Sustainable reservoir computing with liquid egg albumen

Abstract

While physical reservoir computing offers a promising approach for efficient information processing, identifying suitable substrates remains challenging. Here, we demonstrated that colloidal albumen proteins could function as an effective physical reservoir for classifying multivariate datasets and electrocardiogram (ECG) signals. We exploited the nonlinear dynamics of protein macromolecules and ions in the albumen to perform high-dimensional mappings of input data. Our albumen-based reservoir achieved classification accuracy comparable to conventional machine learning methods on benchmark datasets while consuming over 5000 times less energy during training. Notably, the reservoir exhibited short-term plasticity analogous to biological synapses, with conductance spikes and fading memory. This bio-inspired computing paradigm not only offered a sustainable alternative to traditional architectures but also provided insights into the information-processing capabilities of biological systems. Our findings opened new avenues for low-power, environmentally friendly computing solutions with potential applications in real-time health monitoring and edge computing.