A Columnar V1/V2 Visual Cortex Model and Emulation

Chapter
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 4)

Abstract

We have explored the implementation of neurophysiological and psychological constructs to develop a hyper-parallel computing platform. This approach is termed neuromorphic computing. As part of that effort, the primary visual cortex (V1) has been modeled in a high performance computing facility. The current columnar V1 model is being expanded to include binocular disparity and motion perception. Additionally, V2 thick and pale stripes are being added to produce a V1/V2 stereomotion and form perception system. Both the V1 and V2 models are based upon structures approximating neocortical minicolumns and functional columns. The neuromorphic strategies employed include columnar organization, integrate-and-fire neurons, temporal coding, point attraction recurrent networks, Reichardt detectors and “confabulation” networks. The interest is driven by the value of applications which can make use of highly parallel architectures we expect to see surpassing one thousand cores per die in the next few years. A central question we seek to answer is what the architecture of hyper-parallel machines should be. We also seek to understand computational methods akin to how a brain deals with sensation, perception, memory, attention decision-making.

Keywords

Simple Cell Dark Transition Thick Stripe General Purpose Graphical Processing Unit Information Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Livingstone M, Hubel D (1988) Segregation of form, color, movement, and depth: anatomy, physiology, and perception. Science 240(4853):740–749PubMedCrossRefGoogle Scholar
  2. 2.
    Moore MJ, Bishop M, Pino RE, Linderman R (2010) A columnar primary visual cortex (V1) model emulation using a PS3 cell-BE array. 2010 IEEE World Congress on Computational Intelligence, pp 1–8Google Scholar
  3. 3.
    Ahmed B, Anderson JC, Martin KAC, Nelson JC (1997) Map of the synapses onto layer 4 basket cells of the primary visual cortex of the cat. J Comp Neurol 380:230–242PubMedCrossRefGoogle Scholar
  4. 4.
    Anderson JA (1993) The BSB model: a simple nonlinear autoassociative neural network, associative neural memories. Oxford University Press, Inc., New YorkGoogle Scholar
  5. 5.
    Anderson JC, Martin KAC (2001) Does bouton morphology optimize axon length? Nat Neurosci 4:1166–1167PubMedCrossRefGoogle Scholar
  6. 6.
    Anderson JC, Martin KAC (2002) Connection from cortical area V2 to MT in macaque monkey. J Comp Neurol 443:56–70PubMedCrossRefGoogle Scholar
  7. 7.
    Anderson JC, Martin KAC (2005) Connection from cortical area V2 to V3A in macaque monkey. J Comp Neurol 488:320–330PubMedCrossRefGoogle Scholar
  8. 8.
    Anderson JC, Martin KAC (2006) Synaptic connection from cortical area V4 to V2 in macaque monkey. J Comp Neurol 495:709–721PubMedCrossRefGoogle Scholar
  9. 9.
    Anderson JC, Martin KAC, Whitteridge D (1993) Form, function, and intracortical projections of neurons in the striate cortex of the monkey Macacus nemestrinus. Cereb Cortex 3:412–420PubMedCrossRefGoogle Scholar
  10. 10.
    Anderson JC, Binzegger T, Martin KAC, Rockland KS (1998) The connection from cortical area V1 to V5: a light and electron microscopic study. J Neurosci 18:10525–10540PubMedGoogle Scholar
  11. 11.
    Kobatake E, Tanaka K (1994) Neuronal selectivities to complex object features in the ventral pathway of the macaque cerebral cortex. J Neurophysiol 71:856–867PubMedGoogle Scholar
  12. 12.
    Leuba G, Kraftsik R (1994) Changes in volume, surface estimate, three-dimensional shape and total number of neurons of the human primary visual cortex from midgestation until old age. Anat Embryol 190:351–366PubMedCrossRefGoogle Scholar
  13. 13.
    Tovée MJ (1996) An introduction to the visual system. Cambridge University Press, Cambridge, pp 180–198Google Scholar
  14. 14.
    Peters A, Jones EG (1984) Cellular components of cerebral cortex. Plenum, New YorkGoogle Scholar
  15. 15.
    Dougherty RF, Koch VM, Brewer AA, Fischer B, Modersitzki J, Wandell BA (2003) Visual field representations and locations of visual areas V1/2/3 in human visual cortex. J Vis 3(10):586–598. http://journalofvision.org/3/10/1/, doi:10.1167/3.10.1PubMedCrossRefGoogle Scholar
  16. 16.
    Koch C (1999) Biophysics of computation. Information processing in single neurons. Oxford University Press, New York, p 87Google Scholar
  17. 17.
    Mountcastle VB (1997) The columnar organization of the neocortex. Brain 120:701–722PubMedCrossRefGoogle Scholar
  18. 18.
    Lund JS, Angelucci A, Bressloff PC (2003) Anatomical substrates for functional columns in macaque monkey primary visual cortex cereb cortex. J Neurosci 8:1594–1609Google Scholar
  19. 19.
    Buzás P, Eysel UT, Adorján P, Kisvárday ZF (2001) Axonal topography of cortical basket cells in relation to orientation, direction, and ocular dominance maps. J Comp Neurol 437(3):259–285PubMedCrossRefGoogle Scholar
  20. 20.
    Kisvárday ZF, Ferecskó AS, Kovács K, Buzás P, Budd JML, Eysel UT (2002) One axon-multiple functions: specificity of lateral inhibitory connections by large basket cells. J Neurocytol 31:255–264PubMedCrossRefGoogle Scholar
  21. 21.
    Hertz L (2004) Non-neuronal cells of the nervous system: function and dysfunction. Volume 31: part I: structure, organization, development and regeneration. Elsevier, Amsterdam. ISBN 0-444-51451-1 (3 volumes)Google Scholar
  22. 22.
    Gerstner W, Kistler WM (2002) Spiking neuron models. Single neurons, populations, plasticity. Cambridge University Press, CambridgeGoogle Scholar
  23. 23.
    Black PE (2005, March 11) big-O notation. In: Black PE (ed), Dictionary of algorithms and data structures [online]. U.S. National Institute of Standards and Technology, GaithersburgGoogle Scholar
  24. 24.
    Hecht-Nielsen R (2006) Mechanism of cognition. In: Bar-Cohen Y (ed) Biomimetics: biologically inspired technologies. CRC Press, Boca RatonGoogle Scholar
  25. 25.
    Reichert H, Rowell CHF, Gris C (1985) Course correction circuitry translates feature detection into behavioural action in locusts. Nature, Lond 315:142–144CrossRefGoogle Scholar
  26. 26.
    Wielaard J, Sajda P (2006) Neural mechanisms of contrast dependent receptive field size in V1. Adv Neural Inform Process Syst 18:1505–1506Google Scholar
  27. 27.
    Hubel DH, Wiesel TN (1959) Receptive fields of single neurons in the cat’s striate cortex. J Physiol 148:574–591PubMedGoogle Scholar
  28. 28.
    Felleman DJ (2002) Area V2. In: Ramachandran VS (ed), Encyclopedia of the human brain (pp 199–222). Academic Press, San DiegoGoogle Scholar
  29. 29.
    Van Essen DC (2005) Corticortical and thalamocortical information flow in the primate visual system. Prog Brain Res 149:173–185Google Scholar
  30. 30.
    Serre T (2006) Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines, PhD Thesis, CBCL Paper #260/MIT-CSAIL-TR #2006-028, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
  31. 31.
    Sincich L, Horton J (2005) The circuitry of V1 and V2: integration of color, form, and motion. Annu Rev Neurosci 28:303–326PubMedCrossRefGoogle Scholar
  32. 32.
    Roe AW, Ts’o DY (1995) Visual topography in primate V2: multiple representation across functional stripes. J Neurosci 15:3689–3715PubMedGoogle Scholar
  33. 33.
    Roe AW, Ts’o DY (1997) The functional architecture of area V2 in the macaque monkey. In: Rockland KS, Kaas JH, Peters A (eds), Cerebral cortex vol. 12: extrastriate visual cortex in primates. Plenum Press, New York, pp 295–333Google Scholar
  34. 34.
    Prince SJ, Pointon AD, Cumming BG, Parker AJ (2002) Quantitative analysis of the responses of V1 neurons to horizontal disparity in dynamic random-dot stereograms. J Neurophysiol 87:191–208PubMedGoogle Scholar
  35. 35.
    Tootell RBH, Dale AM, Sereno I, Malach R (1996) New images from human visual cortex. Trends Neurosci 19:481–489PubMedCrossRefGoogle Scholar
  36. 36.
    Shushruth S, Ichida JM, Levitt J, Angelucci A (2009) Comparison of spatial summation properties of neurons in macaque V1 and V2. J Neurophysiol 102(4):2069–2083PubMedCrossRefGoogle Scholar
  37. 37.
    Nassi JJ, Lyon DC, Callaway EM (2006) The parvocellular LGN provides a robust disynaptic input to the visual motion area MT. Neuron 50(2):319–327PubMedCrossRefGoogle Scholar
  38. 38.
    Rockland KS, Ichinohe N (2004) Some thoughts on cortical minicolumns. Exp Brain Res 158:265–277. doi:10.1007/s00221-004-2024-9PubMedCrossRefGoogle Scholar
  39. 39.
    Hegdé J, Van Essen DC (2000) Selectivity for complex shapes in primate visual area V2. J Neurosci 20:1–6Google Scholar
  40. 40.
    Ito M, Komatsu H (2004) Representation of angles embedded within contour stimuli in area V2 of macaque monkeys. J Neurosci 24:3313–3324PubMedCrossRefGoogle Scholar
  41. 41.
    Riesenhuber M, Poggio T (1999) Hierarchical models of object recognition in cortex. Nat Neurosci 2:1019–1025PubMedCrossRefGoogle Scholar
  42. 42.
    Serre T, Kouh M, Cadieu C, Knoblich U, Kreiman G, Poggio T (2005) A theory of object recognition: computations and circuits in the feedforward path of the ventral stream in primate visual cortex, CBCL Paper #259/AI Memo #2005-036, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
  43. 43.
    Chen G, Lu HD, Roe AW (2008) A map of horizontal disparity in primate V2. Neuron 58:442–450PubMedCrossRefGoogle Scholar
  44. 44.
    Lu HD, Chen GC, Tanigawa H, Roe AW (2010) A motion direction map in Macaque V2. Neuron 68(5):1002–1013PubMedCrossRefGoogle Scholar
  45. 45.
    Bullier J (2006) What is fed back? In: vanHemmen JL, Sejnowsky TJ (eds) 23 Problems in systems neuroscience. Oxford UP, New York, pp 103–132CrossRefGoogle Scholar
  46. 46.
    Fize D; Vanduffel W, Nelissen K, Denys K, Chef d’Hotel C, Faugeras O, Orban GA (2003) The retinotopic organization of primate dorsal V4 and surrounding areas: a functional magnetic resonance imaging study in awake monkeys. J Neurosci 23(19):7395–7406PubMedGoogle Scholar
  47. 47.
    Tanaka K, Saito H, Fukada Y, Moriya M (1991) Coding visual images of objects in the inferotemporal cortex of the macaque monkey. J Neurophysiol 66:170–189PubMedGoogle Scholar
  48. 48.
    Starnet Communications Corporation (2010) X-Win32 [web site] http://www.starnet.com/products/xwin32/. Retrieved January 2011Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Air Force Research LaboratoryRomeUSA
  2. 2.ITT/AESRomeUSA

Personalised recommendations