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Insects, such as fruit flies and honey bees, can solve simple associativelearning tasks and learn abstract concepts such as "sameness" and "difference",which is viewed as a higher-order cognitive function and typically thought todepend on top-down neocortical processing. Empirical research with fruit fliesstrongly supports that a randomized representational architecture is used inolfactory processing in insect brains. Based on these results, we propose aRandomly Weighted Feature Network (RWFN) that incorporates randomly drawn,untrained weights in an encoder that uses an adapted linear model as a decoder.The randomized projections between input neurons and higher-order processingcenters in the input brain is mimicked in RWFN by a single-hidden-layer neuralnetwork that specially structures latent representations in the hidden layerusing random Fourier features that better represent complex relationshipsbetween inputs using kernel approximation. Because of this specialrepresentation, RWFNs can effectively learn the degree of relationship amonginputs by training only a linear decoder model. We compare the performance ofRWFNs to LTNs for Semantic Image Interpretation (SII) tasks that have been usedas a representative example of how LTNs utilize reasoning over first-orderlogic to surpass the performance of solely data-driven methods. We demonstratethat compared to LTNs, RWFNs can achieve better or similar performance for bothobject classification and detection of the part-of relations between objects inSII tasks while using much far fewer learnable parameters (1:62 ratio) and afaster learning process (1:2 ratio of running speed). Furthermore, we show thatbecause the randomized weights do not depend on the data, several decoders canshare a single randomized encoder, giving RWFNs a unique economy of spatialscale for simultaneous classification tasks.

An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning