BCM and Membrane Potential: Alternative Ways to Timing Dependent Plasticity
The Bienenstock-Cooper-Munroe (BCM) rule is one of the best-established learning formalisms for neural tissue. However, as it is based on pulse rates, it can not account for recent spike-based experimental protocols that have led to spike timing dependent plasticity (STDP) rules. At the same time, STDP is being challenged by experiments exhibiting more complex timing rules (e.g. triplets) as well as simultaneous rate- and timing dependent plasticity. We derive a formulation of the BCM rule which is based on the instantaneous postsynaptic membrane potential as well as the transmission profile of the presynaptic spike. While this rule is neither directly rate nor timing based, it can replicate BCM, conventional STDP and spike triplet experimental data, despite incorporating only two state variables. Moreover, these behaviors can be replicated with the same set of only four free parameters, avoiding the overfitting problem of more involved plasticity rules.
KeywordsSpike Train Presynaptic Activity Spike Timing Dependent Plasticity Postsynaptic Spike Presynaptic Spike
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- 1.Koch, C.: Biophysics of computation. Information processing in single neurons. In: Computational Neuroscience. Oxford University Press, Oxford (1999)Google Scholar
- 5.Bienenstock, E., Cooper, L., Munro, P.: Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. Journal of Neuroscience 2(1), 32–48 (1982)Google Scholar
- 8.Bi, G.Q., Poo, M.M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience 18(24), 10464–10472 (1998)Google Scholar
- 15.Lu, B., Yamada, W., Berger, T.: Asymmetric synaptic plasticity based on arbitrary pre- and postsynaptic timing spikes using finite state model. In: Proceedings of International Joint Conference on Neural Networks (2007)Google Scholar
- 17.Schreiter, J., Ramacher, U., Heittmann, A., Matolin, D., Schüffny, R.: Cellular pulse coupled neural network with adaptive weights for image segmentation and its VLSI implementation. In: Proceedings 16th International Symposium on Electronic Imaging: Science and Technology, vol. 5298, pp. 290–296 (2004)Google Scholar
- 18.Schemmel, J., Brüderle, D., Meier, K., Ostendorf, B.: Modeling synaptic plasticity within networks of highly accelerated I&F neurons. In: ISCAS 2007 (2007)Google Scholar