Research on dendrites has been conducted for decades, providing valuable information for
the development of dendritic computation. Creating an ideal neuron model is crucial for …

A single dendritic neuron model (DNM) that owns the nonlinear information processing
ability of dendrites has been widely used for classification and prediction. Complex-valued …

Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and
parallel computing model, combining the spiking and coupled mechanisms of neurons. This …

Several variants of spiking neural P systems (SNPS) have been presented in the literature to
perform arithmetic operations. However, each of these variants was designed only for one …

This paper proposes a new variant of spiking neural P systems (in short, SNP systems),
nonlinear spiking neural P systems (in short, NSNP systems). In NSNP systems, the state of …

Neurological disorders such as Parkinson's disease, stroke, and spinal cord injury can pose
significant threats to human mortality, morbidity, and functional independence. Brain …

This work proposes a decision tree (DT)-based method for initializing a dendritic neuron
model (DNM). Neural networks become larger and larger, thus consuming more and more …

As the well-known McCulloch–Pitts neuron model has long been criticized to be
oversimplified, different algebra to formulate a single neuron model has received increasing …

The simulation of biological dendrite computations is vital for the development of artificial
intelligence (AI). This article presents a basic machine-learning (ML) algorithm, called …

The simulation of human brain neurons by synaptic devices could be an effective strategy to
break through the notorious “von Neumann Bottleneck” and “Memory Wall”. Herein, opto …