Neuroscientists have long characterized the properties and functions of the nervous system,
and are increasingly succeeding in answering how brains perform the tasks they do. But the …

Convolutional neural networks (CNNs) were inspired by early findings in the study of
biological vision. They have since become successful tools in computer vision and state-of …

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 …

In the modern era, many terms related to artificial intelligence, machine learning, and deep
learning are widely used in domains such as business, healthcare, industries, and military …

Departing from traditional linguistic models, advances in deep learning have resulted in a
new type of predictive (autoregressive) deep language models (DLMs). Using a self …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

How do neural populations code for multiple, potentially conflicting tasks? Here we used
computational simulations involving neural networks to define" lazy" and" rich" coding …

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 …

Flexible behaviors over long timescales are thought to engage recurrent neural networks in
deep brain regions, which are experimentally challenging to study. In insects, recurrent …

Deep neural networks (DNNs) have had extraordinary successes in classifying
photographic images of objects and are often described as the best models of biological …