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The Optimal Human Ventral Stream from Estimates of the Complexity of Visual Objects

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Abstract

The part of the primate visual cortex responsible for the recognition of objects is parcelled into about a dozen areas organized somewhat hierarchically (the region is called the ventral stream). Why are there approximately this many hierarchical levels? Here I put forth a generic information-processing hierarchical model, and show how the total number of neurons required depends on the number of hierarchical levels and on the complexity of visual objects that must be recognized. Because the recognition of written words appears to occur in a similar part of inferotemporal cortex as other visual objects, the complexity of written words may be similar to that of other visual objects for humans; for this reason, I measure the complexity of written words, and use it as an approximate estimate of the complexity more generally of visual objects. I then show that the information-processing hierarchy that accommodates visual objects of that complexity possesses the minimum number of neurons when the number of hierarchical levels is approximately 15.

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References

  • Allman J, Jeo R, Sereno M (1994) The functional organization of visual cortex in owl monkeys. In: Baer JF, Weller RE, Kakoma I (eds) Aotus: the owl monkey. Academic, Orlando, pp 287–320

    Google Scholar 

  • Attneave F (1954) Some informational aspects of visual perception. Psychol Rev 61:183–193

    Article  PubMed  CAS  Google Scholar 

  • Barlow HB (1961) Possible principles underlying the transformation of sensory messages. In: Rosenblith WA (eds) Sensory communication. MIT Press, Cambridge, pp 217–34

    Google Scholar 

  • Barlow HB (1986) Why have multiple cortical areas? Vis Res 26:81–90

    Article  PubMed  CAS  Google Scholar 

  • Boussaoud D, Ungerleider LC, Desimone R (1990) Pathways for motion analysis: cortical connections of the medial superior temporal and fundus of the superior temporal visual areas in the macaque. J Comp Neurol 296:462–495

    Article  PubMed  CAS  Google Scholar 

  • Boussaoud D, Desimone R, Ungerleider LG (1991) Visual topography of area TEO in the macaque. J Comp Neurol 306:554–575

    Article  PubMed  CAS  Google Scholar 

  • Cajal SR (1995) Histology of the nervous system, vol. 1. Oxford University Press, New York

    Google Scholar 

  • Changizi MA (2001a) The economy of the shape of limbed animals. Biol Cybern 84:23–29

    Article  CAS  Google Scholar 

  • Changizi MA (2001b) Principles underlying mammalian neocortical scaling. Biol Cybern 84:207–215

    Article  CAS  Google Scholar 

  • Changizi MA (2001c) Universal scaling laws for hierarchical complexity in languages, organisms, behaviors and other combinatorial systems. J Theor Biol 211: 277–295

    Article  CAS  Google Scholar 

  • Changizi MA (2003a) The brain from 25,000 feet: high level explorations of brain complexity, perception, induction and vagueness. Kluwer, Dordrecht

    Google Scholar 

  • Changizi MA (2003b) The relationship between number of muscles, behavioral repertoire, and encephalization in mammals. J Theor Biol 220:157–168

    Article  Google Scholar 

  • Changizi MA (2005) Scaling the brain and its connections. In: Kaas JH (eds) Evolution of nervous systems. Elsevier, Amsterdam

    Google Scholar 

  • Changizi MA, McDannald MA, Widders D (2002) Scaling of differentiation in networks: nervous systems, organisms, ant colonies, ecosystems, businesses, universities, cities, electronic circuits, and legos. J Theor Biol 218:215–237

    Article  PubMed  CAS  Google Scholar 

  • Changizi M, Shimojo S (2005a) Character complexity and redundancy in writing systems over human history. Proc R Soc Lond B 272:267–275

    Article  Google Scholar 

  • Changizi MA, Shimojo S (2005b) Parcellation and area–area connectivity as a function of neocortex size. Brain Behav Evol 66:88–98

    Article  Google Scholar 

  • Changizi MA, Zhang Q, Ye H, Shimojo S (2006) The structures of letters and symbols throughout human history are selected to match those found in objects in natural scenes. Am Nat (in press)

  • Cherniak C (1992) Local optimization of neuron arbors. Biol Cybern 66:503–510

    Article  PubMed  CAS  Google Scholar 

  • Cherniak C (1994) Component placement optimization in the brain. J Neurosci 14:2418–2427

    PubMed  CAS  Google Scholar 

  • Cherniak C (1995) Neural component placement. Trends Neurosci 18:522–527

    Article  PubMed  CAS  Google Scholar 

  • Cherniak C, Changizi MA, Kang D (1999) Large-scale optimization of neuron arbors. Phys Rev E 59:6001–6009

    Article  CAS  Google Scholar 

  • Cherniak C, Mokhtarzada Z, R-Esteban R, Changizi B (2004) Global optimization of cerebral cortex layout. Proc Nat Acad Sci 101:1081–1086

    Article  PubMed  CAS  Google Scholar 

  • Chklovskii DB, Koulakov AA (2000) A wire length minimization approach to ocular dominance patterns in mammalian visual cortex. Physica A 284:318–334

    Article  Google Scholar 

  • Coogan TA, Burkhalter A (1993) Hierarchical organization of areas in rat visual cortex. J Neurosci 13:3749–3772

    PubMed  CAS  Google Scholar 

  • Cowey A (1979) Cortical maps and visual perception. The Grindley Memorial Lecture. Q J Exp Psychol 31:1–17

    CAS  Google Scholar 

  • Cowey A (1981) Why are there so many visual areas?. In: Schmitt FO, Warden FG, Adelman G, Dennis SG (eds) The organization of the cerebral cortex. MIT Press, Cambridge, pp 395–413

    Google Scholar 

  • Durbin R, Mitchison G (1990) A dimension reduction framework for understanding cortical maps. Nature 343:644–647

    Article  PubMed  CAS  Google Scholar 

  • Felleman DJ, Van Essen DC (1991) Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1:1–47

    Article  PubMed  CAS  Google Scholar 

  • Hasson U, Levy I, Behrmann M, Hendler T, Malach R (2002) Eccentricity bias as an organizing principle for human high-order object areas. Neuron 34:479–490

    Article  PubMed  CAS  Google Scholar 

  • Hasson U, Harel M, Levy I, Malach R (2003) Large-scale mirror-symmetry organization of human occipito-temporal object areas. Neuron 37:1027–1041

    Article  PubMed  CAS  Google Scholar 

  • Hilgetag CC, Grant S (2000) Uniformity, specificity and variability of corticocortical connectivity. Phil Trans R Soc Lond B 355:7–20

    Article  CAS  Google Scholar 

  • Hilgetag CC, O’Neill MA, Young MP (2000) Hierarchical organization of macaque and cat: cortical sensory systems explored with a novel network processor. Phil Trans R Soc Lond B 355:71–89

    Article  CAS  Google Scholar 

  • Jacobs RA, Jordan MI (1992) Computational consequences of a bias toward short connections. J Cogn Neurosci 4:323–336

    Article  Google Scholar 

  • Kaas JH (1977) Sensory representations in mammals. In: Stent GS (eds) Function and formation of neural systems. Dahlem Konferenzen, Berlin, pp 65–80

    Google Scholar 

  • Kaas JH (1989) Why does the brain have so many visual areas?. J Cogn Neurosci 1:121–135

    Google Scholar 

  • Kaas JH (1995) The evolution of isocortex. Brain Behav Evol 46:187–196

    Article  PubMed  CAS  Google Scholar 

  • Kaas JH (1997a) Theories of visual cortex organization in primates. In: Rockland KS, Kaas JH, Peters A (eds) Cerebral cortex vol 12. extrastriate cortex in primates. Plenum, New York, pp 91–125

    Google Scholar 

  • Kaas JH (1997b) Topographic maps are fundamental to sensory processing. Brain Res Bull 44:107–112

    Article  CAS  Google Scholar 

  • Kaas JH (2000) Why is brain size so important: design problems and solutions as neocortex gets bigger or smaller. Brain Mind 1:7–23

    Article  Google Scholar 

  • Kaas JH, Krubitzer LA (1991) The organization of extrastriate visual cortex. In: Dreher B, Robinson SR (eds) Neuroanatomy of the visual pathways and their development. MacMillan, London, pp 302–323

    Google Scholar 

  • Kaas JH, Morel A (1993) Connections of visual areas of the upper temporal lobe of owl monkeys: the MT crescent and dorsal and ventral subdivisions of FST. J Neurosci 13:534–546

    PubMed  CAS  Google Scholar 

  • Klyachko VA, Stevens CF (2003) Connectivity optimization and the positioning of cortical areas. Proc Nat Acad Sci 100:7937–7941

    Article  PubMed  CAS  Google Scholar 

  • Lewis JW, Van Essen DC (2000) Architectonic parcellation of parieto-occipital cortex and interconnected cortical regions in the macaque monkey. J Comp Neurol 428: 79–111

    Article  PubMed  CAS  Google Scholar 

  • Lyon DC, Kaas JH (2002) Evidence for a modified V3 with dorsal and ventral halves in macaque monkeys. Neuron 33:453–461

    Article  PubMed  CAS  Google Scholar 

  • Malach R, Levy I, Hasson U (2002) The topography of high-order human object areas. Trends Cogn Sci 6:176–184

    Article  PubMed  Google Scholar 

  • Mead C (1989) Analog VLSI and neural systems. Addison-Wesley, Boston

    Google Scholar 

  • Mitchison G (1991) Neuronal branching patterns and the economy of cortical wiring. Proc R Soc Lond B 245:151–158

    Article  CAS  Google Scholar 

  • Mitchison G (1992) Axonal trees and cortical architecture. Trends Neurosci 15:122–126

    Article  PubMed  CAS  Google Scholar 

  • Orban G, Van Essen D, Vanduffel W (2004) Comparative mapping of higher visual areas in monkeys and humans. Trends Cogn Sci 8:315–324

    Article  PubMed  Google Scholar 

  • Ringo JL (1991) Neuronal interconnection as a function of brain size. Brain Behav Evol 38:1–6

    Article  PubMed  CAS  Google Scholar 

  • Rockland KS, Pandya DN (1979) Laminar origins and terminations of cortical connections of the occipital lobe in the rhesus monkey. Brain Res 179:3–20

    Article  PubMed  CAS  Google Scholar 

  • Rolls ET, Aggelopoulos NC, Zheng F (2003) The receptive fields of inferior temporal cortex neurons in natural scenes. J Neurosci 23:339–348

    PubMed  CAS  Google Scholar 

  • Rosa MGP (1997) Visuotopic organization of primate extrastriate cortex. In: Rockland KS, Kaas JH, Peters A (eds) Cerebral cortex. vol 12. extrastriate cortex in primates. Plenum, New York, pp 127–203

    Google Scholar 

  • Rosa MGP, Tweedale (2005) Brain maps, great and small: lessons from comparative studies of primate visual cortical organization. Phil Trans R Soc Lond B 360:665–691

    Article  Google Scholar 

  • Ruppin E, Schwartz EL, Yeshurun Y (1993) Examining the volume efficiency of the cortical architecture in a multi-processor network model. Biol Cybern 70:89–94

    Article  PubMed  CAS  Google Scholar 

  • Scannell JW, Blakemore C, Young MP (1995) Analysis of connectivity in the cat cerebral cortex. J Neurosci 15:1463–1483

    PubMed  CAS  Google Scholar 

  • Sereno, Allman JM (1990) Cortical visual areas in mammals. In: Leventhal AG (eds) The neural basis of visual function, vol 4. Macmillan, London, pp 160–172

    Google Scholar 

  • Sereno MI, Tootell RBH (2005) From monkeys to humans: what do we now know about brain homologies? Curr Opin Neurobiol 15:135–144

    Article  PubMed  CAS  Google Scholar 

  • Shannon CE (1951) Prediction and entropy of printed English. Bell Syst Tech J 30:50–64

    Google Scholar 

  • Simoncelli EP, Olshausen BA (2001) Natural image statistics and neural representation. Annu Rev Neurosci 24:1193–1216

    Article  PubMed  CAS  Google Scholar 

  • Traverso S, Morchio R, Tamone G (1992) Neuronal growth and the Steiner problem. Riv Biol 85:405–418

    PubMed  CAS  Google Scholar 

  • Ullman S, Vidal-Naquet M, Sali E (2002) Visual features of intermediate complexity and their use in classification. Nat Neurosci 5:682–687

    PubMed  CAS  Google Scholar 

  • Ungerleider LG, Desimone R (1986) Cortical projections of visual area MT in the macaque. J Comp Neurol 248:190–222

    Article  PubMed  CAS  Google Scholar 

  • Van Essen DC (1997) A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature 385:313–319

    Article  PubMed  Google Scholar 

  • Van Essen DC (2004) Organization of visual areas in macaque and human cerebral cortex. In: Chalupa LM, Werner JS (eds) The visual neurosciences. MIT Press, Cambridge, pp 507–521

    Google Scholar 

  • Zeki SM (2003) Improbable areas in the visual brain. Trends Neurosci 26:23–26

    Article  PubMed  CAS  Google Scholar 

  • Van Essen DC, Maunsell JHR (1983) Hierarchical organization and the functional streams in the visual cortex. Trends Neurosci 6:370–375

    Article  Google Scholar 

  • Van Essen DC, Newsome WT, Maunsell JHR (1984) The visual field representation in striate cortex of the macaque monkey: asymmetries, anisotropies, and individual variability. Vis Res 24:429–448

    Article  PubMed  Google Scholar 

  • Van Essen DC, Felleman DF, DeYoe EA, Olavarria JF, Knierim JJ (1990) Modular and hierarchical organization of extrastriate visual cortex in the macaque. Cold Spring Harb Symp Quant Biol 55: 679–696

    PubMed  Google Scholar 

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Correspondence to Mark A. Changizi.

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Changizi, M.A. The Optimal Human Ventral Stream from Estimates of the Complexity of Visual Objects. Biol Cybern 94, 415–426 (2006). https://doi.org/10.1007/s00422-006-0056-x

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