Self-organization and the brain
The handbook of brain theory and neural networks
Feature extraction through LOCOCODE
Neural Computation
A model of computation in neocortical architecture
Neural Networks - Special issue on organisation of computation in brain-like systems
Self-Organizing Maps
Preintegration lateral inhibition enhances unsupervised learning
Neural Computation
Macrocolumns as Decision Units
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Learning the Gestalt rule of collinearity from object motion
Neural Computation
Neural Computation
Competition and multiple cause models
Neural Computation
Maximal Causes for Non-linear Component Extraction
The Journal of Machine Learning Research
Rapid convergence to feature layer correspondences
Neural Computation
A dynamical model for receptive field self-organization in V1 cortical columns
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Expectation Truncation and the Benefits of Preselection In Training Generative Models
The Journal of Machine Learning Research
Self-organization of topographic bilinear networks for invariant recognition
Neural Computation
Dynamics of cortical columns – self-organization of receptive fields
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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We study self-organization of receptive fields (RFs) of cortical minicolumns. Input driven self-organization is induced by Hebbian synaptic plasticity of afferent fibers to model minicolumns based on spiking neurons and background oscillations. If input in the form of spike patterns is presented during learning, the RFs of minicolumns hierarchically specialize to increasingly small groups of similar RFs in a series of nested group subdivisions. In a number of experiments we show that the system finds clusters of similar spike patterns, that it is capable of evenly cover the input space if the input is continuously distributed, and that it extracts basic features from input consisting of superpositions of spike patterns. With a continuous version of the bars test we, furthermore, demonstrate the system's ability to evenly cover the space of extracted basic input features. The hierarchical nature and its flexibility with respect to input distinguishes the presented type of self-organization from others including similar but non-hierarchical self-organization as discussed in [Lücke J., & von der Malsburg, C. (2004). Rapid processing and unsupervised learning in a model of the cortical macrocolumn. Neural Computation 16, 501-533]. The capabilities of the presented system match crucial properties of the plasticity of cortical RFs and we suggest it as a model for their hierarchical formation.