For a long time, biology and neuroscience fields have been a great source of inspiration for
computer scientists, towards the development of Artificial Intelligence (AI) technologies. This …

Although it may appear infeasible and impractical, building artificial intelligence (AI) using a
bottom-up approach based on the understanding of neuroscience is straightforward. The …

Quantification and reduction of uncertainty in deep-learning techniques have received much
attention but ignored how to characterize the imprecision caused by such uncertainty. In …

The state-of-the art machine learning approach to training deep neural networks,
backpropagation, is implausible for real neural networks: neurons need to know their …

In areas of machine learning such as cognitive modeling or recommendation, user feedback
is usually context-dependent. For instance, a website might provide a user with a set of …

Broad learning system based on neural network (BLS-NN) has poor efficiency for small data
modeling with various dimensions. Tree-based BLS (TBLS) is designed for small data …

Biological organisms must learn how to control their own bodies to achieve deliberate
locomotion, that is, predict their next body position based on their current position and …

Recently emerged technologies based on Deep Learning (DL) achieved outstanding results
on a variety of tasks in the field of Artificial Intelligence (AI). However, these encounter …

Owing to its distinguished nonlinear mapping capability and interpretability, a Takagi–
Sugeno–Kang (TSK) fuzzy classifier is always employed to achieve both enhanced …

Traditional approaches using Deep Neural Networks for classification, while unquestionably
successful, struggle with more general intelligence tasks such as “on the fly” learning as …