To make successful evidence-based decisions, the brain must rapidly and accurately transform sensory inputs into specific goal-directed behaviors. Most experimental work on this subject has focused on forebrain mechanisms. Using a novel evidence-accumulation task for mice, we performed recording and perturbation studies of crus I of the lateral posterior cerebellum, which communicates bidirectionally with numerous forebrain regions. Cerebellar inactivation led to a reduction in the fraction of correct trials. Using two-photon fluorescence imaging of calcium, we found that Purkinje cell somatic activity contained choice/evidence-related information. Decision errors were represented by dendritic calcium spikes, which in other contexts are known to drive cerebellar plasticity. We propose that cerebellar circuitry may contribute to computations that support accurate performance in this perceptual decision-making task. Although the cerebellum is best known for its role in controlling movement, clinical and experimental evidence have long indicated that the posterior cerebellum regulates a wide range of cognitive functions , includiIn the domain of movement control, the cerebellum is thought to use sensory and internal information as a means of adjusting action on a subsecond scale . The cerNeuronal correlates of the perceptual decision-making process have been studied using behavioral tasks in animal models  includinNeurons in multiple brain structures across species have been found to represent various stages in the transformation from sensory information to decision signals. These regions include prefrontal, premotor, parietal, and primary and secondary sensory cortices, striatum, midbrain structures, and possibly others . Many ofWe investigated cerebellar neural activity during decision-making in a head-fixed rodent model. Like previously developed decision-making tasks , our tasTo study decision-making in the cerebellum, we developed a task with five key properties: (1) integration of evidence over seconds , (2) minMice learned to perform this task with high accuracy . BehavioA agonist muscimol bilaterally into crus I. Inactivations reduced choice accuracy while leaving intact the ability to lick and perform trials (We focused our study on the ansiform area (crus I)  of the pm trials . To quanm trials . Fits tom trials . TherefoIn previously investigated brain regions, neurons exhibit choice- and evidence-specific modulations of activity over the duration of evidence accumulation and decision-making . To testCytoplasmic calcium acts as a temporally filtered readout of firing rate, and calcium extrusion in Purkinje cells occurs on a slower time scale see  than in Elsewhere in the brain, neuronal activity during evidence accumulation and decision-making can encode behavioral variables of choice and evidence . To deteBecause choice and evidence are correlated when mice successfully perform the task, we asked whether choice- or evidence-related information existed independently at the population level in the neuronal signals. To separate the two, we determined how decoding accuracy changed after removing information about one of the variables, by shuffling its identity across trials while holding the other variable constant. For example, when the choice on each trial was randomly assigned to another trial with the same sensory evidence, choice decoding accuracy dropped significantly  of cells, cue-period calcium was modulated in the same direction (i.e. upward or downward) without regard to whether the upcoming decision was left or right, while in the remaining 20% (165/843) of cells, activity for left choices and right choices was modulated in opposite directions . In 30% We next asked how Purkinje cells represented evidence. We observed cells in which cue-period activity was modulated by the strength of evidence presented, and responses to individual sensory events were apparent in some cells as puff-triggered averages that rose and fell in approximately 1 s . TherefoIn theories of cerebellar learning, the transformation of mossy-fiber input to Purkinje cell output is refined by climbing fiber-driven error signals which drive plasticity . These iWhen mice make errors, their behaviors, especially their licking patterns, differ from correct trials. We therefore tested whether the elevation in dendritic signaling could reflect a purely motor event such as a lick-cessation signal, since mice cease licking at moments of error . Such a Dendritic signalling was consistently elevated in error trials relative to correct trials across varying trial difficulties, with a modest but non-significant reduction in magnitude in trials with stronger evidence . These eThe present work reports the necessity of the cerebellum in an evidence-accumulation-based decision-making task. We have identified two Purkinje-cell signals that may contribute to this process: somatic activity that reflects evidence and choice, and dendritic signals that report errors. This convergence of task-relevant information onto Purkinje cells suggests that cerebellar activity may play important roles in decision-making, consistent with established hypotheses of cerebellar function in complex domains .Cerebellar crus I communicates with numerous forebrain structures including somatosensory, frontal, and parietal regions  via the Previous studies have established a sophisticated conceptual framework for understanding the computational basis for evidence accumulation and decision-making . Our resOur imaging of Purkinje cell somata revealed ramps of fluorescence. Cytoplasmic calcium acts as a temporally filtered readout of firing rate, limited by calcium removal times that are slower in Purkinje cells see  than in These somatic signals represented task-relevant information related to choice and evidence variables, although it remains an open question as to whether these signals precisely track accumulated evidence over time. They could exhibit firing ramps , steps , or moreCerebellar theories propose that the mossy fiber-granule cell pathway encodes contextual or efference-copy signals which are used to generate short-term predictions . ClimbinWe suppose that these dendritic signals might not represent graded information but rather a more categorical signal for updating the cerebellar representation. It might alternatively be the case that the slight but non-significant trend we did observe, which appears inverted relative to traditional reward error signals, could be an example of inverted reward signalling seen elsewhere in the brain . In all The involvement of the cerebellum, with its clearly delineated cell types and connectivity  opens maPcp2-Cre  and Pcp2-Cre-Ai148 or Ai148 ; for Purkinje cell dendritic imaging from six mice , for Purkinje cell somatic imaging from six mice , for inactivation experiments from six separate mice , and for electrophysiology experiments from another three mice . Mice were housed in a 12 hr:12 hr reverse light:dark cycle facility, and experiments were performed during the dark cycle. During the experimental day, mice were housed in darkness in an enrichment box containing bedding, houses, wheels (Bio-Serv Fast-Trac K3250/K3251), climbing chains, and play tubes. At other times, mice were housed in cages in the animal facility, in groups of 2–4 mice per cage. Mice received 1.0–1.4 mL of filtered water per day. Body weight and condition was monitored daily.Experimental procedures were approved by the Princeton University Institutional Animal Care and Use Committee and performed in accordance with the animal welfare guidelines of the National Institutes of Health. Data for the behavioral task came from 12 mice  of genotypes Pcp2-Cre imaging mice, AAV1.CAG.Flex.GCaMP6f.WPRE.SV40 (Penn Vector Core) virus was injected into crus I and surrounding regions, 220–280 µm below the brain surface , using borosilicate glass pipettes  beveled to 30 degrees with a ~ 10 μm tip opening, and an automated injector system (World Precision Instruments Micro4). In all imaged mice, a window composed of a cannula  glued  to a glass coverslip (Warner Instruments 64–0720) was cemented atop the craniotomy, then a custom-machined titanium headplate  craniotomies were drilled over left and right crus I, and guide pedestals  were implanted over each. After surgery, dummy cannulas were kept in guide pedestals at all times when injections were not being performed, and were changed approximately every 3 days. For electrophysiology experiments, headplates as described above were implanted, and a 2 mm craniotomy was drilled over crus I and the dura removed. Per animal, one 64-channel silicon probe  was placed on a custom-printed probe holder (designed in Blender and printed on a Formlabs Form2 3D printer), and lowered into crus I. During lowering of the probe, recordings were performed as described below to determine the final location of the probe. Ground and reference wires were attached to two stainless steel screws  above the forebrain. Absolute Dentin (Parkell) was used to secure the probe to the skull, and to create a well which was filled with silicone gel (Dow Corning 3–4680) and ophthalmic ointment  to cover the craniotomy before being further secured using dental cement  and dental acrylic . All animals were given buprenorphine (0.1 mg/kg body weight) and rimadyl (5 mg/kg body weight) after surgery and were given at least 5 days of recovery in their home cages before the start of experiments.Mice were anesthetized with isoflurane  and underwent surgical procedures lasting 3–4 hr. For mice in imaging experiments, a 3-mm-diameter craniotomy was drilled over the left posterior hemispheric cerebellum. In eadplate  was cemeBehavioral training, inactivation, and imaging experiments were performed in a light-proof and sound-dampened chamber, with white noise playing at all times. Mice were seated in a custom aluminium tube  with their head protruding out the front and an opening on the top of the tube for access of the imaging objective. For two mice in one session each, the mice stood on a cylindrical treadmill instead of the tube. Mice were head-fixed using custom head bars screwed into the head plate, angled with the anterior end downward at 18.5 degrees from the horizontal. A set of custom-machined polyoxymethylene alignment tools was used to align the headplate in the lateral axis and in yaw for precise repositioning across days.Air puffs were produced by activation of solenoids  with input from an air source regulated to 10 psi (ControlAir Type 850 Miniature Air Pressure Regulator). Air was delivered via two tubes  custom-machined with uniform openings, and positioned parallel to one another, parallel to the anteroposterior axis of the animal, 10 mm apart mediolaterally and ~1 mm anterior to the nose of the animal. Water rewards were produced via activation of similar solenoids, and delivered via similar tubes positioned 4.5 mm apart from one another mediolaterally, and 0.5–1.0 mm anterior to the opening of the animal’s mouth. Licks were detected via completion of one of two parallel electrical circuits for the left and right ports. Puff ports and lick ports were positioned in individual custom-machined polyoxymethylene brackets. The lick port bracket was mounted to a linear actuator  which enabled retraction of the ports to and from the reach of the animal’s tongue (within approximately 300 ms). The ports, brackets, and actuator were mounted via a custom-machined bracket to a micromanipulator  for precise positioning of the apparatus in a unique position for each animal. The entire experimental apparatus was mounted on a translatable stage (Danaher Precision Systems) atop a rotating optical breadboard  to allow custom positioning in the x, y, and rotational axes for imaging. Mice were positioned according to a set of unique coordinates for each mouse on the stage, micromanipulator, and alignment pieces, which were maintained across behavioral sessions.Behavioral movies were acquired using two USB cameras (Playstation Eye), modified by removal of infrared filters and encasings. One camera was positioned directly below the animal’s mouth and the other at the side of the animal’s face. Images were acquired at 30 Hz with 320 × 240 pixel resolution. Illumination was provided by an infrared LED array (Yr.seasons 48-LED Illuminator Light CCTV 850 nm IR Infrared Night Vision). Sounds were delivered to the apparatus by a speaker (Sony Tweeter XS-H20S) mounted below the apparatus.https://github.com/wanglabprinceton/accumulating_puffs; https://github.com/elifesciences-publications/accumulating_puffs). A DAQ board  was used to deliver and read electrical signals from the experimental apparatus. Solenoids were controlled using digital outputs to custom transistor-based switch circuits. Electrical signals corresponding to licks, solenoid signals, and microscope galvanometer position were acquired using analog inputs at 500 Hz. Camera signals were acquired using a custom Python wrapper to the CLEye API . The linear actuator was controlled using an LAC board (Actuonix) and USB control via a custom Python wrapper to the C API (available at https://www.actuonix.com/LAC-Board-p/lac.htm). The micromanipulator was controlled using a Python wrapper to the serial control interface. Experiments were monitored live using a custom interface to display trial information, performance, video input, and electrical signals.All behavioral equipment and data collection were controlled by custom multi-process software written in Python (SCR_014307). Excitation light was provided by a Mai Tai Sapphire laser (Spectra-Physics) at 920 nm. A 16x objective lens  was used with ultrasound gel  as the immersion medium. Excitation power measured at the output of the objective lens ranged from 10 to 50 mW. Images were acquired at 28 Hz or 56 Hz, 512 × 512 or 256 × 512 pixel resolution.A second computer was used to control the two-photon microscope  using the MATLAB software ScanImage 2015  . Signals were sent at multiple timepoints throughout each trial, delivering information corresponding to individual defined moments in the trial, which were then embedded in microscope image frames via ScanImage I2C functionality.Two forms of synchronization signal were sent from the behavior computer to the imaging computer during imaging. The first was a TCP/IP signal indicating animal and session identity. The second was an IExtracellular recordings were performed using 64-channel Neuronexus silicon probes, which were connected to two amplifier boards  using a dual headstage adapter . Recordings were made using an Open Ephys acquisition board at a sampling rate of 30 kHz. Similar to imaging, synchronization pulses containing information about timing of the puffs, licks, and rewards were routed through a National Instruments card (NI USB-8451) and connected to the Open Ephys acquisition board using an I/O board (Open Ephys).The evidence accumulation task was based on a task used in rats . Mice weMice were trained to perform the task through a behavioral shaping procedure which typically spanned a period of 10–20 days. In the first stage of training, trials consisted of a 1 s cue period with the same puff rates as the final task, except all puffs following an initial bilateral puff were presented on the correct side only. A water reward was delivered on the correct side in each trial regardless of the actions of the mouse. When a mouse consumed 14 consecutive rewards, it was advanced to the next stage. In the next stage, trial structure remained similar to the previous stage, except rewards were delivered only if the mouse licked the port on the correct side for a given trial at any point in the 3 s response window. In addition, the delay period was extended to 1.3 s during which periodic (2.5 Hz) guide puffs were delivered on the correct side from the end of the cue period until the animal made a decision. To advance, mice completed at least 100 trials at this stage, with 55% correct in a window of 40 consecutive trials, where a correct trial was defined as a match between the side licked first and the correct side for that trial. In the next stage, trial structure remained similar, except bilateral puffs were included at the end of the cue period. In this stage, rewards were only delivered if the first detected lick was on the correct side. As in the final version, error trials included a buzzer sound and a prolonged inter-trial interval. To advance, mice completed at least 200 trials at this stage, with at least 80% correct trials in a consecutive window of 50 trials. In the next stage, the cue period duration was selected randomly as either 2, 2.8, or 3.8 s, with 0.4, 0.3, and 0.3 probability respectively, with a delay period of 200 ms, and guide puffs were no longer delivered. After 100 trials at this stage, and at least 75% correct in a window of 40 trials, mice were advanced. In the next stage, the cue period was its final 3.8 s duration. After a minimum of 125 trials at this stage, and at least 80% correct trials in a window of 25 consecutive trials, mice were advanced to the next stage. In this stage, mice were required to accumulate evidence, with a generative evidence rate ratio of 1:9 for the two sides. When mice completed at least 300 trials and at least 80% correct in 40 consecutive trials, they were advanced to the final task (described above).Four anti-biasing procedures were implemented during the behavior. First, the probability of drawing a trial on a given side was weighted to produce more trials on the side with worse performance. This mechanism was implemented if in a window of 6 consecutive trials on each side, performance (fraction correct) on one side was 1.5x greater than on the other. In that case, the draw probability of a trial on the worse side was set equal to the ratio of performances on the better to the worse side within the six-trial anti-biasing window. Second, when the same bias metric exceeded 2x, a larger water reward volume  was given with 60% probability. Third, if bias persisted at the 2x threshold for five consecutive trials, the experimental apparatus was shifted 50 µm toward the side of better performance to bring the stimuli and lick ports of the worse side closer to the animal. At the start of each session, a set of 15–30 warm-up trials were delivered drawing from one or two stages preceding the current one in the shaping procedure, and in warm-up trials of the first four shaping stages, the correct side, instead of being randomized, was alternated. Mice were trained for a maximum of 70 min and a minimum of 40 min unless the animal stopped licking for extended periods of time sooner, in which case they were dismounted early. Occasionally, manual rewards were delivered to encourage licking, and stage advancements or decrements were performed manually to suit animal performance.Mice were trained until they achieved at least 70% correct on the final version of the task in two consecutive sessions. Mice were then subjected to injection sessions, corresponding to saline or muscimol in a randomized order across mice. On injection days, mice were anaesthetized using isoflurane (2–3% for induction and maintenance) for approximately 20 min, during which 100–120 nL of either muscimol  or saline were delivered through the injection cannulas  at a rate of 50 nL/min, using an automated injection system (World Precision Instruments Micro4). The injection cannula was left in place for 3 min following each injection to allow diffusion of the solution into the brain. Approximately 1 hr post-injection, mice were mounted on the rig and performed a behavioral session. On at least 2 days prior to the first injection, mice were anesthetized with the same protocol prior to their behavioral sessions in order to acclimatize them to anesthesia. When mice performed below 65% on a session, one or two recovery sessions with no injections were subsequently given such that the animal returned to at least 70% performance, before any additional injection sessions. Confidence intervals for performance in each session were computed as binomial proportion confidence intervals using the Jeffreys method.Following the completion of the study (by approximately 3 weeks), mice were injected under the identical protocol with a fluorescent solution (1.8 mg/mL fluorescein (Fisher Scientific S25328) and 0.5 mg/mL CTB-555 (Invitrogen C22843) in sterile saline) to recover the location and spread of the muscimol injection site. After approximately 1 hr, mice were perfused and brains were extracted. Brains were cleared using iDISCO  and imagSCR_008394) 3.5 and 3.6 , which make use of Numpy 1.12.1 and Scipy 0.19.1  on the previous trial, s was defined as the #R-#L puffs scaled from -1 to 1. The history term p of a rightward choice in the current trial was modeled asEffects of the muscimol inactivation were assessed by fitting behavioral data to a regression model  that conThe four coefficients Imaging data were first motion corrected using a custom template matching-based procedure, then regions of interest (ROI) were selected manually based upon cell morphology and calcium activity. The manually selected dendritic ROI were subsequently refined using the following procedure: for each ROI, the mean activity of all pixels inside the ROI was extracted for each frame in the behavioral session, yielding a single time series. The Pearson correlation coefficient was computed between this time series and every pixel in the dataset. These correlation coefficients were assembled into a correlation coefficient image with the same dimensions as an imaged frame, and regions of interest were automatically extracted from this image by applying a median filter, followed by thresholding the image at two standard deviations above the mean, then detecting and selecting the connected component that most overlapped with the manual ROI. The resulting ROI was excluded if it contained less than 0.5 or greater than 3.5 times the number of pixels in the manually selected ROI. After all ROI were refined in this way, a second processing step merged overlapping and highly correlated ROI to ensure that the resulting ROI corresponded to unique physiological sources. First, mean activity time series traces were extracted for each refined ROI. Next, the pairwise correlation coefficients of traces from each ROI were computed, and an undirected graph was constructed where each ROI composed a node, and edges were placed between nodes with correlation values exceeding a threshold of 0.5. Next, connected component clusters in the graphs with greater than one node were subjected to the following procedure: for each pair of ROI within the cluster, the two ROI were merged if >50% of either ROI’s pixels was contained within the other’s boundaries. A similar graph-based approach was used to detect neighboring ROI with high correlations corresponding to a single cellular origin; if the correlation values of two ROI exceeded 0.8 and they fell within 5 micrometers of each other, they were merged. Finally, pixels within the motion boundaries of the motion-corrected images were removed from all ROI.0 was then computed from these raw traces, with baseline F0 being computed as the minimum of a median-filtered (1 s kernel) 12 s sliding window preceding each time point. Somatic data analysis was restricted to trials with matching 3.8 s cue periods, which comprised 85% of trials. Dendritic data analysis included trials of all cue period durations.For each ROI, the mean activity of all pixels in the ROI was computed for each frame, yielding raw time series data. ∆F/Fr was computed on a trial-by-trial basis. For other analyses, r was computed over the mean somatic signal in the specified time period. For comparison of r in the pre-cue period and cue period, r was computed over a 2 s period immediately preceding, or in the middle of the cue period, respectively. For all other analyses, r was computed over the duration of the cue period. Significance of this modulation was computed by comparing the fraction of cells for which |rcue period| exceeded |rpre-cue period|. The 95% confidence interval for this fraction was computed using bootstrapping: cells were randomly sampled with replacement to create resampled datasets , and the same fraction was computed for each of these datasets. Boundaries of the confidence interval were then computed as the 5th and 95th percentile of these bootstrapped fractions. Return to baseline was computed by comparing the mean activity of each cell before the cue period and after the inter-trial interval; in total, 198,542 such comparisons were made for all cells in the study.Modulation index r was defined as Pearson’s correlation between the somatic calcium signal of a given cell and a uniform time vector (‘cue-period time’). For trial-by-trial analyses , https://github.com/cortex-lab/Kilosort). Spikes were further sorted manually using Phy  puffs on the given side, and excluding bilateral start and end puffs. Mean tSomatic activity or behavioral movie measurements were used to decode the choice or correct side (evidence) on a trial-by-trial basis. Each trial was segmented into 400 ms time bins, in which the mean activity of each cell (somatic decoding) or the mean movement index for each movement feature  was computed. For each time point, the vector of these mean values was used to predict the behavioral variable  by logistic regression. Each regression was scored by k-fold cross validation with k = 5. Because choice and evidence are correlated in the behavioral task, we employed a procedure to investigate their effects separately. First, to determine whether evidence-related information was contained in the neural activity independently of choice-related information: we compared evidence-decoding accuracy with accuracy on a shuffled dataset in which the side of evidence on each trial was randomly assigned to another trial in which the same choice was made as the original trial. In this shuffled dataset, all evidence-decoding accuracy arises from correlated choice-related information, since evidence-related information was removed; therefore, any excess decoding performance relative to this baseline exhibited by the unshuffled dataset arises from independent evidence information. We also did the converse to test for independent choice-related information: that is, shuffled choice across trials within the same evidence category , and evaluated choice decoding accuracy. These ‘independent’ shuffles constitute the ‘Ind’ condition for choice and evidence decoding. In addition, complete shuffles were performed in which choice/evidence was shuffled across trials with no regard for the other variable. These shuffles constitute the ‘Shuffle’ condition.To determine the relationship between somatic signals and puff number on a trial-by-trial basis, a two-factor linear model was constructed for each cell. The factors corresponded to the number of left-sided and right-sided puffs in each trial, and the dependent variable to the mean pre-decision fluorescence (500 ms preceding the decision phase) of the given cell in each trial. The best-fit coefficients were interpreted as the relationship between fluorescence and puff number for that cell. Significance of these coefficients was scored by the p-value associated with each coefficient in the model . Cells with p<0.05 for a given coefficient were considered to have ramps significantly correlated with puffs on the corresponding side. Significance cells were grouped into categories according to the sign of the coefficient (+ or –), and the side of the puffs with significant coefficients . In the shuffle condition, the puff numbers were shuffled across trials while holding the animal’s choice constant, and the same analysis was run.Movements of the mouth, whiskers, nose, and paws were made using the behavioral movies acquired during all behavioral sessions in which calcium imaging was performed. For each movie from each animal, regions of interest (ROI) were manually selected corresponding to the aforementioned regions in the field of view. Full-session traces were extracted as the mean pixel value within the ROI in each frame. Mouth movements were measured using the laterally positioned camera, where deviation of the mouth and chin was best detected. Full-session traces corresponding to mouth movement were mean-subtracted and normalized from 0 to 1. For those measurements, the normalized licking measure was computed as the absolute value of the mouth-movement trace, with a median-filtered (0.5 s kernel) drifting baseline signal subtracted off, normalized from 0 to 1. Deviations in overall image brightness were normalized by a fiducial measurement in the field of view not involving the animal that reliably tracked field-of-view brightness. The remaining movement measurements  were extracted from the camera positioned below the animals’ faces. For these measurements, traces were extracted in the same manner as above, and the normalized movement index was computed as absolute value of the trace derivative, normalized from 0 to 1.0 sparsity penalty, and binarized with a threshold of 0.1.Dendritic events were detected using the OASIS autoregressive deconvolution algorithm , with ARTo test the hypothesis that dendritic signalling encoded motor events, we computed the mean dendritic response to a number of specific motor actions, and compared this response across error and correct contexts on a cell-by-cell basis. In these analyses, lick initiation (‘start licking’) and cessation (‘stop licking’) were defined as licks that were preceded or followed by, respectively, at least 250 milliseconds without licking on that side. For the analysis in For all of the above movement measurements, movement events were split into those which occurred in error contexts (i.e. the time window for which the dendritic response was measured for Dendritic activity was used to decode the outcome (correct or error) on a trial-by-trial basis. For each cell in each trial, the post-decision response was computed as the mean ∆F/F value in the 800 ms following (‘Post-choice’) or preceding (‘Pre-choice’) the decision. The outcome of each trial was predicted by logistic regression using these values as predictors. For each session analyzed, 1000 batches of trials were randomly sampled in which the number of correct and error trials were matched, and a regression was performed on each, scored by k-fold cross validation with k = 3. In the shuffle conditions, the outcomes of the trials were shuffled and the decoding accuracy on the shuffled dataset was reported. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed Richard Ivry as the Senior Editor, a Reviewing Editor, and three reviewers. The reviewers have opted to remain anonymous.Thank you for submitting your article "A cerebellar role in evidence-guided decision-making" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:This is an interesting study describing a potential role for the lateral posterior cerebellum in a novel decision-making task. The cerebellum has received much less focus than cortex in decision-making research despite clear evidence from, among other sources, human imaging and lesion studies that the cerebellum is involved in a wide range of cognitive processes. There is also a rich tradition of theoretical modeling inspired by the cerebellum's unique circuit organization and role in sensorimotor coordination that appears potentially relevant to many cognitive operations, raising interesting questions about what the cerebellum might contribute to decision-making. Therefore, the approach and data reported in the current manuscript are promising and could provide meaningful novel insight into the neural basis of decision-making. Despite this potential, and that it does report some interesting observations, the main limitation of this work is that it is hard to say what the cerebellum is doing during the task. In particular, it is not clear that it is possible to conclude that the cerebellum is truly involved in evidence accumulation. The concern about the support for evidence accumulation holds for the inactivation experiments as well as for the imaging.Essential revisions:1) Inactivation results: Inactivation results are suggestive of an important role for the cerebellum in this task. However, from the data presented, it is not clear that it is possible to conclude that the cerebellar inactivations are interfering with evidence accumulation itself, and not just somatosensation or motor output. At a minimum, the authors should show psychometric curves for the 5 individual muscimol-treated animals. Assessing the effects on the slopes and offsets of the individual psychometric curves might be more revealing about which aspects of task performance were affected by inactivation of crus I. Further, there is the possibility that with this experimental design, muscimol inactivation may not be adequate to determine whether the cerebellum is accumulating evidence, because changes in the psychometric function can also be explained by a decrease in sensory sensitivity alone. Careful modeling of the data may help.2) Evidence for evidence accumulation signals from imaging of somatic calcium in Purkinje cells: The authors report that appear similar to evidence-dependent and choice-selective ramping responses previously reported in many cortical and subcortical areas of primates and rodents. The manuscript reports temporal dynamics of Purkinje cell somatic calcium signals that, in many cells, manifest as a gradual ramping response during the cue period that is correlated with both the number of pulses on one or the other side (evidence) and/or the animal's eventual response (choice). These responses are interpreted with analogy to the ramping responses in trial-averaged spike rates recorded from parietal cortex  that are thought to encode the time-integral of sensory evidence. However, the manuscript does not convincingly demonstrate that cerebellar neurons are actually encoding accumulated evidence. In general, given the uncertainty about the origin of these signals, more caution is warranted in interpretation of the apparent ramping signals. Specifically:a) While there appear to be lots of time-modulated signals, the link to evidence accumulation and choice is not clear. It is apparent that the most salient feature of the recorded cerebellar population is a constant modulation, either upwards or downwards, with the passage of time in the trial. The presence of this signal complicates the interpretation of any ramping activity as being attributable to evidence accumulation. However, the statistical analyses  ignore this component of the responses. This might be valid if the representation of time were truly independent from the representation of evidence. But no such independence is established. For example, cells that are more strongly driven by evidence might also be more strongly driven by time, or more likely to be driven by time in one direction or another. The authors should more carefully consider the relationship between the different components of the cerebellar responses and/or formally control for potentially confounding effects of the time-related responses in the statistical models.b) Given the temporal resolution of the calcium indicator, it is difficult to interpret any evidence-related signal dynamics as reflecting an underlying ramp in neuronal firing rate. The original description of the use of somatic calcium imaging to track changes in Purkinje cell simple spike rate  found that these signals were so slow that step changes in simple spike rate could result in ramps of fluorescence like those seen here. This technical concern would best be addressed by at least some recordings comparing the calcium signals to simple spike rate with electrophysiology. Although the authors have tried to be careful in their writing, the text currently leaves plenty of room for misinterpretation by less technical readers. They should clarify the text further to be much more clear about the limitations of interpreting the temporal dynamics of the calcium signals.c) The relevant decision variable in the pulse accumulation task is the *difference* in pulses between the two sides. The authors seem to know this point well based on how they plot the psychometric functions. Yet at best a small minority of Purkinje cells encode this value. Subsection “Neuronal signatures of choice and evidence in Purkinje cells” reports that 39/843 cells (less than 5%) encode either a sum or difference of the number of pulses on each side. The specific figure for the number of cells encoding a difference is not reported, but Figure 3 suggests that it is about 5 cells . These numbers question strong conclusions about the representation of the decision variable and accumulated evidence in cerebellum. Further, this result could be taken to show that responses in rodent cerebellum are different from those in primate cortex that are often interpreted as a representation of the decision variable for perceptual discrimination. Notably, it also indicates a dissociation between rodent cerebellum and neocortex, as Scott et al., estimate that as many as 1/3 of evidence-modulated cortical cells are better explained by the difference between sides. Therefore, the data strongly suggest that the critical decision-making operations are actually implemented downstream of the recorded cerebellar population. This strikes me as highly relevant to how the overall results should be interpreted, and it should be emphasized more strongly in the summary and discussion.d) The analysis of the representation of accumulated evidence  ignores the time course of representation, focusing on a short window immediately before the decision. Even if we disregard technical problems explained above in 2a-c, it is unclear whether the cerebellar neurons represent integration of evidence over time or merely its final outcome. Note that the population analysis for the representation of evidence  does not answer this question  because evidence is defined as the "correct" choice in that analysis rather than the magnitude of evidence (#R-#L).3) "Error-related" dendritic responsesIn addition to somatic calcium signals, the authors analyzed dendritic calcium signals and found that these were higher on error vs. correct trials. The implications of this observation are emphasized heavily; for example, the Discussion section concludes that "the cerebellum, which learns from error to guide action, may help in the learning and tuning of accurate responses". This is an interesting proposal, but one might have had the same belief prior to seeing the results reported here (given existing human data and theories about the cerebellum), and it's not clear how the data should update it.a) There is speculation about how the observed error responses could be used as error signals to guide learning, in line with existing models of cerebellar learning. However, it was not clear to the reviewers how that would work, especially given that the error responses observed were not directional. Typically, Purkinje cell complex spikes are thought to provide a directional signal for learning, not just a correct/ incorrect signal. There are no analyses to support the proposed functional role of these error related responses as being involved in "tuning" responses or correcting errors. Yet, it should be possible to provide a more thorough analysis of the error signals:What do error signals predict about future behavior? An obvious analysis would be to ask whether the magnitude of the error signal on trial n−1 influences choice accuracy on trial n. This seems to be a clear prediction from the proposal that the cerebellum "tunes" behavior or "corrects errors".c) How do the error signals relate to the strength of evidence on each trial? This is briefly mentioned at the end of the Results section and in Figure 4—figure supplement 1, but without any statistical tests or interpretation. Whether and how the error signals are modulated by the strength of evidence seems key to determining their functional role in updating behavior, if any. In particular, we would have expected that true "error" responses would be higher for easier trials, but the opposite appears to be true.4) Ruling out sensory and motor confounds as potential sources of somatic and dendritic calcium signalsa) Figure 3B indicates that isolated puffs produce a calcium response that gradually rises and falls over ~1 s. On trials with strong evidence, these signals will overlap. Assuming that they sum reasonably linearly, overlapping calcium responses from neurons that encode only the transient presentation of evidence would nevertheless give the impression of a gradual ramp with a slope that depends on the quantity of evidence. Note that this is a different issue from the point raised in the Discussion section about distinguishing single-trial ramps from steps. Rather, it makes it unclear whether the evidence-related cerebellar responses correspond to a representation of the momentary sensory evidence or to the magnitude of accumulated evidence that drives choice. This distinction is critical to interpreting proposed neural implementations of evidence accumulation models. One possible way to address this would be to record responses during stimulus presentation from animals that are not engaged in making a decision.b) The authors attempt to rule out that the somatic signals are related directly to movement of the animal and less related to the "cognitive" variables. The analysis in the supporting figures is in line with the level of detail often applied in the field currently. But, is it possible that the activity is related to other movements of the animal rather than in the orofacial region? The authors should either cite strong evidence showing that these are the only relevant regions for the area of cerebellum examined or provide additional videography results from other parts of the body (e.g. paws). This is of course a significant point to make as strong as possible given the cerebellum's long-established role in motor behaviors.c) The analyses in Figure 4—figure supplement 1 do not convincingly rule out the possibility that the difference in dendritic calcium signals on error trials resulted from differences in licking on error trials. That figure clearly shows that cessation of licking proceeds at different rates on error trials compared to other times. How does trial type (correct vs error) affect the relationship between licking (or changes of rate in licking) and dendritic calcium signals?eLife. Your revised article has been favorably evaluated by Richard Ivry (Senior Editor), Megan Carey (Guest Reviewing Editor), and two reviewers.Thank you for resubmitting your work entitled "A cerebellar role in evidence-guided decision-making" for further consideration at The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:The authors have made a concerted effort to clarify the text of their paper and have provided reasonable responses to many of the points raised. As was noted in the first round of reviews, rebuttal, and in the text, it remains unclear what the cerebellum's specific role or computation is during the task, and the reviewers were somewhat disappointed that most of the key questions are deferred to future studies. However, the reviewers also felt that defining cerebellum's exact role in the task may be asking too much for a first study, particularly given the limitations of the dataset and the conceptual and analytical complexities that prevent the authors from specifying the nature of neural representations and the source of the behavioral deficit following cerebellar inactivation.Overall, there are several interesting leads about the role(s) of cerebellum in perceptual decisions in this paper. While none of these are firmly established by the current study, the reviewers agree that the writing and presentation of the results is generally fair and not greatly over-stated, and favor publication so that the results can be evaluated by the field and followed up in future studies from other groups.The reviewers note that in the process of clarifying what can and cannot be claimed based on the existing data, the scope of the paper is limited to three points: successful training of mice to do the task, reduced accuracy following cerebellar inactivation, and representation of task-relevant variables in cerebellar neural population without specifying the exact nature of the represented variables. Before publication, the reviewers agree that it is important to ensure that after toning down its claims, the manuscript does not leave behind any statements that could mislead readers as to what is actually shown. The reviewers have identified the following statements from the Title, Abstract, and Discussion section that are potentially misleading and should be restated with more specific statements that more accurately match the conclusions that can be supported by the data.TitleA cerebellar role in evidence-guided decision-making.Impact StatementThe lateral posterior cerebellum participates in evidence-accumulation-based decision-making, and Purkinje neurons in this region encode choice-, evidence-, and error-related variables. .Abstract- Here we show that during perceptual decision-making over a period of seconds, decision-, sensory-, and error-related information converge on the lateral posterior cerebellum in crus I, .- Demonstrated that cerebellar inactivation reduces behavioral accuracy without impairing motor parameters of action [Not all motor parameters were controlled for].- We found that Purkinje cell somatic activity encoded choice- and evidence-related variables [Please avoid the suggestion that the specific variables that are encoded have been determined].- Decision errors were represented by dendritic calcium spikes, which are known to drive plasticity [This could misleadingly suggest that they are known to drive plasticity in this context].- We propose that cerebellar circuitry may contribute to the set of distributed computations in the brain that support accurate perceptual decision-making. [Should be more focused on task performance].Discussion section- Cerebellar inactivation reduces animals' use of evidence and increases their use of choice history. .- Given the temporal resolution of calcium measurements, our somatic signals may correspond to firing rate ramps , steps , or more complex response profiles that form a temporal basis for evidence accumulation . .-The task-modulated activity we observe encodes both choice-related and evidence-related variables that may be used during the decision-making process. [Please avoid the suggestion that the specific variables that are encoded have been determined].- We observed an excess of dendritic calcium events coincident with decision errors, demonstrating for the first time observations compatible with error-associated signalling in a decision-making reward context. .eLife. Your revised article has been favorably evaluated by Richard Ivry (Senior Editor), and Megan Carey (Guest Reviewing Editor).Thank you for resubmitting your work entitled "Cerebellar involvement in an evidence-accumulation decision-making task" for further consideration at The manuscript has been improved but there are some final issues that need to be addressed before acceptance, as outlined below:In response to the request to re-evaluate the evidence for ramping signals that could be obtained with the somatic calcium imaging, the authors now state , "Therefore our observed increasing and decreasing time courses of calcium could reflect various firing rate profiles, such as impulse responses, ramps, or steps."In light of this revision, as well as the fact that the electrophysiological evidence provided in Figure 2—figure supplement 2 is from only a few cells, all of which show positive ramps of activity, the following statements should also be revised:- , "We did find that electrically recorded Purkinje cells exhibited gradually increasing rates of firing throughout the cue period , suggesting that on average across trials, the fluorescence signals we observed correspond to firing rate ramps."- (Discussion section), "Our electrical recordings also showed ramps, suggesting that temporally filtered firing rate ramps are sufficient to account for our observed fluorescence signals."In both instances, we suggest removing the second clause, starting with "suggesting that…"Thank you for resubmitting your work entitled "Cerebellar involvement in an evidence-accumulation decision-making task>" for further consideration at eLife. Your revised article has been favorably evaluated by our editors again, but there remain some issues that need to be addressed before acceptance, as outlined below. Given that this is the third request for revisions, we will be unable to follow with any more. Please attend to this final issue one way or the other so that the next letter will be the final one.We appreciate the authors' desire to speculate here. However, in our view, the "suggests/ consistent with" was not the only problem with this sentence. There is also a problem with "sufficient". The electrophysiological evidence in Figure 2—figure supplement 2 is anecdotal and non-quantitative. For this statement to be left in, it would need to be adequately supported. In our view, this would require a quantitative comparison between imaging and electrophysiology results. In particular, we would want to know:- How many Purkinje cells in total were recorded from electrophysiologically? How many of these showed ramping? - Did any Purkinje cells show ramping calcium signals without a transient increase in firing rate?- Did any Purkinje cells show ramping calcium signals without ramps in firing rate ?- Why are no decreasing activity ramps found with electrophysiology, but they are found with imaging?- What accounts for the decreasing ramps that were observed with calcium imaging?- What would the predicted calcium signals be for the examples shown if the spike rates recorded electrophysiologically (with and without the transient increase/ ramping components) were convolved according to Ramirez and Stell (2016)? And/or with the authors' own convolution/ deconvolution methods, from the simultaneous calcium imaging/ electrophysiological recordings that they performed?We give the authors the choice of either fully addressing these points, or using compromise language, such as "Preliminary electrical recordings also showed ramps, consistent with the idea that temporally filtered firing rate ramps may account for the observed fluorescence signals." Summary:This is an interesting study describing a potential role for the lateral posterior cerebellum in a novel decision-making task. The cerebellum has received much less focus than cortex in decision-making research despite clear evidence from, among other sources, human imaging and lesion studies that the cerebellum is involved in a wide range of cognitive processes. There is also a rich tradition of theoretical modeling inspired by the cerebellum's unique circuit organization and role in sensorimotor coordination that appears potentially relevant to many cognitive operations, raising interesting questions about what the cerebellum might contribute to decision-making. Therefore, the approach and data reported in the current manuscript are promising and could provide meaningful novel insight into the neural basis of decision-making. Despite this potential, and that it does report some interesting observations, the main limitation of this work is that it is hard to say what the cerebellum is doing during the task. In particular, it is not clear that it is possible to conclude that the cerebellum is truly involved in evidence accumulation. The concern about the support for evidence accumulation holds for the inactivation experiments as well as for the imaging.We thank the reviewers and editor for their thoughtful comments on the manuscript. We have added new data, analyses, and substantial clarifications that we think improve the quality of the study. In some cases, we think the objections raised by reviewers were consequences of our failure to properly convey the scope of our study.We fully agree that a tremendous amount stands to be resolved with respect to the cerebellar role and computations in our decision-making behavior. Our findings are at an early stage relative to the decision-making field. It is not our intent at this time to dissect precisely the evidence integration process as many studies have, but rather to study the whole perceptual decision-making process with a more agnostic view about cerebellar roles. We cannot yet make strong claims about the particular computational aspect of decision-making to which the cerebellum contributes. In light of this and of the reviewer suggestions, we have emphasized more strongly the extent to which many questions remain unanswered.Of specific note with respect to a number of reviewer comments: we do not interpret our findings as evidence that the cerebellum performs the integration computation in evidence accumulation. Furthermore, we are aware that the particular roles of brain regions in the evidence-accumulation process are intricate, deeply studied, and yet still unresolved .In that light, our goal was to probe for an involvement of cerebellar activity in the perceptual decision-making process, which to the most basic extent has not been previously shown. We asked three fundamental questions: (1) Can we train mice on a new evidence-accumulation decision-making task? (2) Does cerebellar inactivation affect behavioral performance? (3) Are task-relevant variables represented in any way in cerebellar neuronal populations? We reasoned that these three demonstrations would constitute novel and interesting findings and would be a basis for substantially larger-effort endeavors (which we are now pursuing).To make this clearer in the manuscript, we have adjusted the text to emphasize the result: i.e. demonstration of a cerebellar role in perceptual decision-making, using evidence accumulation as a paradigm, but where the cerebellar role may or may not be directly in the computations of updating an evidence integrator per se. We were careful to present it this way in the manuscript and have attempted to clarify it more. We consider follow-up studies with detailed high-resolution measurements and inactivations an important and interesting next step, and we are pursuing that.Essential revisions:1) Inactivation results: Inactivation results are suggestive of an important role for the cerebellum in this task. However, from the data presented, it is not clear that it is possible to conclude that the cerebellar inactivations are interfering with evidence accumulation itself, and not just somatosensation or motor output.We view the inactivation experiment as an initial demonstration of cerebellar necessity, and not a comprehensive modelling effort. Rather, we add a new region of interest to the emerging brainwide view of perceptual decision-making, noting that the inactivations demonstrate a causal role for the cerebellum in effectively converting sensory evidence into a decision at some stage of that complex process. This could correspond to sensory input gain biases, post-categorization biases, or other non-accumulation-specific biases. We have now noted this important distinction more clearly in the Discussion section. Below we address our thoughts on the possibilities of specific roles for the cerebellum but we do not believe to have resolved those yet using this study.First, to address the possibility of interference with motor output: we were particularly mindful of the possibility of a strictly motor deficit, and we believe the licking measurements and control trials we included  address this.Regarding the possibility of sensory deficits and other non-accumulation impairments: as we explain in the initial section of our response, we completely agree: the evidence-accumulation process, as opposed to related sensory gain and premotor processes, is difficult to pin down and we are aware of recent findings in rodent decision-making which indicate non-accumulation roles for regions that have been long studied in decision-making . We do not intend to claim that the cerebellum performs specifically evidence integration.At a minimum, the authors should show psychometric curves for the 5 individual muscimol-treated animals. Assessing the effects on the slopes and offsets of the individual psychometric curves might be more revealing about which aspects of task performance were affected by inactivation of crus I. Further, there is the possibility that with this experimental design, muscimol inactivation may not be adequate to determine whether the cerebellum is accumulating evidence, because changes in the psychometric function can also be explained by a decrease in sensory sensitivity alone. Careful modeling of the data may help.In the submitted version of the manuscript, the evidence sensitivities, biases, and trial history effects of individual animals were represented by the session-by-session fits in the logistic regression model shown in Figure 1—figure supplement 2C (and subsection “A decision-making task for cerebellar investigations”). This behavioral model has been used in multiple published decision-making studies, it considers more variables than a psychometric fit does, and it provides a highly useful view of the effects introduced by the inactivation.Psychometric curvesNevertheless, we now include psychometric curves that similarly demonstrate the reduced sensitivity presented quantitatively in the aforementioned modelling . We also more strongly highlight the findings of the behavioral modelling, which indicate significant changes in animals’ usage of evidence and trial history in guiding decisions (subsection “A decision-making task for cerebellar investigations”).From our data and modelling at this stage, we consider deficits at all stages of the decision-making process  possible and interesting to report. Thus, we do not claim that the cerebellum controls evidence integration per se. We have made this point clearer in the Results section and Discussion section.Some of the shifts in psychometric curves may arise from asymmetry of injections (and we include specific evidence suggesting that such an asymmetry can account for some of the biases we see). We are currently doing experiments with newer technologies that we believe will resolve these questions in a more detailed manner.However, we do note that the trend in our psychometrics is broadly consistent with effects seen in , which shows that curves with this general phenotype (compression and bias) could conceivably be explained by a number of hypotheses ranging from sensory and premotor deficits to accumulation deficits. While it is beyond our current goal in this manuscript to perform this level of modelling, we use it just to demonstrate that our phenotype does not rule out interesting possibilities.Drift diffusion modelsFits to drift diffusion models are an interesting direction for these data, yet also not necessary for our aim to demonstrate general causality for the region in the task. For completeness, we include below preliminary fits to a drift diffusion model showing what appears to be an increase in accumulation noise as a result of muscimol inactivation.Given the poor statistical power of this dataset for this analysis, we don’t feel it substantially changes our main novel point that the cerebellum indeed plays causal role in some component of perceptual decision-making. We are currently performing a temporally specific inactivation study that will be better powered to perform this level of analysis. We intend to pursue more detailed modelling there.2) Evidence for evidence accumulation signals from imaging of somatic calcium in Purkinje cells: The authors report that appear similar to evidence-dependent and choice-selective ramping responses previously reported in many cortical and subcortical areas of primates and rodents. The manuscript reports temporal dynamics of Purkinje cell somatic calcium signals that, in many cells, manifest as a gradual ramping response during the cue period that is correlated with both the number of pulses on one or the other side (evidence) and/or the animal's eventual response (choice). These responses are interpreted with analogy to the ramping responses in trial-averaged spike rates recorded from parietal cortex  that are thought to encode the time-integral of sensory evidence. However, the manuscript does not convincingly demonstrate that cerebellar neurons are actually encoding accumulated evidence. In general, given the uncertainty about the origin of these signals, more caution is warranted in interpretation of the apparent ramping signals. Specifically:We thank the reviewers for pointing out that our claims on this topic came across stronger than intended. As suggested, we have edited our interpretations to be more cautious. Our adjustments clarify what we now intend to claim and eliminate vagueness. We agree, as we note in the responses below, that (1) we have not demonstrated that cerebellar neurons precisely encode the stepwise accumulation of evidence, (2) the coding strategy within the cerebellum may be different than that of neocortex, and (3) our measurements are limited in the ability to answer (1) and (2). The analogy to more finely resolved neocortical signals is intriguing but not crucial to our claims, being more of a discussion point. We have therefore re-worded our conclusions to emphasize these distinctions.On that note, some of the reviews in this section are concerned with how evidence is specifically represented, and how that may relate to specific evidence-accumulation models. As we note at the start of our response, an accumulation role per se for the cerebellum is possible but beyond our data to support. In this first contribution to the study of the cerebellum in this task, we focus on findings that precede such a claim. We consider our main finding to be that choice- and evidence-related information is present with sufficient fidelity to play a role in decision-making computations..b) Given the temporal resolution of the calcium indicator, it is difficult to interpret any evidence-related signal dynamics as reflecting an underlying ramp in neuronal firing rate. The original description of the use of somatic calcium imaging to track changes in Purkinje cell simple spike rate  found that these signals were so slow that step changes in simple spike rate could result in ramps of fluorescence like those seen here. This technical concern would best be addressed by at least some recordings comparing the calcium signals to simple spike rate with electrophysiology. Although the authors have tried to be careful in their writing, the text currently leaves plenty of room for misinterpretation by less technical readers. They should clarify the text further to be much more clear about the limitations of interpreting the temporal dynamics of the calcium signals.We have clarified the text to emphasize the limitations in interpreting our calcium signals . We have more strongly noted that step changes in firing rate are indeed possibly the underlying representation.While simultaneous calcium and electrical recordings are beyond our current technical abilities, we have obtained electrical recordings of Purkinje cells during performance of the task in trained animals. These recordings substantiate our primary findings of task-modulated signalling and suggest that the trial-averaged calcium signals we observe indeed correspond to ramps in firing rate. These recordings are now included in Figure 2—figure supplement 2. We include these important data, but nevertheless remain conservative in our interpretations of calcium data (Discussion section), especially since the nature of these signals remains unresolved at the single-trial level.c) The relevant decision variable in the pulse accumulation task is the *difference* in pulses between the two sides. The authors seem to know this point well based on how they plot the psychometric functions. Yet at best a small minority of Purkinje cells encode this value. Subsection “Neuronal signatures of choice and evidence in Purkinje cells” reports that 39/843 cells (less than 5%) encode either a sum or difference of the number of pulses on each side. The specific figure for the number of cells encoding a difference is not reported, but Figure 3 suggests that it is about 5 cells . These numbers question strong conclusions about the representation of the decision variable and accumulated evidence in cerebellum. Further, this result could be taken to show that responses in rodent cerebellum are different from those in primate cortex that are often interpreted as a representation of the decision variable for perceptual discrimination. Notably, it also indicates a dissociation between rodent cerebellum and neocortex, as Scott et al., estimate that as many as 1/3 of evidence-modulated cortical cells are better explained by the difference between sides. Therefore, the data strongly suggest that the critical decision-making operations are actually implemented downstream of the recorded cerebellar population. This strikes me as highly relevant to how the overall results should be interpreted, and it should be emphasized more strongly in the summary and discussion.While it is true that a subset of cells in  represented the #R-#L quantity, a key conclusion of that study was that left and right evidence may be accumulated mostly independently. See their first discussion paragraph: “Behavioral Analysis and Neural Recordings Support the Existence of Two Weakly Coupled Accumulators.” In this light, we think that representations of single-sided evidence could possibly be important contributors to a decision-making circuit.Nevertheless, we agree with the reviewer that cerebellar computations might not be the site of accumulation per se but possibly a filtering step preceding or following accumulation, as a node in a complex decision-making circuit. It may be the case, for example, that the cerebellar contribution is related to the individual accumulators proposed by , and that the weak coupling occurs in neocortex or elsewhere. We have now emphasized this point (Discussion section).:a) While there appear to be lots of time-modulated signals, the link to evidence accumulation and choice is not clear. It is apparent that the most salient feature of the recorded cerebellar population is a constant modulation, either upwards or downwards, with the passage of time in the trial. The presence of this signal complicates the interpretation of any ramping activity as being attributable to evidence accumulation. However, the statistical analyses  ignore this component of the responses. This might be valid if the representation of time were truly independent from the representation of evidence. But no such independence is established. For example, cells that are more strongly driven by evidence might also be more strongly driven by time, or more likely to be driven by time in one direction or another. The authors should more carefully consider the relationship between the different components of the cerebellar responses and/or formally control for potentially confounding effects of the time-related responses in the statistical models.d) The analysis of the representation of accumulated evidence  ignores the time course of representation, focusing on a short window immediately before the decision. Even if we disregard technical problems explained above in 2a-c, it is unclear whether the cerebellar neurons represent integration of evidence over time or merely its final outcome. Note that the population analysis for the representation of evidence  does not answer this question  because evidence is defined as the "correct" choice in that analysis rather than the magnitude of evidence (#R-#L).Points 2a and 2d are each distinct and important points, which concern the temporal representations observed in Purkinje cells in our study. We address the specifics of these concerns below, but we note generally that their relevance is contingent on an assumption that we are claiming cerebellar neurons represent integration of evidence per se. While we are aware of studies that have made strong claims about the precise representations of accumulated evidence in single-neuron signals, we are not making claims as strong. We have modified the text in the manuscript to clarify this .Our intention is to demonstrate that we observe evidence-related information in neuronal activity. Specifically, we make 2 points about evidence representation in our data:(1) Stimulus information is present throughout the cue period: evidence-side decoding in Figure 2F demonstrates population-level encoding of evidence-related information throughout most of the cue period .(2) The magnitude of single-sided evidence is present in some neurons : shown by linear model for evidence strength effects in Figure 3C.These analyses are comparable to the type shown in Figure 5 of , which demonstrates the presence of task-relevant information but does not resolve its time course. Further, based on new data given in response to review 4a, we know that these representations are task-specific.Notably, we do notclaim that:(1) cerebellar modulation is entirely driven by evidence. Some level of time modulation, as suggested in review 2a is likely in our view, though we note that our signals cannot be exclusively a time representation because of the analyses mentioned above.(2) cerebellar signals represent the integration of evidence on a moment-to-moment basis. Our resolution and signal-to-noise does not allow us to answer this yet. Even if our neuronal population encodes evidence by, for example, a distributed temporal basis, the presence of the necessary information at all is an important finding.Additionally, we know from our upcoming study of neural correlates in the primary and secondary visual cortices that even apparently simple sensory responses  are modulated by time and numerous other factors . The slow calcium time dynamics of Purkinje cell somata makes disentangling time and evidence effects very difficult, and this is better addressed by specific experiments e.g. with carefully designed sampling of stimulus times, which is beyond the scope of our current study.In summary, we think that the concern in 2a (evidence and time modulations may be intertwined) and the concern in 2d  are interesting questions we have not resolved, but that our core conclusions do not depend on the answers. Finally, we note that we are currently performing electrical-recording experiments that address this important issue as part of a follow-up study.3) "Error-related" dendritic responsesIn addition to somatic calcium signals, the authors analyzed dendritic calcium signals and found that these were higher on error vs. correct trials. The implications of this observation are emphasized heavily; for example, the Discussion section concludes that "the cerebellum, which learns from error to guide action, may help in the learning and tuning of accurate responses". This is an interesting proposal, but one might have had the same belief prior to seeing the results reported here (given existing human data and theories about the cerebellum), and it's not clear how the data should update it.In this work we focused on reporting what is, to our knowledge, the first observation of error-related signalling in the Purkinje cell dendritic pathway during a cognitive, decision-making context. We agree with the reviewers that we did not resolve many interesting details of this signalling, in some part due to limitations of the experiment as explained in the specific replies below. Nevertheless, we feel that our result builds a bridge between known roles of error signals in motor contexts and hypothesized but never-observed roles in the domain of decision-making.Beyond describing the finding, at this time we feel limited in our ability to propose specific roles of the observed dendritic signalling. We have therefore removed the heavy emphasis (like that referenced by the reviewer above). We still consider these findings an important result to include as they motivate future studies that we are currently pursuing, and we hope that they will also be of use to others given the current interest in this topic .a) There is speculation about how the observed error responses could be used as error signals to guide learning, in line with existing models of cerebellar learning. However, it was not clear to the reviewers how that would work, especially given that the error responses observed were not directional. Typically, Purkinje cell complex spikes are thought to provide a directional signal for learning, not just a correct/ incorrect signal. There are no analyses to support the proposed functional role of these error related responses as being involved in "tuning" responses or correcting errors. Yet, it should be possible to provide a more thorough analysis of the error signals:What do error signals predict about future behavior? An obvious analysis would be to ask whether the magnitude of the error signal on trial n−1 influences choice accuracy on trial n. This seems to be a clear prediction from the proposal that the cerebellum "tunes" behavior or "corrects errors".We appreciate the trial-by-trial analysis suggestion and we are very interested in knowing the answer to this question as well, especially given the success of similar analyses in recent cerebellar work on head movements , eye movements , and eyeblink conditioning . We attempted this analysis, and encountered an issue of statistical power that is related to slow learning in this behavior and therefore prohibitively low trial counts.To illustrate the point, we have performed a power analysis considering the number of correct and error trials in our dataset. To detect a 10% difference from mean performance (at α=0.05) in fraction correct choices, one needs approximately 16x the number of trials we collected in our imaging study. We also note that this is under a very generous assumption that there would be a 10% difference in performance as a result of dendritic signalling within a session, but we know that in our task, small improvements in performance take place over weeks . This is in contrast to the behaviors used in studies that have successfully performed such analyses, where learning is substantially faster and can thus be expected to be accompanied by robust neural correlates in just tens of trials.We are encouraged to have found this elevated signalling and now that we know it exists, it may be possible to design a study to test this, but it would best involve a new or modified experimental design. We have added text to the Discussion on this topic.c) How do the error signals relate to the strength of evidence on each trial? This is briefly mentioned at the end of the Results section and in Figure 4—figure supplement 1, but without any statistical tests or interpretation. Whether and how the error signals are modulated by the strength of evidence seems key to determining their functional role in updating behavior, if any. In particular, we would have expected that true "error" responses would be higher for easier trials, but the opposite appears to be true.We have added statistics to the reporting of these results, which proved to be not significant despite a possibly interesting trend. If the trend had been significant, it may have been compatible with inverted reward signalling which is known to occur in some other brain regions . We now comment on this in the manuscript (Discussion section).4) Ruling out sensory and motor confounds as potential sources of somatic and dendritic calcium signalsa) Figure 3B indicates that isolated puffs produce a calcium response that gradually rises and falls over ~1 s. On trials with strong evidence, these signals will overlap. Assuming that they sum reasonably linearly, overlapping calcium responses from neurons that encode only the transient presentation of evidence would nevertheless give the impression of a gradual ramp with a slope that depends on the quantity of evidence. Note that this is a different issue from the point raised in the Discussion section about distinguishing single-trial ramps from steps. Rather, it makes it unclear whether the evidence-related cerebellar responses correspond to a representation of the momentary sensory evidence or to the magnitude of accumulated evidence that drives choice. This distinction is critical to interpreting proposed neural implementations of evidence accumulation models. One possible way to address this would be to record responses during stimulus presentation from animals that are not engaged in making a decision.We see the importance of the suggested control, and we have included new data from mice subjected to trials under identical conditions but not trained in the decision-making behavior. These data can be seen in Figure 3—figure supplement 1 (and subsection “Dynamics of choice- and evidence-related information in Purkinje cells”), and they indicate an absence of ramping, evidence-side decoding, or puff count representation in these signals. We cite this, and the electrical recordings given in response to review 2b as evidence that the calcium modulation we observe is not a result of simple sensory responses.b) The authors attempt to rule out that the somatic signals are related directly to movement of the animal and less related to the "cognitive" variables. The analysis in the supporting figures is in line with the level of detail often applied in the field currently. But, is it possible that the activity is related to other movements of the animal rather than in the orofacial region? The authors should either cite strong evidence showing that these are the only relevant regions for the area of cerebellum examined or provide additional videography results from other parts of the body (e.g. paws). This is of course a significant point to make as strong as possible given the cerebellum's long-established role in motor behaviors.We now cite evidence of primarily orofacial representations in crus I . In particular, we note here that “in rats, the largest whisker projection area in the cerebellar cortex is located in crus 1, occupying the largest part of that folium”  ), and that a comprehensive review of cerebellar representations concludes “the physiological role of crus I and crus II in controlling forelimb and hindlimb muscles could no longer be maintained” .Nevertheless, we now include more movie-based measurements of forepaws, which also support the conclusion that movements do not account for the somatic signals .We appreciate the reviewers’ concerns and do not believe we can extensively rule out all movements in the body . That said, it is a less likely explanation than the one we propose, given the known roles of this region and our inactivation results. We have demonstrated that the strongest expected movement-related signals, orofacial movements, are not responsible for the somatic activity that we observe. By adding forepaw analyses, we have furthermore shown that less-likely movement-related signals are also not an explanation. This standard of proof is comparable that rest of the field of evidence accumulation.c) The analyses in Figure 4—figure supplement 1 do not convincingly rule out the possibility that the difference in dendritic calcium signals on error trials resulted from differences in licking on error trials. That figure clearly shows that cessation of licking proceeds at different rates on error trials compared to other times. How does trial type (correct vs error) affect the relationship between licking (or changes of rate in licking) and dendritic calcium signals?We take this concern very seriously and we have thought hard about what analysis properly addresses it. We believe the reviewer’s question is addressed by our analyses in Figure 4F and Figure 4—figure supplement 1. We now explain our rationale more thoroughly in the text , as well as include an additional panel in Figure 4F to visually demonstrate the rationale of the analysis.To our understanding, the specific null hypothesis we need to reject is that the elevation in dendritic signalling with errors is reflective not of an error event but rather of a specific motor event that tends to co-occur with errors. The most likely such motor event would be the cessation of licking, since this is the one that reliably occurs at the moment of errors , but others, such as initiationof licking (perhaps toward the other direction) or movements of other sorts are possible too.not at the moment of errors. These “non-error” lick cessations occur in all correct trials, as noted by the reviewer. We computed the dendritic responses at all such moments of lick cessation, in both error contexts and non-error contexts . We then compared the responses on a cell-by-cell basis across these two contexts, yielding an error:correct activity ratio. If the best explanation for the elevated signalling were a lick cessation-related signal, then these lick cessation events should elicit responses of similar magnitude (i.e. error:correct activity ratio of 1), but we find a statistically elevated level of signalling specifically when lick cessation occurs with errors, as compared to when it occurs elsewhere. This implies that signalling is specifically elevated at moments of error beyond what is expected just when animals stop licking in non-error contexts. In other words, lick cessation in error trials elicits more dendritic response than lick cessation in correct trials–a direct response to the question “How does trial type (correct vs error) affect the relationship between licking and dendritic calcium signals?”The reasoning of our analysis is as follows: consider first the hypothesis that dendritic activity increases not specifically with errors but rather when animals stop licking, an event that nearly always occurs when animals make errors. If this were true, then the cessation of licking, whether or not it occurs at the moment of error, would elicit this elevated signalling. We take advantage of the fact that we have many additional instances where lick cessation occurred We apply the same logic to various definable motor events, such as moments of low, medium, and high rates of licking, as well as lick initiation and orofacial movements. We also apply the lick initiation and cessation analyses over differing time windows, including the dendritic activity preceding and following the lick initiation/cessation, to account for the possibility that a lick-related signal in the cerebellum temporally precedes or follows the licking measurement.We consider this to be the most thorough approach we can take to answer whether there exists elevated error-related dendritic signalling beyond what is expected from specific motor hypotheses.To clarify this analysis for readers, we have added an additional panel  illustrating this type of measurement, and relabelled the descriptions to align with the “correct vs error” phrasing given by the reviewer .The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:The authors have made a concerted effort to clarify the text of their paper and have provided reasonable responses to many of the points raised. As was noted in the first round of reviews, rebuttal, and in the text, it remains unclear what the cerebellum's specific role or computation is during the task, and the reviewers were somewhat disappointed that most of the key questions are deferred to future studies. However, the reviewers also felt that defining cerebellum's exact role in the task may be asking too much for a first study, particularly given the limitations of the dataset and the conceptual and analytical complexities that prevent the authors from specifying the nature of neural representations and the source of the behavioral deficit following cerebellar inactivation.Overall, there are several interesting leads about the role(s) of cerebellum in perceptual decisions in this paper. While none of these are firmly established by the current study, the reviewers agree that the writing and presentation of the results is generally fair and not greatly over-stated, and favor publication so that the results can be evaluated by the field and followed up in future studies from other groups.The reviewers note that in the process of clarifying what can and cannot be claimed based on the existing data, the scope of the paper is limited to three points: successful training of mice to do the task, reduced accuracy following cerebellar inactivation, and representation of task-relevant variables in cerebellar neural population without specifying the exact nature of the represented variables. Before publication, the reviewers agree that it is important to ensure that after toning down its claims, the manuscript does not leave behind any statements that could mislead readers as to what is actually shown. The reviewers have identified the following statements from the Title, Abstract, and Discussion section that are potentially misleading and should be restated with more specific statements that more accurately match the conclusions that can be supported by the data.TitleA cerebellar role in evidence-guided decision-making.Cerebellar involvement in an evidence-accumulation decision-making task.Impact StatementThe lateral posterior cerebellum participates in evidence-accumulation-based decision-making, and Purkinje neurons in this region encode choice-, evidence-, and error-related variables. .In a new evidence-accumulation decision-making task, activity of the lateral posterior cerebellum is necessary for accurate performance, and Purkinje cell somatic and dendritic activity contain choice/evidence and error-related information.Abstract- Here we show that during perceptual decision-making over a period of seconds, decision-, sensory-, and error-related information converge on the lateral posterior cerebellum in crus I, .Sentence removed from abstract.- Demonstrated that cerebellar inactivation reduces behavioral accuracy without impairing motor parameters of action [Not all motor parameters were controlled for].(Abstract) Cerebellar inactivation led to a reduction in the fraction of correct trials.- We found that Purkinje cell somatic activity encoded choice- and evidence-related variables [Please avoid the suggestion that the specific variables that are encoded have been determined].(Abstract) “.…we found that Purkinje cell somatic activity contained choice/evidence-related information”.- Decision errors were represented by dendritic calcium spikes, which are known to drive plasticity [This could misleadingly suggest that they are known to drive plasticity in this context].(Abstract) “Decision errors were represented by dendritic calcium spikes, which in other contexts are known to drive cerebellar plasticity.”- We propose that cerebellar circuitry may contribute to the set of distributed computations in the brain that support accurate perceptual decision-making. [Should be more focused on task performance].(Abstract) “We propose that cerebellar circuitry may contribute to computations that support accurate performance in this perceptual decision-making task."Discussion section- Cerebellar inactivation reduces animals' use of evidence and increases their use of choice history. .(Discussion section) Our fits to a behavioral choice model indicate that reduced performance was accompanied by a decreased weighting of evidence and increased weighting of choice history parameters.- Given the temporal resolution of calcium measurements, our somatic signals may correspond to firing rate ramps , steps , or more complex response profiles that form a temporal basis for evidence accumulation . .(Discussion section) Cytoplasmic calcium acts as a temporally filtered readout of firing rate, limited by calcium removal times that are slower in Purkinje cells  than in neocortical neurons . Cytoplasmic calcium acts as a temporally filtered readout of firing rate, and calcium extrusion in Purkinje cells occurs on a slower time scale  than in neocortical neurons . Therefore, our observed increasing and decreasing time courses of calcium could reflect various firing rate profiles, such as impulse responses, ramps, or steps.-The task-modulated activity we observe encodes both choice-related and evidence-related variables that may be used during the decision-making process. [Please avoid the suggestion that the specific variables that are encoded have been determined].(Discussion section).…the neural activity we observed contains task-relevant information that may be used during evidence accumulation and decision-making.- We observed an excess of dendritic calcium events coincident with decision errors, demonstrating for the first time observations compatible with error-associated signalling in a decision-making reward context. .(Discussion section) We observed an excess of dendritic calcium events coincident with decision errors, which has not been previously reported in the cerebellum.The manuscript has been improved but there are some final issues that need to be addressed before acceptance, as outlined below:In response to the request to re-evaluate the evidence for ramping signals that could be obtained with the somatic calcium imaging, the authors now state , "Therefore our observed increasing and decreasing time courses of calcium could reflect various firing rate profiles, such as impulse responses, ramps, or steps."In light of this revision, as well as the fact that the electrophysiological evidence provided in Figure 2—figure supplement 2 is from only a few cells, all of which show positive ramps of activity, the following statements should also be revised:- , "We did find that electrically recorded Purkinje cells exhibited gradually increasing rates of firing throughout the cue period , suggesting that on average across trials, the fluorescence signals we observed correspond to firing rate ramps." “We did find that electrically recorded Purkinje cells exhibited gradually increasing rates of firing throughout the cue period .”- (Discussion section), "Our electrical recordings also showed ramps, suggesting that temporally filtered firing rate ramps are sufficient to account for our observed fluorescence signals."(Discussion section) “Our electrical recordings also showed ramps, consistent with the idea that temporally filtered firing rate ramps are sufficient to account for our observed fluorescence signals.”The reason we went to the effort to obtain and include electrical recordings, per suggestions of many of our colleagues, was that it could help substantiate the possibility of fluorescence ramps corresponding to firing rate ramps. We think that the Discussion is an appropriate place to at least note the relationship between these two findings and our interpretation of them.Your revised article has been favorably evaluated by our editors again, but there remain some issues that need to be addressed before acceptance, as outlined below. Given that this is the third request for revisions, we will be unable to follow with any more. Please attend to this final issue one way or the other so that the next letter will be the final one.We appreciate the authors' desire to speculate here. However, in our view, the "suggests/ consistent with" was not the only problem with this sentence. There is also a problem with "sufficient". The electrophysiological evidence in Figure 2—figure supplement 2 is anecdotal and non-quantitative. For this statement to be left in, it would need to be adequately supported. In our view, this would require a quantitative comparison between imaging and electrophysiology results. In particular, we would want to know:- How many Purkinje cells in total were recorded from electrophysiologically? How many of these showed ramping? - Did any Purkinje cells show ramping calcium signals without a transient increase in firing rate?- Did any Purkinje cells show ramping calcium signals without ramps in firing rate ?- Why are no decreasing activity ramps found with electrophysiology, but they are found with imaging?- What accounts for the decreasing ramps that were observed with calcium imaging?- What would the predicted calcium signals be for the examples shown if the spike rates recorded electrophysiologically (with and without the transient increase/ ramping components) were convolved according to Ramirez and Stell (2016)? And/or with the authors' own convolution/ deconvolution methods, from the simultaneous calcium imaging/ electrophysiological recordings that they performed?We give the authors the choice of either fully addressing these points, or using compromise language, such as "Preliminary electrical recordings also showed ramps, consistent with the idea that temporally filtered firing rate ramps may account for the observed fluorescence signals."We thank the reviewers and editors for their suggestions. We have made the requested adjustment to the manuscript text, opting to use the exact compromise phrasing beginning with “Preliminary …” proposed by the editors. Below we include the original sentence for reference, followed by the updated sentence:(Discussion section), “Our electrical recordings also showed ramps, consistent with the idea that temporally filtered firing rate ramps are sufficient to account for our observed fluorescence signals.”(Discussion section) “Preliminary electrical recordings also showed ramps, consistent with the idea that temporally filtered firing rate ramps may account for the observed fluorescence signals.”