# Signal Conditioning for Learning in the Wild

## TL;DR

This paper introduces a biologically inspired signal conditioning method that preprocesses diverse sensory data, enabling a single learning network to perform well across various classification tasks without hyperparameter tuning.

## Contribution

The authors develop a signal conditioning pipeline inspired by the mammalian olfactory system that regularizes diverse sensory inputs for robust, hyperparameter-free learning across multiple domains.

## Key findings

- Enables a single network to classify diverse datasets without hyperparameter tuning.
- Improves learning robustness by transforming inputs into a common statistical structure.
- Demonstrates effectiveness on gas sensors, spectral remote sensing, and biological species identification.

## Abstract

The mammalian olfactory system learns rapidly from very few examples, presented in unpredictable online sequences, and then recognizes these learned odors under conditions of substantial interference without exhibiting catastrophic forgetting. We have developed a brain-mimetic algorithm that replicates these properties, provided that sensory inputs adhere to a common statistical structure. However, in natural, unregulated environments, this constraint cannot be assured. We here present a series of signal conditioning steps, inspired by the mammalian olfactory system, that transform diverse sensory inputs into a regularized statistical structure to which the learning network can be tuned. This pre-processing enables a single instantiated network to be applied to widely diverse classification tasks and datasets - here including gas sensor data, remote sensing from spectral characteristics, and multi-label hierarchical identification of wild species - without adjusting network hyperparameters.