A multiple cause mixture model for unsupervised learning
Neural Computation
A Neural Network for PCA and Beyond
Neural Processing Letters
A simple algorithm that discovers efficient perceptual codes
Computational and psychophysical mechanisms of visual coding
Feature extraction through LOCOCODE
Neural Computation
Nonlinear dynamical properties of a somatosensory cortical model
Information Sciences: an International Journal
Preintegration lateral inhibition enhances unsupervised learning
Neural Computation
Macrocolumns as Decision Units
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Biophysiologically plausible implementations of the maximum operation
Neural Computation
Competition and multiple cause models
Neural Computation
Hierarchial self-organization of minicolumnar receptive fields
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Towards cortex sized artificial neural systems
Neural Networks
Learning sensory representations with intrinsic plasticity
Neurocomputing
Learning Image Components for Object Recognition
The Journal of Machine Learning Research
Maximal Causes for Non-linear Component Extraction
The Journal of Machine Learning Research
Rapid convergence to feature layer correspondences
Neural Computation
Spatio-temporal memories for machine learning: a long-term memory organization
IEEE Transactions on Neural Networks
Generalized softmax networks for non-linear component extraction
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A dynamical model for receptive field self-organization in V1 cortical columns
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Learning of lateral connections for representational invariant recognition
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Expectation Truncation and the Benefits of Preselection In Training Generative Models
The Journal of Machine Learning Research
Dynamics of cortical columns – sensitive decision making
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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
Rapid correspondence finding in networks of cortical columns
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Glial cells for information routing?
Cognitive Systems Research
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We study a model of the cortical macrocolumn consisting of a collection of inhibitorily coupled minicolumns. The proposed system overcomes several severe deficits of systems based on single neurons as cerebral functional units, notably limited robustness to damage and unrealistically large computation time. Motivated by neuroanatomical and neurophysiological findings, the utilized dynamics is based on a simple model of a spiking neuron with refractory period, fixed random excitatory interconnection within minicolumns, and instantaneous inhibition within one macrocolumn. A stability analysis of the system's dynamical equations shows that minicolumns can act as monolithic functional units for purposes of critical fast decisions and learning. Oscillating inhibition (in the gamma frequency range) leads to a phase-coupled population rate code and high sensitivity to small imbalances in minicolumn inputs. Minicolumns are shown to be able to organize their collective inputs without supervision by Hebbian plasticity into selective receptive field shapes, thereby becoming classifiers for input patterns. Using the bars test, we critically compare our system's performance with that of others and demonstrate its ability for distributed neural coding.