Skip Nav Destination
Close Modal
Update search
NARROW
Format
Journal
TocHeadingTitle
Date
Availability
1-2 of 2
Stefan C. Kremer
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Journal Articles
Publisher: Journals Gateway
Neural Computation (2003) 15 (6): 1255–1320.
Published: 01 June 2003
Abstract
View articletitled, A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case
View
PDF
for article titled, A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case
Spatiotemporal connectionist networks (STCNs) comprise an important class of neural models that can deal with patterns distributed in both time and space. In this article, we widen the application domain of the taxonomy for supervised STCNs recently proposed by Kremer (2001) to the unsupervised case. This is possible through a reinterpretation of the state vector as a vector of latent (hidden) variables, as proposed by Meinicke (2000). The goal of this generalized taxonomy is then to provide a nonlinear generative framework for describing unsupervised spatiotemporal networks, making it easier to compare and contrast their representational and operational characteristics. Computational properties, representational issues, and learning are also discussed, and a number of references to the relevant source publications are provided. It is argued that the proposed approach is simple and more powerful than the previous attempts from a descriptive and predictive viewpoint. We also discuss the relation of this taxonomy with automata theory and state-space modeling and suggest directions for further work.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2001) 13 (2): 249–306.
Published: 01 February 2001
Abstract
View articletitled, Spatiotemporal Connectionist Networks: A Taxonomy and Review
View
PDF
for article titled, Spatiotemporal Connectionist Networks: A Taxonomy and Review
This article reviews connectionist network architectures and training algorithms that are capable of dealing with patterns distributed across both space and time—spatiotemporal patterns. It provides common mathematical, algorithmic, and illustrative frameworks for describing spatiotemporal networks, making it easier to compare and contrast their representational and operational characteristics. Computational power, representational issues, and learning are discussed. In additional references to the relevant source publications are provided. This article can serve as a guide to prospective users of spatiotemporal networks by providing an overview of the operational and representational alternatives available.