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 …

In this paper, we discuss the nonlinear computational power provided by dendrites in
biological and artificial neurons. We start by briefly presenting biological evidence about the …

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

In the last decade dendrites of cortical neurons have been shown to nonlinearly combine
synaptic inputs by evoking local dendritic spikes. It has been suggested that these …

Dendrite morphological neurons are a type of artificial neural network that works with min
and max operators instead of algebraic products. These morphological operators build …

The dominant view in neuroscience is that changes in synaptic weights underlie learning. It
is unclear, however, how the brain is able to determine which synapses should change, and …

This article highlights specific features of biological neurons and their dendritic trees, whose
adoption may help advance artificial neural networks used in various machine learning …

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 dendritic neuron model with adaptive synapses (DMASs) based on differential evolution
(DE) algorithm training is proposed. According to the signal transmission order, a DNM can …

Information processing in the brain depends on the integration of synaptic input distributed
throughout neuronal dendrites. Dendritic integration is a hierarchical process, proposed to …