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A Comprehensive Survey on Learning Models of Spiking Neural Membrane Systems and Spiking Neural Networks

Core Concepts
Spiking neural networks (SNN) and spiking neural P systems (SNPS) are compared, focusing on learning algorithms and real-life applications.
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.
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].
"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."

Deeper Inquiries

How can the challenges of efficient training for multi-layer SNN and SNPS be addressed in future research?

Efficient training for multi-layer SNN and SNPS can be addressed in future research through several approaches: Development of Novel Algorithms: Researchers can focus on developing new algorithms that are specifically designed to train multi-layer SNN and SNPS efficiently. These algorithms should take into account the unique characteristics of spiking neural networks and spiking neural P systems to optimize the training process. Hybrid Learning Methods: Combining different learning methods, such as spike timing-dependent plasticity (STDP) with gradient descent-based algorithms, can potentially improve the training efficiency of multi-layer SNN and SNPS. By leveraging the strengths of different learning approaches, researchers can overcome the challenges associated with training these complex networks. Hardware Optimization: Exploring hardware implementations that are tailored to the requirements of SNN and SNPS training can significantly enhance efficiency. Customized hardware accelerators or neuromorphic chips designed specifically for spiking neural networks can speed up the training process and reduce energy consumption. Parallel Processing: Utilizing parallel processing techniques can distribute the computational load across multiple processors or cores, enabling faster training of multi-layer SNN and SNPS. This approach can help overcome the computational complexity associated with training deep spiking networks. Transfer Learning: Leveraging transfer learning techniques, where knowledge gained from training one network is transferred to another, can expedite the training process for multi-layer SNN and SNPS. By initializing the network with pre-trained weights or features, researchers can reduce the training time and improve performance.