Preintegration lateral inhibition enhances unsupervised learning
Neural Computation
IEEE Transactions on Neural Networks
Simple model of spiking neurons
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
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Neural network models for unsupervised pattern recognition learning are challenged when the difference between the patterns of the training set is small. The standard neural network architecture for pattern recognition learning consists of adaptive forward connections and lateral inhibition, which provides competition between output neurons. We propose an additional adaptive inhibitory feedback mechanism, to emphasize the difference between training patterns and improve learning. We present an implementation of adaptive feedback inhibition for spiking neural network models, based on spike timing dependent plasticity (STDP). When the inhibitory feedback connections are adjusted using an anti-Hebbian learning rule, feedback inhibition suppresses the redundant activity of input units which code the overlap between similar stimuli. We show, that learning speed and pattern discriminatability can be increased by adding this mechanism to the standard architecture.