Associative learning based on symmetric spike time dependent plasticity

Spike-timing-dependent-plasticity (STDP) is an important learning rule in organisms. In the application of neural computation, it is meaningful to apply symmetric STDP to associative learning. In this paper, an electronic synapse with symmetric STDP features was demonstrated. Meanwhile, taking Pavlov's experiment as an example, a model of neural network was built with this electronic synapse and successfully simulated the Pavlov's experiment, indicating the proposed symmetric STDP synaptic circuit can mimic the working principle of associative learning.