Similar articles

Abstract: A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers, physical mnemonic layer and abstract thinking layer, which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness: (1) the reception process whereby cerebral subsystems group distributed signals into coherent object patterns; (2) the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and (3) the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns’ changes. Using this framework, various sorts of human actions can be explained, leading to a general approach for analyzing brain functions.

[1] Baars, B.J., 1988. A Cognitive Theory of Consciousness. Cambridge University Press, UK.

[2] Carpenter, G.A., Grossberg, S., 1987. ART 2: Stable self-organization of pattern recognition codes for analog input patterns. Appl. Opt., 26:4919-4930.

[3] Grossberg, S., Mingolla, E., Todorovic, D., 1989. A neural network architecture for preattentive vision. IEEE Transactions on Biomedical Engineering, 36:65-84.

[4] Kastner, S., Ungerleider, L.G., 2000. Mechanisms of visual attention in the human cortex. Annual Review of Neuroscience, 23:315-341.

[5] Klir, G.J., Folger, T.A., 1988. Fuzzy Sets, Uncertainty and Information. Prentice Hall.

[6] Kohonen, T., 1995. Self-Organizing Maps. Springer, Berlin, Heidelberg.

[7] Liu, C., Jansen, B.H., Boutros, N.N., 2002. Modeling of Auditory Evoked Potentials. Proceedings of the Second Joint EMBS/BMES Conference, p.234-235.

[8] Rakovic, D., 1992. Neural networks versus brainwaves: A model for dream_like states of consciousness. Engineering in Medicine and Biology Society, 14:2651-2652.

[9] Rakovic, D., 1997. Hierarchical Neural Networks and Brainwaves: Towards A Theory of Consciousness. Proceedings of ECPD Workshop in Brain & Consciousness, p.189-204.

[10] Rennie, C.J., Robinson, P.A., Wright, J.J., 2002. Unified neurophysical model of EEG spectra and evoked potentials. Biological Cybernetics, 86:457-471.

[11] Reynolds, J.H., Chelazzi, L., Desimone, R., 1999. Competitive mechanisms subserve attention in macaque areas V2 and V4. Journal of Neuroscience, 19:1736-1753.

[12] Taylor, J.G., 1994. The Relational Mind. Proceedings of From Perception to Action Conference, p.302-311.

[13] Taylor, J.G., 1999. Neural Networks for Consciousness: The Central Representation. Proceedings of the International Joint Conference on Neural Network, 1:91-96.

[14] Taylor, J.G., 2000. A General Framework for the Functions of the Brain. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Network, 1:35-40.

[15] Taylor, J.G., 2001. A control model of the movement of attention. Neural Networks, 15:309-326.

[16] Vitiello, G., 2003. Quantum dissipation and information: A route to consciousness modeling. NeuroQuantology, 2:266-279.

[17] Wlodzislaw, D., 1996. Computational Physics of the Mind. Computer Physics Communications 97, Elsevier Science, p.136-153.

[18] Yao, Y., Freeman, W.J., 1990. Model of biological pattern recognition with spatially chaotic dynamics. Neural Networks, 3:153-170.