Reward-modulated spike-timing-dependent plasticity with a dynamic spike timing rule and inhibitory plasticity

The viability of spike-timing-dependent plasticity (STDP) to explain learning processes is controversial, although recent developments of reward-modulated STDP (RM-STDP) models provide a plausible substrate. However, evidence has also emerged to show that rewards themselves can modify the STDP rule. In this modeling study, we use a dynamic STDP rule to show that such modification can lead to network instability, and furthermore that inhibitory STDP may be able to balance networks to restore asynchronous, stochastic firing. We conclude that further experimental and modeling work is necessary to arrive at a biologically plausible mechanism of learning.