An artificial neural network (ANN) that mimics the information processing mechanisms and
procedures of neurons in human brains has achieved a great success in many fields, eg …

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 …

Recent researches have provided strong circumstantial support to dendrites playing a key
and possibly essential role in computations. In this paper, we propose an unsupervised …

An approximate logic neuron model (ALNM) based on the interaction of dendrites and the
dendritic plasticity mechanism is proposed. The model consists of four layers: a synaptic …

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 …

Synaptic plasticity is a long-lasting core hypothesis of brain learning that suggests local
adaptation between two connecting neurons and forms the foundation of machine learning …

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 …

Artificial Neural Network (ANN) design is a complex task because its performance depends
on the architecture, the selected transfer function, and the learning algorithm used to train …

Neural dendrites play a critical role in various cognitive functions, including spatial
navigation, sensory processing, adaptive learning, and perception. The spatial layout, signal …

In machine learning, an artificial neural network is the mainstream approach. Such a
network consists of many neurons. These neurons are of the same type characterized by the …