Strategies for the optimization of large scale networks of integrate and fire neurons

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

Volumn 2084 LNCS, Issue PART 1, 2001, Pages 117-125

  Sánchez Montañés, Manuel A ^a  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; NEURONS;

COMPUTATION TIME; CURRENT LIMITS; CURRENT TECHNOLOGY; INTEGRATE AND FIRES; INTEGRATE-AND-FIRE NEURONS; LARGE-SCALE NETWORK; MEAN FIELD APPROXIMATION; NETWORK ACTIVITIES;

COMPUTER SIMULATION; NEURAL NETWORKS;

EID: 84902180723     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (11)

1. Brettle, D., Niebur, E.: Detailed parallel simulation of a biological neuronal network. IEEE Comp. Sci. Eng. 1 (1994) 31-43.
2. Giugliano, M.: Synthesis of generalized algorithms for fast computation of synaptic conductances with markov kinetic models in large network simulations. Neural Computation 12 (2000) 903-31.
3. Koch, C., Segev, I. (eds.): Methods in Neuronal Modeling From Ions to Networks, 2nd edition. MIT Press: Cambridge, Massachusetts (1998).
4. Koch, C.: Biophysics of computation. New York: Oxford UP (1999).
5. Kohn, J., Worgotter, F.: Employing the Z-Transform to optimize the calculation of the synaptic conductance of NMDA and other synaptic channels in network simulations. Neural Computation 10 (1998) 1639-1651.
6. Lago-Fernandez, L.F., Sanchez-Montanes, M. A., Corbacho, F.: A biologically inspired visual system for an autonomous robot. Neurocomputing (2001) in press.
7. Lambert, J. D.: Computational methods in ordinary differential equations. New York: Wiley (1973).
8. Olshausen, B.: Discrete-time difference equations for simulating convolutions (Tech. Memo). Pasadena: California Institute of Technology (1990).
9. Oppenheim, A. V., Schafer, R. W.: Digital-signal processing. London: Prentice Hall International (1975).
10. Sanchez-Montanes, M. A., Konig, P., Verschure, P. F. M. J.: Learning in a neural network model in real time using real world stimuli. Neurocomputing (2001) in press.
11. Verschure, P.F.M.J.: Xmorph: A software tool for the synthesis and analysis of neural systems. Technical Report ETH-UZ, Institute of Neuroinformatics (1997).