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Research Article

Spatial structure of neuronal receptive field in awake monkey secondary visual cortex (V2)

Lu Liu, Liang She, Ming Chen, Tianyi Liu, Haidong D. Lu, Yang Dan, and Mu-ming Poo
PNAS February 16, 2016 113 (7) 1913-1918; first published February 2, 2016; https://doi.org/10.1073/pnas.1525505113
Lu Liu
aInstitute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
bUniversity of Chinese Academy of Sciences, Shanghai 200031, China;
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Liang She
aInstitute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
bUniversity of Chinese Academy of Sciences, Shanghai 200031, China;
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Ming Chen
aInstitute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
bUniversity of Chinese Academy of Sciences, Shanghai 200031, China;
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Tianyi Liu
aInstitute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
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Haidong D. Lu
aInstitute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
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Yang Dan
cDivision of Neurobiology, Department of Molecular and Cell Biology, Howard Hughes Medical Institute, Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720
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Mu-ming Poo
aInstitute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
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  • For correspondence: mpoo@ion.ac.cn
  1. Contributed by Mu-ming Poo, January 6, 2016 (sent for review October 12, 2015; reviewed by Judith Hirsch and Doris Y. Tsao)

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  • Fig. 1.
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    Fig. 1.

    Analysis of RF subunits. (A) Random sequences of natural images were presented and single-unit recordings were made in either V1 or V2. L.S., lunate sulcus; dashed line, V1/V2 border. (B) Linear-nonlinear RF model. Each subunit is represented by a linear filter (Fi) depicting the spatial RF structure. S, stimulus. The response (xi = S·Fi) of each linear filter is passed through a second-order polynomial fi(xi) = ai + bixi + cixi2 before summation. n, number of subunits; i, the ith subunit; R, neuronal response. (C and D) Prediction of responses by RF models of example V1 (C) and V2 (D) cells. (Upper) Left, spatial RF; Center, spatial spectrum of RF; Right, static nonlinear function, with data points (mean ± SEM) depicting measured responses and curves representing polynomial fits of the data. (Lower) Gray, measured responses in test data set; black, model prediction. CC between measured (test data) and predicted responses was 0.38 in C and 0.37 in D. (E and F) Distribution of CCs for V1 and V2 cells. Black dashed line, threshold for all cells used for further RF analysis.

  • Fig. S1.
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    Fig. S1.

    Comparison of PPR with modified spike-triggered average or spike-triggered covariance (mSTA/STC) method. (A) For both model cells: (Left) RFs of model cells; (Center) RFs estimated by PPR; (Right) RFs estimated by modified STC. The distortion of RF by modified STC was most prominent when the orientation of model cell was near horizontal or vertical (e.g., orientation 10° or 80°). (B) Comparison of CCs for measured (test data) and predicted responses between RFs computed by mSTA/STC and PPR for both V1 (gray, n = 124) and V2 (black, n = 360) cells. Each dot represents one cell. Red dashed line, unit line. (Inset) Distribution of delta CC between PPR and mSTA/STC.

  • Fig. 2.
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    Fig. 2.

    RF subunits identified by PPR. (A) Example V1 and V2 RFs. Each box contains subunits from one cell (gray, V1; black, V2). Cell 1, V1 cell with a center-surround subunit. Eccentricity (Ecc), 2.4°. (Left) Linear filter. (Center) RF spectrum. (Right) Nonlinear function (data points in spikes/s; error bars, SEM; curves, polynomial fits). Cell 2, V1 cell with two Gabor subunits. Ecc, 7.6°. Cell 3, V2 cell with a center-surround subunit. Ecc, 3.7°. Cell 4, V2 cell with two Gabor subunits. Ecc, 2.2°. Cells 5 and 6, V2 cells with ultralong subunits. Ecc, 2.2° and 2.1°. Cells 7–9, V2 cells with complex-shaped RF subunits. Ecc, 2°, 3°, and 3.8°. (B) Cumulative distributions of aspect ratio for V1 (n = 99) and V2 (n = 223) cells. (Inset) Gabor fit for subunit 1 of cell 4. X and Y, widths of Gaussian envelope (SD) orthogonal and parallel to the sine wave grating. AR of each cell was averaged from all excitatory subunits. (C) Cumulative distributions of DCI for V1 (n = 88) and V2 (n = 193) cells. (Inset) Fit of subunit 2 of cell 9 by sum of two Gabors (G1, G2). θ, angle between two Gabors; ‖G‖2, ∬G2dxdy. The DCI of each cell was averaged from all excitatory subunits.

  • Fig. S2.
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    Fig. S2.

    Illustration of decomposing V2 complex-shaped RFs and ultralong RFs into sum of Gabor functions. (A) Three example V2 cells with complex-shaped subunits, fitted by the sum of two Gabor functions. (B) An example V2 cell with ultralong RF, fitted by sum of five equal spaced Gabor functions with same size.

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    Fig. S3.

    Performance of RF models with ultralong subunits. (A) Prediction of responses by Gabor-fits of an ultralong RF (two subunits) and by shortened Gabors (40% of original). Arrows, example responses to the above stimuli, which was showed overlapping with RF contours. Note that all stimuli contained long edges aligned with the RF. (B) CCs between measured and predicted responses (of test dataset) by original RF subunits (computed from PPR), Gabor fits of the subunits, and progressively shortened Gabor. (C) Summary of analysis in B for all cells with AR > 2 (n = 16) by comparing the CC change with the best Gabor fits. The CC decrease is significant by shortening to 60% and 40%.

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    Fig. S4.

    Performance of RF models with complex-shaped subunits. (A–D) Prediction of responses before and after removal of subunits from cells with complex-shaped RF. (A and B) The example stimuli contained image features matched to one of the three subunits in the example V2 cell. Arrows, example responses to the above stimuli, which was showed overlapping with RF contours. (C) Three subunits of an example V2 cell are marked 1–3. Removal of the subunits reduced the CCs between measured (test data) and predicted responses. (D) Removal of one (n = 24) or two (n = 9) high DCI subunits from all high DCI cells (DCI > 30). (E and F) Prediction of responses before and after removing components of complex-shaped subunits. Removal of an oriented component from one subunit or from all subunits reduced the CCs between measured and predicted responses for an example cell (E) and all high DCI cells (F) (from one subunit, n1 = 48; from all subunits, nall = 30).

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    Fig. 3.

    Functional domains identified by optical imaging of intrinsic signals. (A) (Left) Cranial window for intrinsic optical imaging and single-unit recording. (Right) Gray circle, location of the window; dashed line, V1/V2 border; L.S., lunate sulcus. (B) Three functional maps (color, orientation, and direction) obtained by optical imaging of intrinsic signals and blood vessel pattern of the same cortical area, overlaid with expected stripe borders defined previously by cytochrome oxidase staining (Materials and Methods). Arrowheads, different stripes. A, anterior; M, medial.

  • Fig. S5.
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    Fig. S5.

    Identification of expected stripes in V2 from functional maps obtained by optical imaging of intrinsic signals. (A) Bright-field image of the cortex through the implanted window. (B) Green light image of the visual cortex showing the pattern of blood vessels. A, anterior; M, medial; L.S., lunate sulcus. (C) Ocular dominance map, which is clear in V1 but not in V2. (D) Color map shows the blobs in V1 and color preference domains within thin stripes of V2. (E) Orientation map showing orientation-selective domains in both V1 and V2. In V2, these domains are found in thick and pale stripes, complementary to the color preference domains. (F) Motion direction map, indicating direction-selective domains in thick stripes of V2. (Scale bar, 2 mm for B–F.) (G) Band-pass filtered color, orientation, and direction maps overlaid with blood vessels in V2. (Bottom) Three overlaid maps, together with estimated stripe borders (Materials and Methods).

  • Fig. 4.
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    Fig. 4.

    Organization of RF properties in V2 stripes. (A) Example RFs of six cells from thin (red), pale (blue), and thick (green) stripes. Eccentricity, 1.9°, 1.7°, 2.0°, 1.7°, 1.9°, and 0.9°. (B) (Left) Aspect ratio vs. normalized location across stripes (0, 1.5, and 3 represent center of thin, pale, and thick stripes, respectively). Data point with different symbol, aspect ratio of neuron from different hemispheres. (Right) Aspect ratios of V1 cells (randomly dispersed horizontally). (C) Cumulative distribution of aspect ratios of V1 (n = 99) and V2 cells in thin (n = 73), pale (n = 63), and thick (n = 19) stripes. Significant differences were found between V1 and V2 cells in pale or thick stripes and between V2 cells in thin and pale or thick stripes. (D) Cumulative distributions of DCI of V1 (n = 88) and V2 cells in thin (n = 58), pale (n = 62), and thick (n = 18) stripes. Significant difference was found between V1 and V2 cells in thin stripes.

  • Fig. S6.
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    Fig. S6.

    Percentage of center-surround cells in different stripes. Most of these cells were located in thin stripes. Error bar, SEM (n = 3 hemispheres). Cells were defined as center-surround simple cell by two criteria: (i) containing only one subunit with half-wave-rectified nonlinear function; and (ii) the goodness-of-fit > 4 by fitting the RF with DOG function (Materials and Methods).

  • Fig. 5.
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    Fig. 5.

    A convergence model for V2 RFs. (A) The model for V2 cells with ultralong RF subunits. Red and blue ellipse, on and off subregions. An ultralong cell may receive convergent projections from multiple V1 cells aligned along their preferred orientation. (B) The model for V2 cells with complex-shaped RF subunits. The cells may receive convergent inputs from V1 cells with different orientations at same or different center locations.

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Spatial structure of receptive fields in monkey V2
Lu Liu, Liang She, Ming Chen, Tianyi Liu, Haidong D. Lu, Yang Dan, Mu-ming Poo
Proceedings of the National Academy of Sciences Feb 2016, 113 (7) 1913-1918; DOI: 10.1073/pnas.1525505113

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Spatial structure of receptive fields in monkey V2
Lu Liu, Liang She, Ming Chen, Tianyi Liu, Haidong D. Lu, Yang Dan, Mu-ming Poo
Proceedings of the National Academy of Sciences Feb 2016, 113 (7) 1913-1918; DOI: 10.1073/pnas.1525505113
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