Nature-inspired computing is a type of human-designed computing motivated by nature,
which is based on the employ of paradigms, mechanisms, and principles underlying natural …

While the training parameters of machine learning models are adapted during the training
phase, the values of the hyperparameters (or meta-parameters) have to be specified before …

Nonlinear spiking neural P (NSNP) systems are a class of neural-like computational models
inspired from the nonlinear mechanism of spiking neurons. NSNP systems have a …

Spiking neural P (SNP) systems are a class of neural-like membrane computing models that
are abstracted by applying the mechanisms of spiking neurons. In SNP systems, each …

Nonlinear spiking neural P (NSNP) systems are distributed parallel neural-like computing
models that abstract the nonlinear spiking mechanisms of biological neurons. Echo state …

Dynamic threshold neural P (DTNP) systems are a new variant of spiking neural P (SNP)
systems, abstracted by the spiking and dynamic threshold mechanisms of biological …

Spiking neural P (SN P) systems are a class of discrete neuron-inspired computation
models, where information is encoded by the numbers of spikes in neurons and the timing of …

In biological nervous systems, the operation of interacting neurons depends largely on the
regulation from astrocytes. Inspired by this biological phenomenon, spiking neural P …

Spiking neural P systems (SN P systems) are models of computation inspired by biological
spiking neurons. SN P systems have neurons as spike processors, which are placed on the …

Dynamic threshold neural P systems (DTNP systems) are recently proposed distributed and
parallel computing models, inspired from the intersecting cortical model. DTNP systems …