Biological brains have a natural capacity for resolving certain classification tasks. Studies on biologically plausible spiking neurons, architectures and mechanisms of artificial neural systems that closely match biological observations while giving high classification performance are gaining momentum. Spiking neural P systems (SN P systems) are a class of membrane computing models and third-generation neural networks that are based on the behavior of biological neural cells and have been used in various engineering applications. Furthermore, SN P systems are characterized by a highly flexible structure that enables the design of a machine learning algorithm by mimicking the structure and behavior of biological cells without the over-simplification present in neural networks. Based on this aspect, this paper proposes a novel type of SN P system, namely, layered SN P system (LSN P system), to solve classification problems by supervised learning. The proposed LSN P system consists of a multi-layer network containing multiple weighted fuzzy SN P systems with adaptive weight adjustment rules. The proposed system employs specific ascending dimension techniques and a selection method of output neurons for classification problems. The experimental results obtained using benchmark datasets from the UCI machine learning repository and MNIST dataset demonstrated the feasibility and effectiveness of the proposed LSN P system. More importantly, the proposed LSN P system presents the first SN P system that demonstrates sufficient performance for use in addressing real-world classification problems. 

A Layered Spiking Neural System for Classification Problems

Two types of cells for bio-inspired computations are neurons and bacteria: the former have “simple” neurons that connect together to become more useful, i.e. structure is important to their overall function; the latter have bacteria “processing” DNA such as plasmids. We combine inspirations from both cell types as neural-like P systems with plasmids or NP P systems: bacteria are nodes in a digraph, with edges as communication links among bacteria. Bacteria use “transmit” and “kill” operations to send or remove plasmids. Another bio-inspiration is homogeneous bacteria, i.e. each bacterium has the same set of rules. Combining a neural-like structure with conjugation using plasmids proves useful: NP P systems are computationally complete even with homogeneous bacteria; bounding the number of plasmids reduces their computing power; we give an estimate of 164 bacteria for homogeneous NP P systems to remain complete.

Neural-like P systems with plasmids

Dynamic threshold neural P systems (DTNP systems, for short) are a kind of distributed parallel computing systems abstracted from the spiking and dynamic threshold mechanisms of neurons. A DTNP system consists of several dynamic threshold neurons, and each neuron has a data unit and a threshold unit. The computational completeness of DTNP systems has been investigated. DTNP systems are synchronous systems, and a global clock is assumed to synchronize all threshold neurons. However, the assumption is biologically non-realistic. In this paper, we discuss DTNP systems working in sequential mode, i.e., sequential DTNP systems (SDTNP systems, in short). Based on the number of spikes of active neurons and the rule-application strategy, four sequentiality strategies are considered. It is proven that SDTP systems working in four sequentiality strategies are Turing universal number generating/accepting devices.

Sequential dynamic threshold neural P systems

https://link.springer.com/content/pdf/10.1007/s41965-021-00089-9.pdf

Computational power of sequential spiking neural P systems with multiple channels

With the advancement of technologies it is becoming imperative to have a stable, secure and uninterrupted supply of power to electronic systems as well as to ensure the identification of faults occurring in these systems quickly and efficiently in case of any accident. Spiking neural P system (SNPS) is a popular parallel distributed computing model. It is inspired by the structure and functioning of spiking neurons. It belongs to the category of neural-like P systems and is well-known as a branch of the third generation neural networks. SNPS and its variants can perform the task of fault diagnosis in power systems efficiently. In this paper, we provide a comprehensive survey of these models, which can perform the task of fault diagnosis in transformers, power transmission networks, traction power supply systems, metro traction power supply systems, and electric locomotive systems. Furthermore, we discuss the use of these models in fault section estimation of power systems, fault location identification in distribution network, and fault line detection. We also discuss a software tool which can perform the task of fault diagnosis automatically. Finally, we discuss future research lines related to this topic.

A Review of Power System Fault Diagnosis with Spiking Neural P Systems

This paper discusses a new variant of spiking neural P systems (in short, SNP systems), spiking neural P systems with extended channel rules (in short, SNP-ECR systems). SNP-ECR systems are a class of distributed parallel computing models. In SNP-ECR systems, a new type of spiking rule is introduced, called ECR. With an ECR, a neuron can send the different numbers of spikes to its subsequent neurons. Therefore, SNP-ECR systems can provide a stronger firing control mechanism compared with SNP systems and the variant with multiple channels. We discuss the Turing universality of SNP-ECR systems. It is proven that SNP-ECR systems as number generating/accepting devices are Turing universal. Moreover, we provide a small universal SNP-ECR system as function computing devices.

Spiking Neural P Systems with Extended Channel Rules.

Spiking neural P systems with inhibitory rules (in short, SNP-IR systems) are a distributed parallel computing model, abstracted by the spiking and inhibitory mechanisms of biological neurons. Computational completeness of SNP-IR systems as number generating/accepting and function computing devices has been studied recently. However, computational completeness of SNP-IR systems as language generating devices still has not been investigated. We discuss the relationship of languages generated by SNP-IR systems with regular languages. Moreover, we prove that SNP-IR systems can generate recursively enumerable languages by means of a projection of inverse-morphic image. 

Computational completeness of spiking neural P systems with inhibitory rules for generating string languages

Spiking neural P systems with inhibitory rules (in short, IR-SN P systems) are a kind of bio-inspired computing systems, which are abstracted by the inhibitory synaptic mechanism of biological neurons. IR-SN P systems work in synchronous mode. This paper investigates their sequential version, sequential IR-SN P systems (in short, IR-SSN P systems). In sequential mode, not only the rules in each neuron are applied sequentially, but also the neurons fire in a sequential manner. The maximum-spike-number strategy is considered in sequential mode, and two sub-modes are further distinguished: max-sequentiality strategy and max-pseudo-sequentiality strategy. Computational completeness of IR-SSN P systems as number generating/accepting devices and function computing devices are discussed. It is proven that IR-SSN P systems are Turing universal number generating/accepting devices. Moreover, a small universal IR-SSN P system for computing functions is established in max-sequential strategy.

Computational completeness of sequential spiking neural P systems with inhibitory rules

Nonlinear spiking neural P (NSNP) system is a distributed parallel computing model inspired from the mechanisms of spiking neurons. Computational completeness of NSNP systems as number generating/accepting devices and function computing devices has been already discussed. However, universality result of NSNP systems as language generating devices has not been established so far. This paper investigates computational power of NSNP systems as language generating devices. The relationships of languages generated by NSNP systems with regular languages are investigated. Moreover, we prove that recursively enumerable languages can be characterized by the projection of inverse-morphic images of languages generated by NSNP systems.

Nan Zhou

Papers

Spiking Neural P Systems with Extended Channel Rules.

Sequential dynamic threshold neural P systems

Computational completeness of spiking neural P systems with inhibitory rules for generating string languages

Computational completeness of sequential spiking neural P systems with inhibitory rules

Nonlinear neural P systems for generating string languages