Over the years, spiking neural P systems (SNPS) have grown into a popular model in
membrane computing because of their diverse range of applications. In this paper, we give a …

Asymmetric cryptographic systems are often more complex and require more computational
power than symmetric systems. This is why they might be implemented using …

Spiking neural P systems are a class of distributed and parallel computing models inspired
by spiking neurons. In this work, the features of neuron division and neuron budding are …

Spiking neural P systems are a class of third generation neural networks belonging to the
framework of membrane computing. Spiking neural P systems with communication on …

Spiking neural P systems are parallel and distributed computation devices which are
inspired by the neuro-physiological behavior of biological neurons. In this paper we will …

In this paper we continue previous studies on the computational efficiency of spiking neural
P systems, under the assumption that some pre-computed resources of exponential size are …

In this paper, we create a matrix representation for spiking neural P systems with structural
plasticity (SNPSP, for short), taking inspiration from existing algorithms and representations …

Spiking neural P systems are a new candidate in spiking neural network models. By using
neuron division and budding, such systems can generate/produce exponential working …

The research within the field of Spiking Neural P systems (SN P systems, for short) is
focusing mainly in the study of the computational completeness (they are equivalent in …

Remaining useful life (RUL) prediction is a key enabler for predictive maintenance. In fact,
the possibility of accurately and reliably predicting the RUL of a system, based on a record of …