Nearest Neighbor Representations of Neurons

CoRR(2024)

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摘要
The Nearest Neighbor (NN) Representation is an emerging computational model that is inspired by the brain. We study the complexity of representing a neuron (threshold function) using the NN representations. It is known that two anchors (the points to which NN is computed) are sufficient for a NN representation of a threshold function, however, the resolution (the maximum number of bits required for the entries of an anchor) is O(nlogn). In this work, the trade-off between the number of anchors and the resolution of a NN representation of threshold functions is investigated. We prove that the well-known threshold functions EQUALITY, COMPARISON, and ODD-MAX-BIT, which require 2 or 3 anchors and resolution of O(n), can be represented by polynomially large number of anchors in n and O(logn) resolution. We conjecture that for all threshold functions, there are NN representations with polynomially large size and logarithmic resolution in n.
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