Numerous neurophysiological studies have revealed that a large number of the primary visual cortex neurons operate in a regime called surround modulation. Surround modulation has a substantial effect on various perceptual tasks, and it also plays a crucial role in the efficient neural coding of the visual cortex. Inspired by the notion of surround modulation, we designed new excitatory-inhibitory connections between a unit and its surrounding units in the convolutional neural network (CNN) to achieve a more biologically plausible network. Our experiments show that this simple mechanism can considerably improve both the performance and training speed of traditional CNNs in visual tasks. We further explore additional outcomes of the proposed structure. We first evaluate the model under several visual challenges, such as the presence of clutter or change in lighting conditions and show its superior generalization capability in handling these challenging situations. We then study possible changes in the statistics of neural activities such as sparsity and decorrelation and provide further insight into the underlying efficiencies of surround modulation. Experimental results show that importing surround modulation into the convolutional layers ensues various effects analogous to those derived by surround modulation in the visual cortex.

The authors report increased accuracy and robustness by adding an extremely simple, linear filtering with a difference-of-Gaussian kernel. While this would be a quite remarkable finding if it turned out to be reliable, I have substantial doubts, which I will outline below: - Instead of using a well-established baseline (e.g. a ResNet), the authors train their own architecture (similar to VGG) on what seems to be their own 100-class variant of ImageNet. Despite these simplifications, their performance is quite poor (a ResNet-18 would achieve 70% top-1 accuracy on full ImageNet). As a consequence, we don't really know whether the improvement by their surround modulation module is an artefact of a poorly trained model or a real effect. Training a ResNet on ImageNet is trivial these days (scripts are provided in the official PyTorch or Tensorflow repositories), so it's really unclear to me why the authors try to establish their own baseline.

This paper adds static linear surround modulation to deep convolutional networks. The authors show that this improves the speed and performance of the networks. They also show that it (but also batch normalization) increases sparsity of neural activity. The performance gains seem to be reduced on larger problems which limited projected significance and excitement in the paper. Limiting suppression to only identical features was also seen as limiting.

Numerous neurophysiological studies have revealed that a large number of the primary visual cortex neurons operate in a regime called surround modulation. Surround modulation has a substantial effect on various perceptual tasks, and it also plays a crucial role in the efficient neural coding of the visual cortex. Inspired by the notion of surround modulation, we designed new excitatory-inhibitory connections between a unit and its surrounding units in the convolutional neural network (CNN) to achieve a more biologically plausible network. Our experiments show that this simple mechanism can considerably improve both the performance and training speed of traditional CNNs in visual tasks. We further explore additional outcomes of the proposed structure.

Numerous neurophysiological studies have revealed that a large number of the primary visual cortex neurons operate in a regime called surround modulation. Surround modulation has a substantial effect on various perceptual tasks, and it also plays a crucial role in the efficient neural coding of the visual cortex. Inspired by the notion of surround modulation, we designed new excitatory-inhibitory connections between a unit and its surrounding units in the convolutional neural network (CNN) to achieve a more biologically plausible network. Our experiments show that this simple mechanism can considerably improve both the performance and training speed of traditional CNNs in visual tasks. We further explore additional outcomes of the proposed structure.