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Most artificial networks today rely on dense representations, whereasbiological networks rely on sparse representations. In this paper we show howsparse representations can be more robust to noise and interference, as long asthe underlying dimensionality is sufficiently high. A key intuition that wedevelop is that the ratio of the operable volume around a sparse vector dividedby the volume of the representational space decreases exponentially withdimensionality. We then analyze computationally efficient sparse networkscontaining both sparse weights and activations. Simulations on MNIST and theGoogle Speech Command Dataset show that such networks demonstrate significantlyimproved robustness and stability compared to dense networks, while maintainingcompetitive accuracy. We discuss the potential benefits of sparsity onaccuracy, noise robustness, hyperparameter tuning, learning speed,computational efficiency, and power requirements.

How Can We Be So Dense? The Benefits of Using Highly Sparse Representations