Global scaling of synaptic efficacy: Homeostasis in silicon synapses

Neurocomputing

Volumn 72, Issue 4-6, 2009, Pages 726-731

  Bartolozzi, Chiara ^a   Indiveri, Giacomo ^b  

^a ISTITUTO ITALIANO DI TECNOLOGIA   (Italy)

^b ETH ZURICH   (Switzerland)

Author keywords

aVLSI; Homeostasis; Neuromorphic; Spike based learning; Synapse; Synaptic scaling

Indexed keywords

AVLSI; HOMEOSTASIS; NEUROMORPHIC; SPIKE-BASED LEARNING; SYNAPSE; SYNAPTIC SCALING;

BODY FLUIDS; CONCURRENCY CONTROL; EDUCATION; MATHEMATICAL MODELS; NEURAL NETWORKS;

LEARNING ALGORITHMS;

SILICON;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER PROGRAM; HOMEOSTASIS; LEARNING; NERVE CELL PLASTICITY; PRIORITY JOURNAL; SPIKE; STATISTICAL ANALYSIS; SYNAPTIC EFFICACY; SYNAPTIC TRANSMISSION;

EID: 58149464971     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.05.016     Document Type: Article

References (22)

1. Mead C. Analog VLSI and Neural Systems (1989), Addison-Wesley, Reading, MA
2. Indiveri G., Chicca E., and Douglas R. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Transactions on Neural Networks 17 1 (2006) 211-221
3. Arthur J., and Boahen K. Learning in silicon: timing is everything. In: Weiss Y., SchF̈olkopf B., and Platt J. (Eds). Advances in Neural Information Processing Systems, vol. 18 (2006), MIT Press, Cambridge, MA
4. Serrano-Gotarredona R., Oster M., Lichtsteiner P., Linares-Barranco A., Paz-Vicente R., Gómez-Rodríguez F., Kolle Riis H., Delbrück T., Liu S.C., Zahnd S., Whatley A.M., Douglas R.J., Häfliger P., Jimenez-Moreno G., Civit A., Serrano-Gotarredona T., Acosta-Jiménez A., and Linares-Barranco B. AER building blocks for multi-layer multi-chip neuromorphic vision systems. In: Becker S., Thrun S., and Obermayer K. (Eds). Advances in Neural Information Processing Systems vol. 15 (2005), MIT Press, Cambridge
5. Choi T.Y.W., Merolla P.A., Arthur J.V., Boahen K.A., and Shi B.E. Neuromorphic implementation of orientation hypercolumns. IEEE Transactions on Circuits and Systems I 52 6 (2005) 1045-1060
6. Chicca E., Whatley A.M., Dante V., Lichtsteiner P., Delbrück T., Del Giudice P., Douglas R.J., and Indiveri G. A multi-chip pulse-based neuromorphic infrastructure and its application to a model of orientation selectivity. IEEE Transactions on Circuits and Systems I, Regular Papers 5 24 (2007) 981-993
7. Turrigiano G. Homeostatic plasticity in neural networks: the more things change, the more they stay the same. Trends in Neuroscience 22 5 (1999) 221-227
8. Marder E., and Prinz A. Modeling stability in neuron and network function: the role of activity in homeostasis. BioEssays 24 12 (2002) 1145-1154
9. Echegoyen J., Neu A., Graber K., and Soltesz I. Homeostatic plasticity studied using in vivo hippocampal activity-blockade: synaptic scaling, intrinsic plasticity and age-dependence. PLoS ONE 2 8 (2007)
10. Turrigiano G., Leslie K., Desai N., Rutherford L., and Nelson S. Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391 (1998) 892-896
11. Abbott L., and Nelson S. Synaptic plasticity: taming the beast. Nature Neuroscience 3 (2000) 1178-1183
12. Triesch J. Synergies between intrinsic and synaptic plasticity mechanisms. Neural Computation 19 (2007) 885-909
13. Bofill-i Petit A., and Murray A.F. Synchrony detection and amplification by silicon neurons with STDP synapses. IEEE Transactions on Neural Networks 15 5 (2004) 1296-1304
14. Riis H., and Hafliger P. Spike based learning with weak multi-level static memory. Proceedings of IEEE International Symposium on Circuits and Systems (2004), IEEE 393-396
15. G. Indiveri, S. Fusi, Spike-based learning in VLSI networks of integrate-and-fire neurons, in: Proceedings of the IEEE International Symposium on Circuits and Systems, ISCAS 2007, 2007, pp. 3371-3374.
16. Liu S., and Minch B. Silicon synaptic adaptation mechanisms for homeostasis and contrast gain control. IEEE Transactions on Neural Networks 13 6 (2002) 1497-1503
17. A. Destexhe, Z. Mainen, T. Sejnowski, Kinetic models of synaptic transmission, Methods in Neuronal Modelling, From Ions to Networks, The MIT Press, Cambridge, MA, 1998, pp. 1-25.
18. Liu S.-C., Kramer J., Indiveri G., Delbrück T., and Douglas R. Analog VLSI: Circuits and Principles (2002), MIT Press, Cambridge
19. Oster M., Whatley A.M., Liu S.-C., and Doublas R.J. A hardware/software framework for real-time spiking systems. In: Duch W., Kacprzyk J., Oja E., et al. (Eds). Artificial Neural Networks: Biological Inspirations-ICANN 2005: 15th International Conference, Warsaw, Poland, September 11-15, 2005, Proceedings, Part I, Lecture Notes in Computer Science, vol. 3696 (2005), Springer, Berlin 161-166
20. Mitra S., Fusi S., and Indiveri G. A VLSI spike-driven dynamic synapse which learns only when necessary. Proceedings of the IEEE International Symposium on Circuits and Systems (2006), IEEE 2777-2780
21. Turrigiano G., and Nelson S. Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience 5 (2004) 97-107
22. Desai N.S., Cudmore R.H., Nelson S.B., and Turrigiano G. Critical periods for experience-dependent synaptic scaling in visual cortex. Nature Neuroscience 5 8 (2002) 783-789