Biological Cybernetics

, Volume 94, Issue 4, pp 325–334

Symbols as Self-emergent Entities in an Optimization Process of Feature Extraction and Predictions

Prospects

Abstract

In the mammalian cortex the early sensory processing can be characterized as feature extraction resulting in local and analogue low-level representations. As a direct consequence, these map directly to the environment, but interpretation under natural conditions is ambiguous. In contrast, high-level representations for cognitive processing, e.g. language, require symbolic representations characterized by expression and syntax. The representations are binary, structured and disambiguated. However, do these fundamental functional distinctions translate into a fundamental distinction of the respective brain areas and their anatomical and physiological properties? Here we argue that the distinction between early sensory processing and higher cognitive functions may not be based on structural differences of cortical areas; instead similar learning principles acting on input signals with different statistics give rise to the observed variations of function. Firstly, we give an account of present research describing neuronal properties at early stages of sensory systems as a consequence of an optimization process over the set of natural stimuli. Secondly, addressing a stage following early visual processing we suggest to extend the unsupervised learning scheme by including predictive processes. These contain the widely used objective of temporal coherence as a special case and are a powerful approach to resolve ambiguities. Furthermore, in combination with a prior on the bandwidth of information exchange between units it leads to a condensation of information. Thirdly, as a crucial step, not only are predictive units optimized, but the selectivity of the feature extractors are adapted to allow optimal predictability. Thus, over and beyond making useful predictions, we propose that the predictability of a stimulus be in itself a selection criterion for further processing. In a hierarchical system the combined optimization process leads to entities that represent condensed pieces of knowledge and that are not analogue anymore. Instead, these entities work as arguments in a framework of transformations that realize predictions. Thus, the criteria of predictability and condensation in an optimization of sensory representations relate directly to the two defining properties of symbols of expression and syntax. In this paper, we sketch an unsupervised learning process that gradually transforms analogue local representations into discrete binary representations by means of four hypotheses. We propose that in this optimization process acting in a hierarchical system, entities emerge at, higher levels that fulfil the criteria defining symbols, instantiating qualitatively different representations at similarly structured low and high levels.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aloimonos J, Shulman D (1989) Integration of visual modules: an extension of the marr paradigm. Academic Press, BostonGoogle Scholar
  2. Ashby FG, Maddox WT (2005) Human category learning. Annu Rev Psychol 56:149–178CrossRefPubMedGoogle Scholar
  3. Barlow H, Blakemore C, Pettigrew JD (1967) The neural mechanisms of binocular depth discrimination. J Physiol (Lond) 193:327–342Google Scholar
  4. Barlow HB (1961) Possible principles underlying the transformation of sensory messages. In: Rosenblith WA (eds). Sensory communication, vol 1961. MIT, Cambridge, pp 217–234Google Scholar
  5. Barlow HB (2001) Redundancy reduction revisited. Network Comput Neural Syst 12(3):241–254CrossRefGoogle Scholar
  6. Berkes P, Wiskott L (2005) Slow feature analysis yields a rich repertoire of complex cell properties. J Vision 5(6):579–602CrossRefGoogle Scholar
  7. Berry MJ II, Brivanlou IH, Jordan TA, Meister M (1999) Anticipation of moving stimuli by the retina. Nature 398:334–338CrossRefPubMedGoogle Scholar
  8. Betsch BY, Einhäuser W, Körding KP, König P (2004) The world from a cat’s perspective-statistics of natural videos. Biol Cybern 90(1): 41–50CrossRefPubMedGoogle Scholar
  9. Bialek W, Nemenman I, Tishby N (2001) Predictability, complexity, and learning. Neural Comput 13(11):2409–2463CrossRefPubMedGoogle Scholar
  10. Braitenberg V, Schüz A (1991) Anatomy of the cortex. Springer, Berlin Heidelberg New yorkGoogle Scholar
  11. Brodmann K (1906) Beiträge zur histologischen Lokalisation der Grosshirnrinde. Fünfte Mitteilung: über den allgemeinen Bauplan des Cortex pallii bei den Mammalieren und zwei homologe Rindenfelder im besonderen. Zugleich ein Beitrag zur Furchenlehre. J Psychol Neurol 6:275–400Google Scholar
  12. Buonomano DV, Merzenich MM (1998) Cortical plasticity: from synapses to maps. Annu Rev Neurosci 21:149–186CrossRefPubMedGoogle Scholar
  13. Callaway EM (1998) Local circuits in primary visual cortex of the macaque monkey. Annu Rev Neurosci 21:47–74CrossRefPubMedGoogle Scholar
  14. Chichocki A, Amari S-I (2002) Adaptive blind signal and image processing. In: Learning algorithms and applications. Wiley, New YorkGoogle Scholar
  15. Douglas RJ, Martin KA (2004) Neuronal circuits of the neocortex. Annu Rev Neurosci 27:419–451CrossRefPubMedGoogle Scholar
  16. Einhäuser W, Kayser C, Körding KP, König P (2003) Learning distinct and complementary feature selectivities from natural colour videos. Rev Neurosci 14(1–2):43–52PubMedGoogle Scholar
  17. Elder JH, Goldberg RM (2002) Ecological statistics of Gestalt laws for the perceptual organization of contours. J Vision 2(4):324–353CrossRefGoogle Scholar
  18. Field D (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc of Am 4(12): 2379–2394CrossRefGoogle Scholar
  19. Geisler WS, Perry JS, Super BJ, Gallogly DP (2001) Edge co-occurrence in natural images predicts contour grouping performance. Vis Res 41:711–724CrossRefPubMedGoogle Scholar
  20. Gibson JJ (1979) The ecological approach to visual perception. Houghton Mifflin, Boston, MAGoogle Scholar
  21. Gilbert CD, Wiesel TN (1989) Columnar specificity and intrinsic horizontal and cortico-cortical connections in cat visual cortex. J Neurosci 9:2432–2442PubMedGoogle Scholar
  22. Grill-Spector K, Malach R (2004) The human visual cortex. Annu Rev Neurosci 27:649–677CrossRefPubMedGoogle Scholar
  23. Hafner VV, Fend M, König P, Körding KP (2004) Predicting properties of the rat somatosensory system by sparse coding. Neural Inf Process 4:11–18Google Scholar
  24. Harnard S (1990) The symbol grounding problem. Physica D 42: 335–346CrossRefGoogle Scholar
  25. Hershler O, Hochstein S (2005) At first sight: a high-level pop out effect for faces. Vis Res 45:1707–1724CrossRefPubMedGoogle Scholar
  26. Hilbert D (1928) Die Grundlagen der Mathematik. Abhandlungen aus dem mathematischen Seminar der Unversität Hamburg 6:65–85Google Scholar
  27. Hipp J, Einhäuser W, Conradt J, König P (2005) Unsupervised learning of somatosensory representations for texture discrimination using a temporal coherence principle. Network Comput Neural Syst (in press)Google Scholar
  28. Honavar V, Uhr L (1994) Artificial intelligence and neural networks: steps toward principled integration. Academic, New York, NYGoogle Scholar
  29. Hubel DH, Wiesel TN (1959) Receptive fields of single neurones in the cat’s striate cortex. J Physiol 148:574–591PubMedGoogle Scholar
  30. Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160: 106–154PubMedGoogle Scholar
  31. Hurri J, Hyvärinen A (2003) Simple-cell-like receptive fields maximize temporal coherence in natural video. Neural Comput 15: 663–691CrossRefPubMedGoogle Scholar
  32. Hyväarinen A, Hoyer P (2000) Emergence of phase- and shift-invariant features by decomposition of natural images into independent feature subspaces. Neural Comput 12:1705–1720CrossRefPubMedGoogle Scholar
  33. Hyvarinen A, Hurri J, Vayrynen J (2003) Bubbles: a unifying framework for low-level statistical properties of natural image sequences. J Opt Soc Am A Opt Image Sci Vis 20(7):1237–1252PubMedCrossRefGoogle Scholar
  34. Jones JP, Palmer LA (1987) An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol 58(6):1233–1258PubMedGoogle Scholar
  35. Kayser C, Körding KP, König P (2004) Processing of complex stimuli and natural scenes in the visual cortex. Curr Opin Neurobiol 14: 468–473CrossRefPubMedGoogle Scholar
  36. Kjaer TW, Gawne TJ, Hertz JA, Richmond BJ (1997) Insensitivity of V1 complex cell responses to small shifts in the retinal image of complex patterns. J Neurophysiol 78(6):3187–3197PubMedGoogle Scholar
  37. Klette R, Schlüns K, Koschan A (1998) Computer vision—three-dimensional data from images. Springer, Berlin Heidelbereg New YorkGoogle Scholar
  38. Körding KP, Kayser C, Einhäuser W, König P (2004) How are complex cell properties adapted to the statistics of natural stimuli?. J Neurophysiol 91(1):206–212CrossRefPubMedGoogle Scholar
  39. Kreiman G, Koch C, Fried I (2000) Imagery neurons in the human brain. Nature 408:357–361CrossRefPubMedGoogle Scholar
  40. Krüger N (1998) Collinearity and parallelism are statistically significant second order relations of complex cell responses. Neural Process Lett 8(2):117–129CrossRefGoogle Scholar
  41. Krüger N, Ackermann M, Sommer G (2002) Accumulation of object representations utilizing interaction of robot action and perception. Knowl Based Syst 15:111–118CrossRefGoogle Scholar
  42. Krüger N, Lappe M, Wörgötter F (2004) Biologically motivated multi-modal processing of visual primitives. AISB J 1(5):417–428Google Scholar
  43. Krüger N, Wörgötter F (2004) Statistical and deterministic regularities: utilisation of motion and grouping in biological and artificial visual systems. Adv Imaging Electron Phys 131:82–147Google Scholar
  44. Krüger N, Wörgötter F (2005) Multi-modal primitives as functional models of hyper-columns and their use for contextual Integration. In: Proceedings of the 1st international symposium on brain, vision and artificial intelligence 2005, LNCS 3704. Springer, Berlin Heidelberg New York, p 157–166Google Scholar
  45. Lettvin JY, Maturana HR, McCulloch WS, Pitts WH (1959) What the frog’s eye tells the frog’s brain. Proc IRE 47:1940–1951CrossRefGoogle Scholar
  46. Linsker R (1988) Self-organization in a perceptual network. Computer 21:105–117CrossRefGoogle Scholar
  47. Maunsell JHR, Newsome WT (1987) Visual processing in monkey extrastriate cortex. Annu Rev Neurosci 10:363–401CrossRefPubMedGoogle Scholar
  48. Nakahara H, Zhang LI, Merzenich MM (2004) Specialization of primary auditory cortex processing by sound exposure in the “critical period”. Proc Natl Acad Sci USA 101:7170–7174CrossRefPubMedGoogle Scholar
  49. Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–609CrossRefPubMedGoogle Scholar
  50. Olshausen BA, Field DJ (2004) Sparse coding of sensory inputs. Curr Opin Neurobiol 14(4):481–487CrossRefPubMedGoogle Scholar
  51. Olshausen BA, Field DJ (2005) How close are we to understanding v1?. Neural Comput 17(8):1665–1699CrossRefPubMedGoogle Scholar
  52. Phillips WA, Singer W (1997) In search of common foundations for cortical computation. Behav Brain Sci 20:657–683CrossRefPubMedGoogle Scholar
  53. Orbach J (1998) The neuropsychological theories of lashley and Hebb. University Press of AmericaGoogle Scholar
  54. Quiroga RQ, Reddy L, Kreiman G, Koch C, Fried I (2005) Invariant visual representation by single neurons in the human brain. Nature 435(7045):1102–1107CrossRefPubMedGoogle Scholar
  55. Ringach RL (2002) Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex. J Neurophysiol 88: 455–463PubMedGoogle Scholar
  56. Ringach DL, Hawken MJ, Shapely R (2002) Receptive field structure of neuons in monkey visual cortex revealed by stimulation with natural image sequences. J Vision 2:12–24CrossRefGoogle Scholar
  57. Ringach DL (2004) Mapping receptive fields in primary visual cortex. J Physiol 558:717–728CrossRefPubMedGoogle Scholar
  58. Roelfsema PR (2002) Do neurons predict the future?. Science 295(5553):227CrossRefPubMedGoogle Scholar
  59. Schiller PH, Finlay BL, Volman SF (1976) Quantitative studies of single-cell properties in monkey striate cortex. III. Spatial frequency. J Neurophysiol 39:1334–1351PubMedGoogle Scholar
  60. Schultz W, Dickinson A (2000) Neuronal coding of prediction errors. Annu Rev Neurosci 23:473–500CrossRefPubMedGoogle Scholar
  61. Steels L (2003) Evolving grounded communication for robots. Trends Cog Sci 7(7):308–312CrossRefGoogle Scholar
  62. Tishby NZ, Pereira F, Bialek W (1999) The information bottleneck method. In: Hajek B, Sreenivas RS (eds) Proceedings of the 37th Allerton Conference on communication, control and computing, Urbana, Illinois, 1999. University of Illinois, IllinoisGoogle Scholar
  63. Ullman S (1979) The interpretation of Visual Motion. MIT, Cambridge, MAGoogle Scholar
  64. Vargha-Khadem F, Gadian DG, Copp A, Mishkin M (2005) FOXP2 and the neuroanatomy of speech and language. Nat Rev Neurosci 6:131–138CrossRefPubMedGoogle Scholar
  65. Verschure PFMJ, Pfeifer R (1992) Categorization, representations, and the dynamics of system-environment interaction: a case study in autonomous systems. In: Meyer JA, Roitblat H, Wilson S (eds) From animals to animats: proceedings of the 2nd international conference on simulation of adaptive behavior, Honolulu, Hawaii. MIT, Cambridge, MA pp 210–217Google Scholar
  66. Watt RJ, Phillips WA (2000) The function of dynamic grouping in vision. Trends Cog Sci 4(12):447–154CrossRefGoogle Scholar
  67. Wiskott L, Sejnowski TJ (2002) Slow feature analysis: unsupervised learning of invariances. Neural Comput 14(4):715–770CrossRefPubMedGoogle Scholar
  68. Wörgötter F, Porr B (2005) Temporal sequence learning, prediction, and control: a review of different models and their relation to biological mechanisms. Neural Comput 17(2):245–319CrossRefPubMedGoogle Scholar
  69. Zhang LI, Bao S, Merzenich MM (2001) Persistent and specific influences of early acoustic environments on primary auditory cortex. Nat Neurosci 4:1123–1130CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Department of Neurobiopsychology, Institute of Cognitive ScienceUniversity OsnabrückOsnabrückGermany
  2. 2.Cognitive Vision Group, Institut for Medieteknologi og IngeniørvidenskabAalborg University CopenhagenBallerupDenmark

Personalised recommendations