  Liquid State Machine (LSM), also known as the recurrent version of Spiking
Neural Networks (SNN), has attracted great research interests thanks to its
high computational power, biological plausibility from the brain, simple
structure and low training complexity. By exploring the design space in network
architectures and parameters, recent works have demonstrated great potential
for improving the accuracy of LSM model with low complexity. However, these
works are based on manually-defined network architectures or predefined
parameters. Considering the diversity and uniqueness of brain structure, the
design of LSM model should be explored in the largest search space possible. In
this paper, we propose a Neural Architecture Search (NAS) based framework to
explore both architecture and parameter design space for automatic
dataset-oriented LSM model. To handle the exponentially-increased design space,
we adopt a three-step search for LSM, including multi-liquid architecture
search, variation on the number of neurons and parameters search such as
percentage connectivity and excitatory neuron ratio within each liquid.
Besides, we propose to use Simulated Annealing (SA) algorithm to implement the
three-step heuristic search. Three datasets, including image dataset of MNIST
and NMNIST and speech dataset of FSDD, are used to test the effectiveness of
our proposed framework. Simulation results show that our proposed framework can
produce the dataset-oriented optimal LSM models with high accuracy and low
complexity. The best classification accuracy on the three datasets is 93.2%,
92.5% and 84% respectively with only 1000 spiking neurons, and the network
connections can be averagely reduced by 61.4% compared with a single LSM.
Moreover, we find that the total quantity of neurons in optimal LSM models on
three datasets can be further reduced by 20% with only about 0.5% accuracy
loss.
