Lifan Long

Sichuan University | SCU · Department of Computer Science and Technology

Spiking neural P (SNP) systems are a class of distributed and parallel neural-like computing models that are inspired by the mechanism of spiking neurons and are 3rd-generation neural networks. Chaotic time series forecasting is one of the most challenging problems for machine learning models. To address this challenge, we first propose a nonlinear...

Nonlinear spiking neural P (NSNP) systems are distributed parallel neural-like computing models that abstract the nonlinear spiking mechanisms of biological neurons. Echo state network (ESN) is a new type of recurrent neural network (RNN) that can overcome the disadvantages of traditional RNN. Inspired by the structure of ESN, this study proposes a...

Multivariate time series forecasting remains a challenging task because of its nonlinear, non-stationary, high-dimensional, and spatial–temporal characteristics, along with the dependence between variables. To address this limitation, we propose a novel method for multivariate time series forecasting based on nonlinear spiking neural P (NSNP) syste...

Nonlinear spiking neural P (NSNP) systems are a recently developed theoretical model, which is abstracted by nonlinear spiking mechanism of biological neurons. NSNP systems have a nonlinear structure and the potential to describe nonlinear dynamic systems. Based on NSNP systems, a novel time series forecasting approach is developed in this paper. D...

Spiking neural P (SNP) systems are a class of neural-like computing models, abstracted by the mechanism of spiking neurons. This article proposes a new variant of SNP systems, called gated spiking neural P (GSNP) systems, which are composed of gated neurons. Two gated mechanisms are introduced in the nonlinear spiking mechanism of GSNP systems, con...

Spiking neural P (SNP) systems are a class of neural-like membrane computing models that are abstracted by applying the mechanisms of spiking neurons. In SNP systems, each spiking neuron has three characteristics: (i) internal state, (ii) spike consumption, and (iii) spike generation. These three characteristics are used to form a parameterised non...