Papers for the WRITTEN and ORAL exams of the Cogmaster's course P1 

Cyril Monier

Written:

  • Cowley, B. R., Smith, M. A., Kohn, A., & Yu, B. M. (2016). Stimulus-Driven Population Activity Patterns in Macaque Primary Visual Cortex. PLoS Computational Biology, 12(12), e1005185. http://doi.org/10.1371/journal.pcbi.1005185
  • Cui, Y., Liu, L. D., McFarland, J. M., Pack, C. C., & Butts, D. A. (2016). Inferring Cortical Variability from Local Field Potentials. The Journal of Neuroscience, 36(14), 4121–4135. http://doi.org/10.1523/JNEUROSCI.2502-15.2016
  • Li, H., Liu, X., Andolina, I. M., Li, X., Lu, Y., Spillmann, L., & Wang, W. (2017). Asymmetries of Dark and Bright Negative Afterimages Are Paralleled by Subcortical ON and OFF Poststimulus Responses. The Journal of Neuroscience, 37(8), 1984–1996. http://doi.org/10.1523/JNEUROSCI.2021-16.2017
  • Liu, L., She, L., Chen, M., Liu, T., Lu, H. D., Dan, Y., & Poo, M.-M. (2016). Spatial structure of neuronal receptive field in awake monkey secondary visual cortex (V2). Proceedings of the National Academy of Sciences, 113(7), 1913–1918. http://doi.org/10.1073/pnas.1525505113
  • Ni, A. M., Murray, S. O., & Horwitz, G. D. (2014). Object-centered shifts of receptive field positions in monkey primary visual cortex. Current Biology, 24(14), 1653–1658. http://doi.org/10.1016/j.cub.2014.06.003
  • van Kerkoerle, T., Self, M. W., Dagnino, B., Gariel-Mathis, M.-A., Poort, J., van der Togt, C., & Roelfsema, P. R. (2014). Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex. Proceedings of the National Academy of Sciences, 111(40), 14332–14341. http://doi.org/10.1073/pnas.1402773111

Oral:

  • Koch, E., Jin, J., Alonso, J. M., & Zaidi, Q. (2016). Functional implications of orientation maps in primary visual cortex. Nature Communications, 7, 13529. http://doi.org/10.1038/ncomms13529
  • Kremkow, J., Jin, J., Komban, S. J., Wang, Y., Lashgari, R., Li, X., et al. (2014). Neuronal nonlinearity explains greater visual spatial resolution for darks than lights. Proceedings of the National Academy of Sciences, 111(8), 3170–3175. http://doi.org/10.1073/pnas.1310442111
  • Nauhaus, I., Nielsen, K. J., & Callaway, E. M. (2016). Efficient Receptive Field Tiling in Primate V1. Neuron, 91(4), 893–904. http://doi.org/10.1016/j.neuron.2016.07.015
  • Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences, 111(23), 8619–8624. http://doi.org/10.1073/pnas.1403112111
  • Ziemba, C. M., Freeman, J., Movshon, J. A., & Simoncelli, E. P. (2016). Selectivity and tolerance for visual texture in macaque V2. Proceedings of the National Academy of Sciences, 113(22), E3140–9. http://doi.org/10.1073/pnas.1510847113
  • Zurawel, G., Shamir, I., & Slovin, H. (2016). Reconstruction of shape contours from V1 activity at high resolution. NeuroImage, 125, 1005–1012. http://doi.org/10.1016/j.neuroimage.2015.10.072

 

Andrei Gorea

Written:

  • Amano, K., Edwards, M., Badcock, D. R., & Nishida, S. (2009). Adaptive pooling of visual motion signals by the human visual system revealed with a novel multi-element stimulus. Journal of Vision, 9(3), 4.1–25. http://doi.org/10.1167/9.3.4
  • Arathorn, D. W., Stevenson, S. B., Yang, Q., Tiruveedhula, P., & Roorda, A. (2013). How the unstable eye sees a stable and moving world. Journal of Vision, 13(10), 22. http://doi.org/10.1167/13.10.22
  • Gepshtein, S., Lesmes, L. A., & Albright, T. D. (2013). Sensory adaptation as optimal resource allocation. Proceedings of the National Academy of Sciences, 110(11), 4368–4373. http://doi.org/10.1073/pnas.1204109110
  • Jogan, M., & Stocker, A. A. (2015). Signal Integration in Human Visual Speed Perception. The Journal of Neuroscience, 35(25), 9381–9390. http://doi.org/10.1523/JNEUROSCI.4801-14.2015
  • Rust, N. C., Mante, V., Simoncelli, E. P., & Movshon, J. A. (2006). How MT cells analyze the motion of visual patterns. Nature Neuroscience, 9(11), 1421–1431. http://doi.org/10.1038/nn1786
  • Sherbakov, L., & Yazdanbakhsh, A. (2013). Multiscale sampling model for motion integration. Journal of Vision, 13(11). http://doi.org/10.1167/13.11.18

Oral:

  • Hedges, J. H., Gartshteyn, Y., Kohn, A., Rust, N. C., Shadlen, M. N., Newsome, W. T., & Movshon, J. A. (2011). Dissociation of neuronal and psychophysical responses to local and global motion. Current Biology, 21(23), 2023–2028. http://doi.org/10.1016/j.cub.2011.10.049
  • Krekelberg, B., Dannenberg, S., Hoffmann, K.-P., Bremmer, F., & Ross, J. (2003). Neural correlates of implied motion. Nature, 424(6949), 674–677. http://doi.org/10.1038/nature01852
  • Nishida, S. (2004). Motion-based analysis of spatial patterns by the human visual system. Current Biology, 14(10), 830–839. http://doi.org/10.1016/j.cub.2004.04.044
  • Roach, N. W., McGraw, P. V., & Johnston, A. (2011). Visual motion induces a forward prediction of spatial pattern. Current Biology, 21(9), 740–745. http://doi.org/10.1016/j.cub.2011.03.031
  • Tadin, D., Lappin, J. S., Gilroy, L. A., & Blake, R. (2003). Perceptual consequences of centre-surround antagonism in visual motion processing. Nature, 424(6946), 312–315. http://doi.org/10.1038/nature01800
  • Whitney, D., Goltz, H. C., Thomas, C. G., Gati, J. S., Menon, R. S., & Goodale, M. A. (2003). Flexible retinotopy: motion-dependent position coding in the visual cortex. Science, 302(5646), 878–881. http://doi.org/10.1126/science.1087839

 

Jean Lorenceau

Written:

  • DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012). How does the brain solve visual object recognition? Neuron, 73(3), 415–434. http://doi.org/10.1016/j.neuron.2012.01.010
  • Hafed, Z. M., Lovejoy, L. P., & Krauzlis, R. J. (2011). Modulation of microsaccades in monkey during a covert visual attention task. The Journal of Neuroscience, 31(43), 15219–15230. http://doi.org/10.1523/JNEUROSCI.3106-11.2011
  • Jin, Z., Watamaniuk, S. N. J., Khan, A. Z., Potapchuk, E., & Heinen, S. J. (2014). Motion integration for ocular pursuit does not hinder perceptual segregation of moving objects. The Journal of Neuroscience, 34(17), 5835–5841. http://doi.org/10.1523/JNEUROSCI.4867-13.2014
  • Kheradpisheh, S. R., Ganjtabesh, M., Thorpe, S. J., & Masquelier, T. (2016). STDP-based spiking deep neural networks for object recognition. arXiv:1611.01421 [cs.CV].
  • Kuang, X., Poletti, M., Victor, J. D., & Rucci, M. (2012). Temporal encoding of spatial information during active visual fixation. Current Biology, 22(6), 510–514. http://doi.org/10.1016/j.cub.2012.01.050
  • Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005). Invariant visual representation by single neurons in the human brain. Nature, 435(7045), 1102–1107. http://doi.org/10.1038/nature03687

Oral:

  • Çukur, T., Nishimoto, S., Huth, A. G., & Gallant, J. L. (2013). Attention during natural vision warps semantic representation across the human brain. Nature Neuroscience, 16(6), 763–770. http://doi.org/10.1038/nn.3381
  • de Gee, J. W., Knapen, T. H. J., & Donner, T. H. (2014). Decision-related pupil dilation reflects upcoming choice and individual bias. Proceedings of the National Academy of Sciences, 111(5), E618–25. http://doi.org/10.1073/pnas.1317557111
  • Ko, H.-K., Poletti, M., & Rucci, M. (2010). Microsaccades precisely relocate gaze in a high visual acuity task. Nature Neuroscience, 13(12), 1549–1553. http://doi.org/10.1038/nn.2663
  • Kok, P., & de Lange, F. P. (2014). Shape perception simultaneously up- and downregulates neural activity in the primary visual cortex. Current Biology, 24(13), 1531–1535. http://doi.org/10.1016/j.cub.2014.05.042
  • Reimer, J., McGinley, M. J., Liu, Y., Rodenkirch, C., Wang, Q., McCormick, D. A., & Tolias, A. S. (2016). Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nature Communications, 7, 13289. http://doi.org/10.1038/ncomms13289
  • Srihasam, K., Vincent, J. L., & Livingstone, M. S. (2014). Novel domain formation reveals proto-architecture in inferotemporal cortex. Nature Neuroscience, 17(12), 1776–1783. http://doi.org/10.1038/nn.3855

 

Pascal Mamassian

Written:

  • Ban, H., Preston, T. J., Meeson, A., & Welchman, A. E. (2012). The integration of motion and disparity cues to depth in dorsal visual cortex. Nature Neuroscience, 15(4), 636–643. http://doi.org/10.1038/nn.3046
  • Bang, D., Aitchison, L., Moran, R., Herce Castanon, S., Rafiee, B., Mahmoodi, A., et al. (2017). Confidence matching in group decision-making. Nature Human Behaviour, 1(6), 0117–7. http://doi.org/10.1038/s41562-017-0117
  • Ehinger, B. V., Häusser, K., Ossandón, J. P., & König, P. (2017). Humans treat unreliable filled-in percepts as more real than veridical ones. eLife, 6, 257. http://doi.org/10.7554/eLife.21761
  • Goncalves, N. R., & Welchman, A. E. (2017). “What Not” Detectors Help the Brain See in Depth. Current Biology, 27(10), 1403–1412.e8. http://doi.org/10.1016/j.cub.2017.03.074
  • Parise, C. V., & Ernst, M. O. (2016). Correlation detection as a general mechanism for multisensory integration. Nature Communications, 7, 11543. http://doi.org/10.1038/ncomms11543
  • Witzel, C., & Gegenfurtner, K. R. (2015). Categorical facilitation with equally discriminable colors. Journal of Vision, 15(8), 22. http://doi.org/10.1167/15.8.22

Oral:

  • Bahrami, B., Olsen, K., Latham, P. E., Roepstorff, A., Rees, G., & Frith, C. D. (2010). Optimally interacting minds. Science, 329(5995), 1081–1085. http://doi.org/10.1126/science.1185718
  • Cammack, P., & Harris, J. M. (2016). Depth perception in disparity-defined objects: finding the balance between averaging and segregation. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 371(1697). http://doi.org/10.1098/rstb.2015.0258
  • Fritsche, M., Mostert, P., & de Lange, F. P. (2017). Opposite Effects of Recent History on Perception and Decision. Current Biology, 27(4), 590–595. http://doi.org/10.1016/j.cub.2017.01.006
  • Holcombe, A. O., Brown, N. J. L., Goodbourn, P. T., Etz, A., & Geukes, S. (2016). Does sadness impair color perception? Flawed evidence and faulty methods. F1000Research, 5, 1778. http://doi.org/10.12688/f1000research.9202.1
  • Sanborn, A. N., & Chater, N. (2016). Bayesian Brains without Probabilities. Trends in Cognitive Sciences, 20(12), 883–893. http://doi.org/10.1016/j.tics.2016.10.003
  • Zhang, P., Jamison, K., Engel, S., He, B., & He, S. (2011). Binocular rivalry requires visual attention. Neuron, 71(2), 362–369. http://doi.org/10.1016/j.neuron.2011.05.035

 

Laura Dugué

Written:

  • Baldauf, D., & Desimone, R. (2014). Neural mechanisms of object-based attention. Science, 344(6182), 424–427. http://doi.org/10.1126/science.1247003
  • Brefczynski, J. A., & DeYoe, E. A. (1999). A physiological correlate of the “spotlight” of visual attention. Nature Neuroscience, 2(4), 370–374. http://doi.org/10.1038/7280
  • Li, X., Lu, Z.-L., Tjan, B. S., Dosher, B. A., & Chu, W. (2008). Blood oxygenation level-dependent contrast response functions identify mechanisms of covert attention in early visual areas. Proceedings of the National Academy of Sciences, 105(16), 6202–6207. http://doi.org/10.1073/pnas.0801390105

Oral:

  • Mitchell, J. F., Sundberg, K. A., & Reynolds, J. H. (2009). Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. Neuron, 63(6), 879–888. http://doi.org/10.1016/j.neuron.2009.09.013
  • Pestilli, F., Carrasco, M., Heeger, D. J., & Gardner, J. L. (2011). Attentional enhancement via selection and pooling of early sensory responses in human visual cortex. Neuron, 72(5), 832–846. http://doi.org/10.1016/j.neuron.2011.09.025
  • Spaak, E., de Lange, F. P., & Jensen, O. (2014). Local entrainment of ? oscillations by visual stimuli causes cyclic modulation of perception. The Journal of Neuroscience, 34(10), 3536–3544. http://doi.org/10.1523/JNEUROSCI.4385-13.2014

 

Gianluigi Mongillo

Written:

  • Berkes, P., Orbán, G., Lengyel, M., & Fiser, J. (2011). Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science, 331(6013), 83–87. http://doi.org/10.1126/science.1195870
  • Wei, X.-X., & Stocker, A. A. (2015). A Bayesian observer model constrained by efficient coding can explain “anti-Bayesian” percepts. Nature Neuroscience, 18(10), 1509–1517. http://doi.org/10.1038/nn.4105
  • Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences, 111(23), 8619–8624. http://doi.org/10.1073/pnas.1403112111

Oral:

  • Froudarakis, E., Berens, P., Ecker, A. S., Cotton, R. J., Sinz, F. H., Yatsenko, D., et al. (2014). Population code in mouse V1 facilitates readout of natural scenes through increased sparseness. Nature Neuroscience, 17(6), 851–857. http://doi.org/10.1038/nn.3707
  • Salinas, E. (2006). How behavioral constraints may determine optimal sensory representations. PLoS Biology, 4(12), e387. http://doi.org/10.1371/journal.pbio.0040387
  • Tripathy, S. J., Padmanabhan, K., Gerkin, R. C., & Urban, N. N. (2013). Intermediate intrinsic diversity enhances neural population coding. Proceedings of the National Academy of Sciences, 110(20), 8248–8253. http://doi.org/10.1073/pnas.1221214110

 

Peter Neri

Written:

  • Haikala, V., Joesch, M., Borst, A., & Mauss, A. S. (2013). Optogenetic control of fly optomotor responses. The Journal of Neuroscience, 33(34), 13927–13934. http://doi.org/10.1523/JNEUROSCI.0340-13.2013
  • Lent, D. D., Graham, P., & Collett, T. S. (2010). Image-matching during ant navigation occurs through saccade-like body turns controlled by learned visual features. Proceedings of the National Academy of Sciences, 107(37), 16348–16353. http://doi.org/10.1073/pnas.1006021107
  • Liu, L., She, L., Chen, M., Liu, T., Lu, H. D., Dan, Y., & Poo, M.-M. (2016). Spatial structure of neuronal receptive field in awake monkey secondary visual cortex (V2). Proceedings of the National Academy of Sciences, 113(7), 1913–1918. http://doi.org/10.1073/pnas.1525505113
  • Nityananda, V., Tarawneh, G., Rosner, R., Nicolas, J., Crichton, S., & Read, J. (2016). Insect stereopsis demonstrated using a 3D insect cinema. Scientific Reports, 6(1), 18718. http://doi.org/10.1038/srep18718
  • Pecka, M., Han, Y., Sader, E., & Mrsic-Flogel, T. D. (2014). Experience-dependent specialization of receptive field surround for selective coding of natural scenes. Neuron, 84(2), 457–469. http://doi.org/10.1016/j.neuron.2014.09.010
  • Vintch, B., Movshon, J. A., & Simoncelli, E. P. (2015). A Convolutional Subunit Model for Neuronal Responses in Macaque V1. The Journal of Neuroscience, 35(44), 14829–14841. http://doi.org/10.1523/JNEUROSCI.2815-13.2015

Oral:

  • Ben-Tov, M., Donchin, O., Ben-Shahar, O., & Segev, R. (2015). Pop-out in visual search of moving targets in the archer fish. Nature Communications, 6, 6476. http://doi.org/10.1038/ncomms7476
  • Carlson, T. A. (2014). Orientation decoding in human visual cortex: new insights from an unbiased perspective. The Journal of Neuroscience, 34(24), 8373–8383. http://doi.org/10.1523/JNEUROSCI.0548-14.2014
  • Clark, D. A., Fitzgerald, J. E., Ales, J. M., Gohl, D. M., Silies, M. A., Norcia, A. M., & Clandinin, T. R. (2014). Flies and humans share a motion estimation strategy that exploits natural scene statistics. Nature Neuroscience, 17(2), 296–303. http://doi.org/10.1038/nn.3600
  • Fournier, J., Monier, C., Pananceau, M., & Fregnac, Y. (2011). Adaptation of the simple or complex nature of V1 receptive fields to visual statistics. Nature Neuroscience, 14(8), 1053–1060. http://doi.org/10.1038/nn.2861
  • Goller, B., & Altshuler, D. L. (2014). Hummingbirds control hovering flight by stabilizing visual motion. Proceedings of the National Academy of Sciences, 111(51), 18375–18380. http://doi.org/10.1073/pnas.1415975111
  • Liu, B.-H., Li, P., Sun, Y. J., Li, Y.-T., Zhang, L. I., & Tao, H. W. (2010). Intervening inhibition underlies simple-cell receptive field structure in visual cortex. Nature Neuroscience, 13(1), 89–96. http://doi.org/10.1038/nn.2443