Computational Consciousness: Building a Self-Preserving Organism

  • Allan Kardec Barros
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 657)


Consciousness has been a subject of crescent interest among the neuroscience community. However, building machine models of it is quite challenging, as it involves many characteristics and properties of the human brain which are poorly defined or are very abstract. Here I propose to use information theory (IT) to give a mathematical framework to understand consciousness. For this reason, I used the term “computational”. This work is grounded on some recent results on the use of IT to understand how the cortex codes information, where redundancy reduction plays a fundamental role. Basically, I propose a system, here called “organism”, whose strategy is to extract the maximal amount of information from the environment in order to survive. To highlight the proposed framework, I show a simple organism composed of a single neuron which adapts itself to the outside dynamics by taking into account its internal state, whose perception is understood here to be related to “feelings”.


Cost Function Receptive Field Internal State Primary Visual Cortex Sparse Code 
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.


  1. Aleksander I, Dunmall B (2003) Axioms and tests for the presence of minimal consciousness in agents. J Conscious Stud 10(4–5):7–18.Google Scholar
  2. Baars, B.J.: A Cognitive Theory of Consciousness (Cambridge Univ. Press, Cambridge, MA, 1988).Google Scholar
  3. Barlow, H.B.: “Unsupervised learning”. Neural Computation, 1:295–311, 1989.CrossRefGoogle Scholar
  4. Barros, A. and Principe, J: “A Model for Neural Regulation of Heart Rate Based on Statistical Independence”. Submitted to neural computation.Google Scholar
  5. Bell. A: “The co-information lattice”. In Proceedings of the 4th Symposium on Independent component analysis and Blind signal separation (ICA2003), pp. 921–926. 2003.Google Scholar
  6. Coppola, D. and Purves, D. “The extraordinary rapid disappearance of entoptic images”, Proc. Natl. Acad. Sci. USA 93: 8001–8004, 1996.PubMedCrossRefGoogle Scholar
  7. Damasio, A.: Body and emotion in the making of consciousness. In Portuguese. Shchwarcz Editora. 2000.Google Scholar
  8. Damasio, A.: Looking for Spinoza: joy, sorrow and the feeling brain. In Portuguese. Shchwarcz Editora 2004.Google Scholar
  9. Dawson, G. D.: The central control of sensory inflow. Proc. Roy. Soc. Med., London 51 (5), 531–535 (1958).Google Scholar
  10. Delfosse, N, Loubaton, P. “Adaptive blind separation of sources: a deflation approach”, Signal Processing. 45: 59–83. 1995.Google Scholar
  11. Edelman, G. M., W. Einar Gall, W. M. Cowan (eds.): Signal and Sense. Local and Global Order in Perceptual Maps. Wiley, New York 1990.Google Scholar
  12. Field, D. J.: “What is the goal of sensory coding?” Neural Computation, 6:559–601. 1994.CrossRefGoogle Scholar
  13. Hagbarth, K. E., D. J. B. Kerr: Central influences on spinal afferent conduction. J. Neurophysiol. 17 (3), 295–297 (1954).Google Scholar
  14. Hassler, R.: Interaction of reticular activating system for vigilance and the corticothalamic and pallidal systems for directing awareness and attention under striatal control. In: Buser et al. (eds.) 1978.Google Scholar
  15. Hunter, J. and Jasper, H.H. “Effects of thalamic stimulation in anaesthetized cats,” EEG Clin. Neurophysiol. 1: 305–315. 1949.Google Scholar
  16. Hyvarinen, A, Karhunen, J, Oja, E. Independent Component Analysis. John Wiley and Sons. 2001.Google Scholar
  17. James, W: Does ‘consciousness’ exist? Reprinted in: G. N. A. Vesey (ed) Body and mind: readings in philosophy. London: George Allen & Unwin, 1970, pp. 202–208. 1904.Google Scholar
  18. Koch, C: The quest for consciousness. A neurobiological approach. Roberts and Company Publishers. 2004.Google Scholar
  19. Kuffler, S.W: “Neurons in the retina: Organization, inhibition and excitatory problems”, Cold Spring Harbor Symp. Quant. Biol. 17: pp. 281–292. 1952.Google Scholar
  20. LeDoux, J. E.: Emotional networks in the brain. In: Lewis, M., J. M. Haviland (eds.): Handbook of Emotions. Guildford Press, New York 1993.Google Scholar
  21. Lee D. D. and Seung, S. “Learning the parts of objects by non-negative matrix factorization”. Nature, Vol 401. pp. 788–791, 1999.Google Scholar
  22. Lewicki, M. S. “Efficient coding of natural sounds”. Nature Neuroscience, vol. 5, 356–363, 2002.PubMedCrossRefGoogle Scholar
  23. Mangun, G. E., S. A. Hillyard, in: Scheibel, A. B., A. F. Wechsler (eds.): Neurobiology of Higher Cognitive Function. Guildford Press, New York 1990.Google Scholar
  24. Meric, C., L. Collet: Attention and otoacoustic emissions. Neuroscience and Behavioral Reviews 18 (2), 215–222 (1994).Google Scholar
  25. Newman, J., B. J. Baars: A neural attentional model for access to consciousness: a global workspace perspective. Conceptions in Neuroscience 4 (2) 255–290 (1993).Google Scholar
  26. Olshausen, B. A. Field, D. J. “Emergence of simple-cell receptive field properties by learning a sparse code for natural images”. Nature Vol. 381, 607–609, 1996.PubMedCrossRefGoogle Scholar
  27. Scheibel, A. B.: The brain stem reticular core and sensory function. In: Handbook of Physiology. The Nervous System, Vol. III, 1. American Physiological Society, Bethesda 1984.Google Scholar
  28. Scheibel, A. B., A. F. Wechsler (eds.): Neurobiology of Higher Cognitive Function. Guildford Press, New York 1990.Google Scholar
  29. Shannon, CE, “A mathematical theory of communication”, Bell System Technical Journal, Vol. 27, pp. 379–423, 1948.Google Scholar
  30. Valdes-Sosa, P., Sanchez-Bornot, JM., Lage-Castellanos A, Vega-Hernandez M, Bosch-Bayard J, Melie-Garcıa L and E Canales-Rodrıguez L, “Estimating brain functional connectivity with sparse multivariate autoregression” Philosophical Transactions of the Royal Society B. Theme Issue on Multimodal Brain Connectivity. (Eds) P. Valdes-Sosa, R. Kotter, K. Friston. in press.Google Scholar
  31. Zeki, S.: Functional specialization in the visual cortex: the generalisation of separate constructs and their multistage integration. In: Edelman, G. M., et al. 1990, pp. 85–130.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Federal University of MaranhãoMaranhãoBrazil

Personalised recommendations