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Fast Learning and Robustness in Insect Olfactory Bio-Inspired Neural Networks: Neural Threshold Adaptation and Sparse Coding Strategies

Marcos Vázquez,F. B. Rodríguez

Published 2025 in IEEE International Conference on Systems, Man and Cybernetics

ABSTRACT

Fast learning remains a fundamental challenge in deep learning. Inspired by the biological insect olfactory system, we study fast learning in a model with structured random connectivity, adaptive thresholds, and sparsely activated neurons. In contrast to conventional random feature learning, our model regulates neuronal activity through dynamic threshold adaptation. Experimental results indicate that the model performs well with fewer training iterations while improving generalization and robustness compared to a traditional MLP. The model integrates principles from random feature learning and sparse coding, similar to the mechanisms observed in the insect olfactory system, particularly in the Antennal Lobe and Kenyon cells. These findings support that our Bio-Inspired Olfaction Neural Network (BONN) is a biologically plausible and computationally efficient alternative to fast learning in neural networks.

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