The Hyper-Cortex of Human Collective-Intelligence Systems

Individual-intelligence research, from a neurological perspective, discusses the hierarchical layers of the cortex as a structure that performs conceptual abstraction and specification. This theory has been used to explain how motor-cortex regions responsible for different behavioral modalities such as writing and speaking can be utilized to express the same general concept represented higher in the cortical hierarchy. For example, the concept of a dog, represented across a region of high-level cortical-neurons, can either be written or spoken about depending on the individual's context. The higher-layer cortical areas project down the hierarchy, sending abstract information to specific regions of the motor-cortex for contextual implementation. In this paper, this idea is expanded to incorporate collective-intelligence within a hyper-cortical construct. This hyper-cortex is a multi-layered network used to represent abstract collective concepts. These ideas play an important role in understanding how collective-intelligence systems can be engineered to handle problem abstraction and solution specification. Finally, a collection of common problems in the scientific community are solved using an artificial hyper-cortex generated from digital-library metadata.Comment: ECCO Working Paper 06-2005 (17-pages) This paper will be shortened to just the multi-layered digital-library hyper-corte

Connectionist Cognitive Processing for Invariant Pattern Recognition

Recently, from classical connectionist and symbolic models, the new field of neurosymbolic integration has emerged, whose aim is to benefit from the advantages of both domains to model human perceptive and cognitive capabilities. To reach this goal, some strategies are envisaged, among which connectionist cognitive processing claims that these desired capabilities can emerge from pure neuronal structures and processes. This approach refers to the substratum of cognition, the human brain and gives rise to perceptually grounded models whose goal is to reach higher cognitive levels. Its principles are presented here and, as an illustration, an application to invariant pattern recognition is described. From basic connectionist models, a biologically inspired model of neuronal networks cooperation is implemented to allow for internal information translation. This mechanism leads to automatic pattern centring in a classical character recognition application with excellent performances. Intr..