Highlights•Artificial and biological neural networks can be analyzed using similar
methods.•Neural analysis has revealed similarities between the representations in artificial …

We propose a theory of structure learning in the primate brain. We argue that the parietal
cortex is critical for learning about relations among the objects and categories that populate …

There are two principle approaches for learning in artificial intelligence: error-driven global
learning and neuroscience-oriented local learning. Integrating them into one network may …

Machine learning has witnessed tremendous growth in its adoption and advancement in the
last decade. The evolution of machine learning from traditional algorithms to modern deep …

Highlights•The three-factor framework describes various learning rules in a unified
way.•Third factors can encode reward, attention, summary statistics, or supervised …

Despite continual debate for the past 30 years about the function of anterior cingulate cortex
(ACC), its key contribution to neurocognition remains unknown. However, recent …

As neuroscience projects increase in scale and cross international borders, different ethical
principles, national and international laws, regulations, and policies for data sharing must be …

Schank (1980) wrote an editorial for Intelligence on “How much intelligence is there in
artificial intelligence?”. In this paper, we revisit this question. We start with a short overview …

Researchers across cognitive, neuro-and computer sciences increasingly reference 'human-
like'artificial intelligence and 'neuroAI'. However, the scope and use of the terms are often …

Abstract Distribution System Operators (DSOs) and Aggregators benefit from novel Energy
Generation Forecasting (EGF) approaches. Improved forecasting accuracy may make it …