Novel stimuli evoke excess activity in the mouse primary visual cortex

Abstract

To explore how neural circuits represent novel versus familiar inputs, we presented mice with repeated sets of images with novel images sparsely substituted. Using two-photon calcium imaging to record from layer 2/3 neurons in the mouse primary visual cortex, we found that novel images evoked excess activity in the majority of neurons. This novelty response rapidly emerged, arising with a time constant of 2.6 ± 0.9 s. When a new image set was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presentations, which decayed to steady state with a time constant of 1.4 ± 0.4 s. When we increased the number of images in the set, the novelty response's amplitude decreased, defining a capacity to store ∼15 familiar images under our conditions. These results could be explained quantitatively using an adaptive subunit model in which presynaptic neurons have individual tuning and gain control. This result shows that local neural circuits can create different representations for novel versus familiar inputs using generic, widely available mechanisms.

Keywords: adaptation; novelty response; predictive coding; primary visual cortex; visual system.

Fig. 1.

Measuring a novelty response. (A) Awake mice were head-fixed and placed on an air-suspended styrofoam ball. Visual stimuli were projected on a toroidal screen surrounding the animal. Neural activity was recorded with a two-photon microscope. (B) Wide-field image of visual areas, as determined by one-photon fluorescence measurements (see Materials and Methods). The black square within area V1 shows the size of the field of view for the two-photon microscope. (C) Portion of a field of view taken by the two-photon microscope with ROIs shown in red. (Scale bar, 50 µm.) (D) Stimulus design. Step 1: Images were constructed from a superposition of randomly chosen Gabor functions. Step 2: A set of different images was formed and presented repeatedly in the same order; image sets are represented by plotting the image index versus time. Step 3: Occasionally, an image was substituted by unique novel images drawn from the same image ensemble. (E) Example activity traces with the times of novel image presentations shown in red; all novel images were unique. (F, Top) Matrix of trial-by-trial responses of an example cell to novel images. (F, Middle) Activity averaged across trials. (F, Bottom) Repeated sequence with the time of novel images shown in red.

Fig. 2.

Population summary for the novelty response. (A, Top) Repeated image set containing novel images shown in orange. (A, Middle) Activity of one example neuron, averaged across trials with and without novel images (red vs. blue). (A, Bottom) Excess activity due to the occurrence of novel images (black). (B) Excess activity for 1,134 trial-averaged neural responses (rows) plotted versus time relative to the occurrence of novel images and sorted by response amplitude (color scale = z-score). (C) Histogram of amplitudes of the excess activity for all cells. (D) Excess neural activity (normalized) for different groups of neurons sorted by response rank (colors). (E) GCaMP6f response to a single spike, taken from ref. . (F) Population-averaged excess activity (black line; ΔF/F) with a curve fit capturing the response dynamics (dotted black line) and a model of the spiking rate (blue line).

Fig. 3.

The novelty response is not caused by running, pupil size, or eye movements. (A) Traces of behavioral variables during the course of a 1-h experiment. (Top) Pupil displacement from resting position. (Middle) Pupil diameter. (Bottom) Running speed. (B) Behavioral variables triggered on the occurrence of novel images (red dotted line). Shaded areas are uncertainty estimates. (Top) Pupil displacement (blue). (Upper Middle) Pupil diameter (green). (Lower Middle) Running speed (red). (Bottom) Trial-averaged response of the neural population (black).

Fig. 4.

The novelty response is not caused by changes of image order. (A) Population-averaged excess activity triggered by novel images. (B) Population-averaged excess activity triggered by an image order violation. In A and B, error bands were computed by first taking the average of all cell responses for each mouse and then computing the SEM across those five traces (gray). Error bands are therefore indicative of mouse-to-mouse variability.

Fig. 5.

Novelty responses emerge quickly within a repeated sequence. (A) In the variable repetition experiment, we formed blocks containing a new, randomly chosen image set (shown in blue; previous block shown in green). This unique image set was presented once and then immediately followed by L repetitions. Next, an image set was presented with a unique novel image substituted (shown by red bars). Finally, two more image sets were presented before a new block began (shown in purple). (Bottom) Neural activity averaged over the entire population for different choices of L (curves offset for clarity). (Top) Amplitude of the novelty response versus the number of repetitions, L. Dotted line: Exponential curve fit; error bars are SEM over n = 5 mice. (B, Top) Design of the repeated image set experiment. A given image set was repeated in a block until adaptation reached steady-state (blue). The same image set was presented again after a variable delay during which other image sets were presented (green). (B, Bottom) Transient response amplitude versus delay with an exponential curve fit (red); error bars are SEM over n = 5 mice.

Fig. 6.

Novelty response from within larger image sets. (A) Image sets with either S = 3, 6, 9, or 12 images were presented 17 times before one of the images was substituted by a unique novel image. All images were presented for 300 ms each. (B) Trial-averaged response of the neural population to novel images during image sets of different size, S (shown in color). (C) Amplitude of the novelty response versus image set size, S. Dotted line is an exponential curve fit; error bars are SEM for n = 5 mice. (D) Population-averaged steady-state neural activity versus image set size, S; error bars are SEM for n = 5 mice.

Fig. 7.

Classical contrast adaptation versus the novelty response. (A, Left) Schematic of classical contrast adaptation. The transition from high to low contrast typically causes a reduction in firing followed by a slow recovery to higher steady-state firing. (A, Right) Schematic of the novelty response. All transitions to a new image set cause a transient response, regardless of the steady-state activity. (B) Example trace of population neural activity for transitions to image sets that drive stronger (Top) versus weaker (Bottom) steady-state activity. (C) Amplitude of the transient response versus the ratio of steady-state response to the current versus preceding image set; dots are for each of 36 rank-ordered groups of neurons (see main text). (D) Transient activity versus steady-state activity; dots are averages over rank-ordered groups of 60 neurons. Black dotted line: unity; red line: linear curve fit. (Upper Inset) Normalized activity of neurons with large steady-state responses (from blue oval). (Lower Inset) Normalized activity of neurons with weak steady-state responses (from magenta oval).

Fig. 8.

Two principal component analysis (PCA) components capture most of the response variance when transitioning from one set of images to the next. (A) The first five PCA components, scaled by their component weights, from responses transitioning from one set of images to the next set of images. The duration of one repeat of an image sequence was 900 ms. (B) Variance explained by each of the first 20 principal components. The two first principal components captured 87% of the variance. (C) The decay in response amplitude of the strongly active cells was well captured by a linear combination of the first two principal components. (D) Similarly, the decay in response amplitude of the weakly active cells was also well captured by another linear combination of the first two principal components.

Fig. 9.

Adaptive subunit model. (A) Schematic of the model: Images, i, provide input to M subunits, j, via synapses, p_ij, and subunits provide input to N neurons, k, via synapses, q_jk. (B) Illustration of subunit dynamics: A sequence of images (Bottom) drives sparse responses in a subunit (Top) that decrease over time due to a reduction in gain (Middle) during each image presentation. (C) Population-averaged firing rate (Bottom) in neurons during presentation of three different image sets (Top). (D) Population-averaged firing rate during adaptation blocks with different numbers of repetitions of the same image set before the presentation of a novel image, L; dotted line is an exponential curve fit. (E) Amplitude of the novelty response, N, versus number of repetitions, L; dotted line is an exponential curve fit. (F) Population-averaged firing rate following presentation of a novel image during adaptation blocks with different sizes of image set, S. (G) Amplitude of the novelty response, N, versus size of the image set, S; dotted line is an exponential curve fit. (H) Amplitude of the transient response, T, plotted as a function of the time interval between image set presentations; dotted line is an exponential curve fit that defines the recovery time, τ_recovery. All error bars are SEM calculated over n = 4 instantiations.