Core Concepts
Spiking neural networks (SNN) and spiking neural P systems (SNPS) are compared, focusing on learning algorithms and real-life applications.
Abstract
The content provides an in-depth comparison between SNN and SNPS, highlighting their architectures, functions, and applications.
It discusses various machine learning algorithms for SNN and SNPS, including supervised and unsupervised learning methods.
Specific models like LSTM-SNP, BiLSTM-SNP, and SDDC-Net are explored for time series forecasting, sentiment classification, and image segmentation.
The challenges of efficient training for multi-layer SNN and SNPS are addressed, along with potential applications in sentiment classification and time series analysis.
Stats
In the past few years, machine learning and deep learning frameworks have been introduced for SNPS models.
The first study of introducing Hebbian learning in the SNPS framework was conducted by Gutiérrez-Naranjo et al. [82].
Adaptive fuzzy SNPS with Widrow-Hoff learning algorithm was proposed for fault diagnosis [84].
SNPS with learning function (Hebbian) was used for recognizing digital English letters [85].
An associative memory network based on SNPS with white holes and Hebbian learning was developed for identification of digits [12].
Quotes
"Spiking neural networks (SNN) are a brain-inspired model of neural communication and computation using individual spikes to transfer information between individual abstract neurons."
"Spiking neural P systems (SNPS) are a variant of spiking neural networks introduced by Ionescu, Paun, Yokomori in 2006."