whether the response size (measured as peak-to-trough) increased as a function of time (see “Methods” section). Stimulus entrainment was observed only in the ORR (Fig. S9F), and only after accounting for early, positively dominated responses, which adapted over the same time scale as the entrainment increased (compare Fig. S9C to Supplementary Fig. 9F), estimated by fitting an envelope to the response (Fig. S9A, B). The omission response did not show entrainment itself, i.e., its shape stayed very different (slow, unipolar) from the stimulus response (rapid, bipolar), and consequently the entrainment measure remained very low (Fig. S9G, H).

In summary, the size of the omission response was sensitive to the regularity of the sequence on the level of the temporal spacing of the stimuli, as shown in the jitter control (Fig. 6F), as well as on the degree to which the regularity has been experienced, as shown by the omission response over the entire duration of the sequence (Fig. 6K, as in ref. 26). On the other hand, the dependence on the more recent regularity, i.e., the number of standard stimuli between omissions, was weak and appeared to be already included in a baseline shift. Together, this suggests that omission responses are regularity dependent, but their build-up extends beyond the longest inter-omission sequence (15 tones) used here.

Omission responses are spatially distinct from prediction errors and stimulus-specific adaptation
Previous research into predictive processes in the auditory system often investigated neural responses to unexpected changes in stimulus properties13,16,30,58. The classical paradigm in this context is regular sequences of tones, with randomly occurring oddball/deviant stimuli interspersed. Previous research has found that the deviant response increases with the stage of the auditory system from the inferior colliculus to the thalamus and the auditory cortex14,19. We asked next how the spatial organization of omission responses related to those attributed to deviant stimuli and prediction errors. To quantify these in accordance with the literature, we ran two classical paradigms (Fig. 7A), oddball/deviant sequences (Dev), and a Multiple Standards Control sequence (MSC). In the Dev case, we replaced the omissions with deviant sounds (4 and 32 kHz as standard or deviant, SOA 200 ms), while in the MSC case, the omissions were replaced by standard stimuli (4 and 32 kHz), and the standards were replaced by pseudo-randomly drawn tones of different frequencies.

In accordance with previous studies, we defined two measures: Repetition suppression (RS) as the difference between the neural responses to the same tone when it is a standard in the Dev case and when it is in the context of random tones inside the MSC (Fig. 7A, right), averaged for both probe frequencies. The rationale is that repeated presentation of the same stimulus leads to a reduction in the response size, be it due to any combination of active suppression or passive adaptation. The response to the same stimulus in the MSC, where it is not preceded by itself, is considered a (rather) unadapted reference in comparison.

Secondly, prediction error (PE) was defined as the difference between the neural response to the same tone when it is a deviant in the Dev case and again in the MSC (Fig. 7A, right). The rationale is that when it is a deviant, the same tone is less expected, and the response could therefore represent a prediction error. Below, we first inspect the spatial maps of the responses before stepping toward the maps of PE and RS.

Deviant responses (orange) to the same tone were generally larger than standard (blue) or MSC (green) responses in both primary and secondary areas (Fig. 7B). Deviant responses showed a late increase in the response (100–200 ms, in particular in pTeA), consistent with the ramping/elevated response observed in omission responses (Fig. 1G). In the MSC case, the average response was particularly large in A1, A2, and AAF (Fig. 7C, F). The Dev response was overall significantly larger than the MSC response across all regions (p < 0.001, Wilcoxon signed-rank test, FDR-corrected using the Benjamini–Hochberg method). The Standard response tended to be smaller than the MSC response, with the largest reductions in A1/A2.

Repetition suppression (RS) showed significant variation across the different cortical areas (Fig. 7G, I, p = 0.019, Kruskal–Wallis across areas), with the highest degrees of response suppression seen in A1 and A2, while no parts of TeA showed adaptation to repeated stimulation. Prediction errors were highest in AAF; however, they did not vary significantly overall across regions (Fig. 7H, I, p = 0.23).

In summary, we find qualitative differences in the degree of stimulus-specific adaptation across different regions, focused on A1/2, while remaining negligible in other regions, in particular, the main contributor to the ORR, the pTeA. Prediction errors did not show big differences across areas, in line with the widely represented deviant response and the lack of direct relation between Deviant and Omission responses reported earlier25.

Methods
Experimental model and subject details
All experiments were performed at the Central Animal Laboratory at the Radboud University Medical Center. The experimental procedures and protocols were approved by the Dutch central commission for animal research (Centrale Commissie Dierproeven, project numbers: 2017-0041, 2023-0031) and implemented in accordance with the local animal welfare body. Female adult CBA/JRj mice (Janvier Labs) were co-housed in a normal 12 h light/dark cycle and provided with ad libitum food and water. CBA/JRj mice display normal hearing for many months, in contrast to C57Bl/6 derived strains, which develop age-related hearing loss at high frequencies early on36,103. Surgeries were performed when the mice were 6-7 weeks of age, and all recordings in this study were obtained within 10 weeks after surgery.

Implant and transfection surgery
Mice received pain medication via the drinking water (Carprofen, 5 mg/kg, based on regular water intake) in the 24 h before the surgery.
At the start of the surgery, mice were anesthetized with isoflurane (3% induction, 1–2% for maintenance) in equal parts oxygen and air. Breath rate was monitored to range between 40 and 80 bpm. Carprofen (5 mg/kg) and Dexamethasone (2 mg/kg) were administered i.p. after induction of anesthesia. Core body temperature was maintained using a homeothermic blanket (RWDRS-485) in combination with a rectal thermometer, and 0.5 ml saline injections were administered s.c. per hour of anesthesia.

Scalp fur was removed using a depilatory cream, and the skin on the scalp was disinfected with Betadine and ethanol (70%). Local anesthesia was provided by injecting a mixture of Lidocaine/Bupivacaine (0.1 ml) s.c. using a fine syringe needle. A surgical incision was made over the midline. The right hemispheric portion of the skull was first exposed, and the periosteum was removed using a spudger in combination with 3% H2O2, after which the skull was slightly scratched using a scalpel. A custom-made aluminum head post was implanted on the right skull hemisphere at a ~45° angle away from the medial axis using dental cement (Superbond, Sun Medical). Next, the left skull hemisphere was exposed down to the ventral portion of the temporal bone and cleaned with 3% H2O2.

The craniotomy center target was 2.4 mm posterior and 4.7–5.0 mm lateral, about 0.5 mm medial to the parietal-temporal ridge, measured point-to-point relative to Bregma using a crosshair laser (650 nm, 5 mW, Quiaoba) mounted perpendicularly to the skull top on a micropositioner (Kopf Instruments). Prior to drilling, the skull around the craniotomy was cleaned, scratched, and stabilized using dental cement. Depending on the cranial window used, a craniotomy with ø = 3–4.3 mm was performed around the estimated center using a combination of a micromotor drill (K.1070-2, Foredom) with a 0.5 mm steel burr (19007-05, Fine Science Tools) and a #4 forceps (Fine Science Tools). To minimize dura and vessel trauma, the central bone piece was lifted out using a handle cemented onto the piece’s center. Once exposed, the dura mater was kept moist by regular application of sterile saline droplets.

For transfection with the activity reporter jGCaMP8m, a pulled glass micropipette (5 μl capillary, inner tip diameter ~20 μm, unbevelled, 555/5, Assistent) was back-filled with mineral oil and then loaded with ~2.5 μl viral solution (2×1012 vg/ml; pGP-AAV9-syn-jGCaMP8m-WPRE; #162375, Addgene). Using a micromanipulator (SMX series, Sensapex, Finland), the micropipette was angled at ~45° with respect to the brain surface, and injection locations were offset medially to result in an 800–1000 μm spaced honeycomb pattern centered on the craniotomy at ~400 μm cortical depth (800 μm injection depth). Per site, 150–250 nl viral solution was injected at a speed of ~30–60 nl/min, followed by 4 min pauses before pipette retraction, to avoid backspilling.

A cranial window was constructed by gluing a stack of 2 small (3/3.5/4.3 mm) and 1 larger (4/4/5 mm) glass platelets (#0 Thickness, Warner instruments), using optical adhesive (NOA68, Norland), and stored in 70% ethanol. It was rinsed with double-deionized water and then cemented into the craniotomy, gently depressing the tissue. Finally, a custom-made, grade 5 titanium headplate was cemented planar with the window and in alignment with the skull’s central fissure using the micropositioner, to ensure accurate positioning across multiple imaging sessions. See S4 for a visual summary of the surgical procedure.

The animals were allowed to recover from the surgery for a week and were then gradually habituated to head fixation and the recording setup for at least one more week, using sugar water as a reward. Viral expression levels were monitored from the second week onwards until they stabilized, before commencing the experiments.

Widefield imaging
Widefield recordings were performed in a dark and sound-insulated chamber, using acoustic foam on the inside (thickness: 5 cm, black surface, Basotect Plan50, BASF). The acoustic foam shields against external noises above ~1 kHz with a sound absorption coefficient > 0.95, defined as the ratio between absorbed and incident sound intensity. This corresponds to > 26 dB of shielding in addition to the acoustic shielding provided by the booth itself. A 470 nm LED (M470L4, Thorlabs) was triggered at 100 mA for intervals of 1 ms at 100 Hz, using a multichannel LED driver (DC4140, DC4104, Thorlabs). The light was passed through a collimation lens (ACL2520U, Thorlabs) and focused on the brain surface vessels using a 4× objective (Plan Fluor 4×/0.13, MRH00041, Nikon), resulting in an illumination intensity of 0.378 mW/mm2. The emitted light was filtered for its green light components using a longpass dichroic mirror (DMLP490R, Thorlabs) and a bandpass filter (MF525-39, Thorlabs) and recorded using a high-speed, global shutter sCMOS camera (U3-3060CP-M-GL, IDS Imaging), recording at 100 fps at 1 ms exposure duration and a resolution of 1920×1200 pixels, spatially averaged 4× in both dimensions during acquisition. To account for idiosyncratic differences in fluorescence levels between animals, the camera’s digital gain factor was adjusted between 1 and 3 to produce a raw signal intensity with maximum pixel intensity at 25–50% of the sensor’s bit range. Overall photobleaching of the fluorophore intensity was very slow and amounted to < 5% of the overall signal intensity over a whole recording session (2–3 h), and was therefore deemed insignificant over the much shorter period of single paradigms. Imaging data was time-aligned per frame to the clock of a multichannel data acquisition device (PCIe-6351, National Instruments) at 10 kHz.

Two-photon imaging
We performed cellular resolution, 2-photon imaging in the same animals, guided by the response maps obtained in widefield imaging. An infrared laser (Chameleon Vision-S, 925 nm, 1467 mW, Coherent) was pulsed at 80 MHz for 75 fs intervals, attenuated with a Pockels cell to 7-90 (mean ~30) mW in the focal plane, depending on the imaging depth and indicator expression levels. The laser’s group delay dispersion was adjusted for maximum fluorescence signal to compensate for chromatic aberrations in the light path. A sound-shielded (5 cm acoustic foam, Basotect BASF, inside a wooden box), resonant-galvo scanner (6SD11798, Novanta) in combination with a photomultiplier tube module (H11706-40, Hamamatsu) was used to image at 30 Hz (dwell time: 127 ns/pixel) at a resolution of 512*512 pixels, covering an area of 485×490 μm. The imaging path was calibrated such that the back objective (16× LWD, 0.8 NA, Nikon) aperture was ~120% filled with the expanded laser beam, and the half-width of the point spread function was < 8 μm in x and y and < 15 μm in z for a 4 μm fluorescent microbead. As the animals were held at their head post in an near upright position, the scope was tilted 38° off the vertical axis. The animals were head-fixed on a 3D-printed bed and restrained in order to minimize the chance of harm to the animal or motion artifacts. A goniometer was added below in order to allow for fine-tuning of the mediolateral tilt.

Centrifuged ultrasound imaging gel was used as an immersion medium, and a custom 3D-printed matte-black sleeve was placed on the objective to bridge the gap to the sample and protect from residual stray light. To minimize shot noise and auditory noise, lights were turned off in the room during all recordings. A laser-safety tent as well as a sound-insulating enclosure (5 cm acoustic foam) around the animal were used to protect the objective from external light and sound sources. Further, the platform motors (X-LRM050A-E03-ENG2333 and X-LRQ050AP-DE51-ENG3237, Zaber Technologies) were sound-shielded with acoustic foam. Acoustic background level was verified to be rather flat inside the enclosure using an ultrasonic microphone (see below), and in particular did not contain a peak at the resonant frequency of the scope (8 kHz). All data acquisition was controlled through the ScanImage (Premium, 2023.1.0, MDF Bioscience104) software system inside Matlab. Imaging data was time-aligned per frame to the clock of a multichannel data acquisition device (USB-6251, National Instruments) at 10 kHz.

Pupil and facial imaging
During widefield recordings, we recorded from the right snout and eye simultaneously, i.e., opposite to the craniotomy to avoid mixing of light sources. Images were acquired using a high-speed camera (GV-5240CP-NIR-GL, IDS, Germany) equipped with a camera zoom lens (XM-300, SainSonic) under infrared illumination (850 nm) using an LED (M850L3, Thorlabs) powered by an LED driver (LEDD1B, Thorlabs) collimated by a lens (ACL2520U, Thorlabs). Images were captured at 50 fps and an exposure duration of 15 ms. Precise temporal alignment was performed by collecting the frame triggers at 10 kHz in the common DAQ device (see above). After the recording, images were compressed to MPEG-4 with the constant rate factor set to 20 and stored for later processing.

Acoustic stimulation
Sound stimuli were generated and recorded using a multichannel data acquisition device (USB-6251, National Instruments) through a custom-written software package (Controller, implemented in Matlab R2018b, The Mathworks). Sounds were presented to the right ear using a high-quality, miniature driver (IE800, Sennheiser) placed at a 2 cm distance from the pinna at a sampling rate of 250 kHz. The output of the speaker was calibrated to have a flat spectral profile over the range of 2–70 kHz within 5 dB using an inverse linear filter (see ref. 105 for details), estimated before the experiment using an ultrasonic free-field microphone (Type 4939, Bruel & Kjaer), amplified by a signal amplifier (Model 1708, Bruel & Kjaer) corrected for its own frequency-dependent sensitivity curve.

Acoustic paradigms
The sounds used for stimulation were of 75 ms duration, including sinusoidal ramps of 5 ms at their beginning and end, presented at 50 dB SPL. The sinusoidal tone frequency was either 4 kHz or 32 kHz for the pure tone paradigms, and white noise stimuli were presented with constant power spectral density between 2 and 64 kHz.

For the oddball paradigms, tones were presented in regular intervals with a stimulus onset asynchrony (SOA) of 100/200/300/400 ms, i.e., the time between subsequent stimulus onsets. For the SOA Jitter control paradigm, SOA was pseudo-randomly jittered around 200 ms by ±[0, 50, 100] ms, such that each individual SOA from the set {100, 150, 200, 250, 300} was presented equally often. An oddball was either a silence (in the case of stimulus omissions, i.e., amplitude set to 0, equal to -Inf dB) or a non-standard pure tone (i.e., 32 kHz or 4 kHz in the Deviant paradigms, depending on the frequency of the standard). The presently used separation between deviant and standard (3 octaves) is larger than typically used in single-cell studies (<= 0.5 octaves16,106–108). We chose a large separation to be able to study large parts of AC in the same paradigm; however, this means that the deviant and standard will typically activate different neural populations. We therefore refer to the deviant as a distant deviant, to avoid confusion with the classical use of deviant in the literature, which refer to a local deviant that is within the tuning curve of cells that also respond to the standard.

After a lead-in sequence of 20 repetitions of the standard stimulus, oddball stimuli were interjected after a pseudo-randomly chosen number of standards. In each trial, the number of standards between oddballs was equally distributed over the integers [1, 2, 3, 4, 5, 7, 9, 11, 13, 15], such that each number of standards appeared 10 times, resulting in 100 oddball presentations per trial, reduced to 81 after excluding sequences with at least 2 standard tones before the oddball. In a subset of animals, we also recorded additional paradigms which included double omissions, i.e., 0 was included in the above set. Trials were thus rather long sequences (820 stimuli, 82–328 s for 100–400 ms SOA, respectively), with the purpose of establishing and maintaining the regularity of the standard sequence. Per parameter set, two trials were run. Paradigms were only directly compared if they were recorded in the same imaging session.

For the Many Standards Control sequences, the probe tone was presented in place of the oddballs, and for each standard, tone frequency was pseudo-randomly drawn from 10 frequencies, log-spaced between 2 and 64 kHz (excluding the probe frequency), such that each frequency appeared with equal likelihood and perceptually at random.

For the tonotopy estimates, 75 ms pure tones with 11 log-spaced frequencies between 2 kHz and 64 kHz were presented at 4 different amplitude levels, linearly spaced between 20 and 80 dB SPL. The SOA was 1 s, though we verified that SOAs of 500 ms and 50 ms produced qualitatively similar results. Per trial, each frequency-amplitude combination was repeated 6 times in differently randomized order, with each randomization repeated 5 times. This design was chosen to allow for post-hoc exclusion of both sequence-specific and unscrambling-related effects. Across trials, each frequency-amplitude combination was presented 30 times.

Data analysis
Widefield image processing: Frame times were synchronized to stimuli based on the NI frameclock timestamps. For rare cases in which a frame was lost during acquisition, the missing frame was replaced by the previous frame to avoid using future data. Images were downsampled by a factor of 2 to a final resolution of 240×150 (MATLAB: imresize). Per trial, the fluorescence baseline of each pixel (F0) was estimated with an 8 s moving median (symmetrically around each time step) and used to calculate the pixel’s relative luminosity over time (ΔF/F0). We verified that using a single pre-trial F0 yielded qualitatively matched results. The moving median baseline provided lower variability in comparison and was therefore chosen. Comparing baselines estimated between trials and animals, we found the baseline to vary over time and between trials, meandering both in the positive and negative direction, both under stimulus-driven and silent conditions (for details see Fig. S7). On average, only a small, systematic decay was observed over the length of the trial (~150–330 s), which we attribute to a slight reduction in indicator brightness-possibly bleaching-over time. In this study, no spatial or temporal deconvolution was performed in order to prevent assumptions on the spatial light mixing or the indicator dynamics, respectively, to bias the readouts of spatio-temporal activity profiles. No temporal low-pass filtering was performed as the onset dynamics of jGCaMP8m provided informative signals up to the full sampling rate of the camera (100 fps). To verify time alignment and sequence unscrambling, we simulated video data based on the raw stimulus waveforms for Oddball and Tuning paradigms and applied our analysis to these. To minimize the effect of small motion artefacts (typically 1 pixel or less) on dF-derived functional metrics on vessel outlines, sub-pixel rigid motion correction (NoRMCorrE package109) was used.

Two-photon image processing: Preprocessing and ROI identification and extraction were performed in suite2p (V0.12.0110). Specifically, we performed the following steps:
(i) Motion correction via nonrigid frame registration. Parameter settings: nonrigid=1, block_size=[128, 128], snr_thresh=1.2, maxregshiftNR=5.
(ii) Functional cell detection. Parameter settings: roidetect=1, denoise=1, spatial_scale=0, threshold_scaling=1.0, max_overlap=0.75, max_iteration=20, high_pass=100, spatial_hp_detect=25.
(iii) ROI selection: Due to variable image quality, manual sub-selection was performed according to the mean image after the functional cell detection. This avoided the inclusion of ROIs with poor quality spatial profiles or locations, i.e., non-cell-like shapes, overexpressed cells, or cells located inside vessel shadows.
(iv) Cell signal and neuropil extraction. Parameter settings: neuropil_extract=1, allow_overlap=0, inner_neuropil_radius=2, min_neuropil_pixels=350. Neuropil decontamination is performed by selecting a correction coefficient β according to the correlation between the extracted raw cell signal F and the corresponding extracted neuropil signal Fneu. The decontaminated signal was computed as: Fdec = F − β · Fneu, where F is the raw cell trace, and Fneu is the corresponding neuropil trace. The coefficient β was estimated from a linear regression of F on Fneu: F ≈ α + β · Fneu. Since calcium signals can be considered mean-centered, the intercept α was omitted. Under this assumption, the least-squares estimate of β is: β = Cov(F, Fneu) / Var(Fneu), which can also be expressed as: β = r · (σF / σFneu), where r is the Pearson correlation coefficient and σ denotes the standard deviation of each signal. In our dataset, the correlation between raw and neuropil traces was relatively low (r = 0.2 ± 0.1), resulting in β values typically in the range of 0.3–0.5.

The cells’ spatial profiles, locations, and corrected activities were then collected for further processing. No further temporal filtering or deconvolution was applied to the Ca2+ signals to analyze the temporal information in the signal without distortions. To avoid biases due to decreasing single-cell quality with depth, we also analyzed the 2p data using a grid-based approach, specifically, each of the raw 2-photon image frames was evenly divided into a 16×16 grid, each block containing 32×32 pixels of the original image. Within each block, the combined signal was computed as the mean value of all pixels within the block of the frame.

Traces extracted were then aligned to the central system clock by recording the frame timestamps on the central DAQ device. Subsample alignment correction was performed for different locations inside an FOV to account for the sequence of scanning by the laser, which started in the top-left corner and progressed to the bottom using bidirectional scanning. In total, this amounted to a maximally corrected delay of ~30 ms (~1/framerate), which is relevant given the rapid onset dynamics of [Ca2+]in as well as jGCaMP8m (<5 ms35). For display purposes, data from different neurons were then commonly binned, and data displayed at a given time correspond to the last scanning time-point before this time. This ensured that no data from the future contributed to the data displayed at a given point in time (which would have been the case when using binning based on minimal distances to bin centers).

Deconvolution was deemed not necessary due to the fast time-scales of the indicator (2.5 ms @ 50% rise time, and ~40 ms @ 50% decay time35, their Extended Data Table 6) in relation to the 2p imaging time resolution (33 ms). We have verified that deconvolution did not change the results qualitatively, in particular, not the omission responses.

Spatial alignment between widefield and 2p data
For each mouse, a high-resolution vessel image of the craniotomy was used to determine an individual unique vessel landmark. At the start of each two-photon imaging session, the center of the imaging field was aligned with this landmark in x, y, and z using widefield illumination, and the scope’s motor coordinates were recorded. With the relative coordinates computed by subtracting landmark coordinates from the current coordinates and taking into account the relative rotation and scaling caused by the scope’s tilt (38°) and the zoom factor (set via the range of the scan mirrors) respectively, all cells’ location including their profile in subsequent imaging fields can be mapped onto a coordinate of the craniotomy at the accuracy of μm.

Tonotopy estimate
To sharpen the pure tone response estimate, the mean over a period of 200 ms preceding stimulus onset was subtracted from each pixel’s ΔF/F0 trace per frequency-amplitude combination per trial. Responses were collected over a time window of [0, 230] ms, relative to stimulus onset (100 ms stimulus duration plus a 130 ms) to account for the dynamics of response and calcium indicator, based on rise-to-peak and half-decay constants reported in ref. 35, over which the maximum value was taken as the response. After taking the mean response across trials, frequency-amplitude combinations were excluded if their mean value did not exceed the 55th percentile of the pixel’s responses across frequencies and amplitudes. The best frequency was then computed using the approach in ref. 36: The first amplitude with an above-threshold response was defined as the hearing threshold for that pixel, and responses were summed over that and the two higher amplitudes, excluding amplitudes above 70 dB SPL. Across frequencies, the weighted normalized average was then taken per pixel, and the maximum was used to select the best frequency. The degree of responsivity per pixel (shown in Fig. 1D as alpha scaling) was computed as the 55th percentile of responses across amplitudes and frequencies. While tonotopies exist and can be recovered, tonotopic maps vary substantially between animals and do not directly match the temporal organization of the auditory cortex and its surrounding areas, consistent with the variability in auditory cortex location and internal division described previously36,111.

Region definitions
As there is no widely accepted consensus as to the precise defining criteria for the primary and secondary auditory fields36,38,40,112, we developed an approach that combines conventional area estimates through tonotopy and low-frequency response centers with an estimate for temporal fidelity, since secondary cortices by definition exhibit slower response dynamics38. We quantified this by measuring the peak-to-trough distance (in ΔF/F0) that a pixel exhibits during a single noise burst presentation (100 ms, 50 dB SPL), as well as the half-max latency using a Fourier transform interpolation of the trace (MATLAB interpft) surrounding the response peak. These measures resulted in stable and well-defined localization of core, fast-responding response areas (encompassing AI, AII, and AAF) due to the narrow time window of the stimulus and their fast rise-to-peak dynamics. Since GCaMP6-based imaging studies do not provide the opportunity to resolve these temporal response characteristics, we argue that previous estimates of the spatial extent of primary auditory cortices may have been overestimated, and we instead characterized some of the posterior tonotopically organized areas as belonging instead to TeA based on their weak tonotopy, long response latency and modulation speed, as well as weak impulse responses, in accordance with classical anatomical labeling in the Allen Brain atlas113, as well as a recent study which characterized TeA with sparse, high-latency auditory responses37. Using a custom-written graphical user interface (GUI), we defined area borders by hand per animal. The approach was to define the AI-AAF intersection in the high-fidelity center, and then constrain further border definitions to a 1–1.5 mm diameter and fidelity of the primary regions, using tonotopical features as augmentations to guide further area border choices. The extent of the secondary cortices was estimated by using the normalized global (i.e., post-stimulus onset) maximum response value to a 100 ms noise burst presented at 50 dB SPL and empirically setting the threshold to the 75th percentile of maximum values outside of the craniotomy (see Supplementary Fig. 4 for an example of the region selection for the animal shown in Fig. 1). This metric for responsivity was then normalized and smoothed with a 3-pixel Gaussian kernel (MATLAB imgaussfilt) to generate an alpha map (see Fig. 1E).

Pupil tracking and facial motion analysis
The videos obtained from facial imaging were used to quantify pupil size and facial motion. Pupil size was estimated by tracking multiple points on the pupil perimeter and the eye using the SLEAP software package, a deep learning framework that enables animal pose estimation114. Specifically, eight nodes on the pupil edge were tracked to cover the horizontal, vertical, and diagonal distances. Furthermore, there were four nodes placed on the left, right, top, and bottom of the eye to establish when the mouse blinked. The model that was used to track these nodes throughout the videos was trained on 426 labeled instances across different animals and recordings. The final pupil size was defined as the median Euclidean distance between all opposite points on the pupil edge. Frames in which the eyelid was partially closed and the pupil thus (partially) occluded were excluded and interpolated between neighboring frames.

To extract a scalar measure of facial motion, we quantified the overall amount of change between subsequent frames115. Specifically, first frame brightness was standardized to the interval [0,1] per recording, by using a random subset of frames to discretize the intensity into 100 bins, and the remaining frames were then mapped to these bins. Next, the motion energy per pixel was computed as the absolute, pixel-wise difference between subsequent frames. This measure was normalized per recording by subtracting the pixel-wise mean over time. Finally, overall facial motion energy (FME) was quantified as the mean across pixels to obtain a scalar measure over time. We also performed SVD-based (singular value decomposition) decomposition into facial motion components49, however, this provided qualitatively similar results and was thus not included in the results.

Oddball response extraction
For the presentation of each standard stimulus or oddball-tone or omission - the corresponding frame times were selected, including frames that fell into a pre- and post-period of 3 times the SOA. For averaging, only repetitions of the standard or oddball were included that had ≥2 standard stimuli preceding and following them. This ensured that the average only contained averages at each point in time over (locally) identical stimulus conditions. The corresponding extraction of frame times was performed for the different data sources alike, i.e., widefield, 2p, and facial video, used for extracting pupil and facial motion data. The average omission response (OR) was calculated as the mean over the response values from the onset of the omission to the onset of the post-omission stimulus. The omission-responsive region (ORR) was determined using the OR. Specifically, we calculated the upper 98th percentile of the omission response values. To refine this region, we applied morphological operations, including dilation and erosion, and Gaussian filtering to smooth the boundaries and remove gaps within the mask.

Spatial clustering of neural responses to double omission
Widefield imaging data was acquired as a three-dimensional array with spatial dimensions X×Y and temporal dimension T. To identify spatially distinct functional regions related to the double omission (dOM) paradigm, k-means clustering was applied to the time activity profiles of each pixel. Specifically, the temporal responses during the dOM paradigm were used to cluster pixels into an empirically selected number of groups (k=9) based on the similarity of their activity patterns. Each pixel was assigned to one of the nine clusters according to its temporal response characteristics. A cluster assignment map was then generated by color-coding each pixel according to its cluster label, producing a spatial representation of functionally distinct regions during the dOM paradigm.

Entrainment analysis
We defined a custom score to quantify entrainment. ΔF/F traces were aligned to stimulus onset. For each animal, upper and lower envelopes were obtained by interpolating between the identified largest local peaks and troughs of the ΔF/F trace in response to each stimulus. The average of the upper and lower envelopes was defined as the overall response component (Fig. S9C), i.e., ignoring the rapid responses to each stimulus. This component was subtracted from the original trace to remove overall response effects prior to calculating entrainment scores. The entrainment score for each stimulus was defined as the difference between the peak and trough within a window extending from stimulus onset to 50 ms after stimulus offset (truncated to 25 ms for 100 ms SOA sequences). Per-animal scores were normalized by dividing raw values by the median entrainment score of the primary region across all stimuli. For omission analysis (Supplementary Fig. 9G), entrainment scores were calculated in the same way as for stimulus periods, then averaged across all omission events within each animal before computing the across-animal mean. In all other analyses, scores were averaged directly across animals after per-animal normalization. Differences between conditions (stimulus vs. omission, PR vs. ORR, or SOA dependency) were tested using linear mixed-effects models (fitlme, MATLAB) with condition as a fixed effect and animal as a random intercept; p-values for fixed effects were taken from the ANOVA table of the model.

Statistical analysis
We performed mostly non-parametric statistical comparisons to avoid distributional assumptions, which are hard to assess on the animal level (n=10). Therefore, we mainly used the Wilcoxon Rank Sum and Signed-Rank test for unpaired and paired comparisons, respectively. Further, for multigroup comparisons, we first used the Kruskal–Wallis ANOVA, using Bonferroni correction for multiple testing. Test outcomes were considered to be significantly unlikely given the null-hypothesis if the p-value was below 0.05 after correction for multiple testing, which is indicated with an asterisk in figures. Two asterisks indicate a p-value less than 0.01, and three indicate a p-value less than 0.001. If different testing strategies were required, this is specifically indicated in the text. Error bars/error hulls show standard errors of the mean, i.e., standard deviation divided by the square root of the number of animals/cells, unless indicated otherwise.

In comparisons involving cells from multiple animals, we used a nested testing approach, i.e., linear mixed-effects models (fitlme, MATLAB), with one fixed effect (e.g., region) and animal as a random intercept. P-values for the fixed effect were obtained from the ANOVA table of the mixed model. We checked the degree of normality of the data (Shapiro–Wilk test combined with the Quantile-Quantile plot), which was typically the case. Due to the lack of a non-parametric alternative for fitlme in MATLAB and the high degree of significance of the comparisons, we applied the assumption of normality in all cases.

Subject exclusion criteria
In total, we performed implant surgeries and ran all/subsets of the above paradigms in 14 mice, out of which 4 were excluded from further analysis if either the auditory cortex was not widely enough transfected (1), there was tissue infection and/or dural regrowth prohibiting imaging (2), or due to implant failure (1).

Histology
Based on earlier research116, AAV9 and AAV1 were candidate vectors for transfecting cortical neurons with the jGCaMP8m construct. In order to verify functionality and relative strength of the vectors, slices of cultured rat hippocampal neurons were transfected using jGCaMP8m in AAV1 and AAV9 serotypes (Addgene #162375, 1×10^12 vg/ml). Both vectors appeared to work equally well in vitro (see Supplementary Fig. 5A/B), and the AAV9 construct was chosen for further testing. To verify that the construct expresses as intended in vivo, one animal was transcardially perfused with DBPS and PFA after termination of the in vivo experiment, and brain slices from ~3.2 mm posterior to bregma were stained using rabbit anti-GFP and anti-rabbit GFP to enhance the signal-to-noise ratio of jGCaMP8m’s intrinsic fluorescence in DPBS34. Slices and fixed culture preparations were imaged using a widefield microscope (Thunder, Leica), to confirm that viral expression is localized to all cellular compartments of cortical neurons in the mouse after in vivo injection (see S5C).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is a very interesting manuscript combining widefield and 2-photon imaging calcium imaging as well as pupillometry and facial movement analysis to provide novel insights into the neural representation of omitted sounds. The most significant aspect of the work is the demonstration of the localisation of omission responses to a particular region of the temporal cortex, which will be extremely valuable to those intending to dissect the circuitry underlying these omission responses.\nThe authors produced a solid piece of work which will certainly have an impact beyond the field auditory neuroscience. The methodology is sound and the conclusion are well supported by the evidence.\nI only have some (very) minor comments.\nFigure S4: put orientation bars on photos\nL.59: processings > processing\nFigure 1H: panel label missing\nFigure 1I: incorrectly labelled as 1H\nFigure S2E: I don't think I understand panel S2E and I'm wondering whether it adds much/anything to what's already shown in S2D.\nFigure 2B: The ORR demarcated in 2B looks much larger than what was identified as ORR in S2D and would seem to encompass parts of A1. Or am I misinterpreting something here? I guess, to some extent, this may be explainable by differences in scale perhaps? Are all data in Figure 2 from a single animal?\nL.155: more steep > steeper\nL.1008: delete 'across'\nL.372: an > a\nL.393: times > timed\nL.402: typically > typical\nL.501: receptive properties > receptive field properties\nL.524: suggest > suggests\nL.669: used image > used to image\nL.778: delete 'but'\nL.785: It is somewhat unclear how the authors dealt with the neuropil contamination. Was the neuropil signal subtracted in full (F-Fneu) or was the neuropil signal scaled before subtraction (i.e. F-0.7*Fneu) as suggested by Chen et al., 2013 (GCaMP6) and Zhang et al., 2023 (GCaMP8). The sentence starting with \"was empirically selected...\" is missing at least one word at its beginning and it is not clear at all what the authors are trying to say here.\nL.788: \"then for further\": a word seems to be missing here.\nL.893: 'region' missing after 'responsive'\nL.974: centrum > centre?\nL.1020: I think I worked out what is displayed here (Fig 3A) but I'm not absolutely certain. This panel legend, like many others, provides the authors' interpretation of the displayed data rather than a detailed and neutral description that would allow the reader to arrive at their own interpretation. What are the axes in the top panels (there are no scale bars)? What is the green vertical line?\nFigure 5J: I don't understand what is displayed here.", "reviewer_2": "This manuscript investigates neural responses to omitted auditory stimuli using widefield and two-photon calcium imaging in mice, identifying spatially localized omission responses and proposing their relevance to predictive processing. The data are extensive and the approach is ambitious, combining neural recordings with behavioral measures (motion energy and pupil size) and a series of control manipulations. However, several technical and interpretive concerns—particularly surrounding the ΔF/F signal processing—raise questions about the robustness of some core findings. The intention here is to highlight areas where clarification or re-analysis could meaningfully strengthen the conclusions.\nCalcium Signal Processing and Temporal Resolution in the Interpretation of Omission Responses\nThe ΔF/F traces shown throughout the manuscript (e.g., Figures 1G, 2C,5B,6B and 7B) exhibit several features that raise questions about signal processing and the suitability of calcium imaging for certain timing-sensitive claims:\nPost-stimulus suppression: A consistent dip in ΔF/F below baseline following evoked peaks appears in both widefield and two-photon recordings. The suppression size is remarkably larger than the evoked activity. This pattern is uncommon, even with faster indicators like jGCaMP8m, and could result from baseline inflation or decay overlap across high-frequency stimuli (e.g., 5 Hz).\nPre-stimulus rise: The sound-evoked activity (ΔF/F) begins to increase prior to stimulus onset in several examples. This observation requires further clarification and investigation. The manuscript should address: 1. Is there a potential timing misalignment between stimulus delivery and the recording system that could account for this apparent pre-stimulus activity? 2. Have the authors ruled out technical factors such as hardware synchronization issues or processing delays in data acquisition? 3. Could this observation reflect a physiological anticipatory response related to the experimental design, such as stimulus entrainment or expectation effects? 4. What steps were taken to ensure proper baseline normalization, particularly with respect to potential contamination from preceding stimuli?\nBaseline strategy: ΔF/F was calculated using the median fluorescence over an 8-second window. While this approach reduces trial-by-trial noise and global drift, it also incorporates activity from multiple preceding stimuli, especially at high presentation rates. This could inflate baseline values and potentially produce apparent “responses” during omissions that reflect returns to true baseline rather than stimulus expectation violations.\nThese signal dynamics directly affect the interpretation of the timing, magnitude, and spatial localization of omission responses—the central focus of the manuscript. From a conceptual standpoint, the interpretation of these responses as “prediction errors” assumes that the neural signals are temporally precise and reflect a violation of an internal model. However, without behaviorally relevant prediction, and in the absence of a task or outcome, it is difficult to rule out alternative mechanisms such as entrainment-related rebound or passive stimulus filtering, which could produce similar dynamics in the absence of cognitive prediction.\nIn this context, I also wonder whether the choice of imaging methods over electrophysiology introduced tradeoffs that affect some of the conclusions. The widefield and two-photon approaches clearly offer valuable spatial insights, particularly in identifying omission response localization in ORR (likely TeA). However, for capturing precise, rapid dynamics such as SOA dependence, ramping, and double-omission responses at 200 ms intervals, the temporal limitations of calcium imaging may pose a challenge. Even with GCaMP8m, the intrinsic integration and decay properties limit the ability to resolve sub-200 ms timing features, especially without deconvolution.\nI raise this not to question the value of imaging, but to ask whether the authors might consider:\nShowing representative raw fluorescence traces and/or overlaying ΔF/F with the baseline F₀ to clarify the impact of preprocessing,\nExploring ΔF/F computed on a trial-by-trial basis, or including deconvolved activity estimates, to better match the temporal structure of the experimental design, and possibly discussing (or incorporating) complementary electrophysiological recordings, even in a limited fashion, to validate key timing-based inferences (e.g., omission latency, SOA scaling, ramping structure).\nOmission vs. Offset Controls\nIn Figure 5, the authors attempt to distinguish omission responses from offset responses by comparing activity in a 200 ms SOA sequence (with omissions) to that following sequence termination in a 400 ms SOA stream. However, these conditions differ not only in the presence or absence of a final tone, but also in their temporal context—specifically, the rate of preceding stimulation, the level of neural adaptation, and the strength of temporal entrainment. These differences could confound any conclusions drawn from the comparison. For instance, fast rhythmic input (as in the 200 ms condition) can induce phase-locked neural activity that gives rise to so-called “echo responses” (https://doi.org/10.1038/s41598-017-18465-w ; ) — rebound-like transients that occur at expected times even after stimulus cessation, potentially reflecting rhythmic resonance rather than prediction error (https://doi.org/10.1523/JNEUROSCI.1642-19.2019).\nAdditionally, the interstimulus response shown in the 400 ms SOA condition (Fig. 5B), which the authors use as an offset control, appears to persist well into the time window where an omission response would occur in the 200 ms condition. This temporal overlap suggests that what is interpreted as an omission response may in fact ride on ongoing offset-related activity, particularly since the omission window occurs just 200 ms after the prior stimulus. This possibility deserves further attention, as it directly bears on the specificity of the omission response as a prediction error signal.\nMore broadly, this raises a conceptual concern: how do we differentiate between a predictive signal that reflects expectation violation, versus a biological rhythm echo or offset-like signal that appears due to entrainment or rebound following regular input?\nThe double-omission control helps to address some of these concerns, but its interpretation may be complicated by the same ΔF/F signal dynamics noted earlier — especially the strong post-stimulus suppression and potentially inflated baselines that arise in high-frequency stimulus contexts. These dynamics may produce rebound-like features that resemble offset or echo responses, further challenging the distinction between prediction-driven and passive neural activity. Clarifying how baseline and decay dynamics were handled in these controls, and providing raw or minimally processed traces where feasible, would greatly aid in interpreting the origins of these signals.\nJittered SOA Condition and Prediction\nThe jitter control in Figure 6 is conceptually valuable and well-designed in intent, but I found it difficult to interpret the presence of any omission response under this condition.\nThe jittered sequence uses a pseudorandom set of SOAs (100–300 ms), eliminating consistent temporal regularity. From a predictive coding perspective, one might not expect strong omission responses in the absence of a stable rhythm to generate precise temporal predictions.\nThe authors do report reduced ORs under jitter (Fig. 6B), but not abolished. The presence of even reduced omission responses in this context raises a question: are these signals truly reflecting a top-down temporal prediction error, or do they reflect bottom-up adaptation or rebound dynamics that are less sensitive to fine-grained regularity? Without a behavioral marker of prediction or evidence of hierarchical feedback, it is difficult to conclude that these signals represent cognitive prediction violations rather than statistical learning or passive entrainment.Additionally, the same ΔF/F signal dynamics described above (e.g., suppression, baselining issues) may behave differently under regular vs. jittered timing, potentially exaggerating this apparent drop.\nIf the omission response reflects a rebound or offset-like phenomenon, this could confound the intended interpretation of the jitter control as evidence for prediction-specific processing.\nStatistical Framework\nA few statistical issues are worth revisiting:\nMANOVA (Fig. 2H) assumes multivariate normality and independence, which is difficult to justify in calcium imaging. Non-parametric alternatives may be more robust here.\nThe data are hierarchically structured (neurons within animals), yet most tests treat each neuron as independent. Animal-level averaging or mixed-effects models would more accurately reflect the design and reduce pseudo-replication.\nBehavioral Measures (Figure 4)\nThe inclusion of facial motion and pupil responses is an important strength, but I would encourage a more cautious interpretation:\nFacial motion increases after omissions but is time-locked to the next sound, not the omission itself. This makes it difficult to argue that the movement reflects prediction error rather than rebound excitation or adaptation. There is also no reported correlation between motion energy and neural responses.\nPupil dilation peaks ~500 ms after the omitted stimulus—well after the neural omission response. This is compatible with a general arousal response, possibly LC-NE-mediated, but not direct evidence for predictive monitoring. Moreover, the dilation overlaps with responses to subsequent stimuli, further complicating interpretation. These signals may be better framed as nonspecific arousal correlates rather than behavioral readouts of predictive timing, particularly given the lack of task engagement.", "reviewer_3": "This manuscript by Peters, Cai et al. describes results of experiments designed to investigate the neural representation of sound omissions (unexpected absences from an otherwise statistically predictable sound sequence) in the auditory cortex of awake mice. Neural responses to unexpected omissions are of great interest to sensory neuroscientists because they arguably represent the purest form of evidence for statistical learning and/or sensory prediction; there is no stimulus present during an unexpected omission and yet the omission evokes neural activity. Omission responses have been widely demonstrated in humans using EEG and also, in a few studies, in animals including rats and mice. However, the neural mechanisms underlying omission responses and their localisation within the brain have remained largely open questions. The answers to these open questions have profound implications for prominent theories about \"predictive coding\" in the brain, with potential to help resolve debates about the extent to which neural responses in different cortical and subcortical regions represent stimulus features, sensory predictions, prediction errors, or something else.\nHere, experiments were conducted in awake passively listening mice, while neural activity across primary and secondary fields of auditory cortex was monitored using widefield imaging and, at the single-neuron level in more targeted fields of view, using two-photon imaging with an fast calcium indicator, jGCaMP8m. . Facial movements were measured during the experiments, providing a spontaneous behavioural measure of arousal. Multiple stimulus paradigms were used to rule out alternative explanations for the results and to test predictions of prominent theories about neural representation, such as that omission responses signal sensory predictions or prediction errors.\nThe manuscript makes at least three major contributions to systems neuroscience:\n(A) Results clearly demonstrate the existence of a secondary region of the auditory cortex in mice (pTeA) in which neurons with strong omission responses are clustered. There are multiple well-designed controls that effectively rule out alternative explanations, and some of these controls are quite novel and innovative in their own right (e.g. the second-omission control, which could also be useful for investigating omission reponses in human EEG studies).\n(B) Intriguingly, the temporal characteristics of the omission responses observed in the mouse brain are inconsistent with some theories of predictive coding. In particular, the observed responses ramp up monotonically following the unexpected omission rather than following the expected timecourse of the omitted stimulus, implying that the responses represent something more like an *integrated* prediction or prediction error signal. This result provides an important new constraint for theories of sensory representation in the auditory cortex, and for analysis of \"mismatch responses\" and other deviant responses in all sensory modalities.\n(C) Finally, the manuscript serves as an excellent early illustration of the power of fast two-photon imaging techniques to address questions about neural representation on ~10ms timescales. The authors used a relatively new and very fast calcium indicator (jGCaMP8m) and rigorously minimised the use of deconvolution and filtering, and thereby provide a very robust demonstration that calcium imaging can be used effectively to explore questions about temporal representation of sound signals that would previously have been robustly accessible only using electrophysiological techniques.\nThe manuscript is also very well-written. The authors should be particularly commended on the thoughtfulness of their Discussion section, which carefully lays out the evidence against possible alternative explanations for the key observations, and explores a wide range of implications of the results for systems, cognitive and computational neuroscience.\nMAJOR COMMENTS:\n1. Data on the on the laminar distribution of omission responses vs standard responses is less convincingly presented than the other results. In particular, Figure 3 is not fully explained. What do the coloured vertical strips in the top lineplots of Figure 3A represent? What are the axes of the matrix plots in Figure 3A? More critically, why is the \"omission response\" line in the Figure 3C not normalised to 100% at 100um, given that the x-axis label says \"Response rel. OR at 100um\"? While the other figures in the manuscript were generally very convincing and largely self-explanatory, Figure 3 is not convincing in its present form.\n2. Several controls provide compelling evidence demonstrating that activity in the ORR is related to omissions rather than offset responses (e.g., comparison between omissions at 200ms SOA and expected sounds at 400ms SOA, and observations regarding the double-omission response). However, the claim that the ORR can be clearly distinguished topographically from regions with offset responses could be better supported. Please provide more information about exactly how offset response magnitude in Supplementary Figure 3 is defined. Peak delF/F over what time interval after offset? Robust identification of offset responses is not as easy as for onset responses because an excitatory offset burst can be difficult to distinguish from a slow decay in sustained activity. There's a lot of overlap in the onset and offset response maps in Supplementary Figure 3; this is potentially unsurprising as a lot of auditory cortex neurons have responses to both transients. However, another possibility is that the offset response measure is slightly confounded here with the onset response measure, because a cell with high sustained response that decays slowly following sound offset might show up as having both a large onset response AND a large offset response, even in the absence of a \"true\" offset response burst. What would maps of the difference in delF/F between the offset period and onset (or onset+sustained) period look like? More generally, how do the authors know that their offset response measure is capturing \"true\" offset responses rather than slow decays of sustained activity?\n3. Also regarding maps in Supplementary Figure 3 (and elsewhere): There seems to be a very prominent outlining of blood vessels in the response latency, response modulation, onset response, offset response and omission response heat maps. Why might this be?\n4. The dependence of omission responses on SOA was non-monotonic with SOA (Figure 5O). Also, the dependence of omission responses on the number of previous presntations of regular stimuli was slightly non-monotonic (increasing to 40-70 trials then slightly decreasing). Both of these observations would seem to suggest that entrainment/oscillation phenomena might be a more accurate and parsimonious explanation for the observed omission responses than integrated prediction or prediction error. Granted, these explanations are not possible to disentangle cleanly, but the non-monotonicities provide an intriguing avenue for further investigation. Do measures of entrainment in the responses to non-omitted stimuli show the same profile as a function of SOA or number of previous stimuli as the omission responses do? If so, this might be good evidence for the entrainment hypothesis --- or at least, good evidence for circuit-level constraints on the more cognitive predictive-coding interpretations, which would not predict non-monotonicity with SOA or number of previous stimuli.\nMINOR COMMENTS:\nAbstract: \"Animals were aware of omissions...\" Aware is something of a loaded word and should be avoided. It is possible that the arousal responses measured in spontaneous behaviour are entirely subcortical and only minimally relevant to \"awareness\".\nAbstract: I think the abstract could be slightly improved to highlight the significance of this paper more clearly. In particular, one of the greatest strengths of this paper is the careful comparison of different stimulus paradigms (varying SOAs, jitter, number of omissions etc) to rule out offset responses or other possible explanations for the observed omission responses. This is summarised mainly in the sentence about omission responses being \"temporally distinct\" from offset responses, which doesn't really do the authors' efforts here full justice.\nFigure 4, panel O: This plot is described in the legend as showing a \"low-pass dependence\" of OR on SOA, but in fact it is bandpass with peak at 200ms. Why might this be? More explanation or at least speculation here would be helpful. The 200ms SOA almost seems a bit of a \"sweet spot\". In fact for SOAs above 300ms it seems that the omission response is plateauing. Could this reflect an interaction with brain oscillations, or (perhaps equivalently) with short-term synaptic plasticity timescales?\nFigure 6: It's very intriguing that the effect of sequence jitter on the omission responses was so minimal once the impact of sequence jitter on baseline activity was taken into account. This observation seems itself worth a bit more attention in the Discussion.\nLine 57: \"tone height\" -> tone frequency\nLine 59: \"predictive processings\" -> predictive processing\nLine 74: \"Calcium\" should be lowercase\nDiscussion: Generally the discussion is excellent (especially the subsections on alternative explanations for omission responses and implications for predictive coding). However, the first subsection on relation to previous studies could be improved; it doesn't flow very well across some seemingly disjointed paragraphs. For example the paragraph about whether omissions are regularly or randomly selected (lines 402-405) feels a bit disconnected from everything else, and it's not really made clear what random or regular selection of omissions means.\nThe Discussion would also benefit from a bit more information about how the areas known to connect to ORR might contribute to generation of the monotonically ramping omission responses (and/or changes in baseline activity with jittering of regularity, which was also a very intriguing observation in itself)."}, "author_response": "1 Response to the reviewers' comments for the manuscript NCOMMS-25-15638-T\n2 \"The representation of omitted sounds in the mouse auditory cortex\"\n3\n5 address in detail below, including several new figures. Changes to the manuscript are highlighted\n6 in blue font.\n7\n9\n11 imaging as well as pupillometry and facial movement analysis to provide novel insights\n12 into the neural representation of omitted sounds. The most significant aspect of the work\n13 is the demonstration of the localisation of omission responses to a particular region of the\n14 temporal cortex, which will be extremely valuable to those intending to dissect the circuitry\n15 underlying these omission responses.\n16\n18 fully agree with the implications this holds for the future analysis of the circuitry, which we are\n19 planning to tackle in the near future.\n20\n22 the field auditory neuroscience. The methodology is sound and the conclusion are well\n23 supported by the evidence.\n24\n27 typos/errors!\n28\n31\n34\n38 this, i.e. with the two panels receiving a single letter, as the description of the area and the time-\n39 course are dependent on each other, and neither sequence with separate letters could be\n40 unambiguous (i.e. in one first shows the time traces, the areas are not yet defined, and if one first\n41 shows the areas, then it is not clear at which time the activity is shown). But we noticed that this\n42 was incorrect in the results text, which we have now corrected to match the figure and its caption.\n43\n1\n45 much/anything to what's already shown in S2D.\n46\n48 animal, while S2D shows the shift of the PureTone ORR center, with respect to the NB ORR\n49 center, across animals. We have added more information to the caption and changed the Panel\n50 title of S2D to clarify this.\n51\n53 in S2D and would seem to encompass parts of A1. Or am I misinterpreting something here?\n55 data in Figure 2 from a single animal?\n57 demarcating the Omission response area was accidentally set to a value that was lower than in\n58 all other plots and given in the methods. We have corrected this mistake and updated the figure,\n59 which now shows that the 2p ROI is encompassing a substantial part of the ORR.\n61 animals. While this was mentioned in the figure caption, we have further clarified it there, and also\n62 clarified it in the associated Results sections.\n63\n66\n69\n72\n75\n78\n81\n84\n87\n2\n90\n92 the neuropil signal subtracted in full (F-Fneu) or was the neuropil signal scaled before\n93 subtraction (i.e. F-0.7*Fneu) as suggested by Chen et al., 2013 (GCaMP6) and Zhang et al.,\n94 2023 (GCaMP8). The sentence starting with \"was empirically selected...\" is missing at least\n95 one word at its beginning and it is not clear at all what the authors are trying to say here.\n97 subtract a scaled version of the neuropil signal. However, we found that the correlation between\n98 the neuropil signal and the cells was typically rather small (~0.2), and we set the scaling factor to\n99 a value <0.7, namely in the range from 0.3-0.5. This choice has been clarified in the methods\n100 section and the formula has been more explicitly described. Using a value of 0.7 led to qualitatively\n101 similar results.\n102\n105\n108\n111\n114 data rather than a detailed and neutral description that would allow the reader to arrive at\n115 their own interpretation. What are the axes in the top panels (there are no scale bars)?\n118 figure and the others, which we have now resolved. In particular we have added scale bars for\n119 time and dF/F, use the same style for the omission and added a label for the stimuli/omission\n120 (vertical bars). Also, the green bars, which are time-indicators for stimulus or omission, have now\n121 been changed to match the color scheme for those two cases, and visually connected to the\n122 panels that are displayed below. These changes have been clarified in the text and in the figure\n123 caption. Regarding the level of interpretation in the captions, we have carefully revisited all figure\n124 captions, removing formulations that were suggestive of an interpretation, rather than a\n125 description. However, for clarity of presentation we still refer to the elevated activity in the omission\n126 period as Omission responses.\n127\n130 adapted the corresponding sections in the Methods (\"Spatial clustering of neural responses to\n131 double omission\") and the Results to clarify it. We would also like to take the opportunity to clarify\n3\n132 it here directly for the reviewers: The motivation to perform the cluster analysis was to highlight\n133 regions which show a heightened response to the second omission in sequence. Since the double\n134 omissions could only be integrated less frequently than the single omissions (as otherwise, the\n135 animal could have expected them), the corresponding regions were less robustly defined than the\n136 omission region. The clustering was performed on one-dimensional response trace profiles from\n137 double omission, and the resulting cluster labels were projected back onto the craniotomy for\n138 spatial visualization.\n139\n4\n141\n143 and two-photon calcium imaging in mice, identifying spatially localized omission\n144 responses and proposing their relevance to predictive processing. The data are extensive\n145 and the approach is ambitious, combining neural recordings with behavioral measures\n146 (motion energy and pupil size) and a series of control manipulations. However, several\n147 technical and interpretive concerns—particularly surrounding the ΔF/F signal\n148 processing—raise questions about the robustness of some core findings. The intention\n149 here is to highlight areas where clarification or re-analysis could meaningfully strengthen\n150 the conclusions.\n152 in the context of recent methods, which bring new possibilities (much improved SNR and temporal\n153 resolution), but we also agree that their introduction to a field requires particular caution, which\n154 we provide below in the form of additional analyses to address the reviewers' specific concerns.\n156 the suggestions of this reviewer, we partially point to other sections of this letter. Generally, we\n157 would like to emphasize here that we mainly provide data that can critically inform candidate\n158 theories of predictive processing, and carefully discuss in how far they are compatible with the\n159 different frameworks, for example 'Predictive Coding'. Instances where descriptions could have\n160 been suggestive, we have carefully adapted to be more neutral, while maintaining an accurate\n161 description of our findings.\n162\n166 exhibit several features that raise questions about signal processing and the suitability of\n167 calcium imaging for certain timing-sensitive claims:\n169 peaks appears in both widefield and two-photon recordings. The suppression size is\n170 remarkably larger than the evoked activity. This pattern is uncommon, even with faster\n171 indicators like jGCaMP8m, and could result from baseline inflation or decay overlap across\n172 high-frequency stimuli (e.g., 5 Hz).\n174 knowledge there is no published data for our technique (GCaMP8m in ACX), the results of which\n175 we will gladly share with the reviewer. Our first question was, whether the negative slope after the\n176 stimulus-related activity peak is only an artifact of the indicator. At the beginning of the trial (see\n177 figure R1 below, grand average across all animals), the strong negative downslope appears to\n178 only develop over multiple presentations of the same stimulus, associated with a speedup of the\n179 excitatory peak as well. This observation, in combination with the indicator’s dynamics (~2.5 ms\n180 rise time and 50% decay time ~40 ms see Extended Data Table 6, (Zhang et al. 2023)) causes\n181 us to conclude that indicator dynamics do not explain the observed dip. This leaves physiological\n182 explanations.\n5\n184 thalamocortical and intracortical synapses produces suppressed firing that last on the order of\n185 tens to hundreds of milliseconds; when stimuli repeat at ~5 Hz, recovery is incomplete and\n186 suppression accumulates across the train, producing a below-baseline period after offsets (Wehr\n187 & Zador 2005; Chung et al. 2002; Ulanovsky et al. 2004). However, in our data we see that the\n188 post-stimulus dip is very comparable between the 200SOA and 400SOA conditions, while the OR\n189 is nearly extinguished for 400SOA (Fig. 5B & O, Fig. S10).\n191 inhibition, which builds up with the sequence, effectively leading to stimulus entrainment (see R3,\n193 on the Slack channel with multiple researchers, among them Loren Looger (Janelia), whose group\n194 developed jGCaMP8m. The conclusion from this conversation was that the observed time-scales\n195 are consistent with neural activity, see Zhang et al. 2023, Fig. 4d (excitatory neurons in VI) with a\n196 50% decay time of ~70ms, and members of this forum agreed that inhibitory inputs could further\n197 shorten the observed dynamics. The hypothesis of inhibitory input would also be consistent with\n198 the relative activation times of excitatory and inhibitory cells in the auditory cortex, with excitation\n199 preceding inhibition (see Fig. 3F from Feigin et al. 2021, reproduced below in Fig. R2 for the next\n200 point).\n202 brief sound can produce a transient suppression of spontaneous firing below baseline after the\n203 initial evoked response (Willmore & King, 2022) — durations typically range from tens to a few\n204 hundred milliseconds depending on brain level (subcortical/cortical) and cell type (Voytenko &\n206 indicators' fast dynamics resolve a real physiological phenomenon, most likely experience-\n207 dependent inhibitory sharpening.\n209 appears to be an adaptation of the overall brightness over time, which starts already from the\n210 beginning of the trial (see Figure R1), even before acoustic stimulation begins (for more details\n211 on this see the Figure R3 in this letter, which details baseline adaptation in silent and stimulus\n212 driven trials). We think that this baseline reduction is in fact likely indicator-related, and mainly\n213 does not originate from an adaptation in neural activity, as it also occurs in silence and persists\n214 after the termination of the stimulus (although it appeared to recover between recording sessions),\n215 and has been observed before (see Fig. R1b below). This motivated our choice of using a\n216 dynamic baseline. However, we agree that the exact level of this baseline is hard to determine\n217 during continuous stimulation as is the case in an oddball paradigm (see Figure R3 for more\n218 details on baseline choices below). Therefore the absolute size of the negative part of the\n219 response after the stimulus might indeed be increased by the choice of the dynamic baseline,\n220 however, it would be important to note that the polarity can still likely be correct (see Fig. R1c\n221 below, which shows neuronal response profiles from the mouse auditory cortex recorded using\n222 electrodes, adapted from (Jamali et al., 2024)).\n224 possibility can be ruled out, since an overlap would have to extend over periods exceeding a\n225 single SOA, i.e. >200ms. On the one hand, the jGCaMP8m response kernel is already quite\n226 decayed at this point (see Zhang et al. 2023, Fig. 4d), and the response components that we\n6\n227 observe in between the last response and the next negative dip show faster dynamics, which\n228 would raise the question why such a potential residual (negative) decay was not visible in between\n229 the stimuli, and would then materialize after the following response.\n231 a limited overestimation of the baseline in combination with an actual buildup of inhibitory input\n232 and would argue that the use of previous calcium indicators (e.g. GCaMP6s/f) would have\n233 obscured such dynamics. We would like to emphasize that this does not affect the differences we\n234 observe between ORR and PR for omissions, which is the main result of the present manuscript.\n236 aspects now in the Methods.\n237\n238\n240 beginning of the sequence. Data in this plot is derived from the median over both trials of all 10 mice for\n241 the 32kHz Omission paradigms, for ORR (red) and PR (blue), aligned to recording start. A large and\n242 relatively undefined response occurs for the first stimulus in the PR, accompanied by a slower excitatory\n243 modulation (~0.5s half-decay time) across both areas. With an increasing number of stimuli, a fast baseline\n244 undershoot develops after the stimulus onset response (see Fig. 1G for mean response shape after\n245 sequence habituation has occurred).\n246\n[FIGURE REDACTED]\n247\n249 (doi.org/10.1093/cercor/bhx079). These recording are also from the auditory cortex of mice, using a\n250 different, slower indicator and a slower stimulation rate (0.5 Hz), however, also here a reduction in the\n251 baseline is observed.\n252\n7\n[FIGURE REDACTED]\n253\n255 (https://doi.org/10.7554/eLife.102702.2). This paper provides electrophysiological recordings from\n256 populations of neurons in the mouse auditory cortex, in response to 5 sounds with similar SOA (250 ms).\n258 reduction of activity in response to the stimulus, while the second one shows below baseline reduction after\n259 the 4th and 5th stimulus, and the third one (right) shows adaptation. In our widefield imaging these clusters\n260 would be combined and might give rise to the observed composite waveform.\n261\n262\n264 onset in several examples. This observation requires further clarification and\n265 investigation.\n266\n268 find cases in which the actual response occurs before the stimulus. We think there are two\n269 reasons for why responses may sometimes appear to occur early, which we would like to\n270 exemplify with Fig. 2C (2p imaging).\n272 as too early. Given the response delay in the mouse auditory cortex is ~5-10ms (see e.g. Feigin\n273 et al. JNeurosci. 2021. Fig. 3F, reproduced below as Fig. R2) and the ~2.5ms onset time (50%\n274 activation) of the jGCaMP8m indicator (Zhang et al. Nat. Methods, 2023, Fig. 1D, and Extended\n276 falls into the first 2p imaging bin which is 33 ms long. Hence, while at first glance seemingly too\n277 fast, the fast dynamics of AC responses and the indicator response are in line with our\n278 observations.\n280 and has also been puzzling to us at first. In addition to the technical checks we performed (see\n281 below), we have compared extensively across cells and paradigms, and have reached the\n282 conclusion that these rises are due to either recovery or anticipatory dynamics following the\n283 preceding stimuli. We base this conclusion on the fact that we find consistent timing at stimulus\n284 onset for many different paradigms, and also the dynamics that we observe during an omission,\n285 as well as the positive inflection in the response size that happens in the first bin after the next\n286 stimulus has been presented (see in particular the single cells shown in Fig. 2C bottom, light\n287 blue).\n289 this would usually be considered correct in principle, it contributes to the observation of the\n290 reviewer, as information from the future (up to 33 ms/2 = 16.5 ms) can add to the data shown at\n291 each point (depending on where a neuron lies in the field of view, and the subsample correction\n292 we performed, see Methods). To avoid this confusion we have now plotted the data always such\n293 that a measurement point represents the data that has been collected in the dT before it, i.e. on\n294 average, this shifts the plot to the right by 16.5 ms. We would like to ask the reviewer to revisit\n297 accordingly, in regards to the points mentioned above.\n298\n[FIGURE REDACTED]\n299\n301 that inhibitory interneurons follow a bit later (right plot) and peak between 50-100ms.\n302\n304 1. Is there a potential timing misalignment between stimulus delivery and the recording\n305 system that could account for this apparent pre-stimulus activity?\n306 2. Have the authors ruled out technical factors such as hardware synchronization issues\n307 or processing delays in data acquisition?\n309 and we have very carefully designed and validated the stimulus generation and alignment to the\n310 recorded data to make sure that there are no temporal misalignments. Below we provide a list of\n311 the checks that we have performed to ensure this (already before submission of the original MS):\n312 ● For the widefield data, we originally started with a rolling-shutter sCMOS camera, which\n313 led to different timing per line in the image (data not included in this manuscript). To ensure\n314 identical timing for all pixels in an image, we transitioned to the global shutter camera that\n315 delivered the data for the present study. For the 2p data, due to the nature of the laser-\n316 scanning process, this issue is unavoidable. To correct for it, we time-shift pixels on a\n317 given line of the image by the expected delay, i.e. for 30 Hz this corresponds to 33ms/512\n318 = 0.064ms (differences between dots on a line are negligible).\n319 ● Both widefield and 2p systems are hardware synchronized to the stimulus, i.e. specifically:\n9\n320 ○ The widefield camera receives frame acquisition triggers which are generated and\n321 rerecorded by the NI DAQ device, which functions as the main clock in the\n322 experiment. The camera also sends acquisition triggers back, which are recorded\n323 on an analog channel by the same DAQ device. The same device also generates\n324 the sound starting at a digital trigger that is also recorded again on an analog\n325 channel. In this way, all these signals are precisely synchronized via the analog\n326 channels recorded in parallel (at a resolution of 0.1 ms).\n327 ○ The 2p scope runs on an intrinsic clock but sends frame acquisition triggers, which\n328 are recorded on an analog channel as well, and are then post-hoc aligned to the\n329 start trigger for the sound output (same as above in widefield).\n330 ● In widefield imaging, frame-losses can occur occasionally due to the high frame rates. We\n331 correct for these posthoc, by comparing the frame triggers, frame acquisition triggers and\n332 recorded frame times. Missing frames (which are ~0.01% of all frames) are replaced by\n333 the previous frame, thus avoiding any shifting forward in time, as feared by the reviewer.\n335 ● The spectral correction of our speaker introduces a delay of a few milliseconds. This delay\n336 is constant and is already corrected for during stimulus production, by forward-shifting the\n337 stimulus by this delay, such that the actual onset of the sound happens at the intended\n338 time (i.e. due to the convolution with our inverse filter kernel and the filtering of the\n339 amplifier/speaker.\n340 ● We have validated the entire synchronization of the recording system by recording the\n341 sound and filming the membrane of the speaker, which showed precise temporal\n342 alignment.\n344 stimulation, video and neural recording in both widefield and 2p imaging. We have added a\n345 shortened version of these checks to the Methods.\n346\n347 3. Could this observation reflect a physiological anticipatory response related to the\n348 experimental design, such as stimulus entrainment or expectation effects?\n350 (we would also like to ask the reviewer to reinspect the updated plots in this respect), we\n351 nonetheless agree that the inter-stimulus dynamics of the neural response might represent other\n352 processes than just residual neural/indicator activity from the previous stimulus. One of R3's major\n353 points (#4) was also about entrainment, we would like to point R2 to the detailed response there.\n355 with the bipolar response size to the stimulus growing at the beginning of the sequence, after a\n356 short phase of adaptation of the positive response. However, we find no evidence that the\n357 omission response's shape entrains to a similar shape as the one of the stimulus response.\n359 response, which - consistently with this interpretation - is also larger in the ORR compared with\n360 the PR. In that sense we think it is most consistent with an expectation effect. We now mention\n361 this interpretation in the discussion, and have substantially expanded our analysis of entrainment\n362 including a new figure (Fig. S9).\n363\n10\n364\n365 4. What steps were taken to ensure proper baseline normalization, particularly with respect\n366 to potential contamination from preceding stimuli?\n368 baseline, which would thus have shifted the response kernel to somewhat more negative values.\n370 other contamination aside from this shift as the average value is formed over the span of 40\n371 stimuli. The 8s window was chosen conservatively from the outset (i.e. only coarse drift is\n372 removed and not all local baseline variability is eliminated, see Fig. R1), in order to err on the side\n373 of caution with respect to the response shapes. We provide additional analyses and details\n374 regarding this question together with the following point, which concerns a related question.\n375\n377 window. While this approach reduces trial-by-trial noise and global drift, it also\n378 incorporates activity from multiple preceding stimuli, especially at high presentation rates.\n380 omissions that reflect returns to true baseline rather than stimulus expectation violations.\n382 are certain that the omission responses do not reflect a return towards a more positive baseline.\n384 baseline - and the omission response first increases relative to this baseline before returning to it\n385 (see Figure R3H below). In fact, this was one of our main concerns initially as well, however, the\n386 additional analyses clearly indicate that the omission response has a specific temporal onset, but\n387 extends in time longer than predicted by typical predictive coding theories. We will go through the\n388 rationale for our baseline choice detail below. Importantly, we would like to emphasize that no\n389 baselining was performed in the 2p recordings and the shape of the omission response was quite\n390 comparable to the one in the widefield case in the omission responsive region.\n391\n393 rather unrelated to the short-term neural response, relating mostly to properties of the indicator\n394 and maybe network state of the animal. We conclude this because:\n395 1. In recordings without acoustic stimulation (with the animal typically sitting quietly in the\n396 dark), the baseline varied to similar degrees as during stimulation (see Fig. R3A).\n397 2. Across animals, the baseline during acoustic stimulation (SOA 200 Omission paradigm)\n398 sometimes increased over the course of minutes, and sometimes decreased (see\n401 which reduce their brightness. We did not observe a systematic decrease in brightness over\n402 longer periods, i.e. recordings on subsequent days showed similar absolute brightness (since\n403 there are other processes happening in parallel, i.e. continued expression as well as potential\n404 regrowth, this statement only holds from ~2 weeks after transfection to ~1 week before sacrifice,\n405 which covers the period of the presently discussed recordings). Therefore we conclude that the\n406 larger changes in baseline within a recording over multiple minutes are likely not due to these\n407 indicator-related effects, which is also in line with the fact that we tried to work with rather low light\n11\n408 intensities. Instead, we think these reflect neural processes, which seem to, however, not be\n409 strongly related to the acoustic paradigm (again, see Fig. R3B), i.e. specifically they do not seem\n410 to reflect some basic adaptation.\n411\n413 and then compare the ORR (red) with the PR (blue), ORR tends to be less reduced (Fig. R3C),\n414 however, shows a bigger omission response (Fig. R3D), which again speaks against the\n415 alternative hypothesis of a return to the original baseline at 0.\n417 the PR (Fig. R3F) that we think best reflects their relationship.\n418\n420 of the omission response from our view. Only at double omissions (see main manuscript, Fig. 5G)\n421 and at the end of the stimulus, can we observe it for longer periods. Due to the length of the\n422 stimulus sequence, we only run two trials, which means that we only have few repetitions to\n423 inspect the omission response at the end of the stimulus sequence. We now provide this analysis\n424 (Fig. R3G/H), which shows in both the single animal case (Fig. R3G, top static baseline) that the\n425 omission response returns close to the moving baseline (Fig. R3G, bottom). Across animals\n426 (Fig. R3H, static baseline) it becomes even clearer that the omission response exceeds the pre-\n427 stimulus baseline, before returning to a lower value, close to the dynamic baseline. It is also very\n428 noteworthy that the omission response shows repetitive peaks (Fig. R3H) after the stimulus\n429 sequence, which are spaced approximately like the SOA, aligning with our observation in the\n430 double omission case (Fig. 5G).\n431\n433 suitable choice for the present data, and that the omission response cannot be interpreted as a\n434 mere return to the prestimulus baseline. Since we agree with the reviewer that this analysis is\n435 absolutely crucial to the interpretation of the omission response, we have added this figure as a\n436 supplementary figure to the manuscript.\n12\n437\n439 characteristics. All traces in the figure are derived from the PRs (blue)/ORRs (red) for the SOA 200 omission paradigm.\n441 static baseline, i.e. F0 was set to the average of the first 5s (before stimulus onset). Moving median baseline (8s window)\n442 overlaid as thick line, in corresponding, brighter color.\n444 increasing and some decreasing over time. Overall, only a small reduction in fluorescence is observed (black, gray\n445 indicates SEM).\n447 static baseline (i.e. same principle as for silent recording in A). Note that the negative transient at the beginning happens\n448 before stimulus presentation and appears to be random. For a detailed view of the post-stimulus response see G).\n450 response is only visible in the ORR, suggesting that the omission response is not caused by a global return to baseline\n452 which is not observed.\n13\n454 intact.\n456 largely unaffected (compared to D).\n458 trace settles back towards peri-stimulation levels, consistent with the present moving baseline estimate (compare top\n459 (static baseline) to bottom (moving baseline)).\n461 stimulus, the traces settle back to the moving baseline, rather than returning to 0. In the ORR, the omission response\n462 is clearly visible, showing multiple peaks which show similar spacing as the stimulus sequence suggesting some degree\n463 of entrainment (however, see response to R3 on entrainment). Note that we only have 2 trials per animal, i.e. 20 trials\n464 in total, for the present analysis, which leads to the poorer SNR here in comparison to the average responses within\n465 the sequence.\n466\n468 spatial localization of omission responses—the central focus of the manuscript. From a\n469 conceptual standpoint, the interpretation of these responses as “prediction errors”\n470 assumes that the neural signals are temporally precise and reflect a violation of an internal\n471 model. However, without behaviorally relevant prediction, and in the absence of a task or\n472 outcome, it is difficult to rule out alternative mechanisms such as entrainment-related\n473 rebound or passive stimulus filtering, which could produce similar dynamics in the\n474 absence of cognitive prediction.\n476 a substantial body of research on oddball responses, from multiple labs in particular Eli Nelken\n477 and Manolo Malmierca, who work primarily in anesthetized animals and have convincingly linked\n478 the increased neural activity in response to oddball stimuli to predictive principles (e.g. by the use\n479 of control sequences etc). In the domain of omission responses, we have recently demonstrated\n480 in a joint study with the Malmierca lab, that the omission responses are substantially greater in\n481 the awake than in the anesthetized state (Lao-Rodriguez et al. Sci. Advances, 2023). In\n482 accordance with this previous ePhys study, we confirm here that the omission response has a\n483 temporally precise onset.\n485 omission response we observe is most likely consistent with an integrated mismatch signal, given\n486 its timed onset, however, without a timed offset. An instantaneous prediction error or prediction\n487 should decay again directly after the end of the expected, but omitted stimulus. Therefore, we see\n488 our data and conclusions as challenging for established models of predictive processing, and\n489 hope that they will inspire new or modified theories of the neural representation of predictions.\n491 demonstrated, but this is very difficult to achieve with mice in the context of fast stimulus\n492 sequences - such as the oddball sequences - where omissions occur frequently, and the animal\n493 would have to respond many times during the sequence. Future, modified paradigms could\n494 resolve this problem, e.g. by creating different predictions, or spacing oddballs much further apart.\n496 strong, in particular because we included a broad range of controls here (in particular the double\n497 omission and the double SOA controls), in addition to the task-free behavior analysis of the pupil\n498 and facial movements, which clearly demonstrates tracking of recent sequence history. As we\n499 argue in detail in the discussion, the temporal dynamics of the omission response are starkly\n14\n500 different from both offset responses and entrained activity (see also new figure in response to\n502 for simply equating them with either of these two. However, we agree that in particular the offset\n503 response might be a simple variant of the omission response, which encodes the 'surprise' of a\n504 rapid change in stimulus intensity. While therefore both offset and omission responses appear to\n505 signal a violation of an expectation, we fully agree - and have expressed this in the discussion -\n506 that they do not directly match predictions of theories like predictive coding (when suitably\n507 extended in time) as the omission responses last longer than the omitted stimulus. We would\n508 therefore argue that the omission response more likely represents an indicator of an integrated,\n509 recent level of violation, which then also decays again (similar to the offset response, however,\n510 the latter on much shorter timescales).\n512 ability to image the whole AC at high temporal resolution, which is a key advance, as it allows us\n513 to compare response properties of different areas. This provides additional evidence in support\n514 of the omission response, as it is regionally specific to a higher auditory area, while being largely\n515 absent in the primary region.\n517 consideration in the discussion.\n518\n520 electrophysiology introduced tradeoffs that affect some of the conclusions. The widefield\n521 and two-photon approaches clearly offer valuable spatial insights, particularly in\n522 identifying omission response localization in ORR (likely TeA). However, for capturing\n523 precise, rapid dynamics such as SOA dependence, ramping, and double-omission\n524 responses at 200 ms intervals, the temporal limitations of calcium imaging may pose a\n525 challenge. Even with GCaMP8m, the intrinsic integration and decay properties limit the\n526 ability to resolve sub-200 ms timing features, especially without deconvolution.\n528 consider:\n531 or including deconvolved activity estimates, to better match the temporal structure of the\n532 experimental design, and possibly discussing (or incorporating) complementary\n533 electrophysiological recordings, even in a limited fashion, to validate key timing-based\n534 inferences (e.g., omission latency, SOA scaling, ramping structure).\n537 consideration. While imaging with GCaMP8m cannot fully match the speed of electrophysiology,\n538 we would like to argue that it is in fact fast enough for the present requirements.\n540 omission responses using electrophysiology in the auditory cortex (Lao-Rodriguez et al. Sci\n542 (Yaron et al. PLOS Biology, 2025) under similar stimulus conditions. These studies both showed\n543 very similar onset times and time-courses, as what we presently observe, which gives us\n15\n544 confidence that the present GCaMP8m data is a faithful representation of the same phenomenon.\n546 although in a slightly different paradigm (Awwad et al. Current Biology, 2023). The primary\n547 purpose of the present study was therefore the localization of these responses both in terms of\n548 areas and layers of the auditory cortex, however, we have added a more detailed discussion of\n549 the comparison of temporal properties between the methods to the discussion.\n551 similarly slow as reported in the two studies mentioned above. Hence, the speed of the indicator\n552 is less critical in this respect, given how fast it is (see next point). Importantly, this also means that\n553 the area differences in relation to the omission response will remain unaffected. In terms of\n554 resolving the stimulus driven responses, we think the widefield recordings convincingly\n555 demonstrate that 100 Hz is sufficient to resolve the response dynamics.\n557 beautifully characterized in the original publication (Zhang et al. Nature Methods, 2023): The 50%\n558 rise time of the indicator itself is ~2.5ms (see Extended Data Table 6), and the 50% decay time is\n559 ~70 ms (assessed with a kernel fit for data from the visual cortex). Depending on the system\n560 (culture, cortex, etc) the observed dynamics for individual neurons will differ, due to different\n561 intrinsic calcium dynamics, and are thus slower than the indicators actual intrinsic dynamics (see\n563 course appears to be on the fast side, closer to 50 ms for 50% decay. Together with the brightness\n564 of GCaMP8m this in principle allows for even single spike deconvolution under optimal recording\n565 conditions.\n567 ● Temporal resolution in 2p: assuming a decay kernel of 40-50ms, would mean that we only\n568 have ~2-3 frames per spike to deconvolve, given our 2p frame rate of 30fps, before the\n569 signal has mostly decayed. In the study that characterized GCaMP8m, the frame rate\n570 could be increased by focussing on a small number of cells, while here we performed\n571 whole FOV recordings at 30fps.\n572 ● Dynamics are qualitatively consistent: The degree of improvement that can be expected\n573 by deconvolution depends on the kernel properties, and diminishes with faster kernels.\nrise\n576 decay (~t =50ms), overall all positive. Deconvolving with this signal, one would obtain\ndecay\n577 an estimated firing rate that will show rapid increases (positive inflections) at the identical\n578 locations as the original signal (within the limits of our resolution). The estimated firing rate\n579 will be more brief, although this effect will be small given our imaging rate, i.e. likely only\n580 extend over 2-3 frames. However, no changes in polarity would be expected. In particular\n581 for slower signals like the omission response, the difference would be minor. We support\n582 this point, by providing deconvolved activity for a set of representative neurons (Fig. R4).\n584 present setting and therefore choose to present the data in a less processed form, while\n585 we of course fully agree to the value of deconvolution for slower indicators.\n586 ● Avoiding assumptions: As mentioned above, the response kernel appears to differ by\n587 system in which the recordings are performed and are a combination of indicator and\n16\n588 activity dynamics. By choosing a kernel to remove the indicator's dynamics, one makes\n589 an assumption that might also lead to distortions of the data, as it might differ by region,\n590 cell-type, etc. Avoiding such an assumption can therefore be considered a safe approach\n591 (if the dynamics are fast enough, see next two points), which was actually strongly\n592 encouraged by Reviewer 3.\n593 ● Comparability of widefield and 2p data: if we were to deconvolve the data from 2p data\n594 only, their time-courses would be harder to relate to each other. Importantly, deconvolution\n595 should to our knowledge only be done on single cell data, as otherwise reductions in\n596 response rate are not correctly handled, in particular after baselining in widefield.\n598 alignment approach, which leads to an updated Fig. 2, which might already address some of the\n599 concerns that led this reviewer to wonder about the timing. We have clarified the reasoning why\n600 not to use deconvolution in the Methods now under the 2p data analysis.\n601\n603 built-in deconv function with a kernel modeled as an instantaneous rise followed by an exponential decay with a time\n604 constant of 40 ms. Example traces shown here are averaged over omissions for a single stimulus responsive cell in\n606 amplifying noise.\n607\n610 by comparing activity in a 200 ms SOA sequence (with omissions) to that following\n611 sequence termination in a 400 ms SOA stream. However, these conditions differ not only\n612 in the presence or absence of a final tone, but also in their temporal context—specifically,\n613 the rate of preceding stimulation, the level of neural adaptation, and the strength of\n614 temporal entrainment. These differences could confound any conclusions drawn from the\n615 comparison.\n617 study is one of few that presents multiple controls, and that each control should not be seen in\n618 isolation, but rather in the context of all the controls. The difference in rate is indeed important to\n619 consider, however, generally we would like to uphold that this control is quite elegant as the same,\n620 temporally local acoustic conditions can be compared with and without expectation of a stimulus.\n622 however, we concluded that it remains a trustable control, as the size of the stimulus driven\n17\n623 response is quite comparable (see Fig. 5B), and - as we demonstrate in Fig. R3A-D - baseline\n624 changes are not primarily driven by adaptation, and we have cases in the data set where the\n625 average baseline of SOA200 > SOA400 and vice versa.\n626\n628 neural activity that gives rise to so-called “echo responses”\n629 (https://doi.org/10.1038/s41598-017-18465-w ; ) — rebound-like transients that occur at\n630 expected times even after stimulus cessation, potentially reflecting rhythmic resonance\n631 rather than prediction error (https://doi.org/10.1523/JNEUROSCI.1642-19.2019).\n633 address, also because a lot of the seeming differences are due to terminology. Specifically, echo\n634 responses appear to be similar to the presently reported omission responses, i.e. they are quite\n635 salient and have a specific temporal onset aligned with the expected time of the next stimulus.\n637 a substantial omission response anymore. The second citation that the reviewer provided (Latimer\n638 et al. 2019) is very interesting and had so far escaped our attention. Their omission responses\n639 (Fig. 7B) are indeed very reminiscent of the present data, although it lacks any kind of spatial\n640 localization in the auditory cortex (beyond the label A1). While their approach is to fit a multi-filter\n641 model to it (with filter lengths of ~2s), they also conclude that: \"These observations indicate that\n642 adaptive processes carry information about changes in the temporal statistics of temporal\n643 sequences, that is, the termination of the stimulus.\" (and they also extend this to omissions in the\n644 subsequent section). We think one of the key concepts here is the focus on adaptive processes\n645 that incorporate information from past experience/stimulation, which matches precisely with our\n646 interpretation of the data. The next step in the research will be to investigate the location/sources\n647 of these adaptive processes. As was recently shown by Tsukano et al. biorxiv (2024), even\n648 seemingly simple adaptive processes are primarily non-local: they clearly demonstrate a top-\n649 down circuit from the orbitofrontal cortex that targets SST cells in the AC. While we cannot draw\n650 the same conclusion here yet, we provide a specific spatial location in secondary areas of AC,\n651 which provides an important step towards resolving the circuit by specifically modulating inputs to\n652 the m/pTeA or the m/pTeA itself.\n654 differences to previous studies that have shown related responses, including the references\n655 provided by the reviewer.\n656\n658 which the authors use as an offset control, appears to persist well into the time window\n659 where an omission response would occur in the 200 ms condition. This temporal overlap\n660 suggests that what is interpreted as an omission response may in fact ride on ongoing\n661 offset-related activity, particularly since the omission window occurs just 200 ms after the\n662 prior stimulus. This possibility deserves further attention, as it directly bears on the\n663 specificity of the omission response as a prediction error signal.\n665 corresponds to the omission in SOA 200. However, the suggestion that this is \"riding on the offset\n666 response\", seems very unlikely to us (see below what we precisely mean by this),\n667 due to the substantial differences in timescale between the two. We had already noted this in the manuscript,\n668 but had not provided a figure directly comparing them. We provide this figure now below (Fig. R5),\n669 and will include it as a supplementary figure into the manuscript (Fig. S10).\n671 trials, separated by multiple seconds), to the within-sequence, after stimulus responses to the\n672 repeated short sounds (75 ms, maroon: SOA 200, orange: ISR 400, all aligned to the sound\n673 offset), indicates that there is fast upslope with a first peak in all traces (~70ms after stim offset),\n674 which matches the single sound offset response dynamics. These dynamics decay after this peak\n675 in the single sound response. However, only in the case of the SOA 200, do we see a clear,\n676 positive inflection with at the expected time of the omission, followed by a substantial increase. In\n677 the case of ISR400, the activity merely returns to the moving baseline (see Fig. R3 above for why\n678 we think that baseline is the best estimate). Note, that we noticed that the data in the original\n679 figure had used an old data export, and therefore the SOA200/ISR400 averages were slightly\n680 different, but this has now been corrected in Fig. 5 as well. We have also verified that the post-\n681 stimulus reduction in the case of the single sound also becomes more negative upon repeated\n682 presentation of the stimulus (see also Fig. R1), approaching the average shape of the\n684 expected, as here, the sound is presented in isolation with random, multisecond brakes in\n685 between).\n687 identifiable even in the fast sequences, and that this argues against a generally changed shape\n688 of it. The omission response starts later and increases for a much longer period than the offset\n689 response, which cannot be explained by any additive effect of the offset response. We hope that\n690 this direct comparison clarified our reasoning. We have included this figure as a new\n691 supplementary figure (Fig. S10) and refer to it in the same place where we had previously\n692 contrasted the shapes of the omission and offset response in the results.\n693\n694\n696 a single, 100 ms sound (light blue, same Paradigm as Fig. 1C, 40 trial average) and the OR200 (maroon, 200 trials =\n697 omissions) / ISR400 (orange, 1440 trials = sounds) responses in the ORR of the same example animal. Within-\n19\n698 sequence traces were baselined using 8s moving median over the entire sequence, the Noise burst trace was baselined\n699 to the pre-stimulus period (400ms window). Stimuli are indicated as horizontal bars, and between the vertical dashed\n700 lines, there was no stimulus present in all conditions. Isolated sounds are followed by a fast offset response, whose\n701 dynamics can be seen initially also in the sequence conditions. During the offset response’s decay period, the within-\n702 sequence data shows no decrease, and the OM200 shows the OR’s characteristic ramping, inflecting at omission onset\n703 (dotted vertical line), which is absent in the ISR400.\n704\n706 predictive signal that reflects expectation violation, versus a biological rhythm echo or\n707 offset-like signal that appears due to entrainment or rebound following regular input?\n709 prediction, prediction errors, entrainment and even offset responses, while rather objectively\n710 described, all lend themselves to interpretations in terms of predictive processing (e.g. Offset\n711 responses can be interpreted as a deviation from a basic continuity of the sensory input), which\n712 we also tried to emphasize in the discussion. We would therefore also advocate to take our data\n713 at face-value, i.e. in particular the spatial localization, time-scale of response, and dependence\n714 on a number of parameters, as shown in the controls. Future studies that modulate top-down or\n715 local inputs will shed light on the question of the network origin and consequently their\n716 interpretation. However, we think that the specific suggestion of entrainment can in fact be\n717 addressed by computing measures of entrainment on the stimulus response, as suggested by\n719 data/argument here, we would kindly point the reviewer to this section of the response letter (R3,\n721\n723 interpretation may be complicated by the same ΔF/F signal dynamics noted earlier —\n724 especially the strong post-stimulus suppression and potentially inflated baselines that\n725 arise in high-frequency stimulus contexts. These dynamics may produce rebound-like\n726 features that resemble offset or echo responses, further challenging the distinction\n727 between prediction-driven and passive neural activity. Clarifying how baseline and decay\n728 dynamics were handled in these controls, and providing raw or minimally processed\n729 traces where feasible, would greatly aid in interpreting the origins of these signals.\n731 on baseline estimation and its influence on the signals, which apply verbatim to the double\n732 omission case. We think these address the points raised in this question.\n733\n736 found it difficult to interpret the presence of any omission response under this condition.\n738 consistent temporal regularity. From a predictive coding perspective, one might not expect\n739 strong omission responses in the absence of a stable rhythm to generate precise temporal\n740 predictions.\n742 of even reduced omission responses in this context raises a question: are these signals\n20\n743 truly reflecting a top-down temporal prediction error, or do they reflect bottom-up\n744 adaptation or rebound dynamics that are less sensitive to fine-grained regularity? Without\n745 a behavioral marker of prediction or evidence of hierarchical feedback, it is difficult to\n746 conclude that these signals represent cognitive prediction violations rather than statistical\n747 learning or passive entrainment. Additionally, the same ΔF/F signal dynamics described\n748 above (e.g., suppression, baselining issues) may behave differently under regular vs.\n749 jittered timing, potentially exaggerating this apparent drop.\n751 confound the intended interpretation of the jitter control as evidence for prediction-\n752 specific processing.\n754 abolishment of the omission response. In hindsight, it, however, can be understood in the\n755 framework of predictions, as there remains a prediction that a sound of the same frequency will\n756 appear within an approximate, limited time-window, which is much more concrete than no\n757 prediction. However, we agree that this is not the only possible interpretation. We agree that\n758 entrainment could be considered as an alternative, however, the time-course of the omission\n759 response and the stimulus-driven response are so different in size and dynamics, that classical\n760 entrainment appears unlikely, which is further detailed in the entrainment analysis we performed\n761 (Fig. S9). We do not comment here on baselining/suppression and refer to the response given\n762 above, where in particular we have argued that the local response shape is unaffected by\n763 baselining and that the omission response 'returns' to the moving baseline, not the prestimulus\n764 baseline. About statistical learning: we are not sure whether that would be incompatible with\n765 prediction-processing. In fact, we would argue that statistical learning implies that the system\n766 adapts to some part of the stimulus statistics and stops responding to it, which immediately implies\n767 that responses are then related to aspects of the sensory environment that have not been learned,\n768 i.e. are unpredicted. We have tried to clarify this further in the discussion, also in particular that\n769 we are not tied to any particular theory of predictive processing or statistical learning.\n770\n774 justify in calcium imaging. Non-parametric alternatives may be more robust here.\n776 the Shapiro–Wilk test combined with the Quantile-Quantile plot (without or with log-transforming).\n778 that the distributions are typically normally distributed. Since we could not locate a suitable test in\n780 question), we kept the normality assumption, in particular given the tiny p-values (~10-10) the\n781 specific assumption of the test would very likely give the same result.\n782\n784 neuron as independent. Animal-level averaging or mixed-effects models would more\n785 accurately reflect the design and reduce pseudo-replication.\n21\n787 that a mixed effects model is more appropriate in this analysis. We use a linear mixed effects\n788 model now, which only led to changes in the specific p-values, but no qualitative changes in the\n789 results (as expected). The Methods section has been adapted accordingly, as well as the\n790 corresponding figures and Results sections.\n791\n794 encourage a more cautious interpretation:\n796 omission itself. This makes it difficult to argue that the movement reflects prediction error\n797 rather than rebound excitation or adaptation. There is also no reported correlation between\n798 motion energy and neural responses.\n800 the omission. Instead we argue that the adaptation of the size of the facial motion as a function\n801 of the number of sounds (Fig. 4F) and its recovery in size after an omission (Fig. 4E), show that\n802 facial motions are not a hardwired reflex to a sound, but show a dependence on the recent\n803 sequence statistics. Given the pattern of response to individual sounds, i.e. rapid after about\n804 70 ms (matching Clayton et al. 2024) and a transient response (see also Fig. 4B bottom), and the\n805 lack of a response to the omission (within the time window expected from the stimulus response),\n806 we think that the hypothesis of a rebound excitation is unlikely.\n808 analyses to address this question.\n810 omission, i.e. first one much larger, and then adapting to a lower value (see Figure R6 below, left\n811 panel). Overall this mirrors the dependence we have reported before using electrophysiology\n812 (Lao-Rodriguez et al. 2023), however, here we in addition report that the adaptation of the\n813 response is stronger in the omission response area (see below and new panel Fig. 4H).\n816 (see Figure R6 below, right panel), although substantial variability exists here. Interestingly, again\n817 the ORR shows a higher degree of correlation. We now mention these correlations in the text.\n818\n22\n819\n823 facial motion (see Fig. 4F). The decay is stronger in the omission responsive region (ORR) than in the\n824 primary region (PR).\n828\n830 response. This is compatible with a general arousal response, possibly LC-NE-mediated,\n831 but not direct evidence for predictive monitoring. Moreover, the dilation overlaps with\n832 responses to subsequent stimuli, further complicating interpretation. These signals may\n833 be better framed as nonspecific arousal correlates rather than behavioral readouts of\n834 predictive timing, particularly given the lack of task engagement.\n836 clear, and the time-course of pupil responses is known to be comparatively slow, as we and others\n837 have shown before (half-max rise time ~0.5s, i.e. very similar in time-scale to the present results,\n838 e.g. Fig. 1E (Clayton et al. Current Biology, 2024) or Fig. 3C bottom (Alishbayli et al., 2025). To\n839 support this further quantitatively we have statistically compared the size of the pupil after the\n840 omission, but before the next stimulus (110 ms after omission onset) to the pupil size 50 ms before\n841 the omission, which already reaches significance (p=0.027, Wilcoxon test). While we think this\n842 provides solid evidence that pupil dilation is indeed triggered by the omission, rather than the\n843 subsequent sound (in which case we would have expected an onset at least 200 ms post-\n844 omission). We would like to emphasize that our overall qualification of these behavioral results is\n845 already quite cautious, i.e. that there are behavioral responses that indicate a certain degree of\n846 monitoring of the sequence statistics. However, we agree that only an experiment involving active\n847 behavior can provide more clarity here. The challenge that we see in setting up this experiment\n848 is that the animal would have to respond multiple times in the sequence, and response times\n23\n849 would have to be rapid to reliably link it to the omission, rather than the next sound. Another\n850 possibility would be to change the stimulus paradigm, e.g. to the global-local paradigm, and\n851 randomly omit the last stimulus in a shorter sequence of stimuli. To address the reviewers\n852 comment we provide the above reasoning and additional test in the Results and have emphasized\n853 the need for additional behavioral confirmation in the Discussion.\n854\n855\n24\n857\n859 investigate the neural representation of sound omissions (unexpected absences from an\n860 otherwise statistically predictable sound sequence) in the auditory cortex of awake mice.\n862 because they arguably represent the purest form of evidence for statistical learning and/or\n863 sensory prediction; there is no stimulus present during an unexpected omission and yet\n864 the omission evokes neural activity. Omission responses have been widely demonstrated\n865 in humans using EEG and also, in a few studies, in animals including rats and mice.\n867 within the brain have remained largely open questions. The answers to these open\n868 questions have profound implications for prominent theories about \"predictive coding\" in\n869 the brain, with potential to help resolve debates about the extent to which neural responses\n870 in different cortical and subcortical regions represent stimulus features, sensory\n871 predictions, prediction errors, or something else.\n872\n874 across primary and secondary fields of auditory cortex was monitored using widefield\n875 imaging and, at the single-neuron level in more targeted fields of view, using two-photon\n876 imaging with an fast calcium indicator, jGCaMP8m. . Facial movements were measured\n877 during the experiments, providing a spontaneous behavioural measure of arousal. Multiple\n878 stimulus paradigms were used to rule out alternative explanations for the results and to\n879 test predictions of prominent theories about neural representation, such as that omission\n880 responses signal sensory predictions or prediction errors.\n881\n883\n884 (A) Results clearly demonstrate the existence of a secondary region of the auditory cortex\n885 in mice (pTeA) in which neurons with strong omission responses are clustered. There are\n886 multiple well-designed controls that effectively rule out alternative explanations, and some\n887 of these controls are quite novel and innovative in their own right (e.g. the second-\n888 omission control, which could also be useful for investigating omission responses in\n889 human EEG studies).\n890\n891 (B) Intriguingly, the temporal characteristics of the omission responses observed in the\n892 mouse brain are inconsistent with some theories of predictive coding. In particular, the\n893 observed responses ramp up monotonically following the unexpected omission rather\n894 than following the expected timecourse of the omitted stimulus, implying that the\n895 responses represent something more like an *integrated* prediction or prediction error\n896 signal. This result provides an important new constraint for theories of sensory\n897 representation in the auditory cortex, and for analysis of \"mismatch responses\" and other\n898 deviant responses in all sensory modalities.\n899\n25\n900 (C) Finally, the manuscript serves as an excellent early illustration of the power of fast two-\n901 photon imaging techniques to address questions about neural representation on ~10ms\n902 timescales. The authors used a relatively new and very fast calcium indicator (jGCaMP8m)\n903 and rigorously minimised the use of deconvolution and filtering, and thereby provide a\n904 very robust demonstration that calcium imaging can be used effectively to explore\n905 questions about temporal representation of sound signals that would previously have\n906 been robustly accessible only using electrophysiological techniques.\n907\n909 on the thoughtfulness of their Discussion section, which carefully lays out the evidence\n910 against possible alternative explanations for the key observations, and explores a wide\n911 range of implications of the results for systems, cognitive and computational\n912 neuroscience.\n913\n915\n917\n918 1. Data on the laminar distribution of omission responses vs standard responses is less\n919 convincingly presented than the other results. In particular, Figure 3 is not fully explained.\n921 the axes of the matrix plots in Figure 3A? More critically, why is the \"omission response\"\n922 line in the Figure 3C not normalised to 100% at 100um, given that the x-axis label says\n923 \"Response rel. OR at 100um\"? While the other figures in the manuscript were generally\n924 very convincing and largely self-explanatory, Figure 3 is not convincing in its present form.\n926 introductory panel that shows the depth stack that we performed. Further, we now closely match\n927 the style of the schematics in Fig. 3B to the schematics in other figures, using a consistent color\n928 scheme throughout for omission and standard response related plots as well as connecting\n929 elements in the plot better, in particular to the sample data plots below. The normalization was\n930 checked, it is done with respect to OR in ORR at 100𝜇m (adapted in the figure). Further, we have\n931 adjusted the labels to be more clear and a more detailed explanation of the depth dependence\n932 was provided in the text.\n933\n934 2. Several controls provide compelling evidence demonstrating that activity in the ORR is\n935 related to omissions rather than offset responses (e.g., comparison between omissions at\n936 200ms SOA and expected sounds at 400ms SOA, and observations regarding the double-\n937 omission response). However, the claim that the ORR can be clearly distinguished\n938 topographically from regions with offset responses could be better supported. Please\n939 provide more information about exactly how offset response magnitude in Supplementary\n941 of offset responses is not as easy as for onset responses because an excitatory offset\n942 burst can be difficult to distinguish from a slow decay in sustained activity. There's a lot\n943 of overlap in the onset and offset response maps in Supplementary Figure 3; this is\n26\n944 potentially unsurprising as a lot of auditory cortex neurons have responses to both\n945 transients. However, another possibility is that the offset response measure is slightly\n946 confounded here with the onset response measure, because a cell with high sustained\n947 response that decays slowly following sound offset might show up as having both a large\n948 onset response AND a large offset response, even in the absence of a \"true\" offset\n949 response burst. What would maps of the difference in delF/F between the offset period and\n950 onset (or onset+sustained) period look like? More generally, how do the authors know that\n951 their offset response measure is capturing \"true\" offset responses rather than slow decays\n952 of sustained activity?\n954 our considerations. We would like to reemphasize that in addition to the differences in spatial\n955 location, the time-scales of offset responses and omission responses are very different, with the\n956 former peaking around 50 ms and then decaying (Fig. 1C, and see (Olsen & Hasenstaub, 2022),\nvii\n958 not show a clear peak up to this point. Furthermore, the OR shows a dependence on time-history\n959 (shown now in Fig. 4H), which is also uncharacteristic of “simple” bottom-up offset responses\n960 (Olsen & Hasenstaub, 2022). However, as pointed out by the reviewer, slow response dynamics\n961 and activity summation can be important confounds when evaluating offset responses, especially\n962 for the short (75 ms) stimuli used here. To reduce this effect, we have adapted Fig. S3 to show\n963 the Offset response relative to the last dF value before stimulus offset, rather than the pre-stimulus\n964 dF value. The resulting map still clearly differs from the OR map, highlighting AAF and an\n965 uncharacterized antero-medial area, but there is also overlap of the two responses in pTeA/mTeA.\n967 discussion, we conclude that offset responses could be a very basic, temporally local version of\n968 the presently observed omission responses, but that the temporal characteristics are so different\n969 that substantial, context-dependent modification is necessary to get from one to the other. Hence\n970 both of them could still represent a violation of an expectation - i.e. offset responses for the\n971 immediate violation in intensity, and omission responses for a delayed, temporal expectation -\n972 however, different experiments that modulate top-down inputs (e.g. the excellent study by\n976\n977 3. Also regarding maps in Supplementary Figure 3 (and elsewhere): There seems to be a\n978 very prominent outlining of blood vessels in the response latency, response modulation,\n979 onset response, offset response and omission response heat maps. Why might this be?\n981 vessel outlines revealed that these were driven by small motion artefacts (typically 1 pixel or less),\n982 which caused the dF-derived metrics on vessel-bordering pixels to be strongly influenced. To\n983 calculate these response maps, we now implemented sub-pixel rigid motion correction\n984 (NoRMCorrE package, Pnevmatikakis & Giovannucci, 2017), which strongly reduces these\n985 motion artefacts. Fig. S3 and Fig. 6C, as well as the Methods section have been updated\n986 accordingly.\n987\n27\n988 4. The dependence of omission responses on SOA was non-monotonic with SOA (Figure\n989 5O). Also, the dependence of omission responses on the number of previous\n990 presentations of regular stimuli was slightly non-monotonic (increasing to 40-70 trials then\n991 slightly decreasing). Both of these observations would seem to suggest that\n992 entrainment/oscillation phenomena might be a more accurate and parsimonious\n993 explanation for the observed omission responses than integrated prediction or prediction\n994 error. Granted, these explanations are not possible to disentangle cleanly, but the non-\n995 monotonicities provide an intriguing avenue for further investigation. Do measures of\n996 entrainment in the responses to non-omitted stimuli show the same profile as a function\n997 of SOA or number of previous stimuli as the omission responses do? If so, this might be\n998 good evidence for the entrainment hypothesis --- or at least, good evidence for circuit-level\n999 constraints on the more cognitive predictive-coding interpretations, which would not\n1000 predict non-monotonicity with SOA or number of previous stimuli.\n1002 entrainment hard to disentangle from those of predictive processes. However, upon further\n1003 consideration and analysis we think we can provide an answer to this question. The reviewer is\n1004 specifically asking whether the observed omission response could be a consequence of\n1005 entrainment. We argue below that this is not supported by our data, however, that there appears\n1006 to be entrainment of the stimulus driven response as a function of time, after an initial adaptation.\n1008 peak-to-trough response size, phase-lag). Upon inspection of the data (see figure R7 below), we\n1009 concluded that peak-to-trough size of the response within a limited time window matched to the\n1010 peak and trough of the average response (which is dominated by the majority of the later\n1011 responses) gave the most reliable estimate of how the response size changed as a function of\n1012 continued exposure to the stimulus (Measures assuming a sinusoidal response shape were\n1013 avoided, as the response was not clearly sinusoidal and there was no reason to assume a basic,\n1014 sinusoidal shape). This also allowed us to compare the omission response's size in relation to the\n1015 average, possibly entrained shape of the stimulus response. Further, we estimated a top and\n1016 bottom envelope through these peaks and troughs of the stimulus response (Fig. R7A, orange\n1017 and yellow), whose average was used as an estimate of the overall response size/adaptation over\n1018 time.\n1020 we observe two patterns:\n1021 ● The overall response size rapidly reduces over the course of ~10 stimuli (Fig. R7C)\n1022 ● The peak-to-trough size increases with a slightly slower time-course (Fig. R7F) and then\n1023 stays at this level (Fig. R7D/E)\n1025 ● The omission response itself does not change its shape to match the stimulus response\n1026 (summary in Fig. R7H, and all main figures showing omission responses) and therefore\n1027 its values are very low in comparison.\n1028 ● After an omission, the entrainment of the stimulus responses is not reduced but is even\n1029 larger for the next stimulus, i.e. very different from the slow build up at the beginning of\n1030 the stimulus (Fig. R7G).\n28\n1031 ● Across SOAs, the shape of the degree of stimulus entrainment of the omission responses\n1032 is similar to what we observed for the size of the omission response, however, this is also\n1033 somewhat a tautology given that the entrainment measure is also measuring the size of\n1034 the omission response, however, at select time points.\n1036 stimulus responses, particularly in the ORR, exhibited a clear entrainment build-up over the first\n1037 ~15 ± 5 stimuli of the sequence. However, applying the same measure to the omission response\n1038 quantifies the fact that the shape of it is very different from the stimulus response, and no\n1039 convergence of the omission responses to the entrained stimulus response is observed, which\n1040 excludes it as an entrained aftereffect of the stimulus response in our assessment.\n1042 a corresponding description in the Results section and discuss the possibility in detail in the\n1044 figure, but are unsure whether this would be allowed by the word count restrictions.\n1045\n1046\n1049 and trough within an empirically selected window starting at stimulus onset and going to 50 ms after stimulus\n1050 offset (for 100 ms SOA, only 25 ms), measuring the similarity to the average stimulus response shape, after\n1051 normalization (see below).\n1053 respectively. Envelopes were drawn with interpolation between peaks (orange) and troughs (yellow) as a\n1054 visual indicator of entrainment score.\n1056 and then computing the mean across animals. For both areas, following the initial increase, overall\n1057 response size rapidly reduces over the course of ~10 stimuli (2s).\n29\n1059 respectively. Following an early, fast rise and slower decline, scores stabilized with fluctuations around a\n1060 steady level. Normalized entrainment score for each animal was obtained by dividing its raw entrainment\n1061 score by the median raw entrainment score of the primary region across all stimuli.\n1063 entrainment scores. ORR exhibited a faster and more pronounced increase than PR.\n1065 all omission events. Entrainment scores were significantly lower during omissions periods compared with\n1066 stimulus periods. The first stimulus after an omission exhibited significantly higher entrainment than\n1067 baseline, followed by a rapid return to baseline levels.\n1069 and all animals. In both areas, entrainment scores during stimuli were significantly higher than during\n1070 omissions (p<10-4, custom permutation test), whereas asymptotic entrainment values did not differ\n1071 significantly between areas.\n1073 from 100 to 200 ms SOA (p<10-4, custom permutation test), and remained stable between 200 and 400 ms\n1076\n1077\n1079\n1081 should be avoided. It is possible that the arousal responses measured in spontaneous\n1082 behaviour are entirely subcortical and only minimally relevant to \"awareness\".\n1084 \"Omissions and sequence statistics were reflected in behavioral responses, namely timed pupil\n1085 dilation and rapid facial motions, respectively.\"\n1086\n1088 this paper more clearly. In particular, one of the greatest strengths of this paper is the\n1089 careful comparison of different stimulus paradigms (varying SOAs, jitter, number of\n1090 omissions etc) to rule out offset responses or other possible explanations for the observed\n1091 omission responses. This is summarised mainly in the sentence about omission\n1092 responses being \"temporally distinct\" from offset responses, which doesn't really do the\n1093 authors' efforts here full justice.\n1095 sentence to put more emphasis on this, i.e. \"Omission responses were also distinct from offset\n1096 responses in temporal dynamics and size, as verified by multiple controls.\"\n1097\n1099 dependence\" of OR on SOA, but in fact it is bandpass with peak at 200ms. Why might this\n1100 be? More explanation or at least speculation here would be helpful. The 200ms SOA almost\n1101 seems a bit of a \"sweet spot\". In fact for SOAs above 300ms it seems that the omission\n1102 response is plateauing. Could this reflect an interaction with brain oscillations, or (perhaps\n1103 equivalently) with short-term synaptic plasticity timescales?\n30\n1105 the fact that in our previous electrophysiology study we had observed the strongest effect at\n1106 125ms SOA. Our choice of 200 ms SOA for the present study was chosen to account for the time-\n1107 constants of the calcium indicator, but before knowing the dependence on SOA reported here.\n1110 underlying mechanisms in this direction, concluding that both brain-oscillations (here: delta (0.5-\n1111 4 Hz) and ~theta (4-8 Hz) bands) as well as short-synaptic plasticity might indeed contribute (see\n1112 the work of Markram et al. 1998, Fig. 4). We discuss this now briefly in relation to a possible role\n1113 of entrainment.\n1114\n1116 was so minimal once the impact of sequence jitter on baseline activity was taken into\n1117 account. This observation seems itself worth a bit more attention in the Discussion.\n1120 of the sound, the spectral predictability remains the same. We have added a section to the\n1121 discussion including an overview of the related human literature.\n1122\n1125\n1128\n1131\n1133 alternative explanations for omission responses and implications for predictive coding).\n1135 doesn't flow very well across some seemingly disjointed paragraphs. For example the\n1136 paragraph about whether omissions are regularly or randomly selected (lines 402-405)\n1137 feels a bit disconnected from everything else, and it's not really made clear what random\n1138 or regular selection of omissions means.\n1140 rereading this section, we agree that at least two paragraphs were not optimally embedded. We\n1141 have now added 4 headings to group the points in this discussion part into Temporal, Spatial,\n1143\n1145 to connect to ORR might contribute to generation of the monotonically ramping omission\n1146 responses (and/or changes in baseline activity with jittering of regularity, which was also\n1147 a very intriguing observation in itself).\n31\n1149 the basis of a few recent preprints and publications, e.g. (Obara et al., 2023; Tsukano et al., 2024).\n1151\n1152\n1153\n1155\n1157 acoustic statistics enables detecting acoustic targets in noise (p. 2025.05.29.656794). bioRxiv.\n1158 https://doi.org/10.1101/2025.05.29.656794\n1161 eLife, 13, RP102702. https://doi.org/10.7554/eLife.102702\n1168 detection in the primate auditory cortex through feedback of prediction error signals. Nature\n1172 https://doi.org/10.1016/j.neuron.2011.01.012\n1175 https://doi.org/10.1523/JNEUROSCI.0494-22.2022\n1177 rigid motion correction of calcium imaging data. Journal of Neuroscience Methods, 291, 83–94.\n1178 https://doi.org/10.1016/j.jneumeth.2017.07.031\n1182 response via orbitofrontal top-down input. https://doi.org/10.1101/2024.09.17.613562\n1184 neurons during sound processing. Neuroscience, 165(4), 1490–1500.\n1185 https://doi.org/10.1016/j.neuroscience.2009.11.070\n1187 https://doi.org/10.1152/physrev.00011.2022\nResponses from the authors formatted in blue.\nReviewer #1 (Remarks to the Author):\nThe authors have fully addressed all my comments.\nThe authors thank the reviewer for their critical and\nconstructive feedback.\nReviewer #2 (Remarks to the Author):\nThe authors have made substantial efforts addressing\nmy concerns by providing more analyses and\nclarifications. The spatial findings—TeA localization,\nlayer-specific distribution, and hierarchical\norganization—are robust and represent valuable\ncontributions to understanding predictive processing.\nHowever, claims about temporal precision throughout\nthe manuscript exceed what the methods can\nestablish. The authors themselves acknowledge:\n\"While it cannot be concluded with certainty...\"\n(response line 190-191) and note \"baseline level is\nhard to determine during continuous stimulation\"\n(response line 216). Given 2-photon imaging at 30 Hz\n(33ms resolution) and jGCaMP8m decay of ~40ms,\nthey observe temporal structure at this scale but\ncannot prove neural computation precision versus\nmeasurement ceiling.\nI would suggest the following text changes:\nLine 15-16 (Abstract): Change \"Neuronal responses\nto the omission of expected sounds were precisely\ntimed\" to \"Neuronal responses to the omission of\nexpected sounds were time-locked to expected\nstimulus onset\"\nDone.\nLines 82-83, 130, 252 (Results): Replace all instances\nof \"precisely timed\" with \"time-locked to expected\nonset\"\nDone where applicable.\nLines 408-414 (Discussion): After \"temporally precise\n(10s of ms) mechanism,\" add: \"While jGCaMP8m\nprovides improved temporal resolution, the precise\ntiming of predictive computations at the millisecond\nscale remains to be confirmed with electrophysiology\"\nDone.\nLine 475 (Discussion on offset responses): Change\n\"effectively ruled out\" to \"substantially reduced the\nlikelihood\" (authors acknowledge: \"While not\nimpossible, we consider this rather implausible\" -\nresponse line 484)\nDone.\nLines 127-128, 157-158 (OR magnitude): Add caveat\nthat magnitude estimates depend on baseline choice\nduring continuous stimulation\nDone, thanks.\nLines 89-92 (Abstract, behavioral): Change \"reflected\nin behavioral responses\" to \"correlated with\nbehavioral changes\"\nDone, thanks.\nLines 583-587 (Discussion, limitations): Add that\nactive paradigms are needed to distinguish prediction\nmonitoring from arousal responses (authors state:\n\"only an experiment involving active behavior can\nprovide more clarity\" - response line 846)\nAdded to Discussion “[...] and enable distinction of\nprediction monitoring from arousal responses” to the\nsentence where this is semantically appropriate.\nWith these revisions, I recommend publication.\nThe authors thank the reviewer for their critical and\nconstructive feedback.\nReviewer #3 (Remarks to the Author):\nThe authors have done a very thorough job of\naddressing my comments and I have no further\nsuggestions for revision. Thank you for the detailed\nexplanations and additional analysis.\nThe authors thank the reviewer for their critical and\nconstructive feedback.\nRegarding the authors' query about whether their new\nFigure S9 addressing questions about entrainment\nshould be added as a main figure --- I think it's\nprobably best as a supplementary figure given the\nlevel of detail, but it is a very nice addition\nnonetheless.\nWe agree, and will keep Figure S9 in the Supplement."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have fully addressed all my comments.", "reviewer_2": "The authors have made substantial efforts addressing my concerns by providing more analyses and clarifications. The spatial findings—TeA localization, layer-specific distribution, and hierarchical organization—are robust and represent valuable contributions to understanding predictive processing. However, claims about temporal precision throughout the manuscript exceed what the methods can establish. The authors themselves acknowledge: \"While it cannot be concluded with certainty...\" (response line 190-191) and note \"baseline level is hard to determine during continuous stimulation\" (response line 216). Given 2-photon imaging at 30 Hz (33ms resolution) and jGCaMP8m decay of ~40ms, they observe temporal structure at this scale but cannot prove neural computation precision versus measurement ceiling.\nI would suggest the following text changes:\nLine 15-16 (Abstract): Change \"Neuronal responses to the omission of expected sounds were precisely timed\" to \"Neuronal responses to the omission of expected sounds were time-locked to expected stimulus onset\"\nLines 82-83, 130, 252 (Results): Replace all instances of \"precisely timed\" with \"time-locked to expected onset\"\nLines 408-414 (Discussion): After \"temporally precise (10s of ms) mechanism,\" add: \"While jGCaMP8m provides improved temporal resolution, the precise timing of predictive computations at the millisecond scale remains to be confirmed with electrophysiology\"\nLine 475 (Discussion on offset responses): Change \"effectively ruled out\" to \"substantially reduced the likelihood\" (authors acknowledge: \"While not impossible, we consider this rather implausible\" - response line 484)\nLines 127-128, 157-158 (OR magnitude): Add caveat that magnitude estimates depend on baseline choice during continuous stimulation\nLines 89-92 (Abstract, behavioral): Change \"reflected in behavioral responses\" to \"correlated with behavioral changes\"\nLines 583-587 (Discussion, limitations): Add that active paradigms are needed to distinguish prediction monitoring from arousal responses (authors state: \"only an experiment involving active behavior can provide more clarity\" - response line 846)\nWith these revisions, I recommend publication.", "reviewer_3": "The authors have done a very thorough job of addressing my comments and I have no further suggestions for revision. Thank you for the detailed explanations and additional analysis.\nRegarding the authors' query about whether their new Figure S9 addressing questions about entrainment should be added as a main figure --- I think it's probably best as a supplementary figure given the level of detail, but it is a very nice addition nonetheless."}, "author_response": "The authors thank the reviewer for their critical and constructive feedback.\nDone.\nDone where applicable.\nDone.\nDone.\nDone, thanks.\nDone, thanks.\nAdded to Discussion “[...] and enable distinction of prediction monitoring from arousal responses” to the sentence where this is semantically appropriate.\nThe authors thank the reviewer for their critical and constructive feedback.\nThe authors thank the reviewer for their critical and constructive feedback.\nWe agree, and will keep Figure S9 in the Supplement."}]

Decision letter