On the phase-space dynamics of systems of spiking neurons. I: Model and experiments

Neural Computation

Volumn 13, Issue 1, 2001, Pages 161-193

  Banerjee, Arunava ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ARTICLE; BIOLOGICAL MODEL; CELL MEMBRANE POTENTIAL; COMPUTER SIMULATION; CYTOLOGY; DIFFERENTIAL THRESHOLD; NEOCORTEX; NERVE CELL; NERVE CONDUCTION; PHYSIOLOGY;

ACTION POTENTIALS; COMPUTER SIMULATION; DIFFERENTIAL THRESHOLD; MEMBRANE POTENTIALS; MODELS, NEUROLOGICAL; NEOCORTEX; NEURONS; REFRACTORY PERIOD, ELECTROPHYSIOLOGICAL;

EID: 0035227738     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976601300014673     Document Type: Article

References (33)

1. Aertsen, A., & Braitenberg, V. (1992). Information processing in the cortex: Experiments and theory. Berlin: Springer-Verlag.
2. Amit, D. J., Gutfreund, H., & Sompolinsky, H. (1987). Information storage in neural networks with low levels of activity. Phys. Rev. A, 35(5), 2293-2303.
3. Babloyantz, A., Salazar, J. M., & Nicolis, C. (1985). Evidence of chaotic dynamics of brain activity during the sleep cycle. Phys. Lett. A, 111, 152-156.
4. Bair, W., & Koch, C. (1996). Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey. Neural Comp., 8, 1185-1202.
5. Bair, W., Koch, C., Newsome, W., & Britten, K. (1994). Reliable temporal modulation in cortical spike trains in the awake monkey. In Proceedings of the Symposium on Dynamics of Neural Processing.
6. Basar, E. (1990). Chaos in brain function. Berlin: Springer-Verlag,.
7. Bernander, O., Douglas, R. J., & Koch, C. (1992). A model of regular-firing cortical pyramidal neurons (CNS Memo 16). Pasadena: California Institute of Technology.
8. Bower, J. M., & Beeman, D. (1995). The book of GENESIS: Exploring realistic neural models with the GEneral NEural Simulation System. New York: TELOS/Springer-Verlag.
9. Braitenberg, V. (1978). Cortical architectonics: General and area. In M. A. B. Brazier & H. Petsche (Eds.), Architectonics of the cerebral cortex (pp. 443-465). New York: Raven Press.
10. Braitenberg, V., & Schüz, A. (1991). Anatomy of the cortex: Statistics and geometry. Berlin: Springer-Verlag.
11. Burns, B., & Webb, A. C. (1976). The spontaneous activity of neurones in the cat's cerebral cortex. Proc. Royal Soc. London (Biol.), 194, 211-223.
12. Cohn, A. (1922). Uber die anzahl der wurzeln einer algebraischen gleichung in einem kreise. Math. Zeit, 14, 110-148.
13. Coolidge, J. L. (1908). The continuity of the roots of an algebraic equation. Annals of Math. (series 2), 9, 116-118.
14. Erdi, P. (1996). Levels, models, and brain activities: Neurodynamics is pluralistic (open peer commentary). Behav. and Brain Sci., 19, 296-297.
15. Freeman, W. J. (1991). Predictions on neocortical dynamics derived from studies in paleocortex. In E. Basar and T. H. Bullock (Eds.), Induced rhythms of the brain (pp. 183-199). Cambridge, MA: Birkhauser Boston.
16. Hines, M. (1993). NEURON - A program for simulation of nerve equations. In F. Eckman (Ed.), Neural systems: Analysis and modeling (pp. 127-136). Norwell, MA: Kluwer.
17. Hopfield, J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U.S.A., 79, 2554-2558.
18. Hornik, K., Stinhcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359-366.
19. Krüger, J. (1991a). Neuronal cooperativity. Berlin: Springer-Verlag.
20. Krüger, J. (1991b). Spike train correlation on slow time scales in monkey visual cortex. In J. Krüger (Ed.), Neuronal cooperativity. Berlin: Springer-Verlag.
21. MacGregor, R. J., & Lewis, E. R. (1977). Neural modeling. New York: Plenum Press.
22. Marden, M. (1948). The number of zeros of a polynomial in a circle. Proc. Natl. Acad. Sci. U.S.A., 34, 15-17.
23. Nunez, P. L. (1989). Generation of human EEG by a combination of long and short range neocortical interactions. Brain Topography, 1, 199-215.
24. Omlin, C. W., & Giles, C. L. (1996). Constructing deterministic finite-state automata in recurrent neural networks. J. ACM, 43, 937-972.
25. Peters, A., & Proskauer, C. C. (1980). Synaptic relationship s between a multipolar stellate cell and a pyramidal neuron in the rat visual cortex: A combined golgi-electron microscope study. J. Neurocytol, 9, 163-183.
26. Peters, A., & Regidor, J. (1981). A reassessment of the forms of nonpyramidal neurons in area 17 of cat visual cortex. J. Comp. Neurol., 203, 685-716.
27. Röschke, J., & Basar, E. (1989). Correlation dimensions in various parts of cat and human brain in different states. In E. Basar and T. H. Bullock (Eds.), Brain dynamics (pp. 131-148). Berlin: Springer-Verlag.
28. Schüz, A. (1992). Randomness and constraints in the cortical neuropil. In A. Aertsen and V. Braitenberg (Eds.), Information processing in the cortex (pp. 3-21). Berlin: Springer-Verlag.
29. Shepherd, G. M. (1998). The synaptic organization of the brain. New York: Oxford University Press.
30. Siegelmann, H. T., & Sontag, E. D. (1992). On the computational power of neuralnets. In Proceedings of the Fifth ACM Workshop on Computational Learning Theory (Pittsburgh, PA).
31. Softky, W. R., & Koch, C. (1993). The highly irregular firing of cortical-cells is inconsistent with temporal integration of random EPSPs. J. Neurosci., 13, 334-350.
32. Strehler, B. L., & Lestienne, R. (1986). Evidence on precise time-coded symbols and memory of patterns in monkey cortical neuronal spike trains. Proc. Natl. Acad. Sci. U.S.A., 83, 9812-9816.
33. Wright, J. J. (1990). Reticular activation and the dynamics of neuronal networks. Biol. Cybern., 62, 289-298.