The Bayesian Confidence Propagation Neural Network (BCPNN) has been implemented in
a way that allows map** to neural and synaptic processes in the human cortexandhas …

Neural networks that can capture key principles underlying brain computation offer exciting
new opportunities for develo** artificial intelligence and brain-like computing algorithms …

Datasets are essential for training and evaluating machine learning (ML) models. However,
they are also at the root of many undesirable model behaviors, such as biased predictions …

Computers have become essential to everyday life as much as electricity, communications
and transport. That is evident from the amount of electricity we spend to power our …

The brain achieves complex information processing and cognitive functions leveraging
synaptic learning mechanisms that are local, asynchronous, online and Hebbian in nature …

Graph neural networks form a class of deep learning architectures specifically designed to
work with graph-structured data. As such, they share the inherent limitations and problems of …

Deep learning models are at the forefront of artificial intelligence research today. Among
them, artificial neural networks are the most commonly used class of models for a wide …

Machine learning (ML) technology may discriminate toward specific social groups. For
example, recent research have revealed that ML applications are more likely to fail in …

One of the most promising approaches for data analysis and exploration of large data sets is
Machine Learning (ML) techniques that are inspired by brain models. Such methods use …