Spike-driven synaptic plasticity: Theory, simulation, VLSI implementation

Neural Computation

Volumn 12, Issue 10, 2000, Pages 2227-2258

  Fusi, Stefano ^a   Annunziato, Mario ^b   Badoni, Davide ^c   Salamon, Andrea ^d   Amit, Daniel J ^a,e  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

^b UNIVERSITY OF PISA   (Italy)

^c UNIVERSITY OF ROME TOR VERGATA   (Italy)

^d UNIVERSITÀ DI ROMA LA SAPIENZA   (Italy)

^e HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; COMPUTER; COMPUTER SIMULATION; ELECTRIC CONDUCTIVITY; LONG TERM POTENTIATION; NERVE CELL INHIBITION; NERVE CELL PLASTICITY; PHYSIOLOGY; PROBABILITY; STATISTICS; SYNAPSE;

ACTION POTENTIALS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; COMPUTERS; ELECTRIC CONDUCTIVITY; LONG-TERM POTENTIATION; MODELS, NEUROLOGICAL; NEURAL INHIBITION; NEURAL NETWORKS (COMPUTER); NEURONAL PLASTICITY; PROBABILITY; STOCHASTIC PROCESSES; SYNAPSES;

EID: 0034293117     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976600300014917     Document Type: Article

References (40)

1. Abbott, L. F., & Song, S. (1998). Temporally asymmetric hebbian learning, spike timing and neuronal response variability. In M. S. Kearns, S. Solla, & D. A. Cohn (Eds.), Advances in neural information processing systems, 11. Cambridge, MA: MIT Press.
2. Amit, D. J. (1989). Modeling brain function. Cambridge: Cambridge University Press.
3. Amit, D. J., & Brunel, N. (1997). Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cerebral Cortex, 7, 237.
4. Amit, D. J., Del Giudice, P., & Fusi, S. (1999). Apprendimento dinamico della memoria di lavoro: una realizzazione in elettronica analogica (in Italian). In Frontiere della vita 3, 599. Rome: Istituto della Enciclopedia Italiana.
5. Amit, D. J., & Fusi, S. (1994). Dynamic learning in neural networks with material synapses. Neural Computation, 6, 957.
6. Amit, D. J., Gutfreund, H., & Sompolinsky, H. (1987). Statistical mechanics of neural networks near saturation, Annals of Physics, 173, 30.
7. Badoni, D., Bertazzoni, S., Buglioni, S., Salina, G., Amit, D. J., & Fusi, S. (1995). Electronic implementation of a stochastic learning attractor neural network. NETWORK Computation in Neural Systems, 6, 125.
8. Badoni, D., Salamon, A., Salina, A., & Fusi, S., (2000). aVLSI implementation of a network with spiking neurons and stochastic learning. In preparation.
9. Bi, G.-q., & Poo, M.-m. (1999). Activity induced synaptic modifications in hippocampal culture: Dependence on spike timing, synaptic strength and cell type. J. Neurosci., 18, 10464-10472.
10. Bienenstock, E. L., Cooper, L. N., & Munro, P. W. (1982). Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex. Journal of Neuroscience, 2, 1, 32.
11. Bliss, T. V. P., & Collingridge, G. L. (1993). A synaptic model of memory: Long term potentiation in the hippocampus. Nature, 361, 31.
12. Brunel, N., Carusi, F., & Fusi, S. (1998). Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network. NETWORK Computation in Neural Systems, 9, 123.
13. Cox, D. R., & Miller, H. D. (1965). The theory of stochastic processes. London: Methuen.
14. Del Giudice, P., Fusi, S., Badoni, D., Dante, V., & Amit, D. J. (1998). Learning attractors in an asynchronous, stochastic electronic neural network. NETWORK Computation in Neural Systems, 9, 183.
15. Diorio, C., Hasler, P., Minch, B. A., & Mead, C. (1998). Floating-gate MOS synapse transistors. In T. S. Lande (Ed.), Neuromorphic systems engineering: Neural networks in silicon. Norwell, MA: Kluwer.
16. Elias, J., Northmore, D. P. M., & Westerman, W. (1997). An analog memory circuit for spiking silicon neurons. Neural Computation, 9, 419.
17. Fusi, S., Del Giudice, P., & Amit, D. J. (in press). Neurophysiology of a VLSI spiking neural network. In Proceedings of the IJCNN 2000. Available online at: http://jupiter.roma1.infn.it.
18. Fusi, S., & Mattia, M. (1999). Collective behavior of networks with linear (VLSI) integrate-and-fire neurons. Neural Computation, 11, 633.
19. Gerstein, G. L., & Mandelbrot, B. (1964). Random walk models for the spike activity of a single neuron. Biophysical Journal, 4, 41.
20. Grossberg, S. (1969). On learning and energy-entropy dependence in recurrent and non recurrent signed networks. Journal of Stat. Phys., 1, 319.
21. Hafliger, P., Mahowald, M., & Watts, L. (1996). A spike based learning neuron in analog VLSI. In M. Hozer, M. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems, 9 (p. 692). Cambridge, MA: MIT Press.
22. Hopfield, J. J. (1982). Neural networks and physical systems with emergent selective computational abilities. Proc. Naty Acad. Sci. USA, 79, 2554.
23. Markram, H., Lubke, J., Frotscher, M., & Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275, 213.
24. Mattia, M., & Del Giudice, P. (in press). Asynchronous simulation of large networks of spiking neurons and dynamical synapses. Neural Computation.
25. Mead, C. (1989). Analog VLSI and Neural System. Reading, MA: Addison-Wesley.
26. Meyer, P. L. (1965). Introductory probability and statistical applications. Reading, MA: Addison-Wesley.
27. Miyashita, Y. (1993). Inferior temporal cortex: Where visual perception meets memory. Ann. Rev. Neurosci., 16, 245.
28. Nadal, J. P., Toulouse, G., Changeux, J. P., & Dehaene, S. (1986). Networks of formal neurons and memory palimpsests. Lurophys. Lett., 1, 535.
29. Parisi, G. (1986). A memory which forgets. J. Phys, A: Math. Gen., 19, L617.
30. Petersen, C. C. H., Malenka, R. C., Nicoll, R. A., & Hopfield, J. J. (1998). All-or-none potentiation at CA3-CA1 synapses. Proc. Natl. Acad. Sci., 95, 4732.
31. Ricciardi, L. M. (1977). Diffusion processes and related topics on biology. Berlin: Springer-Verlag.
32. Sarpeshkar, R. (1998). Analog versus digital: Extrapolating from electronics to neurobiology. Neural Computation, 10, 1601.
33. Sejnowski, T. J. (1976). Storing covariance with nonlinearly interacting neurons. J. Math. Biology, 4, 303.
34. Senn, W., Tsodyks, M., & Markram, H. (in press). An algorithm for modifying neurotransmitter release probability based on pre-and post-synaptic spike timing. Neural Computation.
35. Softky, W. R., & Koch, C. (1992). Cortical cells should spike regularly but do not. Neural Computation, 4, 643.
36. Tsodyks, M. (1990). Associative memory in neural networks with binary synapses. Modern Physics Letters, B4, 713.
37. Tuckwell, H. C. (1988). Introduction to theoretical neurobiology (Vol. 2). Cambridge: Cambridge University Press.
38. van Vreeswijk, C. A., & Sompolinsky, H. (1997). Chaos in neural networks with balanced excitatory and inhibitory activity. Science, 274, 1724.
39. Willshaw, D. (1969). Non-holographic associative memory. Nature, 222, 960.
40. Zhang, L. I., Tao, H. W., Holt, C. E., H arris, W. A., & Poo, M. (1998). A critical window for cooperation and competition among developing retinotectal synapses. Nature, 395, 37.