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Sensory complexity and global gain in a DCNN codetermine optimal arousal state
By leveraging the full observability of our model, we reconcile conflicting findings from previous studies on sensory processing, by showing that both linear as well inverted-U-shaped gain profiles emerge in the interaction of hierarchical sensory processing and global arousal changes.
LocalNorm: Robust Image Classification through Dynamically Regularized Normalization
While modern convolutional neural networks achieve outstanding accuracy on many image classification tasks, they are, compared to humans, much more sensitive to image degradation.
Efficient Computation in Adaptive Artificial Spiking Neural Networks
Adaptive spike-time coding additionally allows for the dynamic control of neural coding precision: we show how a simple model of arousal in AdSNNs further halves the average required firing rate and this notion naturally extends to other forms of attention.
