The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …

The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

Energy and security are major challenges in a wireless sensor network, and they work
oppositely. As security complexity increases, battery drain will increase. Due to the limited …

A few years ago, the first CNN surpassed human performance on ImageNet. However, it
soon became clear that machines lack robustness on more challenging test cases, a major …

The rise and success of large language models undermines virtually every strong claim for
the innateness of language that has been proposed by generative linguistics. Modern …

Considering the increasing importance of Artificial Intelligence in Education (AIEd) and the
absence of a comprehensive review on it, this research aims to conduct a comprehensive …

Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long
been considered an important component of learning and memory. Computational and …

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

There are two general approaches to developing artificial general intelligence (AGI):
computer-science-oriented and neuroscience-oriented. Because of the fundamental …