There is a concerted effort to build domain-general artificial intelligence in the form of
universal neural network models with sufficient computational flexibility to solve a wide …

Brain networks exist within the confines of resource limitations. As a result, a brain network
must overcome the metabolic costs of growing and sustaining the network within its physical …

To examine the biological building blocks of thought and action, we created biologically
realistic local circuits based on detailed well-cited physiological and anatomical …

The goal of computational neuroscience is to find mechanistic explanations of how the
nervous system processes information to support cognitive function and behaviour. At the …

Neural networks underpin both biological intelligence and modern Al systems, yet there is
relatively little theory for how the observed behavior of these networks arises. Even the …

The success of deep neural networks suggests that cognition may emerge from
indecipherable patterns of distributed neural activity. Yet these networks are pattern …

We introduce bio-inspired artificial neural networks consisting of neurons that are
additionally characterized by spatial positions. To simulate properties of biological systems …

Explainable artificial intelligence (XAI) is an area of ongoing research for a variety of
machine learning applications that aims to describe decision making employed by many …

We present a survey of ways in which domain-knowledge has been included when
constructing models with neural networks. The inclusion of domain-knowledge is of special …

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural
networks (ANNs) have exploited biological properties to solve complex problems. However …