Parallel processing of visual space by neighboring neurons in mouse visual cortex

Journal name:
Nature Neuroscience
Year published:
Published online


Visual cortex shows smooth retinotopic organization on the macroscopic scale, but it is unknown how receptive fields are organized at the level of neighboring neurons. This information is crucial for discriminating among models of visual cortex. We used in vivo two-photon calcium imaging to independently map ON and OFF receptive field subregions of local populations of layer 2/3 neurons in mouse visual cortex. Receptive field subregions were often precisely shared among neighboring neurons. Furthermore, large subregions seem to be assembled from multiple smaller, non-overlapping subregions of other neurons in the same local population. These experiments provide, to our knowledge, the first characterization of the diversity of receptive fields in a dense local network of visual cortex and reveal elementary units of receptive field organization. Our results suggest that a limited pool of afferent receptive fields is available to a local population of neurons and reveal new organizational principles for the neural circuitry of the mouse visual cortex.

At a glance


  1. Mapping receptive fields with population calcium imaging and sparse-noise visual stimuli.
    Figure 1: Mapping receptive fields with population calcium imaging and sparse-noise visual stimuli.

    (a,b) Presentation of sparse-noise visual stimuli (a) to a mouse during simultaneous population calcium imaging in visual cortex (b). (c) Deconvolution of the calcium signals using parameters obtained from electrophysiology to obtain estimated spike rates. Micrograph scale bars in b,c, 20 μm; vertical scale bars in c, 20% change in fluorescence, normalized to baseline (ΔF/F) (top), 1 mV (middle), 2 spikes per frame (bottom). (d) Left, the mean correlation coefficient between deconvolved calcium signals and spike rates obtained from simultaneous on-cell recordings was 0.81 ± 0.02. Right, detection reliability as a function of the number of spikes within one frame. Error bars, s.e.m. (e) Computation of a triggered average of stimulus frames, using the deconvolved calcium signals as an estimate of spike rate. Separate ON and OFF maps were generated using the white dots (top) and black dots (bottom). (f) Maps were filtered using a Gaussian kernel and z-scored using an area of the triggered average away from the RF. (g) These z-scored maps were thresholded to obtain RFs.

  2. Receptive field subregions obtained with population calcium imaging.
    Figure 2: Receptive field subregions obtained with population calcium imaging.

    (a) A representative set of examples, illustrating the diversity of subregion sizes and shapes, taken from eight different neurons across three different mice. For each example subregion, the thresholded, z-scored map (left; color scale as in Fig. 1g) and the subregion outline used in subsequent analysis (right) are displayed next to each other. The third and sixth subregions are OFF subregions; the rest, ON subregions. (be) Plots of the distributions of various geometric parameters for all subregions mapped (n = 228 subregions in six mice). (b) The half short axis length. (c) Half long axis length. (d) Aspect ratio of elliptical fits to observed subregions. (e) Distribution of areas of RF subregions. Arrows indicate the mean of each histogram.

  3. Pairwise subregion overlaps are higher than expected for random positioning.
    Figure 3: Pairwise subregion overlaps are higher than expected for random positioning.

    (a) Left, multiple (12–15) overlaid subregions (top, ON; middle, OFF; bottom, OFF) in three different mice. For each point in space, the number of overlapping subregions is coded by color. Scatter is defined as the average deviation of each subregion center from the mean subregion center. Right, the subregions of each ensemble randomly repositioned three times. The random repositioning algorithm was designed to not change the scatter. Note that the random repositioning does not markedly alter the appearance of the ensembles. (b) Pairwise analysis, however, reveals that subregions overlapped more in their observed positions than when randomly repositioned (error bars, s.d.; P < 10−5, Kolmogorov-Smirnov test). (c) Example of apparently shared subregions (left) and clustered subregion centers (right) in visual space. The subregion outlines and center markers have been color coded by hand for clarity. The two gray subregion center markers on the right represent subregions that do not seem to be shared with the green and blue groups.

  4. Features of local receptive field organization: shared subregions and spanned subregions.
    Figure 4: Features of local receptive field organization: shared subregions and spanned subregions.

    (a) Left, four examples showing subregions of individual (color-coded) neurons that are shared. Right, the relative position of the same neurons in visual cortex (same color code; the region of interest used for analysis is highlighted by a circle for ease of identification). (b) The number of shared subregions in the population decreases when the subregions are randomly repositioned. Each point represents one ensemble of subregions. (c) In the case of spanned subregions, the subregion of one neuron overlaps two or more non-overlapping subregions from other neurons. Four examples are shown. The upward (U) and temporal (T) directions in visual space, and the medial (M) and rostral (R) directions along the cortex, are indicated by arrows. (d) Again, the incidence of this spatial arrangement decreases when the subregions are randomly repositioned.

  5. ON and OFF subregions are offset with respect to each other in visual space.
    Figure 5: ON and OFF subregions are offset with respect to each other in visual space.

    (a) The ON subregions in each mouse tend to cluster in an area of visual space spatially segregated from the OFF subregions, which also cluster together. Data from two mice are shown, with overlap density represented by amount of white (example 1 consists of 15 ON subregions and 18 OFF subregions; example 2 consists of 19 ON subregions and 15 OFF subregions). The upward (U) and temporal (T) directions in visual space are indicated by arrows. (b) When the subregions are randomly reassigned as ON or OFF subregions, the spatial segregation is lost. The observed clustering was decreased by randomly reassigning subregions as ON or OFF (P < 0.02, bootstrap method, n = 4 mice).


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  1. Wolfson Institute for Biomedical Research and Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK.

    • Spencer L Smith &
    • Michael Häusser


S.L.S. and M.H. conceived the experiments. S.L.S. performed the experiments and analyzed the data. S.L.S. and M.H. interpreted the data and wrote the paper.

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