Jitter Noise Impact on Analog Spiking Neural Networks: STDP Limitations

Abstract

Accurate spike timing of neurons is essential for the temporal learning rules in Spiking Neural Networks (SNNs), such as the spike-timing dependent plasticity (STDP). Implementing these rules in analog SNNs presents a challenge as it would require a reliable spike timing of analog neurons. This paper investigates the impact of jitter noise on the spike timing of various analog neuron models, and highlights trade-offs in training neurons using spike timing. Post-layout simulation results reveal that noise-induced period jitter significantly affects spike occurrence time. Its values are three orders of magnitude higher than the precise spike timing required for an accurate synaptic weight updates. Moreover, the spike timing becomes a random variable with a σ /µ that exceeds 76%. This study shows that the spiking frequency is a more reliable measure of neuronal activity, as it achieves a σ /µ of only 0.2%.