EVOLUTIONARY NEURAL ARCHITECTURE DESIGN OF LIQUID STATE MACHINE FOR IMAGE CLASSIFICATION

As a recurrent spiking neural network, liquid state machine (LSM) has attracted more and more attention in neuromorphic computing due to its biological plausibility, computation power, and hardware implementation. However, the neural architecture of LSM, such as hidden neuron number, synaptic density, percentage connectivity, and connection state, has significant impact on its model performance. Manually defining a neural architecture will be ineffective and laborious in most cases. Therefore, based on a state-of-the-art differential evolution algorithm, an evolutionary neural architecture design methodology is proposed to automatically build suitable model topologies for the LSM in this study, without any prior knowledge. The effectiveness of the proposed method has been validated on commonly-used image classification tasks.