Motor cortex somatostatin interneurons adaptively shape the structure of action sequences

Domain: biology
Published: 2026-03-18
Editorial outcome: major_revision

Abstract
The brain flexibly reorganizes action sequences to optimize behavioral outcomes through reinforcement learning and adaptive motor control. Although the primary motor cortex (M1) is essential for skill learning and dexterous movement, how cortical microcircuits refine the timing and structure of action sequences remains unclear. We show that M1 somatostatin interneurons (SST-Ins) display synchronized, action-locked calcium activity during acquisition of a lever-press task in freely moving mice, in contrast to the sequential activation of pyramidal neurons. Following extended training under a stable task schedule, SST-IN activity was no longer coupled to action execution. However, when task demands were modified to require faster and more temporally constrained action sequences, SST-IN activity redistributed and correlated with trial-by-trial changes in sequences, rather than diminishing. Inhibition of SST-INs disrupted temporal organization and impaired efficient motor execution. These findings highlight the unexpected role of M1 SST-INs in refining motor programs into more efficient and task-specific structures.

Introduction
Humans and other animals organize action sequences by integrating discrete movements into cohesive units. With extensive, repeated motor training, these learned sequences become automatized and are executed as unified, holistic actions [1-4]. However, in many other behavioral situations, the organization of action sequences requires flexible modulation of their temporal and structural features depending on task demands [5,6]. This neural capacity for dynamic sequence modulation in response to changing behavioral contexts is essential for producing efficient and adaptive motor behaviors [7,8,9]. Despite its importance, the neural circuit mechanisms underlying the regulation of temporal and structural aspects of action sequences remains unclear.

Both cortical and subcortical structures, particularly the striatum and primary motor cortex (M1), are implicated in action sequence organization [10]. The striatum is involved in controlling well-learned action sequences by encoding neural signals related to sequence initiation, execution, and termination [2,3,10]. In contrast, M1 exhibits dynamic activity patterns that vary depending on the task demands and the stage of learning [11,12,13,14]. M1 may become functionally dispensable for executing overtrained behaviors, as evidenced by lesion studies showing that damage to M1 does not impair the performance of well-practiced routines [2, 15]. These findings support the “cortical disengagement” model [16], which proposes that cortical regions are critical for acquiring new skills but become less essential once motor behaviors are extensively trained.

However, other evidence suggests that M1 continues to encode movement or action sequence-related parameters even after extensive training [17]. For example, primate studies have shown that M1 exhibits anticipatory activity representing memorized action sequences during their execution [18]. Additionally, M1 neurons encode discrete elements of rapid, skilled action sequences, maintaining representations of individual movement parameters throughout sequence performance [4]. M1 activity also remains crucial for flexible execution of cue-guided sequences. For instance, cortical lesions impair adaptability to external instructions while sparing the execution of overtrained sequences [19,20]. Thus, it is possible that the neural circuitry in M1 is actively involved in organizing action sequences on a trial-by-trial basis, particularly when tasks demand temporal modulation or flexible reuse of learned action sequences.

Despite extensive research on M1 in general motor skill learning, how distinct neural circuit components within M1 contribute to the temporal and structural modulation of action sequences remains largely unexplored. Pyramidal (PYR) neurons and inhibitory interneurons (INs) constitute [21] key elements of heterogeneous microcircuitry in M1. They orchestrate action learning and execution through dynamic and plastic activity patterns finely tuned to specific movement parameters [22]. In M1 layer 2/3 (L2/3), PYR neurons progressively refine their activity during associative learning [14], encoding both sensory and motor components as learning advances [23]. Also, neighboring cortical INs, particularly somatostatin-positive (SST) interneurons(INs), play a critical role in regulating learning-dependent network plasticity by modulating synaptic and structural dynamics of PYR neurons [24]. Notably, SST IN activity in L2/3 exhibits task-specific modulation correlating with retention and maintenance of previously acquired motor skills [25, 26]. Thus, prior work has primarily focused on the role of SST INs in L2/3 during motor skill learning.

In contrast, neural circuits in M1 layer 5/6 (L5/6) are specialized for relaying motor commands to downstream targets such as the brainstem, spinal cord [27], and striatum [11], via pathway-specific subpopulations that independently encode movement direction and amplitude [10]. During action sequence organization, the PYR projection neurons in L5/6 convey sequence-related information; the repetitive components of these sequences are directly regulated via projections to the dorsolateral striatum (DLS), as demonstrated by optogenetic manipulations [28]. Although the activity of L5/6 PYR neurons is strongly influenced by local inhibitory circuits, the functional role of inhibitory interneurons, such as SST INs, in the generation and control of structured action sequences remains unclear. Given the critical role of M1 L5/6 microcircuitry in sequence organization [28], we hypothesize that SST INs play a distinct role in this process. Specifically, we investigate how SST INs in L5/6 shape the temporal and structural features of action sequences on a trial-by-trial basis.

In this study, we investigated single-cell calcium activity of SST INs in M1 L5/6 while freely moving mice progressively reorganized reinforced action programs —from a single lever press (fixed ratio 1, FR1), to four lever presses (fixed ratio 4, FR4), and ultimately to a rapid action sequence requiring four consecutive presses within a specified time window (time-constrained FR4, or TC-FR4). We found that SST INs in layer 5/6 exhibited action-specific calcium activity in a single lever-press task. To test whether this synchronized SST IN activity did not decrease as the motor program consolidated, we extended the FR1 training while maintaining the same task schedule. We found that the amplitude of synchronized calcium transients in L5/6 SST INs decreased with task consolidation. Conversely, during TC-FR4 training, as mice continuously reorganized more rapid action sequences, SST INs redistributed their activity and maintained a consistent action-locked response during sequence execution. Specifically, we identified two distinct patterns of single SST IN activity that encoded sequence initiation and trial-by-trial sequence duration. Chemogenetic and closed-loop optogenetic inactivation of SST INs interfered with action sequence kinematics and impaired efficient motor program execution. These results highlight the functional contribution of M1 SST INs in actively regulating and maintaining the timing of motor structures, thereby facilitating efficient motor execution and adaptation to varying task demands.

Results
Action-locked SST IN network activation emerges upon motor program acquisition
Our experimental setup and representative calcium traces are shown to illustrate raw activity patterns of cortical neurons in relation to the single lever press action (Fig.1). To examine the activity of identified cortical neuron subtypes during motor program acquisition, we trained mice on a self-paced lever-press task (Fig.1a). In the first paradigm, mice were trained to perform a single lever press to obtain a reward, known as a fixed-ratio 1 (FR1) schedule. This task was implemented over 7 days to characterize M1 neural responses associated with individual lever presses. The same training was then extended 21 days to examine how neural dynamics changes during the execution of a simple, repeated motor program. In a second set of experiments, mice were trained on a more complex paradigm requiring four lever presses per reward (fixed-ratio 4 or FR4). Initially, the task was performed under a non-time-constrained FR4 schedule (NTC-FR4). The timing requirement was then progressively tightened so that mice were required to complete four or more lever presses within a specific time window, forming a time-constrained FR4 schedule (TC-FR4). This design allowed us to examine how neural activity correlates with the organization of lever presses into structured action sequences. During task performance, we monitored in vivo single-cell calcium activity in the M1 with a miniaturized fluorescence microscope and implantable gradient-index (GRIN) lenses. After viral transduction of the genetically-encoded calcium indicator GCaMP6f, we assessed calcium transients in PYR neurons and SST INs across distinct groups of mice while animals performed a fixed-ratio one (FR1) lever-pressing task (Fig.1a). From Day 1 (D1) to Day 7 (D7) of training, animals displayed a dramatic increase in lever-press rate (Fig. 1b), indicating the acquisition of the novel motor task. On D7, calcium traces (ΔF/F in %, where F indicates fluorescence baseline) of individual SST INs exhibited less complex patterns than calcium traces from PYR neurons (Fig.1 c,d). This SST activity decreased when the animal was not actively involved in the FR1 task (Supplementary Fig.1a,b). We reconstructed these task-related calcium traces into peak-normalized activity maps (Fig.1e, f) to identify the timing of peak calcium increases relative to lever press. Consistent with previous studies in head-fixed mice [14] [25], the activity pattern of PYR neurons revealed a sequential action-locked activation pattern (Fig. 1e), which resulted in a distributed calcium event probability (Fig.1g). In contrast, SST INs displayed calcium event probabilities that were highest around lever-presses (Fig.1h). We then characterized the layer-specificity of action-locked calcium traces (ΔF/F in %) in PYR neurons and SST INs by adjusting the depth of GRIN lens implants and recording neurons in L2/3 and L5/6 of the M1 (Fig.1i,j,k).

We found that the action-locked ΔF/F amplitude was higher in SST INs in M1 L5/6 compared to that of SST INs in M1 L2/3 and PYR in both L2/3 and L5/6 (Fig. 1j,k, and Supplementary Fig.1c,d). Representative raw calcium traces that contribute to the population-averaged ΔF/F profiles of SST INs in L2/3 and L5/6 are shown in Fig.1l,m,n and Fig.1o,p,q, respectively. In these example traces, SST activity in layer 2/3 was increased around individual lever presses, but the occurrence and amplitude of the increases showed considerable variability around each press (Fig. 1l). In contrast, activity in layer 5/6 showed more consistent increases, including relatively consistent transient amplitudes, around each press (Fig. 1o). Nevertheless, there are limitations to comparing cell-to-cell activity solely based on ΔF/F₀ values. To examine this characteristic at the network level independently of ΔF/F amplitude, we assessed Pearson’s correlation coefficients across cells. This parameter calculates the linear relationship between a single cell calcium trace and all other neuronal traces during a 10 second time window centered around the lever-press. The analysis revealed that both PYR neurons and SST INs in L5/6 exhibit higher cell-to-cell correlation compared to the calcium responses in L2/3. (Supplementary Fig.1e,f). It must be noted that placement of the GRIN lens for imaging in L5/6 entails some damage to superficial layers in M1, and this could affect L5/6 SST-IN activity by disrupting cross layer communication. Thus, the differences in lever-press related SST-IN activity in the different cortical layers must be interpreted with caution. The remainder of the study focused on L5/6, a layer that plays a central role in motor output control but remains less well characterized than L2/3 in previous imaging studies [24,25,26].

Action-locked M1 SST IN network activity attenuates during motor program consolidation
To understand whether action-locked SST activity would change upon motor program consolidation, we compared neuronal population activity between early (day 7, D7) and extended (day 21, D21) FR1 training in the same experimental subjects (Fig.2a and Supplementary Fig.2a). In M1 L5/6 SST INs, we measured single-cell calcium activity (Fig.2b,c) and observed decreased action-locked fluorescence responses (ΔF/F in %, where F indicates fluorescence baseline) at D21 compared to D7 (Fig.2d-g). We further validated the stability of our ΔF/F measurements over the measurement period to ensure that this action-locked ΔF/F decrease was not due to long-term fluorescence degradation. For this, we assessed the maximum ΔF/F peaks and action-locked ΔF/F responses across training sessions normalized to D1. While the maximum ΔF/F response normalized to D1 did not change across training sessions (Supplementary Fig.2b,c), the normalized action-locked ΔF/F responses decreased over training sessions (Supplementary Fig.2d) and were smaller at D21 compared to D7 (Supplementary Fig.2e). These results indicate that, in these experimental settings, long-term imaging and training did not decrease the overall amplitude of calcium transients, but rather resulted in the selective reduction of action-locked SST IN activation.

By detecting peak locations of ΔF/F responses, we also analyzed discrete calcium events (Fig.2h,i) from individual SST INs. At a single-cell level, action-locked calcium event frequency decreased from D7 to D21 (Fig.2j-m), with no changes in the temporal distribution of event probability (Fig.2n,o). We also observed a significant decrease in Pearson cell-to-cell correlation coefficients of SST activity across subsequent lever presses at D21 compared to D7 (Fig.2p,q). Thus, SST IN activity exhibited a progressive decrease in action-locked calcium responses (Supplementary Fig.2d). These results indicate that when the task schedule remained consistent during extended training, the action-related activation of M1 SST INs was not maintained.

M1 SST IN network activity is redistributed during motor program reorganization.
Next, we examined how stabilized SST network activity in M1 L5/6 changes as animals reorganized simple actions into more complex action sequences, such as time-constrained rapid sequential lever presses. First, we trained animals for seven days on an FR1 schedule followed by seven days of non-time-constrained FR4 (NTC-FR4) training [3] to allow for the acquisition and consolidation of baseline motor programs (Supplementary Fig.3a,b). Next, we trained mice to progressively increase their lever-press rate and eventually construct a sequence of 4 or more consecutive lever presses within progressively shorter time limits (4, 2, and 1 sec) to obtain a reward. This time-constrained schedule (TC-FR4) increased lever-press frequency across sessions (Fig.3a). At the 4Hz schedule in TC-FR4 training, mice began to organize lever pressing into structured sequences of four consecutive presses within 1 second (Fig.3b,c,d). We defined a unit sequence (SEQ) as a series of consecutive lever presses followed by either a head entry or a pause longer than 3 seconds (Fig.3b), then analyzed kinematic parameters within a SEQ. In addition to the overall increase in lever press rate, the TC-FR4 training schedule led mice to increase the frequency (Fig.3c) and the number (Fig.3d) of lever presses within a SEQ compared to the last session of NTC-FR4. This indicates alterations in SEQ structure that were not observed across NTC-FR4 training (Supplementary Fig.3c,d). To simplify the representation of SEQ kinematic parameters, we defined the sequence efficiency (SEQ efficiency, Fig.3e) as a function of lever-press rate and number of lever presses in a SEQ. As such, efficient SEQs (e.g., fast-quadruple lever presses at >4Hz) were always followed by reward delivery and projected into a bright region on the color-coded map (Fig.3e). In contrast, inefficient SEQs (e.g., slow-and-many lever presses or fast-and-few lever presses) have low SEQ efficiency coefficients and fall in the darker regions of the colormap. As mice learned to perform 4Hz-TC-FR4 SEQs, the SEQ efficiency increased compared to NTC-FR4 training sessions (Fig.3f), indicating successful action sequence reorganization. Concurrent with motor program reorganization, the action-locked single-cell or population fluorescence responses of SST INs increased during 4Hz-TC-FR4 compared to NTC-FR4 (Fig.3g,h,I,j,k,l). Note that when the SEQ efficiency remained unchanged during the NTC-FR4-training (Supplementary Fig.3e,f), there was a reduction in the population fluorescence response (Supplementary Fig.3g-j), which indicated SST INs did not continue to exhibit action-related calcium response during the NTC-FR4-training.

We further examined the distribution characteristics of calcium events of SST INs. In the 4Hz-TC-FR4 schedule, the frequency of action-locked calcium events increased compared to the NTC-FR4 schedule (Fig.3m-r). Importantly, the action-locked calcium event probability during the 4Hz-TC-FR4 training schedule was significantly delayed (Fig.3s,t) compared to the NTC-FR4 schedule. Despite this calcium event redistribution, we did not detect differences in Pearson correlation coefficient of SST IN single-cell activities between 4Hz-TC-FR4 and NTC-FR4 training sessions (Fig.3u,v). Consequently, these data demonstrate that, while the temporal distribution of M1 SST activity shifts during the reorganization of complex motor programs, the cell-to-cell correlation of SST IN activity was not significantly altered.

Redistributed SST IN network activity correlates with structural classes of complex action sequences
As mice generated various types of SEQ structures even within a single session, we hypothesized that the amplitude and profile modulation (e.g. ΔF/F0 of GCaMP6f) in the action-locked SST network activity in M1 L5/6 may reflect structural changes of action sequences in each trial. To examine this, we first performed a correlation analysis between SEQ efficiency and the corresponding amplitude of calcium response for all SEQs within the 4Hz-TC-FR4 training session. We observed a positive correlation between these parameters (Fig.4a), suggesting that larger calcium transients underlie more efficient SEQs.

To directly compare calcium transients between SEQs with different kinematic structures, we performed unsupervised hierarchical clustering of SEQs within a 4Hz-FR4 training session (Fig4.b). The analysis revealed three distinct SEQ sub-classes; Class1 (C1), which are the most represented (46.0% of all SEQs) at this training stage, consisted of SEQs with an action-tight lever-press distribution and rapid lever-press rates (Fig.4c). Class 2 (C2, 29.5%) contained SEQs with a less confined lever-press distribution and slower lever-press frequency, while Class 3 (C3,24.5%) comprised SEQs with numerous lever-press and slower lever-press rates (Fig.4c). Thus, we reorganized the fluorescence profiles and behavioral data according to this classification. C3 SEQs showed a higher number of presses per SEQ relative to C1 and C2 (Fig.4d). In contrast, C1 showed the highest frequency within a SEQ (Fig.4e). Thus, action sequences in C1, C2, and C3 were also separately clustered on the SEQ efficiency map (Fig.4f). When we compared fluorescent transients of SST INs across different SEQ classes within the same training session, we observed that the most efficient C1 SEQs had higher peak amplitudes compared to Class 2 and 3 SEQs (Fig. 4g).

To characterize the calcium transients underlying the most efficient SEQs that led to reward retrieval, we further defined a subset of C1 SEQs as “complete SEQ” when they contained fast/confined (>4Hz) consecutive lever presses and were followed by reward retrieval (Fig.4h,i). In contrast, a subset of C3 SEQs were defined as “incomplete SEQ” when mice executed slow and elongated lever press sequences that failed to meet the temporal requirement for consecutive four or more lever presses. In these cases, no reward was dispensed. (Fig.4j). Less than half of C3 SEQs fall in the “incomplete” category (Fig.4h). Incomplete SEQs contained a higher lever-press number (Fig.4k) and had a lower lever-press frequency (Fig.4l) compared to complete SEQs. As a result, complete and incomplete SEQs have high and low SEQ efficiency coefficient, respectively (Fig.4m). Importantly, distinctive neural responses are associated with the execution of these two SEQ subclasses. Calcium transients during complete SEQs had significantly larger peak amplitude than those associated with incomplete SEQs and persisted for a few seconds after action initiation (Fig.4n,o). Notably, plotting the relationship of the amplitude of SST IN calcium responses and SEQ efficiency segregated the complete and incomplete SEQs (Fig.4p). Additionally, C3 SEQs could be also categorized into rewarded and non-rewarded SEQs (Supplementary Fig.4a-c). The rewarded SEQs exhibited increased calcium transients (Supplementary Fig.4d). Altogether, different kinematics and structural classes of SEQs were distinctively encoded in SST IN activity. Specifically, SST IN activity was higher during efficient SEQs that were appropriately structured and led to successful reward retrieval.

Specific SST IN activity reflects trial-by-trial structural changes in complex action sequences
To better understand how trial-by-trial action components in TC-FR4 SEQs relate to SST IN activity at a single-cell level, we broke down the population average of SST IN activity (Fig.3 and Fig.4) in M1 L5/6 into single-cell calcium responses (Fig.5 a,b). The population average of calcium events exhibited a bimodal distribution (Supplementary Data Fig. 5a), suggesting the presence of two distinct modes of calcium activity. We examined whether these activity modes were represented within individual cells or by mutually exclusive groups of cells. We first identified a primary pattern of SST IN activity that consisted of preferential calcium responses just prior to and during SEQ initiation (Fig.5 a,c). The SST INs showing this activity pattern, which we termed 'instant-onset SST,' were characterized by a dominant peak in their averaged calcium response occurring within 0.5 seconds of SEQ initiation. Unexpectedly, among the remaining SST INs that did not respond preferentially at SEQ initiation, we found a distinct set of neurons exhibiting dominant calcium response peaks between 0.5 and 5 seconds after SEQ initiation (Fig.5 b,d). We defined this group as 'delayed-onset SST’. Based on this classification, the two SST IN activity patterns appear to define mutually exclusive neuronal subpopulations that can be distinguished by the timing of peak calcium responses relative to SEQ initiation (Fig. 5e). Also, during extended TC-FR4 SEQ training on a 4Hz schedule, as well as when the schedule was updated to 8Hz (Fig. 5f), these two distinct SST IN activity patterns were consistently observed across multiple sessions (Supplementary Fig.5b-f). Notably, the instant calcium response, time-locked to the onset of action sequences during the 8Hz training schedule, was reduced and showed less variability compared to the 2Hz and 4Hz schedules as training progressed (Supplementary Fig.5g,h). The SEQ structures and calcium profile during performance on the 1Hz schedule were similar to those in NTC-FR4 training, with no significant differences observed. Conversely, the delayed calcium response became more pronounced during performance on both the 4Hz and 8Hz schedules (Supplementary Fig.5i,j) compared to the 1Hz schedule, indicating an increase in calcium activity associated with the more complex structure of action sequences.

While the ‘instant-onset SST’ (Fig.5c, Supplementary Fig.5 c and e) exhibited time-locked calcium responses to the SEQ initiation, we examined if the calcium response of ‘delayed-onset SST’ (Fig.5d, Supplementary Fig.5 d and f) directly correlated with structural parameters of action sequences. We categorized SEQs into two distinct groups based on the distribution of SEQ durations (Fig.5g): short SEQs (lower 30% in the SEQ duration distribution, Fig.5h) and long SEQs (upper 30%, Fig.5i). We then compared the corresponding calcium peaks. In both the 4Hz and 8Hz schedules, we found that the average calcium responses of ‘delayed-onset SST’ were further delayed when mice performed long SEQs compared to short SEQs (Fig.5j).

Next, we investigated whether this SEQ duration-dependent calcium peak modulation occurs at the single-cell level within a session. We selected 'delayed-onset SSTs' that generated pronounced calcium peaks in both short and long SEQs within 0.5 and 5 seconds of SEQ initiation and compared the locations of their calcium peaks. We found that the calcium peak locations of each SST INs were significantly delayed during long SEQs compared to short SEQs within a training session for both 4Hz and 8Hz training schedules (Fig.5 k,l). This suggests that the individual SST INs are actively modulated in relation to on-going action sequences. Finally, when this analysis was extended to general SEQ trials across multiple sessions without specific SEQ categorization, we found a linear relationship between SEQ duration and the calcium peak response in 'delayed-onset SST INs (Fig. 5m,n). These results demonstrate that the specific calcium responses of SST IN are linked to trial-by-trial structural changes in action sequences.

Negative modulation of SST IN function impairs action sequence kinematics
Given the close association between SST INs activity and the structural modulation of action sequences, we hypothesized that inhibition of SST INs would interfere with efficient lever pressing during motor program re-organization. We tested this idea using chemogenetic modulation with the Gi-coupled hM4Di Designer Receptor Exclusively Activated by Designed Drug (DREADD) designed to interfere with SST IN activity. In hM4Di expressing M1 interneurons (Supplementary Fig. 6a, b), we used ex vivo brain slice recording to measure resting membrane potential and observed that hM4Di expression in SST-Cre mice induced neuronal hyperpolarization upon CNO bath application (Supplementary Fig.6c). Next, we analyzed lever-pressing behavior in control (SST Cre-negative) and hM4Di-DREADD expressing mice (SST Cre-positive) during the TC-FR4 training schedule (Fig.6a). Intraperitoneal injection of Clozapine-N-oxide (CNO, 5mg/kg) increased the average lever-press rate (Fig. 6b) in the SST Cre-positive, but not Cre-negative mice, particularly during the most time-constrained training schedules (i.e. 4 and 8 Hz). We found that this increase was due to increased lever press numbers within a SEQ in hM4Di-expressing mice compared to the control group across training days (Fig.6c). This effect also reduced SEQ efficiency in hM4Di-expressing mice compared to control animals (Fig.6d). These data suggest that, despite an overall increase in lever press rate, chemogenetic-mediated hyperpolarization of M1 SST interneurons deteriorates sequence organization. To further investigate this point, we analyzed lever-press distribution within each SEQ. While control mice injected with CNO displayed a temporally confined lever-press distribution around sequence initiation, hM4Di-CNO inhibition led to prolonged lever pressing (Fig. 6e). In fact, the normalized lever-press probability distribution after sequence initiation was elongated in hM4Di-expressing animals compared to controls (Fig.6f). Disruption of efficient lever pressing in hM4Di-expressing mice was also highlighted by an increased and decreased proportion of “incomplete” (Fig.6g) and “complete” sequences (Fig.6h), respectively. The hM4Di induced negative modulation of SST INs did not alter general locomotion in the operant box, tested in the absence of a lever, and did not affect lever pressing during the slower (1-2 Hz) sequences (Fig 6b-d, Supplementary Fig.6 d,e,f). These data indicate a direct role SST IN activity in the organization and execution of efficient operant action sequences.

Action-locked inhibition of SST INs disrupts efficient action sequences
The DREADD approach might interfere with SST interneuron function throughout the duration of each training session, perhaps leading to long-lasting effects on basal SST activity and broader cortical networks on a timescale of minutes-to-hours. To avoid these confounding effects, we aimed to silence action-locked neuronal activity through closed-loop optogenetics specifically during task execution. To do so, we expressed either the inhibitory opsin (eNpHR) or control fluorescence protein (eYFP) in M1 SST INs using SST-Cre mice. We delivered laser light (wavelength: 532nm, intensity: ~4mW) during TC-FR4 training sessions (Fig.7a). Within these sessions, each action initiation (first lever press) triggered a continuous five sec-long laser illumination (Fig. 7a) to inhibit sequence-related neuronal activation that persists typically for several seconds as the lever press sequence continues. Laser illumination in eNpHR-expressing mice led to an increase in average lever-press rate (Fig. 7b) and the number of lever presses within a SEQ (Fig.7c) compared to eYFP animals, particularly during the most time-constrained training schedules (4 and 8Hz). Similar to the results obtained with chemogenetic negative modulation, the SEQ efficiency decreased in eNpHR compared to eYFP-expressing mice (Fig.7d). Additionally, SST optogenetic silencing elongated the lever-press probability distribution at action initiation (Fig. 7e and f), increased the proportions of incomplete sequences and decreased the percentage of complete sequences (Fig. 7g and h) compared to controls.

To determine whether the pronounced behavioral differences observed during the TC-FR4 8 Hz schedule (Fig.7b-d, f-g) required ongoing optogenetic inactivation during task performance, we introduced a Periodic Laser ON/OFF Modulation (PLM) protocol after animals completed TC-FR4 training on the 1, 2, 4, and 8 Hz schedules (Fig. 7i) and examined how alternating laser ON/OFF conditions influenced sequence organization within a session. When we analyzed the action sequences generated during laser-OFF periods, the eNpHR group demonstrated significantly lower lever-press rate (Fig.7j) and fewer lever presses per sequence (Fig.7k) compared with laser-ON periods. This modulation was not observed in the eYFP group (Fig.7j,k). Moreover, the elongated lever-press probability distribution was significantly altered by the Laser-ON/OFF control only in eNpHR group (Supplementary Fig.7a-c). While the proportion of complete SEQs remained unaffected within the PLM training schedule (Fig.7l), the proportion of incomplete SEQs within the eNpHR group during the laser-OFF periods was significantly lower than laser-ON periods (Fig.7m). Sequence efficiency was also reduced when the condition switched from laser-OFF to laser-ON (Fig.7n).

These findings demonstrate that dynamic laser ON/OFF control continued to modulate behavior, indicating that SST-IN activity remains actively engaged in organizing high-frequency action sequences. However, this finding does not necessarily indicate that the change in TC-FR4 8Hz task was only due to immediate optogenetic inactivation as it is possible that cumulative effects induced by long-term optogenetic suppression may also contribute to these behavioral changes.

To clarify the contribution of immediate optogenetic inactivation, we next investigated whether a single-session, immediate SST-IN inactivation could reproduce the behavioral differences observed after long-term optogenetic suppression. We applied a short-term optogenetic inactivation protocol applied exclusively during the 8 Hz schedule, without any prior treatment history (Fig.7o). On the first day of TC-FR4 8 Hz training, SST-IN activity in eNpHR-expressing mice was silenced using lever-locked closed loop laser activation, and behavioral metrics were examined. Lever presses per minute did not differ significantly between eYFP and eNpHR mice (Fig. 7p,q). However, both the lever-press distribution probabilities and their corresponding area under the curve (AUC) showed significant differences between eYFP- and eNpHR-expressing mice (Fig. 7r,s), indicating that even a single session of SST-IN inactivation was sufficient to alter sequence structure.

To determine how these changes in lever-press distribution probabilities were reflected in sequence parameters, we compared each mouse’s performance during the 8 Hz schedule against their own baseline performance at 4 Hz schedule (Fig. 7t-v). Both eYFP controls and eNpHR mice exhibited a significant Group × Laser interaction for sequence parameters, including lever presses per sequence (Fig. 7t; two-way ANOVA, Group × Laser interaction: p = 0.0078), sequence duration (Fig. 7u; p = 0.0174), and sequence efficiency (Fig. 7v; p = 0.0206). Post hoc tests confirmed that the laser-training effect was specific to the eNpHR group. However, the main effects of group (eYFP vs. eNpHR) were not significant for lever presses per sequence (p = 0.1194), sequence duration (p = 0.0950), or sequence efficiency (p = 0.1005). We further introduced the PLM protocol and found similar alteration in lever-press probability distributions (Supplementary Fig.7d,e) and significant laser-dependent modulation in sequence parameters (Supplementary Fig.7f-h). Thus, a single-session, immediate optogenetic inactivation produced changes in sequence structure (Fig.7s–v), but is not sufficient to alter overall lever-press rate (Fig. 7p and q).

In summary, both immediate and long-term SST-IN inactivation disrupted the temporal organization of lever-press sequences (Fig.7f and s). However, significant alteration of lever-press rate by immediate inactivation was observed only after long-term optogenetic suppression (Fig7.b and j). This suggests that cumulative suppression of SST-INs may create a condition permissive for overly exuberant lever pressing. In our TC-FR4 training, this effect was only observed when SST-IN activity was inactivated during high-frequency sequence execution, even following long-term SST-IN inactivation (Fig7.b).

Methods
Animals
All animal protocols were performed in accordance with the guidelines for care and use of laboratory animals of the US National Institutes of Health approved by the US National Institute on Alcohol Abuse and Alcoholism (NIAAA) Animal Care and Use Committee.
Mice were housed in the NIAAA animal facility under standard conditions with a 12 h light/dark cycle, controlled ambient temperature and humidity. Viral injections and implant surgery were performed on 3- to 8-month-old male and female mice. Wild-type C57/Bl6J mice (JAX Stock #: 000664) were purchased from the Jackson Laboratory. Homozygous SST-Cre mice (JAX stock #: 013044; Sst tm2.1(cre)Zjh /J) were crossed with the wild-type C57BL6/J mice (JAX Stock #: 000664) in the NIAAA animal facility. The Cre-positive (SST+) heterozygous offspring were used for imaging and behavioral experiments.
Depending on experimental design, we also obtained Cre-positive (SST+) and Cre-negative (SST-) offspring as cage mates from the wild-type C57BL6/J mice (JAX Stock #: 000664) and the heterozygous SST Cre-positive (SST+) mice. When the viral expression was not driven by Cre recombinase, C57/Bl6J mice (JAX Stock #: 000664) were purchased and used in the behavioral experiments. Calcium imaging animals were single housed or housed in pairs (maximum two mice per cage); no differences in behavior or neural activity were observed between housing conditions. Optogenetic and chemogenetic animals were group housed (1–4 mice per cage), with treatment and control groups assigned based on genotype. All experiments were conducted during the light phase between 06:00 and 18:00, and repeated measurements were performed at similar times on consecutive days for the same animal.

Recombinant adeno-associated viral (AAV) vectors
The following Cre recombinase-dependent adeno-associated viral vectors were used in the calcium imaging, optogenetic, and chemogenetic experiments: AAV1/9 CAG FLEX GCaMP6f (titre 1.9xA1013, Addgene # 100836-AAV1 ), AAV9.CamKII.GCaMP6f (titre, 2.5x1013, Addgene# 100834-AAV9), AAV2-hSyn-DIO-hM4D(Gi)-mCherry (titre 1.9x1013, Addgene# 44362-AAV2), AAV1-Ef1a-DIO-eNpHR 3.0-EYFP (titre 7x1012, Addgene# 26966-AAV1), AAV1-Ef1a-DIO-eYFP (titre 2.7x1012, Addgene# 27056-AAV1).

Virus injections
To achieve Cre-dependent expression of genetically encoded fluorescent calcium indicators in SST+ cells, AAV vectors containing FLEX-GCaMP6f were micro-injected bilaterally (or unilaterally if noted) into the primary motor cortex (forelimb area [44]) of SST-Cre mice by stereotaxic surgery (AP, anterior-posterior to bregma:0.0~1.0mm, ML, medio-lateral to bregma: +/-1.5mm, DV, dorso-ventral:-0.5mm from the brain surface).
For the expression of calcium indicator in pyramidal neurons, AAV vectors containing a CaMKII promotor were injected in C57BL6/J mice. All other viral injections for optogenetic and chemogenetic experiments followed similar microinjection procedures. AAV2-hSyn-DIO-hM4D(Gi)-mCherry,AAV1-Ef1a-DIO eNpHR 3.0-EYFP, and AAV1-Ef1a-DIO EYFP were bilaterally injected depending on the experimental design. First, mice were anesthetized (Isoflurane 1∼2% with respect to oxygen volume) on a secured stereotaxic frame (Kopf Instruments, Germany). After the mice were deeply anesthetized, a longitudinal head skin incision was made to expose the skull. Craniotomies were carried out using a dental drill and the virus solution was microinjected using a 32-gauge syringe (Hamilton,#65458-01) at a volume of 300nL/site with a rate of 50 nL/min. After infusion, the syringe needle was left in place for an additional 5-8 min before removal. Finally, the incisions were closed with VetBond (3M ,1469Sb). The animals were then placed into recovery cages for 3 days with partial heating, and back to home cages for at least 2-3 weeks before secondary chronic implant surgery. In chemogenetic experiments, mice recovered for 5-6 weeks in their home cages without implant surgery.

Imaging lens and fiber implantation
2-3 weeks after the virus injection, craniotomies were performed above the injection site again under Isoflurane anesthesia. The skull surface was cleaned with a diluted hydroperoxide solution. After drilling and enlarging the hole in the skull (1~2mm in diameter) to expose the target brain surface, the dura mater was removed from the surface using gentle aspiration. To image neurons in deep cortical layers (M1 layer 5/6), a 500-μm diameter, 4.0-mm long gradient-index (GRIN) lens (Inscopix,#1050-004417 or GoFoton, # CLES050GFT120) was slowly implanted (~0.2 mm/min) until the target depth was reached (DV:-0.5mm from the brain surface). For superficial M1 layer (layer 2/3) imaging, a GRIN lens with a large diameter (1.0mm, Inscopix#1050-004637 or 1.8mm, Edmund #64-519) was placed on top of the brain surface using a stereotaxic frame. If necessary, a lens adaptor was attached after unilateral implantation of the GRIN lens.
For optogenetic experiments, fiber ferrules (Thorlabs CFMLC12L02, 1.25mm, 0.39NA, L=2mm) were bilaterally implanted (DV:-0.5mm from the brain surface). Once the GRIN lens or fiber ferrules were securely located, cyanoacrylate (Parkell, C&B Metabond, Radiopaque L-Powder, S396) and dental cement (Stoelting, #51459) were applied to fix the optical implant. After the implantation surgery, animals received ketoprofen injections to reduce pain and inflammation for 3 days and then recovered in partially heated cages before returning to their home cages. Mice for calcium imaging experiments were singly housed after surgery. Mice expressing eNpHR or eYFP for optogenetic experiments were littermates and group-housed after fiber ferrule implantation.

In-vivo calcium imaging
2-3 weeks after the implantation surgery, the quality of in-vivo fluorescence images and spontaneous fluorescence transients were assessed in home cages and operant boxes without behavioral tasks (no levers, no cues). Before the operant training, this inspection was conducted for a week to validate the stability of the fluorescence images. During this time, the mice were also habituated to carrying a head-mounted microscope device and to handling. After the daily image inspection, the mice underwent additional habituation training (1hour a day), during which they carried a 3D-printed mock device (2.5g) in their home cages. Once it was confirmed that the fluorescence imaging provided stable recordings of spontaneous cellular calcium dynamics, the mice underwent food restriction protocols for operant training. Fluorescence images were collected through three open-source head-mounted microscopes (LABmaker, Miniscope V3.2) and one commercial head-mounted microscope (Inscopix, nVoke System) with a sampling frequency of 10 Hz. Each head-mounted microscope device and specific recording parameters were exclusively assigned to designated animals throughout the duration of the experiments. Synchronized behavior log acquisition and fluorescence image recording were controlled by a digital controller (Med-Associates, MED-PC IV Behavioral Control Software Suite). The raw video images were analyzed using image processing software (NIH ImageJ 1.53, and Inscopix, IDEAS platform) and custom MATLAB scripts.

Calcium image data analysis
The raw fluorescence images were compressed with a 1:4 spatial ratio using image processing software (NIH, ImageJ 1.53 or Inscopix, IDEAS platform) and movement artifacts were corrected (Inscopix, IDEAS platform). The field of view (FOV) was then adjusted and cropped to include images within the GRIN lens. To generate ΔF/F image stacks, the change in fluorescence (ΔF = F-F0) was calculated for each individual pixel using a custom MATLAB script, where F represents the fluorescence intensity of the pixel, with the baseline F0 set as the fitted average curve of spontaneous fluorescence intensity of individual pixels along the time-axis (analysis code is available online). A CNMF-E algorithm was then employed within the custom MATLAB script to identify the cell locations/features and to extract the single-cell ΔF/F traces. In brief, the CNMF-E algorithm identified the cellular features and their central locations based on pixel-to-pixel signal correlation in the time domain. The custom MATLAB script automatically assigned regions-of-interest (ROIs) around these identified centers of individual cells. Within each ROI, it eliminated rank-1 background fluorescence and crosstalk components from other neurons. The script then recalculated the pixel-to-pixel signal correlation within the ROI and set the boundary of the dominant neuron. The average ΔF/F was computed within the assigned cell boundary. Thus, the ΔF/F in the single cell characteristics (Fig.1 e,f , Supplementary Fig.1 h,I, and Fig.2 i,j) preserves the same unit representation (ΔF/F in %, where F indicates the spontaneous fluorescence baseline) as in the ΔF/F image stacks. If post-analysis normalization was required for single-cell characterization it is indicated. Following automated trace extraction, identified neurons and their corresponding traces were manually inspected, and any misidentified artifact cells were removed.

Single lever-press (FR1) training
Food restriction started 3 days before any behavioral training. During the entire training period, the body weight of mice was maintained around 85%~90% of their baseline weight by restricting food supply. On the first day in an operant chamber, the mice were trained to retrieve food pellets from the magazine with a random delivery schedule (no cue, no levers). On the following 3 days, the mice were trained on a continuous reinforcement schedule with a fixed ratio (FR1) schedule, where mice earned the reward by making a single lever press. Only one active lever was available. The total number of rewards gradually increased to 5, 15, and 30 in each day. Each daily session ended when the maximum session time of 90 min or the maximum number of rewards set for that session was reached, whichever came first. During this 4-day pre-training, all animals were trained under untethered conditions. Then the following day was designated as Day 1(D1) of the FR1 training while calcium imaging was started. We defined Day 7(D7) as the baseline for the FR1 training, and the FR1 schedule was maintained until Day 21 (D21). In-vivo calcium imaging was performed once or twice a week. On days when the calcium image recording was not performed, mice carried a mock device with a 3-D printed structure identical to the head-mounted microscope (minisciope.org 3D-models or inscopix, dummy microscope#1050-003762) during their daily operant task training. All mice underwent the same daily training protocol and received additional daily food supply (1-3g depending on the daily body weight) after the operant task training in their home cages.

Lever-press sequence (NTC-FR4 and TC-FR4) training
Mice underwent the same food restriction and followed the 4-day pre-training schedule (magazine and FR1 training) and the subsequent 7-day FR1 training schedule described above. After completion of this initial FR1 training, the sequence training schedule began. On the subsequent seven days, all mice were trained on a fixed ratio 4 (FR4) schedule, where the reward was delivered after the 4th lever press without a time-limit. We refer to this FR4 training as non-time-constrained (NTC)-FR4 training to contrast with the following time-constrained (TC)-FR4 training. The following day, the TC-FR4 training was conducted, in which mice were trained to perform organized and sequential lever-presses within specific time criteria to earn a reward. In the first stage of the TC-FR4 training, four consecutive lever-press sequence had to be made within 4 sec (1Hz for 4 lever presses) to earn a reward. The 1Hz schedule was maintained until all mice could achieve 20 complete sequences within 90 min. This training took 2 to 5 days. Next, a 2-second time-limit (2Hz for 4 lever presses) was assigned until all mice in a cohort accomplished 20 complete sequences within a 90 min session. If this training was insufficient, some mice could not earn any reward in the next updated schedules and experienced extinction responses. To complete the 2Hz-TC-FR4 sequence, freely moving mice took 5 days, optics-fiber tethered mice took 7 days and mice carrying the head-mounted microscope took 9 days to meet these criteria. Next, the 1-second (4Hz for 4 lever presses) time-limit was assigned. The calcium imaging experiment stopped on D21 after the first day (D1) of the NTC-FR4 training. Otherwise, the 4Hz schedule continued for 5 days in chemogenetic and optogenetic experiments. The final 0.5-second time limit (8Hz for 4 lever presses) was assigned for another 5 days to compare the behavior between control group and experimental group.

Chemogenetic inhibition during sequence lever-press (TC-FR4) training
Operant training was conducted at least 6 weeks after the stereotaxic injection of AAV-DIO-hM4Di-DREADD. The SST-Cre-positive (Cre+) and Cre-negative (Cre-) cagemates were allocated into the hM4Di group and the control group, respectively, and were co-housed during the period of surgical recovery and behavior training. The food restriction, 4-day pre-training (magazine and FR1 training), 7-day FR1 training, and 7-day NTC-FR4 training were the same as described earlier. The progressive TC-FR4 training schedule followed the same criteria as described above. During the food restriction and 4-day pre-training periods, saline (10mL/kg) was injected in the hM4Di and control groups. From the first day of the FR1 training, all mice got i.p. clozapine-N-oxide (CNO) (Tocris,#6329, 5mg/kg 10mL/kg) injection before daily training sessions until the last day of the TC-FR4 training. The CNO injection was performed 40-60 min before the operant task training and the mice stayed in their home cages until each session started. The training chamber assignment and daily training order were counterbalanced within each genotype. Male and Female mouse ratio was 5:5 within each genotype. All mice underwent the same daily training protocol and received additional daily food supply (1-3g depending on the daily body weight) after the operant task training in their home cages.

Optogenetic Inhibition during sequence lever-press (TC-FR4) training
Within the SST-Cre-positive (Cre+) cagemates, individual mice were randomly assigned to either the eNpHR or eYFP groups, and AAV-DIO-eNpHR (or eYFP) was injected accordingly. As a result, all the mice in the eNpHR group had cage mates from the eYFP group, and they were housed together including during the period of surgical recovery and the entire behavioral training. The food restriction protocol started 2-3 weeks after the implantation surgery. Training protocols including food restriction, 4-day pre-training (magazine and FR1 training), 7-day FR1 training, and 7-day NTC-FR4 training remained consistent with previous descriptions. The 4-day pre-training was conducted under untethered conditions, while mice were tethered to optic fibers for one hour per day in their home cages. Starting from the first day of the 7-day FR1 training, all mice were tethered to optic fibers (Doric Lenses Inc, #SBP(2)_200/220/900-0.37_0.5m_FCM-2xMF1.25) and received closed-loop optogenetic inactivation during the operant training tasks. The laser illumination (Shanghai Laser, 532nm, 15mW at the cable tip with 200um core diameter) was turned on for 5 sec (continuous illumination) following every lever-press. The progressive TC-FR4 training schedule followed the same criteria as previously described. The eNpHR and eYFP groups were counterbalanced for daily training order and training chamber assignment. Both male and female mice were used with 5:5 ratio. All mice underwent the same daily training protocol and received additional daily food supply (1-3g depending on the daily body weight) after the operant task training in their home cages. The lever press rate during TC-FR4 1 Hz and 2 Hz training in tethered mice (Fig. 7b) was lower (~10 presses/min) than that observed in the freely moving condition (~20 presses/min, Fig. 6b). Nevertheless, this difference did not affect the overall consistency of behavioral results and their interpretation in the context of our experiments. However, such differences may be important to consider in future studies.

Unsupervised hierarchical clustering for sequence (SEQ) classification
During TC-FR4 training, rewards were delivered by counting the most recent four consecutive lever-presses in real-time. If the total duration of these lever-presses fell within a specified time limit, the rewards were delivered immediately (delay less than 0.1sec). Timestamps for individual lever-presses, reward deliveries, and head entries were recorded in logfiles. In post-hoc analysis, sequence (SEQ) trials were defined as consecutive lever-presses followed by either a head entry or a pause longer than 3 seconds. For the SEQ clustering, each sequence was itemized and stored in a structured database, including key parameters such as lever-press/SEQ, frequency/SEQ, inter-sequence interval, reward success, optogenetic laser activation status, types of injected opsins, biological information (e.g., sex, age, genotype) of mice, acquired ΔF/F traces from calcium imaging, and the calculated SEQ efficiency. These itemized SEQ database, totaling 330 trials across 8 mice, were randomly shuffled before classification. In the unsupervised hierarchical clustering, only three structural kinematic parameters, lever-press/SEQ and frequency/SEQ, inter-sequence intervals, were initially used without considering any other sequence information, such as reward presentation or calculated SEQ efficiency. Once the classes of SEQ (e.g. C1,C2,and C3) were assigned by the clustering algorithm, the classification information was updated onto the itemized SEQ database. Then the data were reorganized depending on these classes (Fig.4c,f, and g). Reward-based classification (Fig. 4h-p and Fig.S4) was performed after the assignment of unsupervised classes. The hierarchical clustering and dendrogram analyses were conducted using a custom Python script (Python 3.10.6) with Ward linkage as implemented in scipy.cluster.hierarchy.linkage and sklearn.cluster.AgglomerativeClustering. The distance metric used was Euclidean.

Definition of sequence (SEQ) efficiency
Sequence (SEQ) efficiency was developed as a mathematical expression for the simple description of structural features of efficient action sequences. It is defined to describe the highest coefficient value when the structure and kinematics of action sequences align with ideal patterns for a given task schedule, such as 4 lever-presses at 4Hz in the 4Hz-TC-FR4 schedule. Although mice could potentially receive rewards by executing more lever-presses at a faster rate during a sequence trial, such structures are inefficient due to wasted kinematics. From this optimal point, SEQ efficiency gradually decreases as the sequence contains an excessive or inadequate number of lever-presses or operates at an exceptionally high or low lever-press frequency. We implemented an equation for SEQ efficiency by employing a positively skewed gaussian distribution, denoted as S(x), which resulted in a 3-dimensional curve as a function of lever-press number (𝒙𝑳𝑬𝑽) and lever-press frequency (𝒙𝑯𝒛) within a sequence. The definition of SEQ efficiency is as follows:
𝑆𝐸𝑄 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦(𝒙𝑳𝑬𝑽,𝒙𝑯𝒛) = 100 × 𝑆(𝒙𝑳𝑬𝑽) ×𝑆(𝒙𝑯𝒛)
The skewed gaussian function S(x) is multiplication of a Gaussian function ∅(𝑥|𝜇,𝜎) and a cumulative distribution function of the Gaussian distribution Φ(𝑥|𝜇,𝛼), and defined by the following formulas.
𝑆(𝑥) = 2∅(𝑥|𝜇,𝜎)×Φ(𝑥|𝜇,𝛼)
∅(𝑥|𝜇,𝜎) = exp×(1 −(𝑥 −𝜇)2 / 2𝜎2)
Φ(𝑥|𝜇,𝛼) = ∫ exp(1 𝛼(𝑥−𝜇) −𝑡2 / 2)𝑑𝑡 = [1+erf(1 𝛼(𝑥 −𝜇) / √2)]
By adjusting coefficients (𝜇,𝜎,𝛼), the asymmetric curvature of the skewed gaussian function is determined. We set 𝜇 = 2,𝜎 = 4,and 𝛼 = 0.5 to generate the mapping function shown in Supplementary Figure 8, where SEQ efficiency reaches its maximum value at Lever-press number/SEQ (𝒙𝑳𝑬𝑽) = 4 and lever-press frequency/SEQ (𝒙𝑯𝒛) = 4Hz. This mapping function empirically aligned with the behavioral data in TC-FR4 training (Fig.3), the modulation of sequence structures (Fig.4), and results in chemogenetic/optogenetic experiments (Fig.6 and 7).

Brain slice electrophysiology recording
Mice were anesthetized with isoflurane and decapitated after deep anesthesia. The brain was rapidly harvested and transferred to a slicing chamber filled with ice-cold sucrose-based cutting solution containing the following (in mM): 194 sucrose, 30NaCl, 4.5 KCl, 26 NaHCO3, 1.2 NaH2PO4, 10 D-glucose,1 MgCl2, saturated with 95% O2/5% CO2. Coronal sections (250–300 μm) were collected using a Leica VT1200S Vibratome (Leica Microsystems, Buffalo Grove, IL, USA) and incubated in a chamber filled with artificial cerebrospinal fluid (aCSF) containing (in mM): 124 NaCl, 4.5 KCl, 26 NaHCO3, 1.2 NaH2PO4,10 D-glucose,1 MgCl2 and 2 CaCl2, saturated with 95% O2/5% CO2. Slices were incubated for 30–60 min at 32°C and then maintained at room temperature prior to recording.
For patch-clamp measurement, brain slices containing hM4Di-mCherry-expressing SST cells in M1 were transferred to an upright microscope (Scientifica, Uckfield, UK) and visualized using a LUMPlanFL N×40/0.80 NA water immersion objective (Olympus, Waltham, MA, USA). For fluorescence imaging, excitation light (wavelength: 509-551nm) was generated via X-Cite LED driver (Lumen dynamics) and a cy3 filter-cube set and delivered through the objective to identify hM4Di-mCherry-expressing SST cells. Current clamp recordings were performed using an intracellular solution containing the following (in mM): 140 mM K-Glu, 10mM HEPES, 0.1 mM CaCl2, 2 mM MgCl2, 1 mM EGTA, 2 mM ATP-Mg, 0.2 mM GTP-Na. Micropipettes were made from 1.5 mm OD/1.12 mm ID borosilicate glass with a filament (World Precision Instruments, Sarasota, FL, USA) and resistance was 2–4 MΩ. Recordings were performed using a Multiclamp 700B and a Digidata 1550B digitizer with a low-pass filter setting of 2-10 kHz and a sampling frequency of 10kHz. Data were analyzed using pClamp 10.6 software (Molecular Devices). After entering the whole cell configuration, we waited for 10 minutes and measured the baseline membrane potential. Afterwards, CNO (Tocris #6329, 10µM) dissolved in aCSF was continuously bath applied for at least 10 min.

Histology
After behavioral training and assessment, the mice were perfused with phosphate-buffered saline (PBS) solution, followed by 4% paraformaldehyde. The brains were then extracted and postfixed in 4% paraformaldehyde. After 24 hours of fixation, brains were transferred to PBS and stored at 4°C. For sectioning, the posterior brain region from the superior colliculi was removed using a flat blade. Then, the flat coronal cross-section of the brain was positioned and fixed on the flat vibratome stage using superglue. 40µm (or 80µm)-thick sections containing M1 were prepared through the anterior to posterior axis using a Leica VT1200S Vibratome (Leica Microsystems, Buffalo Grove, IL, USA). After slice preparation, eNpHR-eYFP-expressing and GCaMP-expressing brain slices were mounted on slides and covered with thin cover glasses after applying DAPI Fluoromount-G (Southern Biotech, 0100-20). The slices expressing hM4Di-mCherry or GCaMP6f were inspected under an epi-fluorescence wide-field microscope (Zeiss AxioZoom.V16), and if necessary, additional immunofluorescence staining was performed. First, the sections were washed four times for 20 minutes each in PBS. Then, the sections were incubated in PBS containing 5% BSA and 0.2% Triton X in room temperature. One hour later, the solution was replaced with a 0.5% BSA and 0.2% Triton X/PBS solution (PBST) containing a 1:1,000 dilution of Rabbit anti-DsRed (Takara, #632496) or Chicken Polyclonal anti-GFP antibody (Abcam, #ab13970), and the sections were incubated for 24 hours at 4°C. After the incubation in the primary solution, the sections were rinsed four times for 20 minutes each with new PBST solution before incubation in a secondary antibody solution containing a 1:1,000 dilution of Goat anti Rabbit-AlexaFluor568 (Invitrogen, A-11011) or Goat anti-chicken AlexaFlour 488(Invitrogen, #A-11039) in PBST for 24 hours at 4°C. The sections were washed four times for 20 minutes in PBS before being mounted on slide glasses with DAPI Fluoromount-G (SouthernBiotech, 0100-20).

Statistics
For the data presentation illustrating neuronal responses (Fig.1 m,n,p,q, and Fig. 2b, c, f, i, n, and Fig. 3g, h, k, q, s), we averaged the trial responses of individual neurons to represent their characteristics. For all remaining data presentations, we initially computed the mean of these neuronal responses to represent an individual mouse and then calculated the averages across these individual mouse averages. The error bars represent the standard error of the mean (s.e.m.) across mouse averages. All unpaired and pairwise comparisons were two-tailed. Exact P values are reported in the figure captions. For analysis of two experimental results (Fig.2p, Fig.3u) or two groups (Fig.6 b,c,d and Fig.7 b,c,d) across multiple time periods, two-way repeated-measures ANOVA was used. For analysis of a single group across variables (Fig.4 d,e,g), one-way repeated-measures ANOVA was used. When there were multiple comparisons, the Sidak post hoc test was used when needed. Pairwise Pearson correlations between cell activity profiles were computed using MATLAB, yielding a cell-by-cell correlation matrix for each mouse. Mean off-diagonal correlation coefficients were used as a descriptive measure of population synchrony and are shown for individual mice

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper is a comprehensive investigation of the role of SST neurons on both single and repeated lever pressing (simple vs complex actions). The results as presented suggest that SST neurons play a role in high-frequency lever pressing behavior. This interpretation is supported by both single cell calcium data as well as optogenetic and chemogenetic manipulations. Overall the paper is a solid contribution to our understanding of how interneurons modulate action.\nI mainly have concerns with the framing/presentation of this paper. In the introduction results from a number of different aspects of motor processing are presented/conflated together (skilled motor learning, action generation, sequence generation, etc.). Thus, I can't tell right now if the main point about this paper is related to an argument linked with learning or instead if the main point about this paper is about action execution.\nThis confusion (in my mind) persists through the results section. Authors seem to suggest that their investigation is on the role of SST neurons on the execution of a learned motor sequence (i.e that its about learning). This could be the case. It could also be the case that SST neurons are simply involved in lever-pressing actions - and thus, when animals lever press at a high frequency, there is more SST neural activity (in other words, that this is unrelated to learning). Despite going over the paper carefully, its unclear to me which argument authors are making...though they seem to be leaning towards the \"learning\" aspect?\nThe second issue (which relates directly to the above): \"action sequence\" to me implies some care about the sequence of the actions being performed. IN other words, if I have to move my arm up, to the right and then down in order to complete some action (or, alternatively, respond in a patterned way \"left lever, right lever, right lever, left lever\". In this case, there is no \"sequence\" per se - its just repetitive motor responding and authors are training the animal on how fast they need to respond. Thus in my mind it shouldn't really be framed as action sequence (and, in fact, there is a confound in naming it as an \"action sequence\", when really the results could just be related to action frequency (its the same action over and over again, and how fast this action is being performed determines the outcome).\nI made some specific suggestions below regarding additional analyses that could be done to help flesh these out.\nIn summary - I think the results are interesting and important, but the framing is confusing on whether the authors are claiming a role of SST neurons on learning vs. action execution; and whether this should be termed an \"action sequence\" as opposed to, again, more simple action execution.\nSome specific comments below:\n1. Intro:\nA. As noted above, I found this intro a bit all over the place. In particular, they mention roles of motor cortex in skilled motor learning; skilled motor execution; execution of action sequences; associate learning, etc.) and discuss theories as if all of these were the same. This makes it a bit hard to follow exactly what it know regarding the role of cortical circuits in an action sequence task (i.e. in the task they are studying). I would recommend editing/revising intro to be a bit more focused on what is known about cortical circuits involved in action sequencing, make that really crystal clear, and then it may be ok to introduce other tasks in a way that makes it clear these are distinct from what is being studied in this paper.\nB. Similarly - I don’t really need to know what VIP and PV neurons do in the intro, and the role that SST neurons play is thus a bit opaque and confusing (I had to read the paragraph a few times to figure out what you are trying to say about the SST neurons specifically). I’d recommend focusing on theories specifically related to SST neurons (discussion of other INs can well be relegated to discussion).\nC. Finally, in the last paragraph, authors jump right into “FR1 and FR4 sequences”, which MAY BE well know to them, but likely deserves at least a quick introduction for other readers in terms of how the task fits into the larger literature of motor tasks referenced.\nIn summary - please spend more time introducing the points directly relevant to this paper, make the hypotheses a bit more clear (based on other papers, what do you expect SST IN to be doing); and describe the task a bit more.\nRESULTS\nI discuss based on figures:\nFig 1\nThere is no statistical reporting that I could see in the results or figure for any of the conclusions made about data from Fig 1. there are many things that, by eye, look like they would be quite statistically significant…but, again, without any form of testing its just hard to say definitively. Authors need to refine the writing/description of this figure in the results section to reflect that findings are “examples” (and its unclear if they are representative examples at that given the lack of significance testing). In other words: either add some statistics or make it very clear that the point of Fig 1 is purely for illustration purposes.\n\"Trial consistency\" subpanel is just the mean effect, which doesn't really tell much about trial consistency.\nThe only statistical analysis I could see from Fig 1 was in the extended fig. In that analysis, (pearson’s correlation with population activity), I wonder if there is a difference in sample size between PYR neurons and SST neurons, and whether this difference in sample size drives differences observed (I imagine the mean Pearson’s correlation statistic is sensitive to very small sample sizes which can then spuriously inflate or reduce effects).\nFig 2. Fig 2d-g (and supp fig). Authors show that the “peak” calcium response is unchanged with continued imaging, but the event locked peak goes down. This is a nice finding. It may be also nice to also analyze a “base-line” period pre lever press (i.e. in the -5 to -4 second period prior to the level press, for example) to, show that the background/baseline calcium activity is similar across days, its just the peak effect post-lever press that is diminished. It is very nice that authors statistically analyzed calcium activity both at a single cell level and at an animal level for the major findings in this figure.\nFig 3. Authors show greater activity in SST neurons from TC-FR4 (4Hz) compared to NTC-FR4. However, as the authors themselves note, in the time window analyzed, there are (on average) 2-4 times more motor actions in the “motor sequence” of TC-FR4 compared to NTC-FR4 (i.e. there is a higher response frequency). This, coupled with the slow calcium dynamics, complicates interpretation. In my opinion, most of the results of this paper could be explained by a more simple idea: SST neurons are simply active with every lever press. Thus, with repeated lever presses (at a higher frequency) result in greater calcium influx (smoothed of course by the slow temporal dynamics of calcium sensors). Even the early findings related to FR1 could be explained by this: often, in motor learning, early on animals move a lot...and with training they settled down and have more efficient movements. Thus, it could well be that on D1 FR1 training animals press the levers multiple times (thus resulting in greater SST activity), but that at D7 FR1 training animals only press the lever once, thus resulting in diminished SST activity.\nThus - my major point for this (and other figures) - there is no clear evidence (YET) that changes in SST activity are a function of learning per se. Authors either need to make this more clear throughout...or perform some further analyses that may be more convincing that this is a learned phenomenon.\nThe authors may have the data to clarify this - mostly, by analyzing FR1 and NTC-FR4 data, and analyzing calcium data by frequency of response/sequence. If they find there is a freqeuncy effect, that would support my idea. if they find that, in animals not trained on TC-FR4, there is no frequency effect on SST activity, this would support something more about learning. To make this more clear: in the early stages of training - take all trials performed at 0.5 HZ, 1 Hz, 2Hz, 3Hz and 4Hz (separately for D1 and D7) and see if there is any difference in SST activity when sorted that way. Analyze the NTC-FR4 and FR1 data the same way (sorted by frequency of responses).\nFig 4 Interesting analysis and a different way of answering the questions I have above - though this figure further bolsters my hypothesis that the elevated SST activity associated with TC-FR4 is not related to learning per se but instead linked with higher frequency motor activity (this figure seems to demonstrate that even in the well-trained animals, the faster frequency responses are linked with elevated SST activity relative to the lower frequency responses). Thus, again, it is unclear if the SST activity noted is at all a learned phenomenon or simply a cortical execution phenomenon (and a proxy of slow calcium dynamics)\nFig 5. Fig 5 is quite interesting and demonstrates, perhaps some evidence of a learned phenomenon. It would be very interesting to analyze the NTC-FR4 and the FR1 dataset in this way and see if the presence/distribution of these delayed onset neurons emerges only in learning, or, again is a consequence of repeated actions. These \"delayed onset\" neurons could reflect some aspect of the motor sequence that is a learned/trained phenomenon\nFig 6-7. Very interesting results - but again, what is the effect of inhibition on frequency of responding?", "reviewer_2": "This study by Lee et al. investigates the dynamics and functions of SST interneurons in motor cortex during a freely moving lever-pressing task. They use miniscopes to record Ca2+ signals from genetically defined SST neurons longitudinally throughout task acquisition and adaptations. The studies are well controlled, with rigorous analyses. Rigor is also enhanced by the inclusion of analyses of both neurons and mice as samples (ensuring that the reported effects aren’t driven by an outlier mouse). The authors make numerous novel findings related to both the dynamics and functions of SST neurons, across a sequence of behavioral challenges. Furthermore, apparent inconsistencies with the related published literature are appropriately contextualized in the discussion. The comments below are included to help strengthen an already strong manuscript.\nMajor points\n1. Perhaps the most important motivating question (as included in the abstract) is “the mechanisms by which cortical microcircuits actively fine-tune the timing and structure of action sequences, enabling organisms to adaptively maintain motor efficiency across varying task demands, remain unclear.” While the manuscript makes multiple important observations towards this goal, the answer is far from complete. To close the gap even further, I recommend the following analyses.\n- Fig 1. How might the SST neurons be influencing PYR activity (and vice versa)? From the population activities (Fig 1j,k) is there a phasic relationship between these populations (e.g., SST leading vs lagging)?\n- The description of SST relationships to ‘rotational dynamics’ and neuronal oscillations is compelling in the FR4 schedule. Obviously, this would be best assessed with simultaneous recordings of PYR neurons. However, perhaps more can be done with the existing dataset. As possibly evident in Fig 3p, were there oscillatory dynamics in the SST population activity? This would have to be assessed either on individual trials (and then average the frequency spectra across trials), or across trials with similar motor sequences (minimal jitter of individual lever presses).\n- Fig 5j-n – for the delayed-onset SST neurons, were there specific phase relationships to individual lever presses? This can be addressed for individual SST neurons and across the population.\n- Fig 6 – Did SST suppression change the frequency distribution of the lever pressing? Again, analyses of individual trials (and then averaging the frequency spectra across trials) would be needed.\n2. Early in the study the authors make important observations regarding differences between L2/3 vs L5 SST neurons. Were such laminar analyses performed in the later studies? For the FR4 studies of dissociable population dynamics, are both populations distributed in superficial and deep layers?\n3. In the FR1 D7 SST data, there appears to be two peaks in the average data. Are these peaks from separable populations, as described in the FR4 schedule data?\n4. The authors describe in the Discussion that their studies cannot rule out changes in more subtle forelimb kinematics during SST suppression. However, the same limitation applies to the observations in Fig 2d vs 2e. Changes in the velocity or trajectory of limb movements may explain the differences in SST activity. If the authors have the body/limb movement data, I encourage them to present the data to address this potential confound. If not, then mentioning this in the discussion would be important.\nMinor points\n1. Fig 2o – expand y-axis to better visualize data\n2. Extended Fig 6c, right- expand y-axis to better visualize data\n3. Line 311 - Extended Fig 7b?\n4. Please clarify: “Although we also observed slight differences in initial motor task learning between tethered and untethered mice, we did not detect significant circuit inhibition effects between experimental and control groups (Fig.6b and Fig.7b).”", "reviewer_3": "In this manuscript, the authors investigate the role of SST interneurons in learned motor behavior by measuring and manipulating neural activity in mice. Mice were trained to perform lever-pressing tasks of varying difficulty and speed to earn rewards. The authors observed task-related activity modulation in both pyramidal neurons and SST interneurons, with higher SST activity in deeper cortical layers. SST activity was elevated during early task learning and varied with movement type across tasks. SST activity appears elevated during early task learning and varies with movement form across tasks. SST cell activity is varied in its relation toward movement, with a preponderance of cells active at movement initiation. Both chemogenetic and optogenetic perturbations of SST neuron activity lead to performance deficits specifically during the most challenging versions of the task.\nThe results overall are novel, interesting, and have relevance to our understanding of M1 function during both motor learning and execution. Below I describe a number of concerns that should be addressed during revision.\nSupport for the specificity of SST cell role\nThe manuscript focuses on SST neurons, and implies that SST neurons play a specific role in executing more challenging lever press tasks. For example, on line 283 they refer to “a direct and specific contribution of action related SST IN activity,” and on line 434, they refer to “insights into the specific functionality of M1 cortical INs.” While it is certainly appropriate to focus on the role of one cell type, the data included in the manuscript do not make a case for a specific role of SST cells, as distinct from that of other cell types. No comparison is performed to other interneuron types, like PV cells for example, and no differentiation from the overall inhibitory interneuron population is demonstrated. These would involve separate experiments using PV-cre or Vgat-cre mice. I am not suggesting that the authors must do such experiments, but instead that they should avoid suggesting that their data supports a specific role for SST interneurons.\nAlong these lines, it is questionable whether the specific perturbation effects reported in Figures 6 and 7 result from perturbing SST cells in particular or from the general disruption this causes to M1 activity. The observation from these experiments, of a task-specific effect of SST cell activity perturbations, could reflect either a specific role for SST cells or a role for M1 in general. Moreover, much existing data suggests that the influence of M1 on movement is specific to tasks that challenge dexterity. For example, mammals retain the capacity to execute many motor behaviors after M1 lesions, but struggle with tasks that are particularly dexterous. Based on these previous results, one might expect that perturbations to M1 activity in general would show similar effects as those reported in Figures 6 and 7. Again, I am not necessarily asking for more experiments here, but this alternative interpretation should be highlighted as well.\nKey features of cortical involvement in movement execution\nThe manuscript could do a better job of reviewing current understanding of M1 in movement execution, and how it relates to the previous observations of cortical disengagement across learning and the observations reported in the present manuscript. The authors here do note that previous observations of cortical disengagement have come after experimental animals have completed very large numbers of trials over many weeks and have become highly stereotyped in their execution. Also important is the fact that these observations come from tasks that do not have a particularly strong requirement for dexterity. In fact, in Kawai et al., Neuron, 2015, the authors state that their task was specifically designed to avoid dexterity, as their central goal was to distinguish the role of M1 in motor learning from its role in executing dexterous movements, which had not been experimentally decoupled in previous studies. A very large amount of previous literature, ranging from older lesion studies (e.g. Lashley, Arch Neuro Psychiatr, 1924 or Travis, Brain, 1955), to more recent studies using optogenetics (e.g. Guo et al., eLife, 2015 or Miri et al., Neuron, 2017) suggest that M1 plays a specific role in the execution of dexterous motor behaviors. Other evidence has implicated M1 specifically in adaptive or corrective movements, when stereotyped movement trajectories need to be deviated from (e.g. Drew et al., Can J Physiol Pharmacol, 1996, Warren et al., eLife, 2021, or Bollu et al., J Neuropys, 2024). These previous results help explain why the role of M1 or M1 SST cells in particular, may be specific to the more challenging lever press task the authors used in their manuscript.\nThis could also help explain the divergence between results with and without head-fixation: lever press behaviors may become more challenging for mice to perform under head fixation, thereby engaging motor cortex in tasks that might otherwise not involve M1 if performed in a more natural, unconstrained setting. This is important to note, given the prevalence of head-fixed behavioral paradigms in motor system studies.\nConcerns about the damage to M1\nIt appears that the bulk of the paper reports activity measurements from L5/6 SST cells imaged through cylindrical GRIN lens inserted into M1. This lens is 0.5 mm in diameter and was inserted to a depth of 0.5 mm into cortex. This volume of tissue disturbed by the implantation is large relative to the overall volume of forelimb M1 in mice. In addition to destroying upper layer circuits that send substantial input to deeper layers (layer 5A begins less than 0.5 mm from the pial surface), this will also disrupt the apical dendrites of deeper layer cells. Moreover, the authors do mention a slower learning in tethered animals, which might reflect damage to M1. Could the increased activation of SST cells in L5/6 activity measurements reflect a compensation for the loss of inhibitory interneurons superficially? It would be reassuring to see results confirmed using a triangular GRIN lens.\nLack of evidence for discretely separable activity patterns\nThe analysis of activity patterns in Figures 4 and 5 is described as implying that there are distinct types of SST cells - separate classes or clusters. For example, on line 231, the authors refer to “mutually exclusive neuronal subpopulations.” Instead, the data presented here are consistent with a continuous distribution of response types. This distribution is important to quantify and flesh out, but the data do not support the existence of discretely separable types. Seeing differences across the averages of clusters, which come from algorithms fated to separate populations into groups, does not support the idea that discrete or substantially distinct clusters exist. If you divide a continuous distribution into 2 halves, the means of each half will differ from one another, even though they each derive from a single underlying distribution. The manuscript does not provide evidence that the differences across neurons are beyond those expected by chance for a single distribution. Moreover, the plot in Figure 1f shows the distribution of response types across cells, which looks continuous. Again, the quantification of response diversity here is important, but the interpretation that discretely different response types exist should be avoided.\nMissing statistical tests for key comparisons\nIn several cases throughout the manuscript, the authors refer to differences between experimental groups without reference to appropriate statistical tests in either the text or figure legends. For example, the difference between plots in Figure 1 n and q is highlighted, but these actually look somewhat similar and no statistics are included to support a significant difference. Similarly, the description of results in Figure 5j described in lines 245-7 does not provide statistics, nor does the figure legend.\nTime course of perturbation effects across learning\nThe increased magnitude of the perturbation effects later in training reported in Figures 6 and 7 is a very interesting finding. However, because of the experimental design, one possibility is that the effects are more severe at later time points because they reflect the accumulated effects of repeated activity perturbation. A control to exclude this possibility should be provided. For example, performing perturbations in a set of animals only after they have reached the 8 Hz training stage.\nInterpreting the activity changes across FR1 learning\nThe changes in SST activity between early and late in learning the FR1 task (Figure 2) are striking and are potentially consistent with a specific role of the SST neurons during acquisition but not later execution. However, the revelation later in the paper that SST activity perturbations early in learning have little effect argues against this possibility. It is not clear how to reconcile these two results, and ultimately what to make of the changes seen in Figure 2. The Discussion should address this.\nThe sequence efficiency metric should be better explained\nIt seems like the sequence efficiency metric penalizes pressing too many times (more than 4) or too quickly (more than 4 hz), but were the mice penalized in terms of reward when they overshoot? Also, how much the authors decided to penalize overshooting is unclear. Why not use a function where there is no penalty for pressing more or pressing faster? I suppose pressing more could result in longer trials leading to less rewards/min, and faster presses might be less efficient in terms of energy expenditure, but none of this is clearly explained. In addition, the authors do not describe how they arrived at the amount of skew that they used for the function. This would directly affect analyses like those shown in Figure 6 where inhibiting SST cells resulted in decreased efficiency leading them to claim that “chemogenetic-mediated hyperpolarization of M1 SST interneurons deteriorates sequence organization” (line 274).\nInterpretation of activity differences between NTC-FR4 and TC-FR4\nWhen looking at the changes in SST IN activity between the NTC-FR4 and TC-FR4(4Hz) conditions, one explanation for the increase in activity could be that the animals are doing more vigorous movements (more presses and/or faster presses). The division into classes in Figure 4 further highlights this, as the more vigorous SEQ class (class 1) seems to have the largest peak in the calcium transients. It seems that if you did a convolution of the lever presses with a smoothing kernel you could produce something that looks a lot like the average dF/F’s.\nMinor concerns:\nLine 32 (also line 322): I would caution against using the word “persisted” when talking about results for the TC-FR4 training, as it gives the impression that the activity is unchanging, when it is in fact changing. Maybe a more descriptive phrase like “did not decrease” is better.\nFigure 1: When using calcium imaging, one must be careful when interpreting differences in dF/F magnitudes across different cell types. The magnitude of these relative changes depends on many factors unrelated to neuronal firing rates, like the expression level of indicator proteins and the calcium handling within cells (e.g. the intracellular concentration of endogenous calcium buffers). Given this, differences like those reported between SST cells in L2/3 and L5/6, which could be genetically distinct, do not necessarily reflect differences in activity level. The observations reported are meaningful and important, but this caveat in interpreting the differences should be made explicit.\nSpecifying the site of activity measurements: The end of the Results section on Figure 1 and the beginning of that for Figure 2 (page 4) seem to imply that all neural analysis is done on SST cells specifically in L5/6. However, L5/6 is not mentioned again specifically until late in the Discussion. Because there are repeated references to SST interneurons in the intervening text, it is a bit ambiguous whether all the neural activity analysis was specifically on L5/6 cells. In addition, from the description of the experiments reported in Figure 6 and 7, including the depth of viral injection, it seems that the perturbation experiments reported in these figures involved all SST cells, not just those in L5/6. I do not see big issues with this difference, but it should be made explicit.\nLine 61: “M1 PYR neurons can even become independent of movement upon extended training” - this statement is a bit redundant given the preceding text.\nLine 98: It would be helpful if this first section of the Results included at least a brief description of the behavioral paradigm.\nLine 120: “synchrony” generally refers to simultaneous spike firing, but the measures used here, especially when applied to calcium sensor measurements, will reflect correlations of activity on longer timescales that do not seem to meet the typical definition of synchrony.\nLine 171: “at” should be “during”\nLine 180: “This indicates the maintenance of SST IN synchrony as mice efficiently reorganized their behavior from simple to more complex motor programs.” This statement reflects an interpretation of the absence of a significant difference as reflecting the absence of a difference. Such inferences are not statistically valid. This interpretation should be removed.\nLine 207: It should be clarified what is meant by “incomplete”. For incomplete sequences, are the animals just choosing not to retrieve the reward, or is the reward not dispensed? If it is the former, is the reward just left there for the animal to get in the next trial, and could this behavioral change be related to satiation and/or motivation?\nLine 217: “INs” should be “IN”\nLine 234-235: Is the sharper response at higher frequency perhaps because the movement is faster? Also, not entirely clear what “stabilized” means here.\nLine 288: “minute-to-hour” should be “minutes-to-hours.”\nLine 301: The authors justify the analysis shown in Figure 7i-7n as looking for changes “within a single experimental subject.” The notion that the statistics here are showing differences within individual mice is not supported - groupwise stats are still used. Moreover, the results in these panels seem expected given the results in 7b-d. A different justification for including this analysis seems necessary, or else further clarification.\nLine 312-313: references to Fig.7m and Fig.7l seem to have their locations switched.\nLine 390-391: This description is confusing, because earlier (line 259) the authors do refer to what they measured as “kinematics.”\nLine 442: “pattern” should be “patterns.”\nLine 447: “might also regulate the termination of action sequences” - this could be easily tested by aligning activity on the last lever push.\nLine 453-454: this bit is unclear - it is not clear what aspect of the figure indicates oscillatory activity. Activity in a motor region during an oscillatory movement to some extent has to be oscillatory. The Churchland observation of rotational dynamics was surprising because it was occurring during ballistic reaching at frequencies not apparent in the movement itself.\nMethods: There is insufficient explanation of how the hierarchical clustering of the different sequence classes was done (was it single linkage? complete linkage? a density based method like DBSCAN? Also, what was the distance metric used?)\nFigures 1b/2a/3a/4a: it is not clear what is being shown in the color bar at the top.\nFigure 1q legend: says “activity map in (n)” but they probably mean “activity map in (k)”\nFigure 3 legend: on line 90, lever is misspelled.\nFigures 6e-h and Figure 7e-n: The figures here and the accompanying text in the Results, do not clarify when during the training schedule these effects were measured (unless I missed it). The previous panels in each figure locate the effects to later in the schedule - were these comparisons done specifically at those later time points? This should be clarified either way.\nExtended Data Figure 5: The text here notes the differences between the Instant and Delayed onset populations, in terms of the differences between lower and higher frequency responses. However, in the figure, it does not appear that the same statistical tests were performed - the brackets in h and j indicate different comparisons. In addition, it is important to keep in mind that the absence and presence of significant differences in two statistical tests is not an appropriate way to establish a difference between two conditions. The appropriate approach here, as is standardly done when comparing drug effects versus those of placebo, is to directly measure the difference between the two conditions (here the response types) and show that the difference is significantly greater than 0.", "reviewer_4": "The authors study how Sst cells participate in a lever press and sequential lever press task. They find that Sst activity is synchronous around lever presses, particularly in deep layers, reduces with training, and increases when mice switch to a more difficult task schedule. Chemo- or optogenetic suppression of Sst activity increased lever pressing but disrupted its structure leading to lower reward rates.\nThe work this paper describes is well done, and the conclusions reasonable. I therefore do not have any major comments or suggestions. Two minor suggestions:\n1. In every figure please state which layer you are recording from. My understanding is that except for Fig. 1 it is all in L5/6, but this would be clearer if explicitly stated\n2. Extended Figure 5a makes it look like the delayed-onset Sst cells are not synchronous, but rather fire in a sequence. If so, that is a contrast to the highly synchronous activity seen at lever pressing. Is that correct? If so it might be worth pointing this out."}, "author_response": "We appreciate the reviewer’s positive overall evaluation. While previous studies have primarily emphasized the role of M1 SST interneurons in motor learning (mostly in layer 2/3), our findings highlight the role of L5 SST interneurons in action control. While the action we are studying is indeed a learned action, our data are most consistent with a role for these neurons in efficient performance of the actions themselves as opposed to learning. In response to this and other comments in the review, we have revised the manuscript to further clarify the focus of our study. Please see our detailed responses to your specific feedback below.\n\nThe second issue (which relates directly to the above): \"action sequence\" to me implies some care about the sequence of the actions being performed. IN other words, if I have to move my arm up, to the right and then down in order to complete some action (or, alternatively, respond in a patterned way \"left lever, right lever, right lever, left lever\". In this case, there is no \"sequence\" per se - its just repetitive motor responding and authors are training the animal on how fast they need to respond. Thus in my mind it shouldn't really be framed as action sequence (and, in fact, there is a confound in naming it as an \"action sequence\", when really the results could just be related to action frequency (its the same action over and over again, and how fast this action is being performed determines the outcome).\n(Response) We understand the question of whether our behavioral task qualifies as an “action sequence.” While “action sequence” is often intuitively understood as a specific order of distinct actions, we adopt a broader view grounded in prior literature that also recognizes temporally structured, repeated actions as meaningful sequences. In this view, both Left–Right (L–R, alternating pattern) and Left–Left (L–L, repeated pattern) lever presses represent structurally distinct forms of action sequences. Even repeated actions can carry distinct functional meanings depending on the number of repetitions (e.g., L–L vs. L–L–L–L) and temporal features such as rhythm, timing precision, or duration. For instance, a rapid, repeated press pattern (e.g., 'L–L–L–L' within 1 or 4 seconds) may differ substantially from a slower or more irregular version in how it is learned, initiated, and executed.\nTo learn and perform the TC-FR4 task (e.g., a 4 Hz schedule requiring 'L–L–L–L' within 1 second), it is important to note that mice do not simply increase lever press frequency through repetitive pressing. Rather, they explore and reorganize combinations of various behavior elements—including sequential lever presses and non-lever-related actions such as brief pauses or port access. Over the course of training, animals iteratively generate and refine numerous combinations of these behavioral elements to discover more efficient and effective behavioral patterns. Notably, brief pauses (visible as gaps in raster plots in Figure 4c) may play a crucial role in concatenating (or chunking) individual actions or some behavior units into longer, cohesive sequences.\nThus, the resulting behavior patterns (Figure 4b and c) were remarkably variable and diverse, requiring computational methods for classification. While simple measures such as lever press rate are one parameter that assesses overall animal behavior, we found that the concept of an action sequence provides a more meaningful framework for understanding how mice organize distinct individual actions into more efficient, cohesive behavioral structures found in our experimental results.\nThis broader conceptualization originated from extensive prior studies on the control and organization of action sequences. For example, studies by X. Jin et al. (Nature, 2010; Nature Neuroscience, 2014) defined action sequences primarily as temporally structured series of lever presses—often involving a single lever—rather than alternating lever actions. Similarly, Vandaele et al. (eNeuro, 2021; iScience, 2023) classified repeated lever presses into distinct sequence categories based on timing, structural completeness, and the combination of other motor behavior, such as the presence of within-sequence port entries. Furthermore, Kawai et al. (Neuron, 2015) and Wolff et al. (Science Advances, 2022) treated the temporal spacing between lever presses as a key characteristic of action sequence learning. Notably, all of these studies were conducted in the context of single-lever-press tasks.\nIn our experiment, we believe that the term “action sequence” offers a useful framework for interpreting the diverse behavioral patterns that reflect the organization of temporally cohesive action structures. It also helps compare our findings on cortical contributions to action sequence control in contrast to prior work focused on basal ganglia function—making the interpretation more accessible and coherent to a general neuroscience audience."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors did a truly commendable job in addressing all of the reviewers comments - a strong article is now even stronger and in my opinion is ready for publication.", "reviewer_2": "I commend the authors on their thorough responses to all reviewers. My comments/concerns were adequately addressed. While some of the 'reviewer figures' could be helpful to include in the manuscript, I trust the judgement of the authors on these decisions.", "reviewer_3": "I commend the authors for doing a thorough job of addressing the reviewer concerns. The bulk of my concerns have been suitably addressed. A few issues remain, which I address below.\n- Concerns about the damage to M1\nApologies for not making my original concern clear. My concern is specifically with the comparison between L2/3 and L5/6 SST cells. While this comparison is only done in Figure 1, it does seem important, as the authors use it to justify their subsequent focus on L5/6 (line 140). I do not see a control included that assesses whether the difference in activity between L2/3 and L5/6 SST cells is due to the different degree of damage between the L2/3 and L5/6 imaged animals. It is also not clear how such a control could be done, since the cells immediately above the imaged L5/6 SST cells are the ones providing strong input to L5.\nI think it is necessary that this confound be explicitly addressed in the Results when describing the differences seen between L2/3 and L5/6 SST cell activity.\n- Lack of evidence for discretely separable activity patterns\nThis result shown in Figure R6 is great - it should be added to the manuscript to support the claim of \"mutually exclusive neuronal subpopulations\" which I see remains in the manuscript.\n- Missing statistical tests for key comparisons (FIG1)\nI still do not see a statistical test either in the text or figure legend that supports the claims made about Fig. 5j, that the responses are further delayed. One should be provided, or the claim should be omitted.\n- Time course of perturbation effects across learning\nThe additional data presented here in Figure R7 seem to corroborate my original concern. These data present a very different picture of the effects of SST neurons on task performance at later time points. Though a significant difference is seen in the event distribution, the effect is relatively small compared to what is shown in Figure 6 and 7. Figure R7b does not align with Figure 6b-d or Figure 7b-d. Thus a substantial degree of the effects shown in those panels do seem to be a byproduct of repeated inactivation and not from the immediate effects of silencing SST neurons during the movement. The problem is compounded by the fact that the authors stress the difference in effects between easier and harder versions of the task, especially throughout the Discussion section on task specific inhibition effects. I think Figure 7 should be updated to include the data shown in Figure R7 and the interpretation of the inactivation effects in the manuscript should be updated to account for these new results. This would include eliminating the claims about stronger effects during the higher frequency tasks, which clearly are substantially confounded. If inactivation effects depend on the history of inactivation an animal has experienced during previous sessions, then comparing effects across stages of the TC-FR4 training does not seem appropriate.\n- Interpreting the activity changes across FR1 learning\nMy concern here has not been well addressed. I understand the reasons why we might not see an effect of inactivation on behavior in certain cases. But we are still left without positive evidence that the activity of these neurons is important for the easier versions of the task. This is problematic because much of the paper regards these easier versions of the task, like the changes across learning shown in Figure 2. The manuscript should explicitly describe the sense in which these changes are meaningful, in the absence of evidence that the activity is important for behavior.\nAlso, to be clear, the discrepancy referred to on line 413 is not the discrepancy between the change in SST activity and the absence of a functional effect, as the authors state in their rebuttal on this issue. Instead, the discrepancy they refer to on line 413 is one between the authors’ inactivation results and those from other studies. Thus, the passage beginning on line 413 does not directly address my concern.\n- Figure 1: When using calcium imaging, one must be careful when interpreting differences in dF/F magnitudes across different cell types...\nThe issue here is that because calcium indicators serve as calcium buffers, their expression level influences the magnitude of intracellular free calcium changes after action potentials, which in turn affects the dF/F change caused by each spike. The “background fluorescence” and “non-action related calcium signals” the authors refer to are not the primary problem with comparing across cell types. Cell types could differ systematically in their indicator expression level, or in the expression of endogenous calcium buffers. Both will affect the size of spike-induced dF/F changes, confounding comparisons across cell types.", "reviewer_4": "Again no major comments. However my first minor comment (to explicitly say what layer was recorded) was specifically about the figure legends - I would suggest to add a statement of what layer was recorded to Figures 2, 3, etc, in either the main figure panel or in the legend. It may say so in the main text, but there will be no ambiguity if it is in the figure itself."}, "author_response": "We sincerely thank the Reviewer for the very positive feedback and for recognizing the improvements made in response to the previous round of comments. We are delighted that the revisions have strengthened the manuscript and appreciate your recommendation for publication."}, {"round": 3, "reviewer_comments": {"reviewer_3": "The authors have done a thorough and very thoughtful job addressing my concerns. With the updates to clarify and qualify a number of points, I think the manuscript is overall stronger. My concerns have been sufficiently addressed. Just for the sake of clarification (I am not asking for further changes), my previous concern about comparing dF/F values across cell types pertained mostly to the comparisons made between PYR and SST cells in Figure 1 and Extended Data Figure 1. These are distinct types that could exhibit systematic differences in the relation between spiking and dF/F because of differences in calcium handling between the two types. I think the authors are getting the right answer here, but I might have done the analysis using the normalized responses shown in 1e,f."}, "author_response": ""}]

Decision letter