Notes
A collection of fragments of understanding in the pursuit of deeper questions.
Contents
- Computational Vision
- Introduction to Data Science
- Translational Neuromodeling
- Deep Learning in Artificial & Biological Neuronal Networks
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Triplet Rule (Pfister & Gerstner)
The Triplet Rule
Papers: "Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity" & "A Triplet Spike-Timing-Dependent Plasticity Model Generalizes the BCM rule to Higher-Order Spatiotemporal Correlations".
The triplet rule extends the classical Hebbian STDP idea. Instead of only looking at single presynaptic and postsynaptic spikes, multiple recent spikes within a specified time window are considered. As shown above in the figure, LTD generally occurs when a presynaptic spike occurs just after a postsynaptic spike, even if (as is A3^-^) another presynaptic spike preceded the postsynaptic spike. Analogously, LTP tends to occur when a postsynaptic spike preceded the presynaptic spike, even if (as in A3^+^) another postsynaptic spike preceded the presynaptic spike. In this fashion, the consideration of the third spikes within the time window can lead to different outcomes. In both the triplet and BCM rules, above some threshold as the spiking frequency increases, the weight updates also increase (and are positive). The fundamental differences between these two rules are unclear.
Note that if we set A3+ = 0 and A3- = 0, the model becomes a classical pair-based STDP model (BCM).