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Population code in mouse V1 facilitates readout of natural scenes through increased sparseness


Neural codes are believed to have adapted to the statistical properties of the natural environment. However, the principles that govern the organization of ensemble activity in the visual cortex during natural visual input are unknown. We recorded populations of up to 500 neurons in the mouse primary visual cortex and characterized the structure of their activity, comparing responses to natural movies with those to control stimuli. We found that higher order correlations in natural scenes induced a sparser code, in which information is encoded by reliable activation of a smaller set of neurons and can be read out more easily. This computationally advantageous encoding for natural scenes was state-dependent and apparent only in anesthetized and active awake animals, but not during quiet wakefulness. Our results argue for a functional benefit of sparsification that could be a general principle governing the structure of the population activity throughout cortical microcircuits.

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Figure 1: Experimental setup and example session.
Figure 2: Image features contributing to population activation in V1.
Figure 3: Higher order correlations in natural images change population structure.
Figure 4: The population representation of natural movie scenes is easier to discriminate.
Figure 5: A sparse coding model with normalization is consistent with the results.
Figure 6: Active but not quiet wakefulness resembles the anesthetized state.

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We thank W.J. Ma for discussions, P. Storer for help setting up the two-photon system, D. Murray, A. Laudano and A.M. Morgan for organizational help and V. Kalatsky for help with the intrinsic-imaging system. This work was supported by US National Institutes of Health grants NIH-Pioneer award DP1-OD008301, NEI DP1-EY023176 and NIDA RO1-DA028525 to A.S.T.; a McKnight Scholar Award and Beckman Foundation Young Investigator Award to A.S.T.; NEI P30-EY002520; the German National Academic Foundation (P.B.); the Bernstein Center for Computational Neuroscience (FKZ 01GQ1002); and the German Excellency Initiative through the Centre for Integrative Neuroscience Tübingen (EXC307).

Author information

Authors and Affiliations



E.F., P.B., M.B. and A.S.T. designed the study; E.F. and P.B. created the stimuli; R.J.C., E.F. and P.S. built the two-photon microscope; E.F., R.J.C. and D.Y. wrote preprocessing code; E.F. performed the experiments; E.F., P.B. and A.S.E. analyzed the data; E.F., A.S.E., F.H.S. and M.B. performed the modeling; and P.B. and E.F. wrote the paper with input from A.S.E., F.H.S., M.B. and A.S.T.; P.B. and A.S.T. supervised all stages of the project.

Corresponding authors

Correspondence to Philipp Berens or Andreas S Tolias.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Examples of raw data for 3D and 2D scanning

(a) 3D scanning: ΔF/F calcium signals from the 20 cells shown in Fig. 1d out of the 139 total recorded in this site. The 4 different colors indicate presentations of multiple repetitions of the 4 different movies. (b) 2D scanning: Raw ΔF/F calcium signals from the 19 cells recorded in this site.

Supplementary Figure 2 Histograms of all measures of population activity

Histograms of all the measures of population activity shown in Fig. 3 for main dataset (L2/3; anesthetized animals; n = 315 sites). Columns from left to right: histograms for natural movies, phase scrambled movies and the percentage difference between the two conditions relative to the natural condition. Black line and number indicate the median of each distribution.

Supplementary Figure 3 Dependence of sparseness on population and bin size

Population sparseness for 100 ms, 500 ms and 1000 ms bins (indicated by the line style) and natural (blue) and phase scrambled (red) movies. Population sparseness does not depend on population size but larger bin sizes reduce the apparent sparseness. Shaded area around the mean represents ± 1SEM.

Supplementary Figure 4 Correlation of lifetime and population sparseness

(a) Scatter plot of population and lifetime sparseness for the main dataset (N=315). The two measures are highly correlated (linear correlation, r = 0.87; p < 0.001). Gray line indicates best fitting linear regression. (b) Scatter plot of difference in population and lifetime sparseness between the responses to natural and phase scrambled stimuli. The two measures are strongly correlated (linear correlation, r = 0.61, p < 0.001).

Supplementary Figure 5 Control analysis for the effect of eye movements

Median difference in measures of population activity between unprocessed and phase scrambled version of the virtual reality environment movies generated by Wallace et al.20 (n = 13 sites; blue), a natural image sequence with motion vectors extracted from the virtual reality movie and their phase scrambled version (n = 13 sites; red), and between stimulation with natural and phase scrambled movies from the mouse cam lacking eye movements (blue). Positive values indicate that the measure is higher under stimulation with natural stimuli. Error bars encompass the 95%-confidence intervals of the median.

Supplementary Figure 6 Measures of population activity for the V1 models

(a) Median of the measures of population activity for the population responses of model 1 (LNP). Error bars encompass the 95%-confidence intervals of the median. (b) As in (a) but for model 2 (adaptive non-linearity). (c) As in (a) but for model 3 (divisive normalization). (d) Median difference in measures of population activity for the population responses between natural and phase scrambled sets separately for Model 1, Model 2, Model 3 and in vivo data (purple, green, yellow and gray, respectively). Positive values indicate that the measure is higher under stimulation with natural movies. Error bars encompass the 95%-confidence intervals of the median.

Supplementary Figure 7 Example behavioral traces during the awake experiments.

Linear pupil displacement from the median position across the experiment in millimeters, the whisker velocity and the ball velocity in mm/s are shown from top to bottom (red, yellow and blue, respectively). Periods with large pupil displacements, or running and whisking activity between the upper and lower boundaries (dashed lines), were discarded (gray background). Periods during which the animal was not whisking or not running were considered as quiet (green background, below green dashed line; see Methods). Periods during which the animal was either whisking, running or both were considered as active (purple trace, above purple dashed line; see Methods).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Tables 1 and 2 (PDF 1118 kb)

Example natural movie stimulus

Example of a natural stimulus used in this study acquired with a camera mounted on the head of a mouse (MP4 5019 kb)

Example phase-scrambled movie generated from Supplementary Movie 1.

Example of a phase scrambled stimulus generated from Supplementary Movie 1 (MP4 6144 kb)

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Froudarakis, E., Berens, P., Ecker, A. et al. Population code in mouse V1 facilitates readout of natural scenes through increased sparseness. Nat Neurosci 17, 851–857 (2014).

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