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New Threshold Updating Mechanism to Stabilize Activity of Hebbian Neuron in a Dynamic Stochastic ‘Multiple Synaptic’ Network, similar to Homeostatic Synaptic Plasticity Process

International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 36 - Number 3
Year of Publication: 2011
Subha Fernando
Koichi Yamada
Ashu Marasinghe

Subha Fernando, Koichi Yamada and Ashu Marasinghe. Article: New Threshold Updating Mechanism to Stabilize Activity of Hebbian Neuron in a Dynamic Stochastic 'Multiple Synaptic' Network, similar to Homeostatic Synaptic Plasticity Process. International Journal of Computer Applications 36(3):29-37, December 2011. Full text available. BibTeX

	author = {Subha Fernando and Koichi Yamada and Ashu Marasinghe},
	title = {Article: New Threshold Updating Mechanism to Stabilize Activity of Hebbian Neuron in a Dynamic Stochastic 'Multiple Synaptic' Network, similar to Homeostatic Synaptic Plasticity Process},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {36},
	number = {3},
	pages = {29-37},
	month = {December},
	note = {Full text available}


Unconstrained growth of synaptic activity and lack of references to synaptic depression in Hebb’s postulate have diminished its value as a learning algorithm. The existing synaptic scaling mechanisms such as weight normalization, threshold updating, and spike time dependent plasticity have greatly contributed to address these issues in Hebb’s postulate. However, all these mechanisms are based on the networks with single synaptic connection between neurons which process according to a central clock. This article presents a new threshold updating mechanism which behaves similar to the biological process called Homeostatic synaptic plasticity process and helps Hebb’s presynaptic neuron to stabilize its activity in a dynamic stochastic multiple synaptic network. Our modeled network had neurons that (1) processed signals in different time scales, (2) having dynamic and multiple synaptic connections between neurons. And these synapses were modeled as stochastic computational units which had computational power to calculate their own signal release probability as a function of signal arrival time to these synapses, and (3) neurons regulated their own local excitation through the threshold updating mechanism. Under these significant features of the modeled network, the examined neuron exhibited the behavior similar to the presynaptic neuron of Hebb’s postulate when its activity synchronized with the postsynaptic neuron’s activity. And it demonstrated the behavior similar to the Stent’s and Lisman’s anti-Hebbian postulates when its activity asynchronized with the activity of the postsynaptic neuron.


  • Hebb D.O. 1949, The Organization of Behavior. The first stage of the perception: Growth of the Assembly, New York: Wiley, pp. 60-78.
  • Miller K.D. and MacKey D.J, 1994. The Role of Constraints in Hebbian Learning, Neural Computation, Vol 6, pp.100-126.
  • Goodhill G.J. and Barrow H.G., 1993. The Role of Weight Normalization in Competitive Learning, Neural Computation, Vol 6, pp.255-269.
  • William H., 2005. Homeostatic plasticity improves continuous-time recurrent neural networks as a behavioral substrate, proceedings of the 3rd International Symposium on Adaptive Motion in Animals and Machines.
  • William H. and Noblea J., 2007. Homeostatic plasticity improves signal propagation in continuous time recurrent neural networks, Biosystem, Vol 87, No 2-3, pp. 252-259.
  • Bienenstock E.L., Cooper L.N., and Munro P.W., 1982. Theory for the development of neuron selectivity: Orientation Specificity and Binocular interaction in visual cortex, Journal of Neuroscience, Vol 2, pp. 32-48.
  • Song S., Miller K.D., and Abbott L.F., 2000. Competitive Hebbian learning through spike-timing dependent plasticity, Nature Neuroscience, Vol (9), pp. 919-925.
  • van Rossum M.C.W., Bi G.Q. and Turrigiano, G.G., 2000, Stable Hebbian Learning from Spike Timing Dependent Plasticity, Journal of Neuroscience, Vol 20(23), pp. 8812-8821.
  • Abbott L.F., and Gerstner W., 2003. Homeostasis and Learning through Spike-Timing Dependent Plasticity, Presented at Summer School in Neurophzsics, Les Houches.
  • Abraham W. C., 1997. Metaplasticity, a new vista across the field of synaptic plasticity, Prog. Neurobiol. Vol 52, pp.303–323.
  • Branco T. and Staras K., 2009.The probability of neurotransmitter release: variability and feedback control at single synapse, Nature Reviews Neuroscience, Vol 10, pp. 373-383.
  • Abbott L.F., and Nelson S.B., 2000. Synaptic plasticity: taming the beast, Nature Neuroscience, Vol 3, pp. 1178-1183.
  • Bi G. and Poo M., 1998. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type, Journal of Neuroscience. Vol 18(24), pp-10464-10472.
  • Lisman J. and Spruston N.. 2005. Postsynaptic depolarization requirements for LTP and LTD: a critique of spike timing- dependent plasticity, Neuroscience, Vol 8, pp. 839-841.
  • Butts D.A., and Kanold P.O., 2010. The applicability of spike dependent plasticity to development, Frontier in Synaptic Neuroscience, Vol 2(30).
  • Izhikevich E.M. and Desai N.S., 2003. Relating STDP to BCM. Neural Computation, Vol 15, pp 1511–1523.
  • Turrigiano G. G., 1999. Homeostatic plasticity in neural networks: the more things change, the more they stay the same, Trends in Neuroscience, Vol 22, pp. 221-227.
  • Turrigiano G. G., and S.B. Nelson, 2004. Homeostatic Plasticity in the Developing Nervous System, Nature Neuroscience, Vol 5, pp. 97-107.
  • Stent G.S., 1973. A Physiological Mechanism for Hebb’s Postulate of Learning, in Proc.Nat.Acad.Sci.USA, pp.997-1001.
  • Lisman J., 1989. A mechanism for the Hebb and anti-Hebb process underlying learning and memory, in Proc.Nat.Acad.Sci.USA, pp. 9574-9578.
  • Maass W, and Zador A.M. 1999. Dynamic stochastic synapses as computational units, Neural Computation, Vol 11(4), pp. 903-917.
  • Abbott L.F. and Regehr W.G., 2004. Synaptic computation, Nature, Vol 431, pp. 796-803.
  • Zucker R.S., 1989. Short-term synaptic plasticity, Annual Review of Neuroscience, Vol 12, pp. 13-31.
  • Maass W., and Zador A.M., 1999. Computing and Learning with dynamic synapses, Pulsed Neural Network, MIT, pp. 321-326.
  • Levy W. B., and Desmond N. L. 1985. The rules of elemental synaptic plasticity, In Synaptic Modification, Neuron Selectivity and Nervous System Organization, pp. 105–121.