Mouse prefrontal cortex represents learned rules for categorization

Author : LavadaCrooks
Publish Date : 2021-04-22 06:16:32
Mouse prefrontal cortex represents learned rules for categorization

Data reporting

No statistical methods were used to predetermine sample size. Mice were randomly assigned to the categorization rule ‘spatial frequency’ or ‘orientation’. The investigators were not blinded to allocation during experiments and outcome assessment.

Animals

All procedures were performed in accordance with the institutional guidelines of the Max Planck Society and the local government (Regierung von Oberbayern). Twenty female C57BL/6 mice (postnatal day (P) 63–P82 at the day of surgery) were housed in groups of four to six littermates in standard individually ventilated cages (IVC, Tecniplast GR900). Mice had access to a running wheel and other enrichment material such as a tunnel and a house. All mice were kept on an inverted 12 h light/12 h dark cycle with lights on at 22:00. Before and during the experiment, the mice had ad libitum access to standard chow (1310, Altromin Spezialfutter). Before the start of behavioural experiments, mice had ad libitum access to water. At the end of the experiments, mice were perfused with 4% paraformaldehyde (PFA) in PBS and their brains were post-fixed in 4% PFA in PBS at 4 °C.

Surgical procedures

Before surgery, a prism implant was prepared by attaching a 1.5 mm × 1.5 mm prism (aluminium coating on the long side, MPCH-1.5, IMM photonics) to a 0.13 mm thick, 3 mm diameter glass coverslip (41001103, Glaswarenfabrik Karl Hecht) using UV-curing optical glue (Norland optical adhesive 71, Norland Products) and was left to fully cure at room temperature for a minimum of 24 h. Mice were anaesthetized with a mixture of fentanyl, midazolam and medetomidine in saline (0.05 mg kg−1, 5 mg kg−1 and 0.5 mg kg−1 respectively, injected intraperitoneally). As soon as sufficient depth of anaesthesia was confirmed by absence of the pedal reflex, carprofen in saline (5 mg kg−1, injected subcutaneously) was administered for general analgesia. The eyes were covered with ophthalmic ointment (IsoptoMax/Bepanthen) and lidocaine (Aspen Pharma) was applied on and underneath the scalp for topical analgesia. The scull was exposed, dried and subsequently scraped with a scalpel to improve adherence of the head plate. The scalp surrounding the exposed area was adhered to the skull using Histoacryl (B. Braun Surgical). A custom-designed head plate was centred at ML 0 mm, approximately 3 mm posterior to bregma, attached with cyanoacrylate glue (Ultra Gel Matic, Pattex) and secured with dental acrylic (Paladur). A 3 mm diameter craniotomy, centred at anterior–posterior (AP) 1.9 mm, medial–lateral (ML) 0 mm, was performed using a dental drill. The hemisphere for prism insertion was selected based on the pattern of bridging veins. Before inserting the prism, two injections (50 nl min−1) of 200–250 nl of virus solution (AAV2/1.hSyn.mRuby2.GSG.P2A.GCaMP6m.WPRE.SV40, titre: 1.02 × 1013 genome copies (GC) ml−1, Plasmid catalogue 51473, Addgene) were targeted at the medial prefrontal cortex opposite to the prism implant, coordinates: AP 1.4 mm to AP 2.8 mm, ML 0.25 mm, dorsal–ventral (DV) 2.3 mm (Nanoject, Neurostar). The left hemisphere was injected in 11 mice, and the right hemisphere in 9 mice. Subsequently, a durotomy was performed using microscissors (15070-08, Fine Science Tools) over the contralateral hemisphere, next to the medial sinus. The prism implant was inserted, gently pushing the medial sinus aside until the target cortical region became visible through the prism (for a detailed description, see ref. 29). The coverslip was attached to the surrounding skull using cyanoacrylate glue and dental acrylic. After surgery, the anaesthesia was antagonized with a mixture of naloxone, flumazenil and atipamezole in saline (1.2 mg kg, 0.5 mg kg−1 and 2.5 mg kg−1 respectively, injected subcutaneously) and the mice were placed under a heat lamp for recovery. Post-operative analgesia was provided for two subsequent days with carprofen (5 mg kg−1, injected subcutaneously).

Visual stimuli

Stimuli for behavioural training were presented in the centre of a gamma corrected LCD monitor (Dell P2414H; resolution: 1,920 by 1,080 pixels; width: 52.8 cm; height: 29.6 cm; maximum luminance: 182.3 Cd m−2). The centre of the monitor was positioned at about 0° azimuth and 0° elevation at a distance of 18 cm, facing the mouse straight on. The stimuli were 36 different sinusoidal gratings, each with a specific orientation and spatial frequency combination, shown in full contrast on a grey background (see Extended Data Fig. 1 for schematic of stimuli and task stages). Orientations ranged from 0° to 90°, the spatial frequencies from 0.023 cycles per degrees (cyc/°) to 0.25 cyc/° (orientations: [0, 15, 30, 60, 75, 90] °, spatial frequencies: [0.023, 0.027, 0.033, 0.06, 0.1, 0.25] cyc/°). The stimulus size was 45 retinal degrees in diameter, including an annulus of 4 degrees blending into the equiluminant grey background. The gratings drifted with a temporal frequency of 1.5 cycles per s.

In a subset of experiments (n = 3 mice), a dense stimulus space was presented, consisting of 49 stimuli ranging from 15° to 75° in orientation and from 0.027 cyc/° to 0.1 cyc/° in spatial frequency (orientations: [15, 30, 37.5, 45, 52.5, 60, 75]°, spatial frequencies: [0.027, 0.033, 0.036, 0.043, 0.052, 0.06, 0.1] cyc/°). Stimuli on the category boundary (either having an orientation of 45° or a spatial frequency of 0.043 cyc/°) were assigned to both categories, hence rewarded in 50% of trials.

All stimuli were created and presented using the Psychophysics Toolbox extensions of MATLAB48,49,50.

Behaviour

Behavioural experiments started seven days after surgery. The water restriction regime and the behavioural apparatus were previously described51. In short, mice were restricted to 85% of their initial weight on the starting date by individually adjusting the daily water ration. First, mice were accustomed to the experimenter and head fixation in the setup by daily handling sessions lasting 10 min. During these sessions, the water ration was offered in a handheld syringe. The remainder was supplemented in an individual drinking cage after a delay of approximately 30 min. After four to seven days of handling, mice were pre-trained to lick for reward, while being head-fixed on the spherical treadmill52,53,54 in absence of visual stimulation. Whenever a mouse ceased to run (velocity below 1 cm s−1) and made a lick on the spout, a water reward (drop size 8 μl) was delivered via the spout. A baseline imaging time point (T1) was acquired once the mice consumed more than 50 drops per session (35 to 45 min) on two consecutive days (requiring about three days of pre-training).

Subsequently, daily sessions of visual discrimination training for two initial stimuli started. Each mouse was randomly assigned to one of two groups. One group was first trained on the orientation rule, then on the spatial frequency rule. For the other group, the sequence of the rules was reversed (Extended Data Fig. 1). Each rule defined a Go category and a NoGo category, separated by a boundary at either 45° (orientation rule) or at 0.043 cyc/° (spatial frequency rule). Trials started with an inter-trial interval of 5 s. After that, the mouse could initiate stimulus presentation by halting and refraining from licking for a minimum of 0.5 s. A single stimulus was subsequently shown for 1.3 ± 0.2 s. At any time during stimulus presentation, the mouse could make a lick to indicate a Go choice. Trials with a Go choice in response to a Go category stimulus triggered a water reward and were classified as hits; trials in which the mice failed to lick during Go category stimulus presentation were considered misses. Correct withholding of a lick to a NoGo category stimulus was classified as a correct rejection, and did not result in a water reward. A lick during a NoGo category stimulus counted as a false alarm. Initially, false alarms only led to the termination of the current trial; later during training, false alarms were followed by a time-out of 5–7 s showing a time-out stimulus (a narrow, horizontal, black bar). Time-outs were included to reduce a Go bias that mice typically showed. The second imaging session (T2) was carried out after a mouse performed at more than 66% correct Go choices in a given session (requiring 11 to 40 sessions).

For the next training stage (leading up to imaging session T3) further stimuli were added (Extended Data Fig. 1a), such that both the Go category and the NoGo category consisted of three stimuli differing in the feature either irrelevant to the category rule (T3a, n = 6 mice), or relevant to the category rule (T3b, n = 5 mice). Whenever a mouse’s performance exceeded 66% correct Go choices in one session, we proceeded to the next training (and imaging) stage; 6 stimuli per category, 9 stimuli per category (imaging session T4), and finally 18 stimuli per category (imaging session T5), the latter serving as a crucial test for generalization behaviour.

Rule-switch: After successful learning of rule 1, mice (n = 11) were retrained using the previously irrelevant dimension. This stage, known as rule-switch training, started with two exemplar stimuli for the new rule, and then proceeded with the same steps as for rule 1 and ended with another generalization test of rule 2 (18 stimuli per category, imaging session T8).

Task change: After successful learning of rule 1 (T5), the categorization performance of mice (n = 9) was tested with a different operant response, in a left/right choice task. For this session, the behavioural setup was slightly modified to create a left/right choice task. Instead of one lick spout centred in front of the mouse, the mouse was now presented with two lick-spouts, one offset to the left and one offset to the right. Stimuli of the previous Go category were assigned a new GoRight response (rewarded after a lick on the right lick spout). Stimuli of the previous NoGo category were assigned a new GoLeft response (rewarded after a lick on the left lick spout). The original stimulus to category assignment—that is, the categorization rule—remained the same throughout the task change. Before the first stimulus presentation, ten drops were manually given on each lick spout to motivate the mice to lick on both sides.

Throughout training, stimuli from the Go category and the NoGo category were presented in a pseudorandomized fashion, showing not more than three stimuli of the same category in a row. The behavioural training program was a custom written MATLAB routine (Mathworks).

Imaging

Two-photon imaging55 through the implanted prism was performed at 5–8 time points in each mouse throughout the training paradigm (T3 was omitted in two mice; for detailed timing of the imaging sessions see Extended Data Fig. 1a). In some mice (n = 5) we followed two regions in the same mouse; in these cases, two imaging sessions were acquired on consecutive days during the same training stage. Imaging was done using a custom-built two-photon laser-scanning microscope (resonant scanning system) and a Mai Tai eHP Ti:Sapphire laser (Spectra-Physics) tuned to a wavelength of 940 nm. Images were acquired with a sampling frequency of 10 Hz and 750 × 800 pixels per frame. The mice in the task change experiment were imaged using a customized commercially available two-photon laser-scanning microscope (Thorlabs; same laser specifications as described above), operated with Scanimage 456. In these experiments, images were acquired at 30 Hz and 512 × 512 pixels per frame. The average laser power under the objective ranged from 50 to 80 mW. Note that the laser power was higher than for imaging through a conventional cranial window due to a substantial power loss over the prism29. We used a 16×, 0.8 NA, water immersion objective (Nikon) and diluted ultrasound gel (Dahlhausen) on top of the implant as immersion medium. Two photomultiplier tubes detected the red fluorescence signal of the structural protein mRuby2 (570–690 nm) and the green fluorescence signal of GCaMP6m (500–550 nm)57. During imaging, the monitor used for stimulus presentation was shuttered to minimize light contamination58. The imaging data were acquired using custom LABVIEW software (National Instruments; software modified from the colibri package by C. Seebacher) and the synchronization of imaging data with behavioural readout and stimulus presentation was done using DAQ cards (National Instruments).

Tracking of postural markers

In two-photon imaging sessions of a subset of experiments, the mouse was video-tracked using infrared cameras (The Imaging Source Europe). Two cameras were aimed at the eyes, and a third camera was positioned at a slight angle behind the mouse, in order to record body movements in-task. The eyes of the mouse were back-lit by the infrared two-photon imaging laser and the body was illuminated using an infrared light source (740 nm; Thorlabs). Key eye and body features (see Extended Data Fig. 10) were manually defined and automatically annotated using DeepLabCut41,42. From the x and y coordinates of these features, we calculated three eye parameters and four postural parameters (pupil diameter, eye position, eyelid opening, front paw angle, hind paw angle of the left hind paw, body elongation/rotation, tail angle; see Extended Data Fig. 10). Supplementary Video 1 shows both eye and body cameras of an example mouse.

Data analysis

The analysis of behaviour and imaging data was performed using custom written MATLAB routines.

Behavioural data

Behavioural performance is shown as the sensitivity index, d′. For every training session, d′ was calculated as the difference between the z-scored hit rate and the z-scored false alarm rate. The hit rate was defined as the number of correct category 2 trials divided by the total number of category 2 trials per session. Similarly, the false alarm rate was calculated as the number of incorrect category 1 trials divided by the total number of category 1 trials. In case a mouse performed two training sessions at time points T1, T3, T4, T5, T7 and T8, because two regions were imaged, the displayed value in the learning curve is the average across the two imaging sessions.

The fraction of correct Go choices was calculated as the number of hit trials divided by the number of all trials in which the mouse made a Go choice (the sum of ‘hits’ and ‘false alarms’). The number of days until a mouse reached performance criterion was the amount of daily sessions until the fraction of correct Go choices exceeded 0.66. Pre-training sessions without visual stimulation were not included in this quantification.

To investigate categorization behaviour across the entire stimulus space, we calculated the ‘fraction chosen’: The number of Go choices in response to a specific stimulus divided by the total number of presentations for that stimulus (see example in Fig. 1d; for all mice see Extended Data Fig. 2). Finally, we constructed psychometric curves showing the effect of each feature (that is, rule-relevant versus rule-irrelevant) on the behaviour of the mice (Fig. 1j). For that, the stimulus-specific ‘fraction chosen’ values were averaged along the irrelevant or the relevant feature dimension, respectively (see Fig. 1i).

To estimate learning rates, each individual learning curve was fitted with a sigmoid function:

$$y(x)=p1+frac{p2}{1+{{ m{e}}}^{p3(x-p4)}}$$

in which p1 determines the minimum of the sigmoid curve (for curve fitting fixed to 0), p2 the maximum, p3 the slope and p4 the inflection point. The parameter defining the minimum was fixed at a d′ of 0. Learning curves for rule 1 and rule 2 were fitted independently. Goodness of fit was determined as the root-mean-square error between the learning curve and the fitted curve.

Imaging data processing

The imaging data were first preprocessed by performing dark-current subtraction (using the average signal intensity during a laser-off period) and line shift correction. Rigid xy image displacement was first calculated on the structural red fluorescence channel using the cross correlation of the 2D Fourier transform of the images59, and subsequently corrected on both channels. For each imaging session, cells were manually segmented using the average image of the red fluorescence channel across the entire session. The cell identity was then manually matched across all imaging time points and only cells that could be identified in every session from T1 to T8 or T5 to left/right were included in the analysis. This criterion excluded one mouse (M06) from all further analyses, because of lost optical access at T8. The average green fluorescence signal was extracted for each cell and then corrected for neuropil contamination by subtracting the signal of 30 μm surrounding each cell multiplied by 0.7 and adding the median multiplied by 0.7 (refs. 57,60). From this fluorescence trace, we calculated ΔF/F as (F − F0)/F0 per frame. F0 was defined as the 25th percentile of the fluorescence trace in a sliding window of 60 s. From this trace, we inferred the spiking activity of each cell using the constrained foopsi algorithm61,62,63. The inferred spike rate during the stimulus presentation period was used for all further calculations and in all figure panels, except for the HLS maps and the left panels of Fig. 2d, e, where we display the ΔF/F trace for comparison.

To display lick-triggered neuronal activity (Extended Data Fig. 8), we averaged the inferred spike rate centred on the onset of the mouse’s lick-bouts. A lick-bout was defined as a sequence of licks, in which the interval between every two consecutive licks did not exceed 500 ms. Thus, a lick was part of a lick-bout if it happened within 500 ms after the previous lick. The onset of each lick-bout was the time of the first lick in the lick-bout.

Category-tuning index

For every cell, we calculated the CTI as previously described30. In short, we quantified the mean inferred spike rate during stimulus presentation for every stimulus. Next, we calculated the mean difference in inferred rate between stimuli of the same category (within), subtracted it from the mean difference between stimuli belonging to the two different categories (across) and normalized by the sum (across + within). This calculation results in an index ranging from −1 to 1, with category-unselective cells showing CTIs close to and below 0 and an ideal category-selective cell having an index of 1. Category-selective cells were defined as cells with a CTI value larger than 0.1. This threshold was chosen based on the distribution of CTIs in the naive population (T1), where individual cells rarely crossed this value. As a control, we used other thresholds (0.07, 0.15 and 0.20) and found no qualitative difference in the results other than that the fraction of category-selective cells scaled.

The fraction of category-selective cells was calculated as the number of neurons above threshold per imaging region, divided by the total number of chronically recorded neurons in that imaging region. Category-selective cells, determined by their CTI at time points T5 and T8, were divided in a Go category-selective and a NoGo category-selective group; neurons with higher average activity in Go category trials than in NoGo category trials were grouped as Go category-selective cells and conversely, cells with a higher average activity in NoGo category trials were labelled as NoGo category-selective. The overlap between the Go and NoGo category-selective groups was calculated between T5 and T8. The expected range of overlap assuming random independent sampling was calculated from the data, but with shuffled neuron identities (using the 95% percentile of the shuffled distribution). For time points at which not all stimuli were presented (T2, T3, T4, T6 and T7), we approximated category-tuning from the average responses to Go category trials and NoGo category trials.

Bayesian decoding

We decoded category identity from trial-by-trial activity patterns of a single neuron up to groups of ten neurons using Bayes theorem:

$$p(c|r)=frac{p(r|c)p(c)}{p(r)}$$

in which p(r|c) is the probability of a single trial response r when observed in either category 1 or 2 trials (calculated from an exponential distribution), p(c) as the prior probability of observing each category, and p(r) as the probability of observing the response. To cross-validate decoding performance, trials were first split into a training and test set (70% and 30%, respectively). The trial-averaged inferred spike rates followed an exponential distribution, which we estimated for each category individually (using the training set). Then, for each trial in the test set, we calculated the probability that the neuronal response came from those distributions. The distribution that gave the higher probability was determined as the decoder’s prediction. Decoder performance was calculated as the fraction of correctly predicted trials. As a control, decoding performance was also calculated after shuffling category identities across trials.

Selectivity time course

Average selectivity of individual neurons was calculated as the mean difference between responses to all Go category stimuli and all NoGo category stimuli, at every imaging time point (T1–T8). For linear regression, we defined three characteristic selectivity time courses (shown in Extended Data Fig. 7), resembling acquired selectivity for reward/choice, categorization rule 1 and categorization rule 2. Within each of these time courses, maximum selectivity was assigned the value 1 and no selectivity the value 0. The characteristic time courses were used as predictors in a model fitting the development of selectivity of individual neurons over time.

Generalized linear models to assess the influence of individual task parameters

We performed multilinear regression on neurons that were identified in all imaging time points of the rule-switch experiment. The regression model predicted the trial-wise mean spike rate of each cell during the stimulus presentation periods at imaging time point T5. Categorical predictors were: Category identity of the presented stimulus (0: category 1, 1: category 2), choice of the mouse (0: NoGo, 1: Go), and reward (0: no reward, 1: reward). The average running speed during the trial was modelled as a continuous predictor. A positive predictor weight indicated that the activity of a neuron was increased in trials where the value of the predictor was higher. A negative predictor weight reflected an inverse relation between the predictor’s value and the neuron’s firing rate. We normalized the predictor weights for overall differences in response amplitudes, by dividing each weight by the sum of all absolute predictor weights (including the intercept).

Hierarchical clustering was performed on relative predictor weights of neurons, including only cells with an R2 value larger than 0.05. The optimal number of clusters was calculated using gap statistic values, determined as the smallest cluster number k that fulfilled the criterion (here nine clusters):

$${ m{Gap}}(k)ge { m{Gapmax}}-{ m{s.}}{ m{e.}},({ m{Gapmax}})$$

in which Gap(k) is the gap statistic for k clusters, Gapmax is the largest gap value, and s.e.(Gapmax) is the standard error corresponding to the largest gap value.

We obtained linkage and relative predictor weights of the clusters from the MATLAB clusterdata algorithm.

To probe the influence of operant motor behaviour in the task change experiment, we concatenated all trials of sessions T5 (generalization session, Go/NoGo task) and L/R (left/right choice task). A stepwise linear regression model predicted the trial-averaged inferred spike rate of all recorded neurons individually. The predictors were the following categorical variables: category identity of the stimulus (0: category 1; 1: category 2), Go response of the mouse (0: NoGo, 1: all forms of Go, that is, Go/GoRight/GoLeft), reward (0: no reward, 1: reward) and two predictors that were specific to a motor response in the left/right session: GoRight and GoLeft. We only considered significant predictor weights, determined from an F-statistic comparing a model with and without a predictor. Predictor weights were normalized by dividing each weight by the maximum of all predictor weights.

Linear regression assessing the influence of instructed and uninstructed behaviours

The trial-averaged inferred spike rate of all recorded neurons in session T5 of a subset of experiments was fitted using a linear model. Body and eye parameters describing uninstructed behaviours were included in the model as continuous predictors. In addition, we included three categorical task-relevant predictors: category identity of the presented stimulus, choice of the mouse, and reward. For each predictor, we determined its maximum predictive power (cvR2) and its unique contribution (ΔR2), similar to the approach previously described40. Maximum predictive power (cvR2) was calculated as the predictive performance (R2) of a model with all parameters shuffled, except for the parameter of interest. A parameter’s unique contribution (ΔR2) was quantified as the difference between the full model’s R2 and the R2 of a model in which the parameter of interest was shuffled.

Stereotaxic coordinates of imaging regions

We determined the stereotaxic coordinates of the centres of all imaging regions (included in Fig. 2g, h) to place the imaged regions within a common reference frame (Mouse Brain Atlas)64. First, we cut 60-μm thick sagittal sections of both hemispheres using a freezing microtome. The AP coordinates outlining the full extent of the prism were identified from a section of the hemisphere into which the prism had been implanted (Extended Data Fig. 4). On the basis of this information, we calculated the exact AP coordinate of the centre of each imaging field of view. We calculated the dorso-ventral coordinate relative to the brain surface, which was aligned with the dorsal border of the prism. Finally, we determined the medio-lateral coordinate of the imaged field of view from the imaging depth of the field of view relative to the medial pia mater.

Statistical procedures

All data are presented as mean ± s.e.m. unless stated otherwise. Tests for normal distribution were carried out using the Kolmogorov–Smirnov test. Normally distributed data were tested using the two-tailed paired-samples t-test. Non-normally distributed data were tested using the two-tailed WMPSR test for paired samples, and the Kruskal–Wallis test for multiple, independent groups. A Bonferroni aData reporting

No statistical methods were used to predetermine sample size. Mice were randomly assigned to the categorization rule ‘spatial frequency’ or ‘orientation’. The investigators were not blinded to allocation during experiments and outcome assessment.

Animals

All procedures were performed in accordance with the institutional guidelines of the Max Planck Society and the local government (Regierung von Oberbayern). Twenty female C57BL/6 mice (postnatal day (P) 63–P82 at the day of surgery) were housed in groups of four to six littermates in standard individually ventilated cages (IVC, Tecniplast GR900). Mice had access to a running wheel and other enrichment material such as a tunnel and a house. All mice were kept on an inverted 12 h light/12 h dark cycle with lights on at 22:00. Before and during the experiment, the mice had ad libitum access to standard chow (1310, Altromin Spezialfutter). Before the start of behavioural experiments, mice had ad libitum access to water. At the end of the experiments, mice were perfused with 4% paraformaldehyde (PFA) in PBS and their brains were post-fixed in 4% PFA in PBS at 4 °C.

Surgical procedures

Before surgery, a prism implant was prepared by attaching a 1.5 mm × 1.5 mm prism (aluminium coating on the long side, MPCH-1.5, IMM photonics) to a 0.13 mm thick, 3 mm diameter glass coverslip (41001103, Glaswarenfabrik Karl Hecht) using UV-curing optical glue (Norland optical adhesive 71, Norland Products) and was left to fully cure at room temperature for a minimum of 24 h. Mice were anaesthetized with a mixture of fentanyl, midazolam and medetomidine in saline (0.05 mg kg−1, 5 mg kg−1 and 0.5 mg kg−1 respectively, injected intraperitoneally). As soon as sufficient depth of anaesthesia was confirmed by absence of the pedal reflex, carprofen in saline (5 mg kg−1, injected subcutaneously) was administered for general analgesia. The eyes were covered with ophthalmic ointment (IsoptoMax/Bepanthen) and lidocaine (Aspen Pharma) was applied on and underneath the scalp for topical analgesia. The scull was exposed, dried and subsequently scraped with a scalpel to improve adherence of the head plate. The scalp surrounding the exposed area was adhered to the skull using Histoacryl (B. Braun Surgical). A custom-designed head plate was centred at ML 0 mm, approximately 3 mm posterior to bregma, attached with cyanoacrylate glue (Ultra Gel Matic, Pattex) and secured with dental acrylic (Paladur). A 3 mm diameter craniotomy, centred at anterior–posterior (AP) 1.9 mm, medial–lateral (ML) 0 mm, was performed using a dental drill. The hemisphere for prism insertion was selected based on the pattern of bridging veins. Before inserting the prism, two injections (50 nl min−1) of 200–250 nl of virus solution (AAV2/1.hSyn.mRuby2.GSG.P2A.GCaMP6m.WPRE.SV40, titre: 1.02 × 1013 genome copies (GC) ml−1, Plasmid catalogue 51473, Addgene) were targeted at the medial prefrontal cortex opposite to the prism implant, coordinates: AP 1.4 mm to AP 2.8 mm, ML 0.25 mm, dorsal–ventral (DV) 2.3 mm (Nanoject, Neurostar). The left hemisphere was injected in 11 mice, and the right hemisphere in 9 mice. Subsequently, a durotomy was performed using microscissors (15070-08, Fine Science Tools) over the contralateral hemisphere, next to the medial sinus. The prism implant was inserted, gently pushing the medial sinus aside until the target cortical region became visible through the prism (for a detailed description, see ref. 29). The coverslip was attached to the surrounding skull using cyanoacrylate glue and dental acrylic. After surgery, the anaesthesia was antagonized with a mixture of naloxone, flumazenil and atipamezole in saline (1.2 mg kg, 0.5 mg kg−1 and 2.5 mg kg−1 respectively, injected subcutaneously) and the mice were placed under a heat lamp for recovery. Post-operative analgesia was provided for two subsequent days with carprofen (5 mg kg−1, injected subcutaneously).

Visual stimuli

Stimuli for behavioural training were presented in the centre of a gamma corrected LCD monitor (Dell P2414H; resolution: 1,920 by 1,080 pixels; width: 52.8 cm; height: 29.6 cm; maximum luminance: 182.3 Cd m−2). The centre of the monitor was positioned at about 0° azimuth and 0° elevation at a distance of 18 cm, facing the mouse straight on. The stimuli were 36 different sinusoidal gratings, each with a specific orientation and spatial frequency combination, shown in full contrast on a grey background (see Extended Data Fig. 1 for schematic of stimuli and task stages). Orientations ranged from 0° to 90°, the spatial frequencies from 0.023 cycles per degrees (cyc/°) to 0.25 cyc/° (orientations: [0, 15, 30, 60, 75, 90] °, spatial frequencies: [0.023, 0.027, 0.033, 0.06, 0.1, 0.25] cyc/°). The stimulus size was 45 retinal degrees in diameter, including an annulus of 4 degrees blending into the equiluminant grey background. The gratings drifted with a temporal frequency of 1.5 cycles per s.

In a subset of experiments (n = 3 mice), a dense stimulus space was presented, consisting of 49 stimuli ranging from 15° to 75° in orientation and from 0.027 cyc/° to 0.1 cyc/° in spatial frequency (orientations: [15, 30, 37.5, 45, 52.5, 60, 75]°, spatial frequencies: [0.027, 0.033, 0.036, 0.043, 0.052, 0.06, 0.1] cyc/°). Stimuli on the category boundary (either having an orientation of 45° or a spatial frequency of 0.043 cyc/°) were assigned to both categories, hence rewarded in 50% of trials.

All stimuli were created and presented using the Psychophysics Toolbox extensions of MATLAB48,49,50.

Behaviour

Behavioural experiments started seven days after surgery. The water restriction regime and the behavioural apparatus were previously described51. In short, mice were restricted to 85% of their initial weight on the starting date by individually adjusting the daily water ration. First, mice were accustomed to the experimenter and head fixation in the setup by daily handling sessions lasting 10 min. During these sessions, the water ration was offered in a handheld syringe. The remainder was supplemented in an individual drinking cage after a delay of approximately 30 min. After four to seven days of handling, mice were pre-trained to lick for reward, while being head-fixed on the spherical treadmill52,53,54 in absence of visual stimulation. Whenever a mouse ceased to run (velocity below 1 cm s−1) and made a lick on the spout, a water reward (drop size 8 μl) was delivered via the spout. A baseline imaging time point (T1) was acquired once the mice consumed more than 50 drops per session (35 to 45 min) on two consecutive days (requiring about three days of pre-training).

Subsequently, daily sessions of visual discrimination training for two initial stimuli started. Each mouse was randomly assigned to one of two groups. One group was first trained on the orientation rule, then on the spatial frequency rule. For the other group, the sequence of the rules was reversed (Extended Data Fig. 1). Each rule defined a Go category and a NoGo category, separated by a boundary at either 45° (orientation rule) or at 0.043 cyc/° (spatial frequency rule). Trials started with an inter-trial interval of 5 s. After that, the mouse could initiate stimulus presentation by halting and refraining from licking for a minimum of 0.5 s. A single stimulus was subsequently shown for 1.3 ± 0.2 s. At any time during stimulus presentation, the mouse could make a lick to indicate a Go choice. Trials with a Go choice in response to a Go category stimulus triggered a water reward and were classified as hits; trials in which the mice failed to lick during Go category stimulus presentation were considered misses. Correct withholding of a lick to a NoGo category stimulus was classified as a correct rejection, and did not result in a water reward. A lick during a NoGo category stimulus counted as a false alarm. Initially, false alarms only led to the termination of the current trial; later during training, false alarms were followed by a time-out of 5–7 s showing a time-out stimulus (a narrow, horizontal, black bar). Time-outs were included to reduce a Go bias that mice typically showed. The second imaging session (T2) was carried out after a mouse performed at more than 66% correct Go choices in a given session (requiring 11 to 40 sessions).

For the next training stage (leading up to imaging session T3) further stimuli were added (Extended Data Fig. 1a), such that both the Go category and the NoGo category consisted of three stimuli differing in the feature either irrelevant to the category rule (T3a, n = 6 mice), or relevant to the category rule (T3b, n = 5 mice). Whenever a mouse’s performance exceeded 66% correct Go choices in one session, we proceeded to the next training (and imaging) stage; 6 stimuli per category, 9 stimuli per category (imaging session T4), and finally 18 stimuli per category (imaging session T5), the latter serving as a crucial test for generalization behaviour.

Rule-switch: After successful learning of rule 1, mice (n = 11) were retrained using the previously irrelevant dimension. This stage, known as rule-switch training, started with two exemplar stimuli for the new rule, and then proceeded with the same steps as for rule 1 and ended with another generalization test of rule 2 (18 stimuli per category, imaging session T8).

Task change: After successful learning of rule 1 (T5), the categorization performance of mice (n = 9) was tested with a different operant response, in a left/right choice task. For this session, the behavioural setup was slightly modified to create a left/right choice task. Instead of one lick spout centred in front of the mouse, the mouse was now presented with two lick-spouts, one offset to the left and one offset to the right. Stimuli of the previous Go category were assigned a new GoRight response (rewarded after a lick on the right lick spout). Stimuli of the previous NoGo category were assigned a new GoLeft response (rewarded after a lick on the left lick spout). The original stimulus to category assignment—that is, the categorization rule—remained the same throughout the task change. Before the first stimulus presentation, ten drops were manually given on each lick spout to motivate the mice to lick on both sides.

Throughout training, stimuli from the Go category and the NoGo category were presented in a pseudorandomized fashion, showing not more than three stimuli of the same category in a row. The behavioural training program was a custom written MATLAB routine (Mathworks).

Imaging

Two-photon imaging55 through the implanted prism was performed at 5–8 time points in each mouse throughout the training paradigm (T3 was omitted in two mice; for detailed timing of the imaging sessions see Extended Data Fig. 1a). In some mice (n = 5) we followed two regions in the same mouse; in these cases, two imaging sessions were acquired on consecutive days during the same training stage. Imaging was done using a custom-built two-photon laser-scanning microscope (resonant scanning system) and a Mai Tai eHP Ti:Sapphire laser (Spectra-Physics) tuned to a wavelength of 940 nm. Images were acquired with a sampling frequency of 10 Hz and 750 × 800 pixels per frame. The mice in the task change experiment were imaged using a customized commercially available two-photon laser-scanning microscope (Thorlabs; same laser specifications as described above), operated with Scanimage 456. In these experiments, images were acquired at 30 Hz and 512 × 512 pixels per frame. The average laser power under the objective ranged from 50 to 80 mW. Note that the laser power was higher than for imaging through a conventional cranial window due to a substantial power loss over the prism29. We used a 16×, 0.8 NA, water immersion objective (Nikon) and diluted ultrasound gel (Dahlhausen) on top of the implant as immersion medium. Two photomultiplier tubes detected the red fluorescence signal of the structural protein mRuby2 (570–690 nm) and the green fluorescence signal of GCaMP6m (500–550 nm)57. During imaging, the monitor used for stimulus presentation was shuttered to minimize light contamination58. The imaging data were acquired using custom LABVIEW software (National Instruments; software modified from the colibri package by C. Seebacher) and the synchronization of imaging data with behavioural readout and stimulus presentation was done using DAQ cards (National Instruments).

Tracking of postural markers

In two-photon imaging sessions of a subset of experiments, the mouse was video-tracked using infrared cameras (The Imaging Source Europe). Two cameras were aimed at the eyes, and a third camera was positioned at a slight angle behind the mouse, in order to record body movements in-task. The eyes of the mouse were back-lit by the infrared two-photon imaging laser and the body was illuminated using an infrared light source (740 nm; Thorlabs). Key eye and body features (see Extended Data Fig. 10) were manually defined and automatically annotated using DeepLabCut41,42. From the x and y coordinates of these features, we calculated three eye parameters and four postural parameters (pupil diameter, eye position, eyelid opening, front paw angle, hind paw angle of the left hind paw, body elongation/rotation, tail angle; see Extended Data Fig. 10). Supplementary Video 1 shows both eye and body cameras of an example mouse.

Data analysis

The analysis of behaviour and imaging data was performed using custom written MATLAB routines.

Behavioural data

Behavioural performance is shown as the sensitivity index, d′. For every training session, d′ was calculated as the difference between the z-scored hit rate and the z-scored false alarm rate. The hit rate was defined as the number of correct category 2 trials divided by the total number of category 2 trials per session. Similarly, the false alarm rate was calculated as the number of incorrect category 1 trials divided by the total number of category 1 trials. In case a mouse performed two training sessions at time points T1, T3, T4, T5, T7 and T8, because two regions were imaged, the displayed value in the learning curve is the average across the two imaging sessions.

The fraction of correct Go choices was calculated as the number of hit trials divided by the number of all trials in which the mouse made a Go choice (the sum of ‘hits’ and ‘false alarms’). The number of days until a mouse reached performance criterion was the amount of daily sessions until the fraction of correct Go choices exceeded 0.66. Pre-training sessions without visual stimulation were not included in this quantification.

To investigate categorization behaviour across the entire stimulus space, we calculated the ‘fraction chosen’: The number of Go choices in response to a specific stimulus divided by the total number of presentations for that stimulus (see example in Fig. 1d; for all mice see Extended Data Fig. 2). Finally, we constructed psychometric curves showing the effect of each feature (that is, rule-relevant versus rule-irrelevant) on the behaviour of the mice (Fig. 1j). For that, the stimulus-specific ‘fraction chosen’ values were averaged along the irrelevant or the relevant feature dimension, respectively (see Fig. 1i).

To estimate learning rates, each individual learning curve was fitted with a sigmoid function:

$$y(x)=p1+frac{p2}{1+{{ m{e}}}^{p3(x-p4)}}$$

in which p1 determines the minimum of the sigmoid curve (for curve fitting fixed to 0), p2 the maximum, p3 the slope and p4 the inflection point. The parameter defining the minimum was fixed at a d′ of 0. Learning curves for rule 1 and rule 2 were fitted independently. Goodness of fit was determined as the root-mean-square error between the learning curve and the fitted curve.

Imaging data processing

The imaging data were first preprocessed by performing dark-current subtraction (using the average signal intensity during a laser-off period) and line shift correction. Rigid xy image displacement was first calculated on the structural red fluorescence channel using the cross correlation of the 2D Fourier transform of the images59, and subsequently corrected on both channels. For each imaging session, cells were manually segmented using the average image of the red fluorescence channel across the entire session. The cell identity was then manually matched across all imaging time points and only cells that could be identified in every session from T1 to T8 or T5 to left/right were included in the analysis. This criterion excluded one mouse (M06) from all further analyses, because of lost optical access at T8. The average green fluorescence signal was extracted for each cell and then corrected for neuropil contamination by subtracting the signal of 30 μm surrounding each cell multiplied by 0.7 and adding the median multiplied by 0.7 (refs. 57,60). From this fluorescence trace, we calculated ΔF/F as (F − F0)/F0 per frame. F0 was defined as the 25th percentile of the fluorescence trace in a sliding window of 60 s. From this trace, we inferred the spiking activity of each cell using the constrained foopsi algorithm61,62,63. The inferred spike rate during the stimulus presentation period was used for all further calculations and in all figure panels, except for the HLS maps and the left panels of Fig. 2d, e, where we display the ΔF/F trace for comparison.

To display lick-triggered neuronal activity (Extended Data Fig. 8), we averaged the inferred spike rate centred on the onset of the mouse’s lick-bouts. A lick-bout was defined as a sequence of licks, in which the interval between every two consecutive licks did not exceed 500 ms. Thus, a lick was part of a lick-bout if it happened within 500 ms after the previous lick. The onset of each lick-bout was the time of the first lick in the lick-bout.

Category-tuning index

For every cell, we calculated the CTI as previously described30. In short, we quantified the mean inferred spike rate during stimulus presentation for every stimulus. Next, we calculated the mean difference in inferred rate between stimuli of the same category (within), subtracted it from the mean difference between stimuli belonging to the two different categories (across) and normalized by the sum (across + within). This calculation results in an index ranging from −1 to 1, with category-unselective cells showing CTIs close to and below 0 and an ideal category-selective cell having an index of 1. Category-selective cells were defined as cells with a CTI value larger than 0.1. This threshold was chosen based on the distribution of CTIs in the naive population (T1), where individual cells rarely crossed this value. As a control, we used other thresholds (0.07, 0.15 and 0.20) and found no qualitative difference in the results other than that the fraction of category-selective cells scaled.

The fraction of category-selective cells was calculated as the number of neurons above threshold per imaging region, divided by the total number of chronically recorded neurons in that imaging region. Category-selective cells, determined by their CTI at time points T5 and T8, were divided in a Go category-selective and a NoGo category-selective group; neurons with higher average activity in Go category trials than in NoGo category trials were grouped as Go category-selective cells and conversely, cells with a higher average activity in NoGo category trials were labelled as NoGo category-selective. The overlap between the Go and NoGo category-selective groups was calculated between T5 and T8. The expected range of overlap assuming random independent sampling was calculated from the data, but with shuffled neuron identities (using the 95% percentile of the shuffled distribution). For time points at which not all stimuli were presented (T2, T3, T4, T6 and T7), we approximated category-tuning from the average responses to Go category trials and NoGo category trials.

Bayesian decoding

We decoded category identity from trial-by-trial activity patterns of a single neuron up to groups of ten neurons using Bayes theorem:

$$p(c|r)=frac{p(r|c)p(c)}{p(r)}$$

in which p(r|c) is the probability of a single trial response r when observed in either category 1 or 2 trials (calculated from an exponential distribution), p(c) as the prior probability of observing each category, and p(r) as the probability of observing the response. To cross-validate decoding performance, trials were first split into a training and test set (70% and 30%, respectively). The trial-averaged inferred spike rates followed an exponential distribution, which we estimated for each category individually (using the training set). Then, for each trial in the test set, we calculated the probability that the neuronal response came from those distributions. The distribution that gave the higher probability was determined as the decoder’s prediction. Decoder performance was calculated as the fraction of correctly predicted trials. As a control, decoding performance was also calculated after shuffling category identities across trials.

Selectivity time course

Average selectivity of individual neurons was calculated as the mean difference between responses to all Go category stimuli and all NoGo category stimuli, at every imaging time point (T1–T8). For linear regression, we defined three characteristic selectivity time courses (shown in Extended Data Fig. 7), resembling acquired selectivity for reward/choice, categorization rule 1 and categorization rule 2. Within each of these time courses, maximum selectivity was assigned the value 1 and no selectivity the value 0. The characteristic time courses were used as predictors in a model fitting the development of selectivity of individual neurons over time.

Generalized linear models to assess the influence of individual task parameters

We performed multilinear regression on neurons that were identified in all imaging time points of the rule-switch experiment. The regression model predicted the trial-wise mean spike rate of each cell during the stimulus presentation periods at imaging time point T5. Categorical predictors were: Category identity of the presented stimulus (0: category 1, 1: category 2), choice of the mouse (0: NoGo, 1: Go), and reward (0: no reward, 1: reward). The average running speed during the trial was modelled as a continuous predictor. A positive predictor weight indicated that the activity of a neuron was increased in trials where the value of the predictor was higher. A negative predictor weight reflected an inverse relation between the predictor’s value and the neuron’s firing rate. We normalized the predictor weights for overall differences in response amplitudes, by dividing each weight by the sum of all absolute predictor weights (including the intercept).

Hierarchical clustering was performed on relative predictor weights of neurons, including only cells with an R2 value larger than 0.05. The optimal number of clusters was calculated using gap statistic values, determined as the smallest cluster number k that fulfilled the criterion (here nine clusters):

$${ m{Gap}}(k)ge { m{Gapmax}}-{ m{s.}}{ m{e.}},({ m{Gapmax}})$$

in which Gap(k) is the gap statistic for k clusters, Gapmax is the largest gap value, and s.e.(Gapmax) is the standard error corresponding to the largest gap value.

We obtained linkage and relative predictor weights of the clusters from the MATLAB clusterdata algorithm.

To probe the influence of operant motor behaviour in the task change experiment, we concatenated all trials of sessions T5 (generalization session, Go/NoGo task) and L/R (left/right choice task). A stepwise linear regression model predicted the trial-averaged inferred spike rate of all recorded neurons individually. The predictors were the following categorical variables: category identity of the stimulus (0: category 1; 1: category 2), Go response of the mouse (0: NoGo, 1: all forms of Go, that is, Go/GoRight/GoLeft), reward (0: no reward, 1: reward) and two predictors that were specific to a motor response in the left/right session: GoRight and GoLeft. We only considered significant predictor weights, determined from an F-statistic comparing a model with and without a predictor. Predictor weights were normalized by dividing each weight by the maximum of all predictor weights.

Linear regression assessing the influence of instructed and uninstructed behaviours

The trial-averaged inferred spike rate of all recorded neurons in session T5 of a subset of experiments was fitted using a linear model. Body and eye parameters describing uninstructed behaviours were included in the model as continuous predictors. In addition, we included three categorical task-relevant predictors: category identity of the presented stimulus, choice of the mouse, and reward. For each predictor, we determined its maximum predictive power (cvR2) and its unique contribution (ΔR2), similar to the approach previously described40. Maximum predictive power (cvR2) was calculated as the predictive performance (R2) of a model with all parameters shuffled, except for the parameter of interest. A parameter’s unique contribution (ΔR2) was quantified as the difference between the full model’s R2 and the R2 of a model in which the parameter of interest was shuffled.

Stereotaxic coordinates of imaging regions

We determined the stereotaxic coordinates of the centres of all imaging regions (included in Fig. 2g, h) to place the imaged regions within a common reference frame (Mouse Brain Atlas)64. First, we cut 60-μm thick sagittal sections of both hemispheres using a freezing microtome. The AP coordinates outlining the full extent of the prism were identified from a section of the hemisphere into which the prism had been implanted (Extended Data Fig. 4). On the basis of this information, we calculated the exact AP coordinate of the centre of each imaging field of view. We calculated the dorso-ventral coordinate relative to the brain surface, which was aligned with the dorsal border of the prism. Finally, we determined the medio-lateral coordinate of the imaged field of view from the imaging depth of the field of view relative to the medial pia mater.

Statistical procedures

All data are presented as mean ± s.e.m. unless stated otherwise. Tests for normal distribution were carried out using the Kolmogorov–Smirnov test. Normally distributed data were tested using the two-tailed paired-samples t-test. Non-normally distributed data were tested using the two-tailed WMPSR test for paired samples, and the Kruskal–Wallis test for multiple, independent groups. A Bonferroni alpha correction was applied when multiple tests were done on the same data. Correlations were assessed using Pearson’s correlation coefficient, if the data were nData reporting

No statistical methods were used to predetermine sample size. Mice were randomly assigned to the categorization rule ‘spatial frequency’ or ‘orientation’. The investigators were not blinded to allocation during experiments and outcome assessment.

Animals

All procedures were performed in accordance with the institutional guidelines of the Max Planck Society and the local government (Regierung von Oberbayern). Twenty female C57BL/6 mice (postnatal day (P) 63–P82 at the day of surgery) were housed in groups of four to six littermates in standard individually ventilated cages (IVC, Tecniplast GR900). Mice had access to a running wheel and other enrichment material such as a tunnel and a house. All mice were kept on an inverted 12 h light/12 h dark cycle with lights on at 22:00. Before and during the experiment, the mice had ad libitum access to standard chow (1310, Altromin Spezialfutter). Before the start of behavioural experiments, mice had ad libitum access to water. At the end of the experiments, mice were perfused with 4% paraformaldehyde (PFA) in PBS and their brains were post-fixed in 4% PFA in PBS at 4 °C.

Surgical procedures

Before surgery, a prism implant was prepared by attaching a 1.5 mm × 1.5 mm prism (aluminium coating on the long side, MPCH-1.5, IMM photonics) to a 0.13 mm thick, 3 mm diameter glass coverslip (41001103, Glaswarenfabrik Karl Hecht) using UV-curing optical glue (Norland optical adhesive 71, Norland Products) and was left to fully cure at room temperature for a minimum of 24 h. Mice were anaesthetized with a mixture of fentanyl, midazolam and medetomidine in saline (0.05 mg kg−1, 5 mg kg−1 and 0.5 mg kg−1 respectively, injected intraperitoneally). As soon as sufficient depth of anaesthesia was confirmed by absence of the pedal reflex, carprofen in saline (5 mg kg−1, injected subcutaneously) was administered for general analgesia. The eyes were covered with ophthalmic ointment (IsoptoMax/Bepanthen) and lidocaine (Aspen Pharma) was applied on and underneath the scalp for topical analgesia. The scull was exposed, dried and subsequently scraped with a scalpel to improve adherence of the head plate. The scalp surrounding the exposed area was adhered to the skull using Histoacryl (B. Braun Surgical). A custom-designed head plate was centred at ML 0 mm, approximately 3 mm posterior to bregma, attached with cyanoacrylate glue (Ultra Gel Matic, Pattex) and secured with dental acrylic (Paladur). A 3 mm diameter craniotomy, centred at anterior–posterior (AP) 1.9 mm, medial–lateral (ML) 0 mm, was performed using a dental drill. The hemisphere for prism insertion was selected based on the pattern of bridging veins. Before inserting the prism, two injections (50 nl min−1) of 200–250 nl of virus solution (AAV2/1.hSyn.mRuby2.GSG.P2A.GCaMP6m.WPRE.SV40, titre: 1.02 × 1013 genome copies (GC) ml−1, Plasmid catalogue 51473, Addgene) were targeted at the medial prefrontal cortex opposite to the prism implant, coordinates: AP 1.4 mm to AP 2.8 mm, ML 0.25 mm, dorsal–ventral (DV) 2.3 mm (Nanoject, Neurostar). The left hemisphere was injected in 11 mice, and the right hemisphere in 9 mice. Subsequently, a durotomy was performed using microscissors (15070-08, Fine Science Tools) over the contralateral hemisphere, next to the medial sinus. The prism implant was inserted, gently pushing the medial sinus aside until the target cortical region became visible through the prism (for a detailed description, see ref. 29). The coverslip was attached to the surrounding skull using cyanoacrylate glue and dental acrylic. After surgery, the anaesthesia was antagonized with a mixture of naloxone, flumazenil and atipamezole in saline (1.2 mg kg, 0.5 mg kg−1 and 2.5 mg kg−1 respectively, injected subcutaneously) and the mice were placed under a heat lamp for recovery. Post-operative analgesia was provided for two subsequent days with carprofen (5 mg kg−1, injected subcutaneously).

Visual stimuli

Stimuli for behavioural training were presented in the centre of a gamma corrected LCD monitor (Dell P2414H; resolution: 1,920 by 1,080 pixels; width: 52.8 cm; height: 29.6 cm; maximum luminance: 182.3 Cd m−2). The centre of the monitor was positioned at about 0° azimuth and 0° elevation at a distance of 18 cm, facing the mouse straight on. The stimuli were 36 different sinusoidal gratings, each with a specific orientation and spatial frequency combination, shown in full contrast on a grey background (see Extended Data Fig. 1 for schematic of stimuli and task stages). Orientations ranged from 0° to 90°, the spatial frequencies from 0.023 cycles per degrees (cyc/°) to 0.25 cyc/° (orientations: [0, 15, 30, 60, 75, 90] °, spatial frequencies: [0.023, 0.027, 0.033, 0.06, 0.1, 0.25] cyc/°). The stimulus size was 45 retinal degrees in diameter, including an annulus of 4 degrees blending into the equiluminant grey background. The gratings drifted with a temporal frequency of 1.5 cycles per s.

In a subset of experiments (n = 3 mice), a dense stimulus space was presented, consisting of 49 stimuli ranging from 15° to 75° in orientation and from 0.027 cyc/° to 0.1 cyc/° in spatial frequency (orientations: [15, 30, 37.5, 45, 52.5, 60, 75]°, spatial frequencies: [0.027, 0.033, 0.036, 0.043, 0.052, 0.06, 0.1] cyc/°). Stimuli on the category boundary (either having an orientation of 45° or a spatial frequency of 0.043 cyc/°) were assigned to both categories, hence rewarded in 50% of trials.

All stimuli were created and presented using the Psychophysics Toolbox extensions of MATLAB48,49,50.

Behaviour

Behavioural experiments started seven days after surgery. The water restriction regime and the behavioural apparatus were previously described51. In short, mice were restricted to 85% of their initial weight on the starting date by individually adjusting the daily water ration. First, mice were accustomed to the experimenter and head fixation in the setup by daily handling sessions lasting 10 min. During these sessions, the water ration was offered in a handheld syringe. The remainder was supplemented in an individual drinking cage after a delay of approximately 30 min. After four to seven days of handling, mice were pre-trained to lick for reward, while being head-fixed on the spherical treadmill52,53,54 in absence of visual stimulation. Whenever a mouse ceased to run (velocity below 1 cm s−1) and made a lick on the spout, a water reward (drop size 8 μl) was delivered via the spout. A baseline imaging time point (T1) was acquired once the mice consumed more than 50 drops per session (35 to 45 min) on two consecutive days (requiring about three days of pre-training).

Subsequently, daily sessions of visual discrimination training for two initial stimuli started. Each mouse was randomly assigned to one of two groups. One group was first trained on the orientation rule, then on the spatial frequency rule. For the other group, the sequence of the rules was reversed (Extended Data Fig. 1). Each rule defined a Go category and a NoGo category, separated by a boundary at either 45° (orientation rule) or at 0.043 cyc/° (spatial frequency rule). Trials started with an inter-trial interval of 5 s. After that, the mouse could initiate stimulus presentation by halting and refraining from licking for a minimum of 0.5 s. A single stimulus was subsequently shown for 1.3 ± 0.2 s. At any time during stimulus presentation, the mouse could make a lick to indicate a Go choice. Trials with a Go choice in response to a Go category stimulus triggered a water reward and were classified as hits; trials in which the mice failed to lick during Go category stimulus presentation were considered misses. Correct withholding of a lick to a NoGo category stimulus was classified as a correct rejection, and did not result in a water reward. A lick during a NoGo category stimulus counted as a false alarm. Initially, false alarms only led to the termination of the current trial; later during training, false alarms were followed by a time-out of 5–7 s showing a time-out stimulus (a narrow, horizontal, black bar). Time-outs were included to reduce a Go bias that mice typically showed. The second imaging session (T2) was carried out after a mouse performed at more than 66% correct Go choices in a given session (requiring 11 to 40 sessions).

For the next training stage (leading up to imaging session T3) further stimuli were added (Extended Data Fig. 1a), such that both the Go category and the NoGo category consisted of three stimuli differing in the feature either irrelevant to the category rule (T3a, n = 6 mice), or relevant to the category rule (T3b, n = 5 mice). Whenever a mouse’s performance exceeded 66% correct Go choices in one session, we proceeded to the next training (and imaging) stage; 6 stimuli per category, 9 stimuli per category (imaging session T4), and finally 18 stimuli per category (imaging session T5), the latter serving as a crucial test for generalization behaviour.

Rule-switch: After successful learning of rule 1, mice (n = 11) were retrained using the previously i



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