Collective behavior of networks with linear (VLSI) integrate-and-fire neurons

Neural Computation

Volumn 11, Issue 3, 1999, Pages 633-652

  Fusi, Stefano ^a   Mattia, Maurizio ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL; ARTIFICIAL NEURAL NETWORK; CELL MEMBRANE POTENTIAL; NERVE CELL; NONLINEAR SYSTEM; PHYSIOLOGY; REVIEW; SENSORY NERVE; SIGNAL TRANSDUCTION; STATISTICAL MODEL;

AFFERENT PATHWAYS; ANIMALS; LINEAR MODELS; MEMBRANE POTENTIALS; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); NEURONS; NONLINEAR DYNAMICS; SIGNAL TRANSDUCTION;

EID: 0033110047     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976699300016601     Document Type: Article

References (22)

1. Amit, D. J., & Brunel, N. (1997a). Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cerebral Cortex, 7, 237-252.
2. Amit, D. J., & Brunel, N. (1997b). Dynamics of a recurrent network of spiking neurons before and following learning. Network, 8, 373-404.
3. Amit, D. J., Fusi, S., & Yakovlev, V. (1997). Paradigmatic working memory (attractor) cell in IT. Neural Computation, 9, 1071-1092.
4. Amit, D. J., & Mattia, M. (1998). Simulations and mean field analysis of a structured recurrent network of linear (VLSI) spiking neurons before and following learning. Unpublished manuscript. Rome: Instituto di Fisica, University of Rome La Sapienza.
5. Amit, D. J., & Tsodyks, M. V. (1991). Quantitative study of attractor neural network retrieving at low spike rates: I. Substrate - spikes, rates and neuronal gain. Network, 2, 259.
6. Annunziato, M. (1995). Hardware implementation of an attractor neural network with IF neurons and stochastic learning. Thesis, Università degli Studi di Roma La Sapienza.
7. Annunziato, M., Badoni, B., Fusi, S. & Salamon, A. (1998). Analog VLSI implementation of a spike driven stochastic dynamical synapse. In L. Niklasson, M. Boden, T. Ziemke (Eds.), Proceedings of the 8th International Conference on Artificial Neural Networks, Skovde, Sweden (Vol 1, pp. 475-480). Berlin: Springer-Verlag.
8. Brunel, N. & Sergi, S. (1999). Firing frequency of leaky integrate-and-fire neurons with synaptic dynamics, Journal of Theoretical Biology, in press.
9. Brunel, N., & Hakim, V. (1999). Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Computation, in press.
10. Bulsara, A. R., Elston, T. C., Doering, C. R., Lowen, S. B., & Lindenberg, K. (1996). Cooperative behaviour in periodically driven noisy integrate-fire models of neuronal dynamics. Physical Review E, 53, 3958-3969.
11. Cox, D. R., & Miller, H. D. (1965). The theory of stochastic processes. London: Metheuen.
12. Diorio, C., Hasler, P., Minch, B. A., & Mead, C. (1996). A single transistor silicon synapse. IEEE Trans. Electronic Devices, 43, 1972-1980.
13. Elias, J., Northmore, D. P. M., & Westerman, W. (1997). An analog memory circuit for spiking silicon neurons. Neural Computation, 9, 419-440.
14. Gerstein, G. L., & Mandelbrot, B. (1964). Random walk models for the spike activity of a single neuron. Biophysical Journal, 4, 41-68.
15. Knight, B., Manin, D., & Sirovich, L. (1996). Dynamical models of interacting neuron populations in visual cortex. In E. C. Gerf (Ed.), Symposium on Robotics and Cybernetics; Computational Engineering in System Applications. Liue, France: Cite Scientifique.
16. Mead, C. (1989). Analog VLSI and neural system. Reading, MA: Addison-Wesley.
17. Miyashita, Y., & Chang, H. S. (1988). Neuronal correlate of pictorial short-term memory in the primate temporal cortex. Nature, 331, 68.
18. Tuckwell, H. C. (1988). Introduction to theoretical neurobiology (Vol. 2). Cambridge: Cambridge University Press.
19. Usher, M., Stemmler, M., Koch, C., & Olami, Z. (1994). Network amplification of local fluctuations causes high spike rate variability, fractal firing patterns and oscillatory local field potentials. Neural Computation, 6, 795.
20. van Vreeswijk, C. A., & Hasselmo, M. E. (1999). Self-sustained memory states in a simple model with excitatory and inhibitory neurons. Biol. Cybern., in press.
21. van Vreeswijk, C. A., & Sompolinsky, H. (1996). Chaos in neural networks with balanced excitatory and inhibitory activity. Science, 274, 1724-1726.
22. Wilson, F. A. W., Scalaidhe, S. P. O., & Goldman-Rakic, P. S. (1993). Dissociation of object and spatial processing domains in primate pre-frontal cortex. Science, 260, 1955.