Previous research efforts endeavored to understand the functionality of STDP's learning window in spiking neural networks (SNNs).  In this study, we investigate the interaction among different encoding/decoding schemes, STDP learning windows and normalization rules for the SNN classifier, trained and tested on MNIST, NIST and ETH80-Contour datasets.  Finally, normalization of output weights is shown to improve the performance of a first-spike decoding classifier, which reveals the importance of weight normalization to SNN.
SNN
The impact of encoding-decoding schemes and weight normalization in spiking neural networks.Spike-timing Dependent Plasticity (STDP) is a learning mechanism that can capture causal relationships between events.  STDP is considered a foundational element of memory and learning in biological neural networks.  Previous research efforts endeavored to understand the functionality of STDP's learning window in spiking neural networks (SNNs).  In this study, we investigate the interaction among different encoding/decoding schemes, STDP learning windows and normalization rules for the SNN classifier, trained and tested on MNIST, NIST and ETH80-Contour datasets.  The results show that when no normalization rules are applied, classical STDP typically achieves the best performance. 
STDP
In this study, we investigate the interaction among different encoding/decoding schemes, STDP learning windows and normalization rules for the SNN classifier, trained and tested on MNIST, NIST and ETH80-Contour datasets. 
MNIST
In this study, we investigate the interaction among different encoding/decoding schemes, STDP learning windows and normalization rules for the SNN classifier, trained and tested on MNIST, NIST and ETH80-Contour datasets. 
NIST
In this study, we investigate the interaction among different encoding/decoding schemes, STDP learning windows and normalization rules for the SNN classifier, trained and tested on MNIST, NIST and ETH80-Contour datasets. 
ETH80-Contour datasets