Additive function approximation in the brainDownload PDF

11 Sept 2019, 14:22 (modified: 08 Jul 2022, 17:26)Real Neurons & Hidden Units @ NeurIPS 2019 PosterReaders: Everyone
Keywords: sparse networks, random features, associative learning
TL;DR: We advocate for random features as a theory of biological neural networks, focusing on sparsely connected networks
Abstract: Many biological learning systems such as the mushroom body, hippocampus, and cerebellum are built from sparsely connected networks of neurons. For a new understanding of such networks, we study the function spaces induced by sparse random features and characterize what functions may and may not be learned. A network with d inputs per neuron is found to be equivalent to an additive model of order d, whereas with a degree distribution the network combines additive terms of different orders. We identify three specific advantages of sparsity: additive function approximation is a powerful inductive bias that limits the curse of dimensionality, sparse networks are stable to outlier noise in the inputs, and sparse random features are scalable. Thus, even simple brain architectures can be powerful function approximators. Finally, we hope that this work helps popularize kernel theories of networks among computational neuroscientists.
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