On the stationary state of Kohonen's self-organizing sensory mapping
Biological Cybernetics
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Asymptotic Level Density of the Elastic Net Self-Organizing Feature Map
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Applying Mutual Information to Adaptive Mixture Models
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Data Mining and Knowledge Discovery in Medical Applications Using Self-Organizing Maps
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
2005 Special Issue: Unifying cost and information in information-theoretic competitive learning
Neural Networks - 2005 Special issue: IJCNN 2005
Neural network explanation using inversion
Neural Networks
Magnification control for batch neural gas
Neurocomputing
Topographic Infomax in a Neural Multigrid
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Feature Discovery by Enhancement and Relaxation of Competitive Units
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Information Maximization in a Linear Manifold Topographic Map
Neural Processing Letters
Enhancing and Relaxing Competitive Units for Feature Discovery
Neural Processing Letters
Game-based learning model using fuzzy cognitive map
MTDL '09 Proceedings of the first ACM international workshop on Multimedia technologies for distance learning
Self-enhancement learning: self-supervised and target-creating learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Structural enhanced information to detect features in competitive learning
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Magnification control in winner relaxing neural gas
Neurocomputing
Competitive learning by information maximization: eliminating dead neurons in competitive learning
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
The infomin criterion: an information theoretic unifying objective function for topographic mappings
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A spiking neuron as information bottleneck
Neural Computation
Maximizing the ratio of information to its cost in information theoretic competitive learning
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Automatic inference of cabinet approval ratings by information-theoretic competitive learning
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
The infomin principle for ICA and topographic mappings
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Investigation of topographical stability of the concave and convex self-organizing map variant
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Hi-index | 0.00 |
A learning rule that performs gradient ascent in the average mutual information between input and an output signal is derived for a system having feedforward and lateral interactions. Several processes emerge as components of this learning rule: Hebb-like modification, and cooperation and competition among processing nodes. Topographic map formation is demonstrated using the learning rule. An analytic expression relating the average mutual information to the response properties of nodes and their geometric arrangement is derived in certain cases. This yields a relation between the local map magnification factor and the probability distribution in the input space. The results provide new links between unsupervised learning and information-theoretic optimization in a system whose properties are biologically motivated.