Abstract
The idea that synaptic plasticity holds the key to the neural basis of learning and memory is now widely accepted in neuroscience. The precise mechanism of changes in synaptic strength has, however, remained elusive. Neurobiological research has led to the postulation of many models of plasticity, and among the most contemporary are spike-timing dependent plasticity (STDP) and long-term potentiation (LTP). The STDP model is based on the observation of single, distinct pairs of pre- and post- synaptic spikes, but it is less clear how it evolves dynamically under the input of long trains of spikes, which characterise normal brain activity. This research explores the emergent properties of a spiking artificial neural network which incorporates both STDP and LTP. Previous findings are replicated in most instances, and some interesting additional observations are made. These highlight the profound influence which initial conditions and synaptic input have on the evolution of synaptic weights.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abraham, W., et al.: Heterosynaptic metaplasticity in the hippocampus in vivo: A BCM-like modifiable threshold for LTP. PNAS 98(19), 10924–10929 (2001)
Bekkers, J.M., et al.: Origin of variability in quantal size in cultured hippocampal neurons and hippocampal slices. PNAS 87, 5359–5362 (1990)
Bi, G.-q., Poo, M.-m.: Synaptic modifications in cultured hippocampal neurons: de-pendence on spike timing, synaptic strength and post-synaptic cell type. Journal of Neuroscience 18, 10464–10472 (1998)
Bienenstock, E., Cooper, L., Munro, P.: Theory for the development of neuron selec-tivity: Orientation specificity and binocular interaction in the visual cortex. Journal of Neuroscience 2, 32–48 (1982)
Di Paolo, E.: Evolving spike-timing-dependent plasticity for single-trial learning in robots. Phil. Trans. R. Soc. London 361, 2299–2319 (2003)
Goldenberg, E., Garcowski, J., Beer, R.: May we have your attention: Analysis of a selective attention task. In: Proc. Eighth Int. Conf. Sim. Adap. Behaviour, pp. 49–56 (2004)
Hebb, D.: The Organisation of Behaviour: A Neuropsychological theory. Wiley, New York (1949)
Iglesias, J., Eriksson, J.L., Pardo, B., Tomassini, M., Villa, A.E.P.: Stimulus-Driven Unsupervised Synaptic Pruning in Large Neural Networks. In: De Gregorio, M., Di Maio, V., Frucci, M., Musio, C. (eds.) BVAI 2005. LNCS, vol. 3704, pp. 59–68. Springer, Heidelberg (2005)
Izhikevich, E., Desai, N.: Relating STDP to BCM. Letters to Neural Computation 15, 1511–1523 (2003)
Izhikevich, E.: Which model to use for Cortical spiking neurons? IEEE Transactions on Neural Networks 15(5), 1063–1070 (2004)
Izhikevich, E., Gally, J., Edelman, G.: Spike timing dynamics of neuronal groups. Cerebral Cortex 14, 933–944 (2004)
Lomo, T., Bliss, T.: Long-lasting potentiation of synaptic transmission in the dentate area of the anesthetized rabbit following stimulation of the perforant path. Journal Physioogy 232, 331–341 (1973)
Malenka, R., Nicoll, R.: Long-term potentiation – A decade of progress? Science 285, 1870–1874 (1999)
Miller, K.D., McKay, D.J.: The role of constraints in Hebbian learning. Neural Computation 6, 100–126 (1994)
Miller, K.D.: Synaptic economics: competition and co-operation in synaptic plasticity. Neuron 17, 371–374 (1996)
Roberts, P., Bell, C.: Spike timing dependent plasticity in biological systems. Biological Cybernetics 87, 392–403 (2002)
Song, S., Miller, K., Abbott, L.F.: Competitive Hebbian learning through spike timing dependent synaptic plasticity. Nature Neuroscience 3, 919–926 (2000)
van Rossum, M.C.W., Bi, G.Q., Turrigiano, G.G.: Stable Hebbian learning from spike timing dependent plasticity. Journal of Neuroscience 20(23), 8812–8821 (2000)
van Rossum, M.C.W., Turrigiano, G.G.: Correlation based learning from spike timing de-pendent plasticity. Neurocomputing 38-40, 409–415 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bush, D., Philippides, A., Husbands, P., O’Shea, M. (2006). Investigating STDP and LTP in a Spiking Neural Network. In: Nolfi, S., et al. From Animals to Animats 9. SAB 2006. Lecture Notes in Computer Science(), vol 4095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840541_27
Download citation
DOI: https://doi.org/10.1007/11840541_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38608-7
Online ISBN: 978-3-540-38615-5
eBook Packages: Computer ScienceComputer Science (R0)