Titlebook: Recruitment Learning; Joachim Diederich,Cengiz Günay,James M. Hogan Book 2011 Springer-Verlag Berlin Heidelberg 2011 Computational Intelli

MELON

Palate

Representation and Recruitment book, in Chapter 5, we come somewhat closer to the biological context in which the ideas were developed, recruiting sparse random networks of this type to encode the spatial relations semantics of simple images.

Entirety

Spiking Neural Networks and Temporal Bindingearch is reviewed to describe the theory of spiking neural network models. Then, the development of concept representation in neural networks and the binding problem is reviewed to present the relationship between recruitment learning and the temporal binding hypothesis. A historical account of these developments is also given.

悶熱

有惡臭

One-Shot Learning – Specialization and Generalizationlic artificial intelligence as it is in nature. This chapter begins our exploration of recruitment learning by examining a connectionist learning system capable of rapidly integrating a new fact into an existing knowledge base in response to a single instruction expressed in a formal description lan