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 …

Advances in experimental neuroscience have transformed our ability to explore the structure
and function of neural circuits. At the same time, advances in machine learning have …

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

Deep learning algorithms trained to predict masked words from large amount of text have
recently been shown to generate activations similar to those of the human brain. However …

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

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 learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …

Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance
based on deep learning. Unique to Neuroscience, deep learning models can be used not …

Quality-of-Service (QoS), which describes the non-functional characteristics of Web service,
is of great significance in service selection. Since users cannot invoke all services to obtain …

Fear and anxiety play a central role in mammalian life, and there is considerable interest in
clarifying their nature, identifying their biological underpinnings, and determining their …