Experimental design

(A) Awake head-fixed mice viewed sequences of oriented gratings while undergoing two-photon imaging of V1. Location of V1 was determined by widefield retinotopic mapping prior to experiment. Mice saw ABCD, ABBD, and ACBD for day 0 (pre-training) and day 5 (test) and ABCD only for the four days in between (training). Each image was shown for 250 ms, and sequences were separated by an 800 ms gray screen. (B) Fluorescence extracted from ROIs was deconvolved prior to analysis. (C) Trial-averaged responses of 1368 cells on day 0 to sequence ABCD.

Stimulus selectivity

Stimulus-selectivity for day 0 (n=1368) and day 5 (n=1500). A cell was considered stimulus-selective for an element if the average activity evoked by that stimulus was more than two standard deviations higher than any other stimulus.

Prediction errors

(A) Diagram of putative prediction error (PE) responses to omissions (middle) and substitutions (right) where the omitted/substituted element is expected to drive elevated responses on day 5 compared with day 0 (x indicates that this response is not present in our data). (B) PE ratios were computed by dividing trial- and time-averaged activity to the deviant image by activity during a corresponding standard image. (C) Average trace of B-responsive cells to ABCD with bootstrapped 95% confidence intervals. (D) Average trace of B-responsive cells to ABBD (left) and distributions of omission-type PE ratios (right) on days 0 (gray) and 5 (red). Note that there is no change in visual stimulus at the B1B2 transition. On day 0, mean PE=1.1 (n=138). On day 5, mean PE=1.4 (n=107). Distributions were significantly different (p << 0.05; KS-test). (E) Average trace of C-responsive cells to ACBD (left) and ABCD (middle). (right) Distributions of substitution-type PE ratios for C-responsive cells. On day 0, mean PE=0.88 (n=88). On day 5, mean PE=0.84 (n=39). Day 0 and day 5 distributions were not significantly different (p>0.05; KS-test).

Principal Component and Sparsity Analysis

(A) Trial average population responses, sorted by time to peak latency in each cell, to each sequence before and after training. (B) Prior to training, activity is driven along principal components jointly in complex combinations. After training, each of the most significant principal components correspond neatly to individual stimuli. In both datasets, the first five components explain ∼80% of the variance. (C) To test whether changes in principal component space reflected the decorrelation of responses, we computed Pearson-correlation coefficients between all four images for each sequence presentation individually. Empirical PDFs (top panels) and CDFs (bottom panels) of Pearson-correlation coefficients. After training, activity became significantly less correlated (p < 0.05; KS-test) for ABCD and ACBD. Delta (Δ) on bottom panels indicates area between curves on CDFs.

Stimulus decoding and representational drift

(A) Average confusion matrices (100 iterations) for decoders trained on responses to all images. Average decoder accuracy was 78% and 76% for days 0 and 5, respectively. Both are well above chance (6.7% ± 0.4%). The ability to differentiate correctly between the same image in different contexts, such as ABCD vs ABBD, prompted us to consider whether responses slowly drifted over time since sequences were presented in large blocks. (B) A decoder trained on individual elements (for example, ABCD) accurately classifies which block responses came from, with errors decreasing along with distance between blocks (e.g., block 1 is often confused with block 2 but not block 5). Decoder accuracy was 68% on day 0 and 56% on day 5. (C) We measured drift by computing Pearson-correlation coefficients between all pairs of population vectors driven by a particular sequence element and grouped these values by how far apart the pairs were in time/trial. Responses clearly become less correlated as distance between trials increases during both stimulus-evoked and gray periods. The largest change in overall temporal correlation was seen between gray periods on days 0 and 5.

Stimulus responses

(A) Heatmaps display trial-averaged responses to all sequences sorted by peak response to ABCD. Histograms reflect locations of peak activity for each cell and shift towards stimulus locked phasic responses after training. Red and green rectangles highlight area of decreased and increased activity on day 5 relative to baseline. (B) Combining histograms across all stimulus conditions shows how temporal response latency patterning changes over days. Note that the first two time bins (66 ms) after onsets are omitted in histograms to compensate for the transmission delay from retina to L2/3 cells. Prior to training, responses slowly build up after onset. After training, responses are robust at onset and undergo quick depression prior to ramping up for the next element. Histograms based only on early trials (first 100) show that this pattern does not change significantly over the course of the imaging session. (C) To test whether changes to temporal patterning might reflect a change in the ability to discern durations, we trained decoders on responses from different time bins following sequence onset. Decoder accuracy did not change significantly with training.

Gray responses

(A) Heatmaps displaying trial-averaged responses during gray periods following sequence presentations. Cells were sorted by peak response time to ABCD (top panels) and gray periods only (middle panels). Histograms (bottom) reflect locations of peak activity for each cell and show that there is an increase in active population size about 300 ms after gray onset and a second uptick towards preceding the next sequence presentation at 800 ms. (B) Average confusion matrices (100 iterations) for decoders trained on responses at different delays from gray onset for day 0 (left), day 5 (middle), and the difference between them (right). (C) Overall decoder accuracy as a function of time since gray onset did not change significantly with training.