The hard problem of consciousness is the problem of explaining how we experience qualia
or phenomenal experiences, such as seeing, hearing, and feeling, and knowing what they …

Seeing seems effortless, despite the need to segregate and integrate visual information that
varies in quality, quantity, and location. The extent to which seeing passively recapitulates …

Chaotic resonance (CR) is the response of a nonlinear system to weak signals enhanced by
internal or external chaotic activity (such as the signal derived from Lorenz system). The …

Abstract Deep Reinforcement Learning (RL) demonstrates excellent performance on tasks
that can be solved by trained policy. It plays a dominant role among cutting-edge machine …

The nature of distributed computation in complex systems has often been described in terms
of memory, communication and processing. This thesis presents a complete information …

Adaptive Resonance Theory, or ART, is a neural model that explains how normal and
abnormal brains may learn to categorize and recognize objects and events in a changing …

Abstract Computational Cognitive Neuroscience (CCN) is a new field that lies at the
intersection of computational neuroscience, machine learning, and neural network theory …

The FACADE model, and its laminar cortical realization and extension in the 3D LAMINART
model, have explained, simulated, and predicted many perceptual and neurobiological data …

This article surveys the development of mathematical laws, circuits, and architectures that
model how our brains make our minds, and shows how these contributions provide a …

Complexity and heterogeneity are intrinsic to neurobiological systems, manifest in every
process, at every scale, and are inextricably linked to the systems' emergent collective …