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

Movies, audio stories, and virtual reality are increasingly used as stimuli for functional brain
imaging. Such naturalistic paradigms are in sharp contrast to the tradition of experimental …

In the last decade, deep learning has become a major component of artificial intelligence.
The workhorse of deep learning is the optimization of loss functions by stochastic gradient …

A cornerstone of theoretical neuroscience is the circuit model: a system of equations that
captures a hypothesized neural mechanism. Such models are valuable when they give rise …

This short survey reviews the recent literature on the relationship between the brain structure
and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) …

Object recognition relies on inferior temporal (IT) cortical neural population representations
that are themselves computed by a hierarchical network of feedforward and recurrently …

Task-trained artificial recurrent neural networks (RNNs) provide a computational modeling
framework of increasing interest and application in computational, systems, and cognitive …

Deep neural networks (DNNs) have revolutionized computer science and are now widely
used for neuroscientific research. A hot debate has ensued about the usefulness of DNNs as …

Advances in cognitive neuroscience are often accompanied by an increased complexity in
the methods we use to uncover new aspects of brain function. Recently, many studies have …

As conversational CRM systems communicate with human customers and not other
computer systems, they need to tackle human emotions in a way to optimize the outcome of …