  Spiking Neural Networks (SNN) have gained increasing attention for its low
power consumption. But training SNN is challenging. Liquid State Machine (LSM),
as a major type of Reservoir computing, has been widely recognized for its low
training cost among SNNs. The exploration of LSM topology for enhancing
performance often requires hyper-parameter search, which is both
resource-expensive and time-consuming. We explore the influence of input scale
reduction on LSM instead. There are two main reasons for studying input
reduction of LSM. One is that the input dimension of large images requires
efficient processing. Another one is that input exploration is generally more
economic than architecture search. To mitigate the difficulty in effectively
dealing with huge input spaces of LSM, and to find that whether input reduction
can enhance LSM performance, we explore several input patterns, namely
fullscale, scanline, chessboard, and patch. Several datasets have been used to
evaluate the performance of the proposed input patterns, including two spatio
image datasets and one spatio-temporal image database. The experimental results
show that the reduced input under chessboard pattern improves the accuracy by
up to 5%, and reduces execution time by up to 50% with up to 75\% less input
storage than the fullscale input pattern for LSM.
