Imposing biological constraints onto an abstract neocortical attractor network model

Neural Computation

Volumn 19, Issue 7, 2007, Pages 1871-1896

  Johansson, Christopher ^a   Lansner, Anders ^b  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

^b STOCKHOLM UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; CYTOLOGY; HUMAN; NEOCORTEX; NERVE CELL; NERVE TRACT; NONLINEAR SYSTEM; PHYSIOLOGY; PROBABILITY; SYNAPSE;

ANIMALS; HUMANS; NEOCORTEX; NEURAL NETWORKS (COMPUTER); NEURAL PATHWAYS; NEURONS; NONLINEAR DYNAMICS; PROBABILITY; SYNAPSES;

EID: 34447262844     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2007.19.7.1871     Document Type: Article

References (55)

1. Amirikian, B., & Georgopoulos, A. P. (2003). Modular organization of directionally tuned cells in the motor cortex: Is there a short-range order? Proc. Natl Acad. Sci., 100, 12474-12479.
2. Attwell, D., & Laughlin, S. B. (2001). An energy budget for signaling in the grey matter of the brain, J. Cereb. Blo. Flo. Met., 21, 1133-1145.
3. Beaulieu, C., & Colonnier, M. (1989). Number and size of neurons and synapses in the motor cortex of cats raised in different environmental complexities. J. Comp. Neurol., 289, 178-187.
4. Bohland, J. W., & Minai, A. A. (2001). Efficient associative memory using small-world architecture. Neurocomputing, 38-40, 489-496.
5. Bolle, D., Cools, R., Dupont, P., & Huyghebaert, J. (1993). Mean-field theory for the Q-state Potts-glass neural network with biased patterns. J. Phys. A: Math. Gen., 26, 549-562.
6. Bosking, W. H., Zhang, Y., Schofield, B., & Fitzpatrick, D. (1997). Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. J.Neurosci., 17, 2112-2127.
7. Braitenberg, V., & Schuz, A. (1998). Cortex: Statistics and geometry of neuronal connectivity. New York: Springer-Verlag.
8. Buhmann, J., Divko, R., & Schulten, K. (1989). Associative memory with high information content. Phys. Rev. A, 39, 2689-2692.
9. Buxhoeveden, D. P., & Casanova, M. F. (2002a). The minicolumn and evolution of the brain. Brain, Behavior and Evolution, 60, 125-151.
10. Buxhoeveden, D. P., & Casanova, M. F. (2002b). The minicolumn hypothesis in neuroscience. Brain, 125, 935-951.
11. Cheng, K., Waggoner, R. A., & Tanaka, K. (2001). Human ocular dominance columns as revealed by high-field functional magnetic resonance imaging. Neuron, 32, 359-374.
12. Chklovskii, D. B., Mel, B. W., & Svoboda, K. (2004). Cortical rewiring and information storage. Nature, 431, 782-788.
13. DeFelipe, J., Conley, M., & Jones, E. G. (1986). Long-range focal collateralization of axons arising from corticocortical cells in monkey sensory-motor cortex. J. Neurosci., 6, 3749-3766.
14. Destexhe, A., & Paré, D. (1999). Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. J. Neurophysiol., 81, 1531-1547.
15. Fransén, E., & Lansner, A. (1998). A model of cortical associative memory based on a horizontal network of connected columns. Network: Comput. in Neural Systems, 9, 235-264.
16. Fulvi Mari, C. (2004). Extremely dilute modular neuronal networks: Neocortical memory retrieval dynamics. J. Comp. Neursci., 17, 57-79.
17. Fulvi Mari, C., & Treves, A. (1998). Modeling neocortical areas with a modular neural network. BioSystems, 48, 47-55.
18. Gilbert, C. D., & Wiesel, T. N. (1989). Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. J. Neurosci., 9, 2432-2442.
19. Goldman, P. S., & Nauta, W. J. H. (1977). Columnar distribution of cortico-cortical fibers in the frontal association, limbic, and motor cortex of the developing rhesus monkey. Brain Res., 122, 393-413.
20. Hellwig, B. (2000). A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex. Biol. Cybern., 82, 111-121.
21. Holmgren, C., Harkany, T., Svennenfors, B., & Zilberter, Y. (2003). Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. Journal of Physiology, 551, 139-153.
22. Hubel, D. H., & Wiesel, T. N. (1977). Functional architecture of macaque monkey visual cortex. Proc. R. Soc. Lond. B., 198, 1-59.
23. Johansson, C., & Lansner, A. (2004). Towards cortex sized artificial nervous systems. In M. G. Negoita, R. J. Howlett, & L. C. Jain (Eds.), Proc. of Knowledge-Based Intelligent Information and Engineering Systems (8th ed.). New York: Springer.
24. Johansson, C., & Lansner, A. (2007). Towards cortex sized artificial neural systems. Neural Networks, 20, 48-61.
25. Johansson, C., Rehn, M., & Lansner, A. (2006). Attractor neural networks with patchy connectivity. Neurocomputing, 69, 627-633.
26. Kanter, I. (1988). Potts-glass models of neural networks. Phys. Rev. A, 37, 2739-2742.
27. Kropff, E., & Treves, A. (2005). The storage capacity of Potts models for semantic memory retrieval. JSTAT, P08010.
28. Lansner, A., Fransén, E., & Sandberg, A. (2003). Cell assembly dynamics in detailed and abstract attractor models of cortical associative memory. Theory in Biosci., 122, 19-36.
29. Lansner, A., & Holst, A. (1996). A higher order Bayesian neural network with spiking units. Int. J. Neural Systems, 7, 115-128.
30. Lennie, P. (2003). The cost of cortical computation. Current Biology, 13, 493-497.
31. Little, W. A., & Shaw, G. L. (1975). A statistical theory of short and long term memory. Behavioral Biology, 14, 115-133.
32. Lundqvist, M., Rehn, M., Djurfeldt, M., & Lansner, A. (2006). Attractor dynamics in a modular network model of the neocortex. Network: Comput. in Neural Systems, 17, 253-276.
33. Lundqvist, M., Rehn, M., & Lansner, A. (2006). Attractor dynamics in a modular network model of the cerebral cortex. Neurocomputing, 69, 1155-1159.
34. Markram, H. (2006). The Blue Brain Project. Nature Rev. Neurosci., 7, 153-160.
35. Masuda, N., & Aihara, K. (2004). Global and local synchrony of coupled neurons in small-world networks. Biol. Cybern., 90, 302-309.
36. McGraw, P. N., & Menzinger, M. (2003). Topology and computational performance of attractor neural networks. Phys. Rev. E, 68, 047102.
37. Montgomery, J. M., & Madison, D. V. (2004). Discrete synaptic states define a major mechanism of synapse plasticity. Trends in Neurosci., 27, 744-750.
38. Morelli, L. G., Abramson, G., & Kuperman, M. N. (2004). Associative memory on a small-world neural network. Eur. Phys. J. B, 38, 495-500.
39. Mountcastle, V. B. (1997). The columnar organization of the neocortex. Brain, 120, 701-722.
40. O'Kane, D., & Treves, A. (1992a). Short- and long-range connections in autoassociative memory. Journal of Physics A, 25, 5055-5069.
41. O'Kane, D., & Treves, A. (1992b). Why the simplest notion of neocortex as an autoassociative memory would not work. Network: Comput. in Neural Systems, 3, 379-384.
42. Pakkenberg, B., Pelvig, D., Marner, L., Bundgaard, M. J., Gundersen, H. J., Nyengaard, J. R., & Regeur, L. (2003). Aging and the human neocortex. Experimental Gerontology, 38, 95-99.
43. Palm, G. (1980). On associative memory. Biol. Cybern., 36, 19-31.
44. Palm, G. (1981). Towards a theory of cell assemblies. Biol. Cybern., 39, 181-194.
45. Rakic, P. (1995). A small step for the cell, a giant leap for mankind: A hypothesis of neocortical expansion during evolution. Trends in Neurosci., 18, 383-388.
46. Rolls, E. T., & Treves, A. (1998). Neural networks and brain function. New York: Oxford University Press.
47. Roudi, Y., & Treves, A. (2004). An associative network with spatially organized connectivity. JSTAT, P07010.
48. Roudi, Y., & Treves, A. (2006). Localised activity profiles and storage capacity of rate-based autoassociative networks. Phys. Rev. E, 73, 061904.
49. Scannell, J. W., Blakemore, C., & Young, M. P. (1995). Analysis of connectivity in the cat cerebral cortex. Journal of Neuroscience, 15, 1463-1483.
50. Stepanyants, A., & Chklovskii, D. B. (2005). Neurogeometry and potential synaptic connectivity. Trends in Neurosci., 28, 387-394.
51. Stepanyants, A., Hof, P. R., & Chklovskii, D. B. (2002). Geometry and structural plasticity of synaptic connectivity. Neuron, 34, 275-288.
52. Treves, A. (2005). Frontal latching networks: A possible neural basis for infinite recursion. Cognitive Neuropsychology, 22, 276-291.
53. Tsodyks, M. V., & Feigelman, M. V. (1988). The enhanced storage capacity in neural networks with low activity level. Europhysics Letters, 6, 101-105.
54. Tsunoda, K., Yamane, Y., Nishizaki, M., & Tanifuji, M. (2001). Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns. Nature Neurosci., 4, 832-838.
55. Willshaw, D. J., Buneman, O. P., & Longuet-Higgins, H. C. (1969). Non-holographic associative memory. Nature, 222, 960-962.