An Introduction to Neural and Electronic Networks
An Introduction to Neural and Electronic Networks
Map-Seeking Circuits in Visual Cognition: A Computational Mechanism for Biological and Machine Vision
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Maplets for correspondence-based object recognition
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Hierarchial self-organization of minicolumnar receptive fields
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Bilinear Sparse Coding for Invariant Vision
Neural Computation
Topographic Independent Component Analysis
Neural Computation
Separating Style and Content with Bilinear Models
Neural Computation
What is the optimal architecture for visual information routing?
Neural Computation
Rapid convergence to feature layer correspondences
Neural Computation
Dynamic Link Matching between Feature Columns for Different Scale and Orientation
Neural Information Processing
Synaptic Formation Rate as a Control Parameter in a Model for the Ontogenesis of Retinotopy
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
A parallel computation that assigns canonical object-based frames of reference
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A bilinear model for consistent topographic representations
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Self-organization of steerable topographic mappings as basis for translation invariance
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Glial cells for information routing?
Cognitive Systems Research
Learning features and predictive transformation encoding based on a horizontal product model
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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We present a model for the emergence of ordered fiber projections that may serve as a basis for invariant recognition. After invariance transformations are self-organized, so-called control units competitively activate fiber projections for different transformation parameters. The model builds on a well-known ontogenetic mechanism, activity-based development of retinotopy, and it employs activity blobs of varying position and size to install different transformations. We provide a detailed analysis for the case of 1D input and output fields for schematic input patterns that shows how the model is able to develop specific mappings. We discuss results that show that the proposed learning scheme is stable for complex, biologically more realistic input patterns. Finally, we show that the model generalizes to 2D neuronal fields driven by simulated retinal waves.