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Modelling fast forms of visual neural plasticity using a modified second-order motion energy model

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Abstract

The Adelson-Bergen motion energy sensor is well established as the leading model of low-level visual motion sensing in human vision. However, the standard model cannot predict adaptation effects in motion perception. A previous paper Pavan et al.(Journal of Vision 10:1–17, 2013) presented an extension to the model which uses a first-order RC gain-control circuit (leaky integrator) to implement adaptation effects which can span many seconds, and showed that the extended model’s output is consistent with psychophysical data on the classic motion after-effect. Recent psychophysical research has reported adaptation over much shorter time periods, spanning just a few hundred milliseconds. The present paper further extends the sensor model to implement rapid adaptation, by adding a second-order RC circuit which causes the sensor to require a finite amount of time to react to a sudden change in stimulation. The output of the new sensor accounts accurately for psychophysical data on rapid forms of facilitation (rapid visual motion priming, rVMP) and suppression (rapid motion after-effect, rMAE). Changes in natural scene content occur over multiple time scales, and multi-stage leaky integrators of the kind proposed here offer a computational scheme for modelling adaptation over multiple time scales.

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References

  • Adelson, E. H., & Bergen, J. R. (1985). Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A, 2, 284–299.

    Article  CAS  Google Scholar 

  • Amit, D. J. (1989). Modeling brain function. New York: Cambridge University Press.

    Book  Google Scholar 

  • Arnold, D. H., & Clifford, C. W. (2002). Determinants of asynchronous processing in vision. Proceedings of the Royal Society of London, B269, 579–583.

    Article  Google Scholar 

  • Bergen, J. R., & Wilson, H. R. (1985). Prediction of flicker sensitivities from temporal three pulse data. Vision Research, 25, 577–582.

    Article  CAS  PubMed  Google Scholar 

  • Boudreau, C. E., & Ferster, D. (2005). Short-term depression in thalamocortical synapses of cat primary visual cortex. Journal of Neuroscience, 25, 7179–7190.

    Article  CAS  PubMed  Google Scholar 

  • Buchel, C., Josephs, O., Rees, G., Turner, R., Frith, C. D., & Friston, K. J. (1998). The functional anatomy of attention to visual motion. A functional MRI study. Brain, 121, 1281–1294.

    Article  PubMed  Google Scholar 

  • Castro-Alamancos, M., & Connors, B. W. (1996). Short-term synaptic enhancement and long-term potentiation in neocortex. Proceedings of the National Academy of Sciences of the United States of America, 93, 1335–1339.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Cavanagh, P. (1992). Attention based motion perception. Science, 257, 1563–1565.

    Article  CAS  PubMed  Google Scholar 

  • Challinor, K. L., & Mather, G. (2010). A motion-energy modelpredicts the direction discrimination and MAE duration of two-stroke apparent motion at high and low retinal illuminance. Vision Research, 50, 1109–1116.

    Article  PubMed Central  PubMed  Google Scholar 

  • Chance, F. S., Nelson, S. B., & Abbott, L. F. (1998). Synaptic depression and the temporal response characteristics of V1 cells. Journal of Neuroscience, 18, 4785–4799.

    CAS  PubMed  Google Scholar 

  • Culham, J., Verstraten, F. A. J., Ashida, H., & Cavanagh, P. (2000). Independent aftereffects of attention and motion. Neuron, 28, 607–615.

    Article  CAS  PubMed  Google Scholar 

  • Daelli, V., & Treves, A. (2010). Neural attractor dynamics in object recognition. Experimental Brain Research, 203, 241–248.

  • Daelli, V., van Rijsbergen, N., & Treves, A. (2010). How recent experience affects the perception of ambiguous objects. Brain Research, 1322, 81–91.

    Article  CAS  PubMed  Google Scholar 

  • Emerson, R. C., Bergen, J. R., & Adelson, E. H. (1992). Directionally selective complex cells and the computation of motion energy in cat visual cortex. Vision Research, 32, 203–218.

    Article  CAS  PubMed  Google Scholar 

  • Fuortes, M. G., & Hodgkin, A. L. (1964). Changes in time scaleand sensitivity in the ommatidia of Limulus. Journal of Physiology, 172, 239–263.

    CAS  PubMed Central  PubMed  Google Scholar 

  • Georgeson, M. A., & Scott-Samuel, N. E. (1999). Motion contrast: a new metric for direction discrimination. Vision Research, 39, 4393–4402.

    Article  CAS  PubMed  Google Scholar 

  • Giaschi, D., Douglas, R., Marlin, S., & Cynader, M. (1993). The time course of direction-selective adaptation in simple and complex cells in cat striate cortex. Journal of Neurophysiology, 70, 2024–2034.

    CAS  PubMed  Google Scholar 

  • Glasser, D. M., Tsui, J. M. G., Pack, C. C., & Tadin, D. (2011). Perceptual and neural consequences of rapid motion adaptation. Proceedings of the National Academy of Sciences of the United States of America, 108(45).

  • Finlayson, P. G., & Cynader, M. S. (1995). Synaptic depression in visual cortex tissue slices: An in vitro model for cortical neuron adaptation. Experimental Brain Research, 106, 145–155.

    Article  CAS  PubMed  Google Scholar 

  • Groppe, D. M., Urbach, T. P., & Kutas, M. (2011). Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. Psychophysiology, 48(12), 1711–1725.

    Article  PubMed Central  PubMed  Google Scholar 

  • Haug, B. A., Baudewig, J., & Paulus, W. (1998). Selective activation of human cortical area V5A by a rotating visual stimulus in fMRI; implication of attentional mechanisms. Neuroreport, 9, 611–614.

    Article  CAS  PubMed  Google Scholar 

  • Hershenson, M. (1993). Linear and rotation motion aftereffects as a function of inspection duration. Vision Research, 33, 1913–1919.

    Article  CAS  PubMed  Google Scholar 

  • Hempel, C. M., Hartman, K. H., Wang, X.-J., Turrigiano, G. G., & Nelson, S. B. (2000). Multiple forms of short-term plasticity at excitatory synapses in rat medial prefrontal cortex. Journal of Neurophysiology, 83, 3031–3941.

    CAS  PubMed  Google Scholar 

  • Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65–70.

    Google Scholar 

  • Huk, A. C., Ress, D., & Heeger, D. J. (2001). Neuronal basis of the motion aftereffect reconsidered. Neuron, 32, 161–172.

    Article  CAS  PubMed  Google Scholar 

  • Kanai, R., & Verstraten, F. A. (2005). Perceptual manifestations of fast neural plasticity: Motion priming, rapid motion aftereffect and perceptual sensitization. Vision Research, 45, 3109–3116.

    Article  PubMed  Google Scholar 

  • Lingnau, A., Ashida, H., Wall, M. B., & Smith, A. T. (2009). Speed encoding in human visual cortex revealed by fMRI adaptation. Journal of Vision, 9(13), 1–14. 3.

    Article  PubMed  Google Scholar 

  • Lisberger, S., & Movshon, J. (1999). Visual motion analysis for pursuit eye movements in area MT of macaque monkeys. Journal of Neuroscience, 19, 2224–2246.

    CAS  PubMed  Google Scholar 

  • Mather, G., Pavan, A., Campana, G., & Casco, C. (2008). The motion aftereffect reloaded. Trends in Cognitive Sciences, 12, 481–487.

    Article  PubMed Central  PubMed  Google Scholar 

  • Moutoussis, K., & Zeki, S. (1997). A direct demonstration of perceptual asynchrony in vision. Proceedings of the Royal Society of London B, 264(1380), 393–399.

    Article  CAS  Google Scholar 

  • Nelson, S. B. (1991). Temporal interactions in the cat visual system: I. Orientation-selective suppression in the visual cortex. Journal of Neuroscience, 11, 344–356.

    CAS  PubMed  Google Scholar 

  • Pavan, A., Campana, G., Guerreschi, M., Manassi, M., & Casco, C. (2009). Separate motion-detecting mechanisms for first- and second-order patterns revealed by rapid forms of visual motion priming and motion aftereffect. Journal of Vision, 27, 1–16.

    Google Scholar 

  • Pavan, A., Campana, G., Maniglia, M., & Casco, C. (2010). The role of high-level visual areas in short- and longer-lasting forms of neural plasticity. Neuropsychologia, 48, 3069–3079.

    Article  PubMed  Google Scholar 

  • Pavan, A., Contillo, A., & Mather, G. (2013). Modelling adaptation to directional motion using the Adelson-Bergen energy sensor. PloS One, 8(3), e59298.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Pavan, A., & Skujevskis, M. (2013). The role of stationary and dynamic test patterns in rapid forms of motion aftereffect. Journal of Vision, 10, 1–17.

    Google Scholar 

  • Pinkus, A., & Pantle, A. (1997). Probing visual motion signals with a priming paradigm. Vision Research, 37, 541–552.

    Article  CAS  PubMed  Google Scholar 

  • Priebe, N. J., Churchland, M. M., & Lisberger, S. G. (2002). Constraints on the source of short-term motion adaptation in macaque area MT: I. The role of input and intrinsic mechanisms. Journal of Neurophysiology, 88, 354–369.

    PubMed Central  PubMed  Google Scholar 

  • Priebe, N. J., & Lisberger, S. G. (2002). Constraints on the source of short-term motion adaptation in macaque area MT II. Tuning of neural circuit mechanisms. Journal of Neurophysiology, 88, 370–382.

    PubMed Central  PubMed  Google Scholar 

  • Rainville, S. J., Makous, W. L., & Scott-Samuel, N. E. (2005). Opponent-motion mechanisms are self-normalizing. Vision Research, 45, 1115–1127.

  • Rainville, S. J., Scott-Samuel, N. E., & Makous, W. L. (2002). The spatial properties of opponent-motion normalization. Vision Research, 42, 1727–1738.

    Article  PubMed  Google Scholar 

  • Rees, G., Frith, C. D., & Lavie, N. (1997). Modulating irrelevant motion perception by varying attentional load in an unrelated task. Science, 278, 1616–1619.

    Article  CAS  PubMed  Google Scholar 

  • Rolls, E. T. (1989). The representation and storage of information in neuronal networks in the primate cerebral cortex and hippocampus. In R. Durbin, C. Miall, & G. Mitchison (Eds.), The computing neuron, Ch. 8 (pp. 125–159). England: Addison-Wesley, Wokingham.

    Google Scholar 

  • Rushton, W. A. H. (1962). Visual adaptation. Proceedings of the Royal Society B, 986, 20–46.

    Google Scholar 

  • Seidemann, E., & Newsome, W. T. (1999). Effect of spatial attention on the responses of area MT neurons. Journal of Neurophysiology, 81, 1783–1794.

    CAS  PubMed  Google Scholar 

  • Stratford, K. J., Tarczy-Hornuch, K., Martin, K. A. C., Bannister, N. J., & Jack, J. J. B. (1996). Excitatory synaptic inputs to spiny stellate cells in cat visual cortex. Nature, 382, 258–261.

    Article  CAS  PubMed  Google Scholar 

  • Strout, J. J., Pantle, A., & Mills, S. L. (1994). An energy model of interframe interval effects in single-step apparent motion. Vision Research, 34, 3223–3240.

    Article  CAS  PubMed  Google Scholar 

  • Takeuchi, T., & De Valois, K. K. (1997). Motion-reversal reveals two motion mechanisms functioning in scotopic vision. Vision Research, 37, 745–755.

    Article  CAS  PubMed  Google Scholar 

  • Taylor, M. M. (1963). Tracking the decay of the after-effect of seen rotary movement. Perceptual and Motor Skills, 16, 119–129.

    Article  CAS  PubMed  Google Scholar 

  • Treue, S., & Maunsell, J. H. (1996). Attentional modulation of visual motion processing in cortical areas MT and MST. Nature, 382, 539–541.

    Article  CAS  PubMed  Google Scholar 

  • Treue, S., & Maunsell, J. H. (1999). Effects of attention on the processing of motion in macaque middle temporal and medial superior temporal visual cortical areas. Journal of Neuroscience, 19, 7591–7602.

    CAS  PubMed  Google Scholar 

  • van de Grind, W. A., Lankheet, M. J. M., & Tao, R. (2003). A gain-control model relating nulling results to the duration of dynamic motion aftereffects. Vision Research, 43, 117–133.

    Article  PubMed  Google Scholar 

  • Vautin, R. G., & Berkley, M. A. (1977). Responses of single cells in cat visual cortex to prolonged stimulus movement: neural correlates of visual aftereffects. Journal of Neurophysiology, 40, 1051–1065.

    CAS  PubMed  Google Scholar 

  • Varela, J. A., Song, S., Turrigiano, G. G., & Nelson, S. B. (1999). Differential depression at excitatory and inhibitory synapses in visual cortex. Journal of Neuroscience, 19(11), 4293–4304.

    CAS  PubMed  Google Scholar 

  • Verstraten, F. A. J., & Ashida, H. (2005). Attention-based motion perception and motion adaptation: What does attention contribute? Vision Research, 45, 1313–1319.

    Article  PubMed  Google Scholar 

  • Wark, B., Fairhall, A., & Rieke, F. (2009). Timescales of inference in visual adaptation. Neuron, 61(5), 750–761.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Zengel, J. E., & Magleby, K. L. (1982). Augmentation and facilitation of transmitter release. A quantitative description at the frog neuromuscular junction. Journal of General Physiology, 80, 583–611.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

Author AP was supported by grants from the Alexander von Humboldt Foundation, Author AC was supported by the Deutsche Forschungsgemeinschaft (DFG) within the Emmy-Noether program (Grant SA/1975 1–1) and by the Università degli Studi di Ferrara, Author GM was supported by the University of Lincoln.

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The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Institut für Psychologie, Universität Regensburg, Universitätsstr. 31, 93053, Regensburg, Germany

    Andrea Pavan

  2. Theoretical High Energy Physics Faculty of Science, Radboud University Nijmegen, 6500, GL, Nijmegen, The Netherlands

    Adriano Contillo

  3. Dipartimento di Fisica e Scienze della Terra, Università degli Studi di Ferrara, Via Saragat 1, 44122, Ferrara, Italy

    Adriano Contillo

  4. School of Psychology, University of Lincoln, Brayford Pool, Lincoln, LN6 7TS, UK

    George Mather

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  1. Andrea Pavan
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  2. Adriano Contillo
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Correspondence to Andrea Pavan.

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Action Editor: Alessandro Treves

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Pavan, A., Contillo, A. & Mather, G. Modelling fast forms of visual neural plasticity using a modified second-order motion energy model. J Comput Neurosci 37, 493–504 (2014). https://doi.org/10.1007/s10827-014-0520-x

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  • DOI: https://doi.org/10.1007/s10827-014-0520-x

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