Design of a molecular exchange-based robust perceptron for biomolecular neural network

A molecular perceptron is of immense interest due to its computing and classification ability in biophysical and aqueous environments. Because such a perceptron relies on biochemical interactions, it must adapt to perturbations and be resilient against stochastic fluctuations to maintain faithful \emph{in vivo} classification. In this paper, we design a molecular exchange mechanism (MEM)-based perceptron following a set of evolutionarily preserved \emph{in vivo} signaling steps, including negative feedback known for noise regulation. The efficacy study of the MEM-perceptron demonstrates an improved adaptation against perturbations and noise.