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Space–time wiring specificity supports direction selectivity in the retina

Nature volume 509, pages 331–336 (2014)Cite this article

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Abstract

How does the mammalian retina detect motion? This classic problem in visual neuroscience has remained unsolved for 50 years. In search of clues, here we reconstruct Off-type starburst amacrine cells (SACs) and bipolar cells (BCs) in serial electron microscopic images with help from EyeWire, an online community of ‘citizen neuroscientists’. On the basis of quantitative analyses of contact area and branch depth in the retina, we find evidence that one BC type prefers to wire with a SAC dendrite near the SAC soma, whereas another BC type prefers to wire far from the soma. The near type is known to lag the far type in time of visual response. A mathematical model shows how such ‘space–time wiring specificity’ could endow SAC dendrites with receptive fields that are oriented in space–time and therefore respond selectively to stimuli that move in the outward direction from the soma.

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Figure 1: Starburst amacrine cell and its direction selectivity.
Figure 2: EyeWire combines crowd and artificial intelligence.
Figure 3: 3D reconstructions of Off BCs and SACs.
Figure 4: BC–SAC contact.
Figure 5: BC–SAC co-stratification.
Figure 6: Mathematical model of the BC–SAC circuit.

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Acknowledgements

This research was made possible by funding from the Gatsby Charitable Foundation, the Howard Hughes Medical Institute, the Human Frontier Science Program, an anonymous donor, and the National Institutes of Health. K.L. was supported by a Samsung Scholarship. Support from the AWS Research Grants Program gave EyeWire global reach through Amazon Cloudfront. We thank K. Briggman for providing the e2198 data set. J. Mutch created the CNS framework on which CNPKG is based. D. Jia, R. Shearer, and B. Warne assisted in early stages of software development, and W. Silversmith with recent modifications. R. Prentki, L. Trawinski, M. Sorek, A. Ostojic, C. David, R. Avery, S. Temple, A. Bost, M. Greenstein and M. Evans worked in the laboratory to reconstruct neurons, and the first six also served as GrimReaper and hosted EyeWire competitions. Additional reconstructions were provided by R. Han, M. Gavrin, G. Lu, A. Ortiz and D. Udvary. All were trained by R. Prentki, who also created training videos for EyeWirers. We are grateful to A. Norton for 3D renderings, and to E. Almeida for EyeWire graphics. We acknowledge discussions with T. Baden, M. Berry, B. Borghuis, A. Borst, E. J. Chichilnisky, D. Chklovskii, D. Clark, J. Demb, T. Euler, M. Helmstaedter, A. Huberman, S. Lee, R. Masland, J. Sanes and Z. Zhou.

Author information

Author notes

  1. Mark Richardson, Srinivas C. Turaga & H. Sebastian Seung

    Present address: Present addresses: 601 N 42nd Street, Seattle, Washington 98103, USA (M.R.); Princeton Neuroscience Institute and Computer Science Deptartment, Princeton, New Jersey 08544, USA (H.S.S.); Gatsby Computational Neuroscience Unit, London WC1N 3AR, UK (S.C.T.).,

  2. Jinseop S. Kim and Matthew J. Greene: These authors contributed equally to this work.

Authors and Affiliations

  1. Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA

    Jinseop S. Kim, Matthew J. Greene, Kisuk Lee, Mark Richardson, Srinivas C. Turaga, Michael Purcaro, Matthew Balkam, Amy Robinson & H. Sebastian Seung

  2. Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA

    Aleksandar Zlateski

  3. Qualcomm Research, 5775 Morehouse Drive, San Diego, 92121, California, USA

    Bardia F. Behabadi & Michael Campos

  4. Max-Planck Institute for Medical Research, D-69120 Heidelberg, Germany,

    Winfried Denk

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Consortia

the EyeWirers

Contributions

J.S.K. created algorithms, software and procedures for crowd intelligence and learning, and applied them to generate neuron reconstructions. J.S.K. and M.J.G. classified bipolar cells. M.J.G. analysed contact and co-stratification, aided by code from A.Z. and input from W.D. H.S.S. devised the model with help from B.F.B. and M.C. S.C.T. trained the convolutional network. M.P. and M.B. implemented software and algorithms created by A.Z. for interactive segmentation and 3D visualization, with guidance from S.C.T. M.R. created the EyeWire game and M.B. its data infrastructure. K.L. quantified EyeWirer accuracy and learning. A.R. mobilized and studied the EyeWire community. EyeWirers reconstructed neurons and built extensions to EyeWire. H.S.S. wrote the paper with help from J.S.K., M.J.G. and A.R.

Corresponding author

Correspondence to H. Sebastian Seung.

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Competing interests

W.D. receives license income for SBEM technology from Gatan Inc.

Additional information

https://eyewire.org

Extended data figures and tables

Extended Data Figure 1 EyeWire screenshots.

a, Numerical score after gameplay of a cube, with leaderboard below. b, Overview mode with neuron under reconstruction (centre), global chat (bottom left), progress bar for neuron (top left), leaderboard (right), settings and help (bottom right). c, Tutorial play.

Extended Data Figure 2 Questionnaire administered to EyeWirers.

Extended Data Figure 3 EyeWire demographics.

a, b, Data based on 729 responses to the questionnaire in Extended Data Fig. 2. Age distribution of (a) all respondents and (b) those among the top 100 players ranked by number of cubes submitted. c, Gender distribution of all respondents and those among the top 100 players. d, Distribution of educational levels.

Extended Data Figure 4 Entirety of reconstructed SACs.

Only the central region of this plexus of SAC dendrites is portrayed in Fig. 3b. Scale bar, 50 μm.

Extended Data Figure 5 Clustering procedure for BCs.

a, Cells were divided by the 75th percentile of their stratification profiles. b, The shallow cluster BC1/2 was separated into BC1 and BC2 using stratification width, defined as the difference between 75th and 25th percentiles. c, The deep cluster BC3/4 was divided by 10th percentile into BC4 and BC3. d, BC3 was divided by axonal volume to yield BC3a and BC3b. Scatter plots of the BC1/2 (e) and BC3/4 (f) divisions show swaps made to eliminate mosaic violations. No swaps between BC1/2 and BC3/4 were needed.

Extended Data Figure 6 Mosaics of Off BC types.

a–e, Reconstructed BCs of types 1, 2, 3a, 3b and 4 (a through e, respectively). BC1/2 mosaics appear complete. BC3/4 mosaics show some gaps, probably because some thin axons were missed in the INL (Methods). Scale bar, 50 μm. f, Statistics of BC types. Means and standard deviation of the hull area (area of the convex hull around the cell) are in μm2. Type densities are the number of cells (n) divided by the area of the union of hulls of that cell type, and are in cells per mm2 without compensation for tissue shrinkage (Methods). Our densities resemble those of Wässle et al.6, who found 2,233, 3,212, 1,866, 3,254 and 3,005 cells per mm2.

Extended Data Figure 7 Alternative contact analysis.

Analysis based on summing over BC–SAC pairs rather than averaging as in the main text. a, Total BC–SAC contact versus distance from the SAC soma. b, Total SAC area within the union of convex hulls of each BC type versus distance. The peak at 80 μm is the location of maximum dendritic branching. The sharp decrease at larger distances is due to thinning and termination of branches. The graphs differ across BC types, which in our sample do not cover exactly the same retinal areas. c, Fraction of SAC area in contact with BC types, estimated by dividing contact area (a) by SAC area (b). This estimate is similar to that of Fig. 4d, but lacks error bars. d, Fraction of SAC area contacted by all BC types, the sum of the contact fractions in c. Also plotted is the contact predicted by co-stratification, the sum of the curves from Fig. 5b.

Source data

Extended Data Figure 8 Proximity versus contact.

Neurons that intermingle may or may not contact each other. a, b, Type 2 (a) and 3a BCs (b) contacting SACs. The cells are roughly 24 and 21 μm wide, respectively. c, d, Other SACs are well within the arborizations of the same two BCs, yet make no contact at all.

Extended Data Figure 9 Model direction selectivity index (DSI) versus stimulus speed.

The graphs are for travelling sine waves of various wavelengths λ (units of Δx). Speed is in units of Δx/τ. The preferred speed (horizontal location of each peak) is λ/(2π). Note that responses are cut off at high speeds by the temporal filters of the model, but the DSI can decay more slowly.

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Supplementary information

Supplementary Information

This file contains Supplementary Equations showing detailed derivation of the mathematical model of direction selectivity, calculation of the direction selectivity index for a special case that does not depend on the detailed forms of the filters, comparison with experiments, and cable theory estimate of dendritic conduction time. It also contains Supplementary Notes, which include EyeWire demographics, community structure, competitions, and list of EyeWirers who reconstructed SACs. (PDF 161 kb)

Off SAC with BC2 and BC3a axons.

Off SAC with BC2 and BC3a axons. Off SAC in red, BC2 axon in yellow, and BC3a axon in blue. (MOV 19301 kb)

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Kim, J., Greene, M., Zlateski, A. et al. Space–time wiring specificity supports direction selectivity in the retina. Nature 509, 331–336 (2014). https://doi.org/10.1038/nature13240

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