Abstract
Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by which we can better understand and predict the principles of neural computation. Computational modeling of the neuronal pathways of the visual cortex has been successful in developing theories of biological motion processing. This review describes a range of computational models that have been inspired by neurophysiological experiments. Theories of local motion integration and pattern motion processing are presented, together with suggested neurophysiological experiments designed to test those hypotheses.
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Research ethics: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
References
Adelson, E.H. and Bergen, J.R. (1985). Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2: 284–299, https://doi.org/10.1364/josaa.2.000284.Search in Google Scholar PubMed
Adelson, E.H. and Movshon, J.A. (1982). Phenomenal coherence of moving visual patterns. Nature 300: 523–525, https://doi.org/10.1038/300523a0.Search in Google Scholar PubMed
Almasi, A., Meffin, H., Cloherty, S. L., Wong, Y., Yunzab, M., and Ibbotson, M.R. (2020). Mechanisms of feature selectivity and invariance in primary visual cortex. Cerebr. Cortex 30: 5067–5087, https://doi.org/10.1093/cercor/bhaa102.Search in Google Scholar PubMed
Bayerl, P. and Neumann, H. (2004). Disambiguating visual motion through contextual feedback modulation. Neural Comput. 16: 2041–2066, https://doi.org/10.1162/0899766041732404.Search in Google Scholar PubMed
Beck, C. and Neumann, H. (2011). Combining feature selection and integration—a neural model for MT motion selectivity. PLoS One 6: e21254, https://doi.org/10.1371/journal.pone.0021254.Search in Google Scholar PubMed PubMed Central
Born, R.T. and Bradley, D.C. (2005). Structure and function of visual area MT. Annu. Rev. Neurosci. 28: 157–189.10.1146/annurev.neuro.26.041002.131052Search in Google Scholar PubMed
Borst, A. (2007). Correlation versus gradient type motion detectors: the pros and cons. Philos. Trans. R. Soc. B Biol. Sci. 362: 369–374, https://doi.org/10.1098/rstb.2006.1964.Search in Google Scholar PubMed PubMed Central
Bradley, D.C. and Goyal, M.S. (2008). Velocity computation in the primate visual system. Nat. Rev. Neurosci. 9: 686–695, https://doi.org/10.1038/nrn2472.Search in Google Scholar PubMed PubMed Central
Chey, J., Grossberg, S., and Mingolla, E. (1998). Neural dynamics of motion processing and speed discrimination. Vis. Res. 38: 2769–2786, https://doi.org/10.1016/s0042-6989(97)00372-6.Search in Google Scholar PubMed
Collett, T. and Land, M. (1978). How hoverflies compute interception courses. J. Comp. Physiol. 125: 191–204, https://doi.org/10.1007/bf00656597.Search in Google Scholar
De Valois, R.L., William Yund, E., and Hepler, N. (1982). The orientation and direction selectivity of cells in macaque visual cortex. Vis. Res. 22: 531–544.10.1016/0042-6989(82)90112-2Search in Google Scholar PubMed
Foster, K., Gaska, J.P., Nagler, M., and Pollen, D. (1985). Spatial and temporal frequency selectivity of neurones in visual cortical areas V1 and V2 of the macaque monkey. J. Physiol. 365: 331–363, https://doi.org/10.1113/jphysiol.1985.sp015776.Search in Google Scholar PubMed PubMed Central
Fukushima, K. and Kikuchi, M. (1995). Brain processes, theories, and models. An international conference in honor of WS McCulloch 25 years after his death. In: Binding of form and motion by selective visual attention: a neural network model. MIT Press, pp. 436–445.Search in Google Scholar
Goodale, M.A. and Milner, A.D. (1992). Separate visual pathways for perception and action. Trends Neurosci. 15: 20–25, https://doi.org/10.1016/0166-2236(92)90344-8.Search in Google Scholar PubMed
Grossberg, S. (1994). 3-D vision and figure-ground separation by visual cortex. Percept. Psychophys. 55: 48–121, https://doi.org/10.3758/bf03206880.Search in Google Scholar PubMed
Grossberg, S., Mingolla, E., and Viswanathan, L. (2001). Neural dynamics of motion integration and segmentation within and across apertures. Vis. Res. 41: 2521–2553, https://doi.org/10.1016/s0042-6989(01)00131-6.Search in Google Scholar PubMed
Gur, M. and Snodderly, D.M. (2007). Direction selectivity in V1 of alert monkeys: evidence for parallel pathways for motion processing. J. Physiol. 585: 383–400, https://doi.org/10.1113/jphysiol.2007.143040.Search in Google Scholar PubMed PubMed Central
Huang, X., Albright, T.D., and Stoner, G.R. (2008). Stimulus dependency and mechanisms of surround modulation in cortical area MT. J. Neurosci. 28: 13889–13906.10.1523/JNEUROSCI.1946-08.2008Search in Google Scholar PubMed PubMed Central
Hubel, D.H. and Wiesel, T.N. (1965). Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. J. Neurophysiol. 28: 229–289, https://doi.org/10.1152/jn.1965.28.2.229.Search in Google Scholar PubMed
Ibbotson, M., Hung, Y.-S., Meffin, H., Boeddeker, N., and Srinivasan, M. (2017). Neural basis of forward flight control and landing in honeybees. Sci. Rep. 7: 1–15, https://doi.org/10.1038/s41598-017-14954-0.Search in Google Scholar PubMed PubMed Central
Ibbotson, M.R. and Goodman, L.J. (1990). Response characteristics of four wide-field motion-sensitive descending interneurones in Apis mellifera. J. Exp. Biol. 148: 255–279, https://doi.org/10.1242/jeb.148.1.255.Search in Google Scholar
Krekelberg, B., Dannenberg, S., HoffmannBremmer, K.P.F., and Ross, J. (2003). Neural correlates of implied motion. Nature 424: 674–677, https://doi.org/10.1038/nature01852.Search in Google Scholar PubMed
Kumbhani, R.D., El-Shamayleh, Y., and Movshon, J.A. (2015). Temporal and spatial limits of pattern motion sensitivity in macaque MT neurons. J. Neurophysiol. 113: 1977–1988.10.1152/jn.00597.2014Search in Google Scholar PubMed PubMed Central
Liden, L. and Pack, C. (1999). The role of terminators and occlusion cues in motion integration and segmentation: a neural network model. Vis. Res. 39: 3301–3320, https://doi.org/10.1016/s0042-6989(99)00055-3.Search in Google Scholar PubMed
Majaj, N.J., Carandini, M., and Movshon, J.A. (2007). Motion integration by neurons in macaque MT is local, not global. J. Neurosci. 27: 366–370.10.1523/JNEUROSCI.3183-06.2007Search in Google Scholar PubMed PubMed Central
Mather, G. (1984). Luminance change generates apparent movement: implications for models of directional specificity in the human visual system. Vis. Res. 24: 1399–1405, https://doi.org/10.1016/0042-6989(84)90195-0.Search in Google Scholar PubMed
Maunsell, J. and van Essen, D.C. (1983). The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey. J. Neurosci. 3: 2563–2586, https://doi.org/10.1523/jneurosci.03-12-02563.1983.Search in Google Scholar PubMed PubMed Central
Mingolla, E., Todd, J.T., and Norman, J.F. (1992). The perception of globally coherent motion. Vis. Res. 32: 1015–1031, https://doi.org/10.1016/0042-6989(92)90003-2.Search in Google Scholar PubMed
Movshon, J.A., Adelson, E.H., Gizzi, M.S., and Newsome, W.T. (1985). The analysis of moving visual patterns. In: Experimental brain research supplementum. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 117–151.10.1007/978-3-662-09224-8_7Search in Google Scholar
Movshon, J.A. and Newsome, W.T. (1996). Visual response properties of striate cortical neurons projecting to area MT in macaque monkeys. J. Neurosci. 16: 7733–7741, https://doi.org/10.1523/jneurosci.16-23-07733.1996.Search in Google Scholar
Movshon, J.A., Thompson, I.D., and Tolhurst, D.J. (1978). Receptive field organization of complex cells in the cat’s striate cortex. J. Physiol. 283: 79–99.10.1113/jphysiol.1978.sp012489Search in Google Scholar PubMed PubMed Central
Nordström, K. and O’Carroll, D.C. (2006). Small object detection neurons in female hoverflies. Proc. R. Soc. B Biol. Sci. 273: 1211–1216, https://doi.org/10.1098/rspb.2005.3424.Search in Google Scholar PubMed PubMed Central
Nover, H., Anderson, C.H., and DeAngelis, G.C. (2005). A logarithmic, scale-invariant representation of speed in macaque middle temporal area accounts for speed discrimination performance. J. Neurosci. 25: 10049–10060.10.1523/JNEUROSCI.1661-05.2005Search in Google Scholar PubMed PubMed Central
Pack, C.C. and Born, R.T. (2001). Temporal dynamics of a neural solution to the aperture problem in visual area MT of macaque brain. Nature 409: 1040–1042.10.1038/35059085Search in Google Scholar PubMed
Pack, C.C., Gartland, A.J., and Born, R.T. (2004). Integration of contour and terminator signals in visual area MT of alert macaque. J. Neurosci. 24: 3268–3280.10.1523/JNEUROSCI.4387-03.2004Search in Google Scholar PubMed PubMed Central
Pack, C.C., Livingstone, M.S., Duffy, K.R., and Born, R.T. (2003). End-stopping and the aperture problem: two-dimensional motion signals in macaque V1. Neuron 39: 671–680, https://doi.org/10.1016/s0896-6273(03)00439-2.Search in Google Scholar PubMed
Perrone, J.A. (2012). A neural-based code for computing image velocity from small sets of middle temporal (MT/V5) neuron inputs. J. Vis. 12: 1, https://doi.org/10.1167/12.8.1.Search in Google Scholar PubMed
Perrone, J.A. and Krauzlis, R.J. (2008). Spatial integration by MT pattern neurons: a closer look at pattern-to-component effects and the role of speed tuning. J. Vis. 8: 1–1.10.1167/8.9.1Search in Google Scholar PubMed
Perrone, J.A. and Thiele, A. (2001). Speed skills: measuring the visual speed analyzing properties of primate MT neurons. Nat. Neurosci. 4: 526–532, https://doi.org/10.1038/87480.Search in Google Scholar PubMed
Perrone, J.A. and Thiele, A. (2002). A model of speed tuning in MT neurons. Vis. Res. 42: 1035–1051, https://doi.org/10.1016/s0042-6989(02)00029-9.Search in Google Scholar PubMed
Priebe, N.J., Cassanello, C.R., and Lisberger, S.G. (2003). The neural representation of speed in macaque area MT/V5. J. Neurosci. 23: 5650–5661, https://doi.org/10.1523/jneurosci.23-13-05650.2003.Search in Google Scholar PubMed PubMed Central
Raudies, F. and Neumann, H. (2010). A neural model of the temporal dynamics of figure–ground segregation in motion perception. Neural Networks 23: 160–176.10.1016/j.neunet.2009.10.005Search in Google Scholar PubMed
Reichardt, W. (1961). Autocorrelation, a principle for evaluation of sensory information by the central nervous system. In: Symposium on principles of sensory communication 1959. MIT press, pp. 303–317.Search in Google Scholar
Rodman, H.R. and Albright, T.D. (1989). Single-unit analysis of pattern-motion selective properties in the middle temporal visual area (MT). Exp. Brain Res. 75: 53–64, https://doi.org/10.1007/bf00248530.Search in Google Scholar PubMed
Rust, N.C., Mante, V., Simoncelli, E.P., and Movshon, J.A. (2006). How MT cells analyze the motion of visual patterns. Nat. Neurosci. 9: 1421–1431, https://doi.org/10.1038/nn1786.Search in Google Scholar PubMed
Sharpee, T.O., Sugihara, H., Kurgansky, A.V., Rebrik, S.P., Stryker, M.P., and Miller, K.D. (2006). Adaptive filtering enhances information transmission in visual cortex. Nature 439: 936–942.10.1038/nature04519Search in Google Scholar PubMed PubMed Central
Simoncelli, E.P. and Heeger, D.J. (1998). A model of neuronal responses in visual area MT. Vis. Res. 38: 743–761, https://doi.org/10.1016/s0042-6989(97)00183-1.Search in Google Scholar PubMed
Smith, M.A., Majaj, N.J., and Movshon, J.A. (2005). Dynamics of motion signaling by neurons in macaque area MT. Nat. Neurosci. 8: 220–228, https://doi.org/10.1038/nn1382.Search in Google Scholar PubMed
Smith, M.A., Majaj, N.J., and Movshon, A. (2009). Dynamics of visual motion processing: neuronal, behavioral, and computational approaches. Springer US, Boston, MA.Search in Google Scholar
Srinivasan, M., LehrerKirchner, M.W., and Zhang, S. (1991). Range perception through apparent image speed in freely flying honeybees. Vis. Neurosci. 6: 519–535, https://doi.org/10.1017/s095252380000136x.Search in Google Scholar PubMed
Tsui, J.M., Hunter, J.N., Born, R.T., and Pack, C.C. (2010). The role of V1 surround suppression in MT motion integration. J. Neurophysiol. 103: 3123–3138, https://doi.org/10.1152/jn.00654.2009.Search in Google Scholar PubMed PubMed Central
Van Essen, D.C. and Maunsell, J.H. (1983). Hierarchical organization and functional streams in the visual cortex. Trends Neurosci. 6: 370–375, https://doi.org/10.1016/0166-2236(83)90167-4.Search in Google Scholar
Van Santen, J.P. and Sperling, G. (1985). Elaborated reichardt detectors. J. Opt. Soc. Am. A 2: 300–321, https://doi.org/10.1364/josaa.2.000300.Search in Google Scholar PubMed
Von der Heydt, R., Peterhans, E., and Baumgartner, G. (1984). Illusory contours and cortical neuron responses. Science 224: 1260–1262, https://doi.org/10.1126/science.6539501.Search in Google Scholar PubMed
Zarei Eskikand, P., Kameneva, T., Ibbotson, M.R., Burkitt, A.N., and Grayden, D.B. (2016). A possible role for end-stopped V1 neurons in the perception of motion: a computational model. PLoS One 11: e0164813, https://doi.org/10.1371/journal.pone.0164813.Search in Google Scholar PubMed PubMed Central
Zarei Eskikand, P., Kameneva, T., Ibbotson, M.R., Burkitt, A.N., and Grayden, D.B. (2018). A biologically-based computational model of visual cortex that overcomes the X-junction illusion. Neural Networks 102: 10–20, https://doi.org/10.1016/j.neunet.2018.02.008.Search in Google Scholar PubMed
Zarei Eskikand, P., KamenevaBurkitt, T.A.N., Grayden, D.B., and Ibbotson, M.R. (2019). Pattern motion processing by MT neurons. Front. Neural Circ. 13: 43–59, https://doi.org/10.3389/fncir.2019.00043.Search in Google Scholar PubMed PubMed Central
Zarei Eskikand, P., Kameneva, T., Burkitt, A.N., Grayden, D.B., and Ibbotson, M.R. (2020). Adaptive surround modulation of MT neurons: a computational model. Front. Neural Circ. 14: 529345, https://doi.org/10.3389/fncir.2020.529345.Search in Google Scholar PubMed PubMed Central
Zeki, S. (1971). Convergent input from the striate cortex (area 17) to the cortex of the superior temporal sulcus in the rhesus monkey. Brain Res. 28: 338–340, https://doi.org/10.1016/0006-8993(71)90665-2.Search in Google Scholar PubMed
Zeki, S.M. (1974). Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey. J. Physiol. 236: 549–573, https://doi.org/10.1113/jphysiol.1974.sp010452.Search in Google Scholar PubMed PubMed Central
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