There exists a large conceptual gap between symbolic models and emergent models for the
mind. Many emergent models work on low-level sensory data, while many symbolic models …

Development imposes great challenges. Internal ldquocorticalrdquorepresentations must be
autonomously generated from interactive experiences. The eventual quality of these …

This paper describes the design of a single learning network that integrates both object
location (ldquowhererdquo) and object type (ldquowhatrdquo), from images of learned …

The Where-What Network (WWN-3) is an artificial developmental network modeled after
visual cortical pathways, for the purpose of attention and recognition in the presence of …

In autonomous learning, value-sensitive experiences can improve the efficiency of learning.
A learning network needs be motivated so that the limited computational resources and the …

How our brains develop disparity tuned V 1 and V 2 cells and then integrate binocular
disparity into 3-D perception of the visual world is still largely a mystery. Moreover …

This is a theoretical, modeling, and algorithmic paper about the spatial aspect of brain-like
information processing, modeled by the developmental network (DN) model. The new brain …

The article deals with intelligent systems that contain artificial neural networks. After a close
comparison of artificial and biological neural networks the authors reveal some fundamental …

This paper introduces a concept called task muddiness as a metric for higher intelligence.
Task muddiness is meant to be inclusive and expendable in nature. The intelligence …

Informed by brain anatomical studies, we present the developmental network (DN) theory on
brain-like temporal information processing. The states of the brain are at its effector end …