Membrane Computing - Key Concepts and Definitions

Membrane computing is a new branch of natural computing. In recent years, with the rapid development, membrane systems are divided into three categories: Cell-type membrane systems, Tissue membrane system, Neuronal membrane system. Spiking neural membrane system Spiking Neural P Systems is a special type of neuronal membrane system, referred to as SN P system, which is a new branch of membrane computing. It not only has the scientific significance of the computer, but also in terms of biological modeling and simulation have potential applications. In this paper, the concept and principles of spiking neural membrane system, the current species and varieties spiking neural membrane system are analyzed, some research progress theory expounded.

For the attitude control of unmanned helicopters used in the intelligent patrolling of coal mines, a spiking neural membrane system is introduced for attitude optimisation control. First, a geometry-based attitude dynamics model suitable for coal mine scenarios is constructed. Second, in accordance with the attitude dynamics model, an extended spiking neural membrane system (ESNMS) is constructed, and an optimised spiking neural membrane system (OSNMS) and accompanying algorithm are designed to optimise the ESNMS. Then, the attitude control performance of the developed system is theoretically analysed. Finally, through the simulation of a semiphysical experimental platform, trajectory tracking is effectively realised. Under normal and wind disturbance conditions, comparisons with the traditional synovium controller (TSC) and linear feedback controller (LFC) show that the performance of the designed OSNMS is greatly improved, and the experimental results show that the OSNMS has good stability and effectiveness.

Rule-based and object-based event structures for membrane systems

Spiking neural P systems (SN P systems), inspired by biological neurons, are introduced as symbolical neural-like computing models that encode information with multisets of symbolized spikes in neurons and process information by using spike-based rewriting rules. Inspired by neuronal activities affected by enzymes, a numerical variant of SN P systems called enzymatic numerical spiking neural P systems (ENSNP systems) is proposed wherein each neuron has a set of variables with real values and a set of enzymatic activation-production spiking rules, and each synapse has an assigned weight. By using spiking rules, ENSNP systems can directly implement mathematical methods based on real numbers and continuous functions. Furthermore, ENSNP systems are used to model ENSNP membrane controllers (ENSNP-MCs) for robots implementing wall following. The trajectories, distances from the wall, and wheel speeds of robots with ENSNP-MCs for wall following are compared with those of a robot with a membrane controller for wall following. The average error values of the designed ENSNP-MCs are compared with three recently fuzzy logical controllers with optimization algorithms for wall following. The experimental results showed that the designed ENSNP-MCs can be candidates as efficient controllers to control robots implementing the task of wall following.

Artificial neural systems for computation were first proposed three quarters of a century ago and the concepts developed by the pioneers still shape the field today. The first generation of neural systems was developed in the nineteen forties in the context of analogue electronics and the theoretical research in logic and mathematics that led to the first digital computers in nineteen forties and fifties. The second generation of neural systems implemented on digital computers was born in the nineteen fifties and great progress was made in the subsequent half century with neural networks being applied to many problems in pattern recognition and machine learning. Through this history there has been an interplay between biologically inspired neural systems and their implementation by engineers on digital machines. This thesis concerns the third generation of neural networks, Spiking Neural Networks, which is making possible the creation of new kinds of brain inspired computing architectures that offer the potential to increase the level of realism and sophistication in terms of autonomous machine behaviour and cognitive computing. This thesis presents the development and demonstration of a new theoretical architecture for third generation neural systems, the Integrate-and-Fire based Spiking Neural Model with extended Neuro-modulated Spike Timing Dependent Plasticity capabilities. This proposed architecture overcomes the limitation of the homosynaptic architecture underlying existing implementations of spiking neural networks that it lacks a natural spike timing dependent plasticity regulation mechanism, and this results in ‘run away’ dynamics. To overcome this ad hoc procedures have been implemented to overcome the ‘run away’ dynamics that emerge from the use of spike timing dependent plasticity among other hebbian-based plasticity rules. The new heterosynaptic architecture presented, explicitly abstracts the modulation of complex biochemical mechanisms into a simplified mechanism that is suitable for the engineering of artificial systems with low computational complexity. Neurons work by receiving input signals from other neurons through synapses. The difference between homosynaptic and heterosynaptic plasticity is that, in the former the change in the properties of a synapse (e.g. synaptic efficacy) depends on the point to point activity in either of the sending and receiving neurons, in contrast for heterosynaptic plasticity the change in the properties of a synapse can be elicited by neurons that are not necessary presynaptic or postsynaptic to the synapse in question. The new architecture is tested by a number of implementations in simulated and real environments. This includes experiments with a simulation environment implemented in Netlogo, and an implementation using Lego Mindstorms as the physical robot platform. These experiments demonstrate the problems with the traditional Spike timing dependent plasticity homosynaptic architecture and how the new heterosynaptic approach can overcome them. It is concluded that the new theoretical architecture provides a natural, theoretically sound, and practical new direction for research into the role of modulatory neural systems applied to spiking neural networks.

Spiking neural systems are based on biologically inspired neural models of computation since they take into account the precise timing of spike events and therefore are suitable to analyze dynamical aspects of neuronal signal transmission. These systems gained increasing interest because they are more sophisticated than simple neuron models found in artificial neural systems; they are closer to biophysical models of neurons, synapses, and related elements and their synchronized firing of neuronal assemblies could serve the brain as a code for feature binding and pattern segmentation. The simulations are designed to exemplify certain properties of the olfactory bulb (OB) dynamics and are based on an extension of the integrate-and-fire (IF) neuron, and the idea of locally coupled excitation and inhibition cells. We introduce the background theory to making an appropriate choice of model parameters. The following two forms of connectivity offering certain computational and analytical advantages, either through symmetry or statistical properties in the study of OB dynamics have been used: a) all-to-all coupling, b) receptive field style coupling. Our simulations showed that the inter-neuron transmission delay controls the size of spatial variations of the input and also smoothes the network response. Our IF extended model proves to be a useful basis from which we can study more sophisticated features as complex pattern formation, and global stability and chaos of OB dynamics.

Spiking neural networks (SNNs) are the basis for many energy-efficient neuromorphic hardware systems. While there has been substantial progress in SNN research, artificial SNNs still lack many capabilities of their biological counterparts. In biological neural systems, memory is a key component that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning in artificial and SNNs. Here, we propose that Hebbian plasticity is fundamental for computations in biological and artificial spiking neural systems. We introduce a novel memory-augmented SNN architecture that is enriched by Hebbian synaptic plasticity. We show that Hebbian enrichment renders SNNs surprisingly versatile in terms of their computational as well as learning capabilities. It improves their abilities for out-of-distribution generalization, one-shot learning, cross-modal generative association, language processing, and reward-based learning. This suggests that powerful cognitive neuromorphic systems can be built based on this principle.

Neuro-inspired models and systems have great potential for applications in unconventional computing. Often, the mechanisms of biological neurons are modeled or mimicked in simulated or physical systems in an attempt to harness some of the computational power of the brain. However, the biological mechanisms at play in neural systems are complicated and challenging to capture and engineer; thus, it can be simpler to turn to a data-driven approach to transfer features of neural behavior to artificial substrates. In the present study, we used an evolutionary algorithm (EA) to produce spiking neural systems that emulate the patterns of behavior of biological neurons in vitro. The aim of this approach was to develop a method of producing models capable of exhibiting complex behavior that may be suitable for use as computational substrates. Our models were able to produce a level of network-wide synchrony and showed a range of behaviors depending on the target data used for their evolution, which was from a range of neuronal culture densities and maturities. The genomes of the top-performing models indicate the excitability and density of connections in the model play an important role in determining the complexity of the produced activity.

Skin lesion segmentation is critical in medical image processing, but the segmentation task faces numerous challenges due to the differences in size, color, shape, and texture of skin lesions between patients, as well as the blurring of the boundary between lesions and normal skin. While many models improve segmentation performance by introducing complex modules, their computational resource constraints make applications limited in a clinical setting. To cope with this problem, we propose a lightweight multi-fusion network (LMFUNet) with parameters of only 0.100M and GFLOPs of 0.106. LMFUNet uses an Efficient Multi-scale Feature Extraction block (EMFE) in deep stages, which uses grouping of features by convolution with different dilation rates to reduce model complexity and effectively capture multi-scale features. By using the Multi-level Feature Fusion module (MFF) in skip connections, different levels of information are combined step by step, realizing the first step of fusion of low-level details and high-level contextual information, which helps to accurately localize the lesion area. After MFF, we designed the Spatial-channel Fusion module (SCF). This module further optimizes the fusion of feature information output from MFF in terms of spatial and channel dimensions, significantly enhancing the recognition of lesion boundaries. We did in-depth tests on three public datasets that are typical of others: ISIC2017, ISIC2018 and PH2 datasets. LMFUNet demonstrates superior performance because it excels at segmentation and doesn’t require a lot of computing power.

A detection method based on nonlinear spiking neural systems for infrared small targets

In the period of software improvement, programming dependability expectation turned out to be exceptionally critical for creating nature of programming in the product business. Time to time, numerous product dependability models have been introduced for evaluating unwavering quality of programming in programming forecast models. However, building precise forecast model is hard because of intermittent changes in information in the space of programming designing. As needs be, we propose a novel procedure, i.e. spiking neural system to anticipate programming unwavering quality. The key goal of this paper is to exhibit another approach which upgrades the exactness of programming unwavering quality prescient models when utilized with the product disappointment dataset. The viability of quality of a product is exhibited on dataset taken from the literature, where execution is measured by utilizing normalized root mean square error (NRMSE) obtained in the test dataset.

Time Series Analysis of Spiking Neural Systems via Transfer Entropy and Directed Persistent Homology

Biological neural networks enable rapid communication between cells, which has inspired the development of artificial smart neuro‐mimetic systems. In this work, freestanding elastomeric membranes are fabricated with 3D structured holes to induce nonlinear ion‐based transport. These 3D microholes are embedded in a soft polymeric membrane via a grayscale lithographical process and demonstrate ionic rectification, negative differential ionic resistance, ionic conductivity gating, and spiking under an applied electric field bias. The ion transport behavior of these membranes is affected by the shape, size, number, and chemical functionalization of the embedded microholes. The microholes are functionalized with responsive poly(N‐isopropylacrylamide) polymer brush layers allowing to alter the electric gating behavior by external stimuli that includes temperature and chemical composition of the surrounding environment. The electrically biased membranes with a linear array of eight equidistantly spaced microholes exhibit synchronous gating of ionic transport through each one. These individual microholes are equipped with circumjacent blocking stretchable electrodes to simultaneously measure the exact onset time delay of the spikes during the synchronous gating. The operation of the freestanding elastomeric membranes with embedded microholes mimics the behavior of neural membranes. Membranotronics will pave the way toward bioinspired soft artificial neuro‐mimetic systems mechanically and electrochemically comparable to their biological counterpart.