A potentially organizing goal of the brain and cognitive sciences is to accurately explain
domains of human intelligence as executable, neurally mechanistic models. Years of …

Artificial neural networks (ANNs) are essential tools in machine learning that have drawn
increasing attention in neuroscience. Besides offering powerful techniques for data analysis …

Evolution is a blind fitting process by which organisms become adapted to their
environment. Does the brain use similar brute-force fitting processes to learn how to …

The emergence of powerful artificial intelligence (AI) is defining new research directions in
neuroscience. To date, this research has focused largely on deep neural networks trained …

Naturalistic experimental paradigms in neuroimaging arose from a pressure to test the
validity of models we derive from highly-controlled experiments in real-world contexts. In …

Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal
to the beautiful recordings of today, neuroscientists have been striving to unravel the …

Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as
a modeling framework for neural computations in the primate brain. Just like individual …

Backpropagation (BP) has been the most successful algorithm used to train artificial neural
networks. However, there are several gaps between BP and learning in biologically …

We introduce a principled method to train end-to-end analog neural networks by stochastic
gradient descent. In these analog neural networks, the weights to be adjusted are …

The success of deep learning, a brain-inspired form of AI, has sparked interest in
understanding how the brain could similarly learn across multiple layers of neurons …