Sensitivity Analysis for additive STDP rule
This addresses the robustness of synaptic learning in neuroscience, but it is incremental as it focuses on sensitivity analysis of an existing rule.
The paper investigates how small variations in spike timing cause significant changes in synaptic weight evolution under the additive STDP rule, highlighting its sensitivity in biologically plausible spiking regimes.
Spike Timing Dependent Plasticity (STDP) is a Hebbian like synaptic learning rule. The basis of STDP has strong experimental evidences and it depends on precise input and output spike timings. In this paper we show that under biologically plausible spiking regime, slight variability in the spike timing leads to drastically different evolution of synaptic weights when its dynamics are governed by the additive STDP rule.