Deep Neural Networks (DNNs) are popular for classifying large noisy analogue data. However, DNNs suffer from several known issues, including explainability, efficiency, catastrophic interference, and a need for high‐end computational resources. Our simulations reveal that psychologically‐inspired symbolic deep networks (SDNs) achieve similar accuracy and robustness to noise as DNNs on common ML problem sets, while addressing these issues.