Abstract Systems neuroscience seeks explanations for how the brain implements a wide
variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to …

Neural network models are potential tools for improving our understanding of complex brain
functions. To address this goal, these models need to be neurobiologically realistic …

Recurrently connected networks of spiking neurons underlie the astounding information
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well
established that it depends on pre-and postsynaptic activity. However, models that rely …

We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for training deep
neural networks. The HSIC bottleneck is an alternative to the conventional cross-entropy …

From the conception of Baddeley's visuospatial sketchpad, visual working memory and
visual attention have been closely linked concepts. An attractive model has advocated unity …

Training deep neural networks with the error backpropagation algorithm is considered
implausible from a biological perspective. Numerous recent publications suggest elaborate …

For both humans and machines, the essence of learning is to pinpoint which components in
its information processing pipeline are responsible for an error in its output, a challenge that …

Convolutional Neural Networks (CNN) are a class of machine learning models
predominately used in computer vision tasks and can achieve human-like performance …

Recent advances in artificial intelligence (AI) and neuroscience are impressive. In AI, this
includes the development of computer programs that can beat a grandmaster at GO or …