Figure 1.

Weight-dependent learning rules: (a) A model of spike-timing-dependent plasticity (STDP). Synaptic strength change (Δw) depends on the relative spike timing between pre- and postsynaptic neurons (Δt). Spikes of Δt > 0 leads to long term potentiation (LTP), while spike of Δt < 0 leads to long-term depression (LTD). (b) Asymmetric learning rate (AR) model: Learning rates for LTP (red) and LTD (blue) are asymmetric for strong and weak synapses. (c) Symmetric learning rate (SR) model: Learning rates for LTP (red) and LTD (blue) are symmetric for strong and weak synapses. (d) Instability of the synapses (Δw²) for AR and SR models. (e,f) Weight density function for AR and SR was predicted from Boltzmann distribution (colored lines), and simulated using a single synapse model (histograms).