Polychronization: Computation with Spikes
Neural Computation
A fast learning algorithm for deep belief nets
Neural Computation
Detection of Straight Lines Using a Spiking Neural Network Model
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 02
Design tools for artificial nervous systems
Proceedings of the 49th Annual Design Automation Conference
IEEE Transactions on Neural Networks
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A computational model of a self-structuring neuronal net is presented in which repetitively applied pattern sets induce the formation of cortical columns and microcircuits which decode distinct patterns after a learning phase. In a case study, it is demonstrated how specific neurons in a feature classifier layer become orientation selective if they receive bar patterns of different slopes from an input layer. The input layer is mapped and intertwined by self-evolving neuronal microcircuits to the feature classifier layer. In this topical overview, several models are discussed which indicate that the net formation converges in its functionality to a mathematical transform which maps the input pattern space to a feature representing output space. The self-learning of the mathematical transformis discussed and its implications are interpreted. Model assumptions are deduced which serve as a guide to apply model derived repetitive stimuli pattern sets to in vitro cultures of neuron ensembles to condition them to learn and execute a mathematical transform.