Winner-take-all networks of O(N) complexity
Advances in neural information processing systems 1
Shunting inhibition does not have a divisive effect on firing rates
Neural Computation
Self-organizing maps
On the Computational Power of Max-Min Propagation Neural Networks
Neural Processing Letters
A canonical neural circuit for cortical nonlinear operations
Neural Computation
Competitive stdp-based spike pattern learning
Neural Computation
Computation with spikes in a winner-take-all network
Neural Computation
Cortical circuitry implementing graphical models
Neural Computation
A class of sparsely connected autoassociative morphological memories for large color images
IEEE Transactions on Neural Networks
An unbiased implementation of regularization mechanisms
Image and Vision Computing
Biophysical models of neural computation: max and tuning circuits
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Computing the maximum using presynaptic inhibition with glutamate receptors
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
A differential model of the complex cell
Neural Computation
Lattice neural networks with spike trains
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Tangent bundle curve completion with locally connected parallel networks
Neural Computation
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Visual processing in the cortex can be characterized by a predominantly hierarchical architecture, in which specialized brain regions along the processing pathways extract visual features of increasing complexity, accompanied by greater invariance in stimulus properties such as size and position. Various studies have postulated that a nonlinear pooling function such as the maximum (MAX) operation could be fundamental in achieving such selectivity and invariance. In this article, we are concerned with neurally plausible mechanisms that may be involved in realizing the MAX operation. Different canonical models are proposed, each based on neural mechanisms that have been previously discussed in the context of cortical processing. Through simulations and mathematical analysis, we compare the performance and robustness of these mechanisms. We derive experimentally verifiable predictions for each model and discuss the relevant physiological considerations.