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Dropout is a technique that silences the activity of units stochasticallywhile training deep networks to reduce overfitting. Here we introduce QuantalSynaptic Dilution (QSD), a biologically plausible model of dropoutregularisation based on the quantal properties of neuronal synapses, thatincorporates heterogeneities in response magnitudes and release probabilitiesfor vesicular quanta. QSD outperforms standard dropout in ReLU multilayerperceptrons, with enhanced sparse encoding at test time when dropout masks arereplaced with identity functions, without shifts in trainable weight or biasdistributions. For convolutional networks, the method also improvesgeneralisation in computer vision tasks with and without inclusion ofadditional forms of regularisation. QSD also outperforms standard dropout inrecurrent networks for language modelling and sentiment analysis. An advantageof QSD over many variations of dropout is that it can be implemented generallyin all conventional deep networks where standard dropout is applicable.

Quantal synaptic dilution enhances sparse encoding and dropout regularisation in deep networks