Hebbian Learning
Hebbian learning is a type of activity-dependent synaptic plasticity in which the connection between presynaptic and postsynaptic neurons is strengthened by coordinated activation of the two neurons [1]. Hebb proposed the learning principle in 1949, claiming that if a presynaptic neuron A is successful in triggering a postsynaptic neuron B repeatedly while itself (neuron A) is functioning, it would gradually grow more effective in activating neuron B. In both experimental and computational neuroscience, the notion is widely used and researched. In our project, we resort to the hebbian learning rule for updating RNN-G connectivity.