Towards Cortex Sized Artificial Nervous Systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3213, Issue , 2004, Pages 959-966

  Johansson, Christopher ^a   Lansner, Anders ^a  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; MAMMALS;

ARCHITECTURAL STRUCTURE; BAYESIAN; COMPUTATIONAL DEMANDS; CORTICAL STRUCTURES; REAL-TIME OPERATION;

KNOWLEDGE BASED SYSTEMS;

EID: 35048860860     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30132-5_129     Document Type: Article

References (31)

1. Lansner, A., E. Fransén, and A. Sandberg, Cell assembly dynamics in detailed and abstract attractor models of cortical associative memory. Theory in Biosciences, 2003. 122: p. 19-36.
2. Fransén, E. and A. Lansner, A model of cortical associative memory based on a horizontal network of connected columns. Network: Comp. in Neural Systems, 1998. 9(2): p. 235-264.
3. Rolls, E.T. and A. Treves, Neural Networks and Brain Function. 1998, New York: Oxford University Press.
4. Palm, G., Neural Assemblies: An Alternative Approach to Artificial Intelligence. Studies of Brain Function, ed. V. Braitenberg. Vol. 7. 1982, New York: Springer-Verlag.
5. Cossart, R., D. Aronov, and R. Yuste, Attractor dynamics of network UP states in the neocortex. Nature, 2003. 423(6937): p. 283-288.
6. Shu, Y., A. Hasenstaub, and D.A. Mccormick, Turning on and off recurrent balanced cortical activity. Nature, 2003.423(6937): p. 288-293.
7. Koch, C., Biophysics of Computation: Information Processing in Single Neurons, ed. M. Stryker. 1999, New York: Oxford University Press.
8. Fuster, J.M., Memory in the Cerebral Cortex. 1995.
9. Rockel, A.J., R.W. Hiorns, and T.P.S. Powell, The Basic Uniformity in Structure of the Neocortex. Brain, 1980. 103: p. 221-244.
10. Braitenberg, V. and A. Schuz, Cortex: Statistics and Geometry of Neuronal Connectivity. 1998, New York: Springer Verlag.
11. Hofman, M.A., Size and Shape of the Cerebral Cortex in Mammals: I. The Cortical Surface. Brain Behav. Evol., 1985. 27: p. 28-40.
12. Beaulieu, C., et al., Quantitative Distribution of GABA-immunopositive and - immunonegative Neurons and Synapses in the Monkey Striate Cortex (Area 17). Cerebral Cortex, 1992. 2(4): p. 295-309.
13. Beaulieu, C. and M. Colonnier, Number and Size of Neurons and Synapses in the Motor Cortex of Cats Raised in Different Environmental Complexities. J. Comp. Neurol., 1989. 289: p. 178-187.
14. Pakkenberg, B., et al., Aging and the human neocortex. Exper. Gerontol., 2003. 38: p. 95-99.
15. Dombrowski, S., C. Hilgetag, and H. Barbas, Quantitative Architecture Distinguishes Prefrontal Cortical Systems in the Rhesus Monkey. Cer. Cortex, 2001.11(10): p. 975-988.
16. Thomson, A.M. and A.P. Bannister, Interlaminar Connections in the Neocortex. Cerebral Cortex, 2003.13(1): p. 5-14.
17. Mountcastle, V.B., The columnar organization of the ncocortex. Brain, 1997. 120: p. 701-722.
18. Buxhoeveden, D.P. and M.F. Casanova, The minicolumn hypothesis in neuroscience. Brain, 2002.125(5): p. 935-951.
19. Buxhoeveden, D., et al., Quantitative analysis of cell columns in the cerebral cortex. Journal of neuroscience methods, 2000. 97: p. 7-17.
20. Mountcastle, V.B., Modality and Topographic Properties of Single Neurons of Cat's Somatic Sensory Cortex. Journal of neurophysiology, 1957. 20: p. 408-434.
21. Hubel, D.H. and T.N. Wiesel, Functional architecture of macaque monkey visual cortex. Proc. R. Soc. Lond. B., 1977. 198: p. 1-59.
22. Leise, E.M., Modular construction of nervous systems: a basic principle of design for invertebrates and vertebrates. Brain Research Reviews, 1990.15: p. 1-23.
23. Johansson, C. and A. Lansner. Towards Cortex Sized Attractor ANN. in Bio-ADIT04. 2004. Lausanne, Switzerland.
24. Cürüklü, B. and A. Lansner. An Abstract Model of a Cortical Hypercolumn. in In Proc. of the 9th International Conference on Neural Information Processing. 2002. Singapore: IEEE.
25. Barabási, A.-L. and R. Albert, Emergence of Scaling in Random Networks. Science, 1999. 286(5439): p. 509-512.
26. Sandberg, A., et al., A Bayesian attractor network with incremental learning. Network: Computation in Neural Systems, 2002.13(2): p. 179-194.
27. Johansson, C. and A. Lansner, BCPNN Implemented with Fixed-Point Arithmetic, TRITANA-P0403. 2004, Nada, KTH: Stockholm.
28. Bailey, J. and D. Hammerstrom. Why VLSI Implementations of Associative VLCNs Require Connection Multiplexing, in Proc. of Internat. Conf. on Neural Networks. 1988. San Diego.
29. Deiss, S.R., R.J. Douglas, and A.M. Whatley, A Pulse-Coded Communication Infrastructure for Neuromorphic Systems, in Pulsed Neural Networks, W. Maass and C.M. Bishop, Editors. 1999, MIT Press, p. 157-178.
30. Jonsson, J., Pilot Study of a Parallel Implementation of a Bayesian Confidence Propagating Neural Network, TRITA-NA-E03158. 2003, Nada: Stockholm.
31. Johansson, C. and A. Lansner, Mapping of the BCPNN onto Cluster Computers, TRITANA-P0305. 2003, Nada, KTH: Stockholm.