  Molecular Communications (MC) underpins signaling in biological systems,
enabling information transfer through biochemical molecules. The prospect of
engineering this natural communication mechanism has inspired the Internet of
Bio-Nano Things (IoBNT) applications, which rely on heterogeneous collaborative
networks of natural and engineered biological devices, as well as artificial
micro/nanomachines. A key attribute of natural MC systems is their
adaptability, ensuring accurate information transmission in dynamic,
time-varying biochemical environments. Therefore, integrating biological
adaptation techniques into artificial MC networks, which are expected to
operate in various biochemical environments, such as inside human body, is
essential for robust and biocompatible IoBNT applications. This study explores
the design of bio-inspired adaptive MC receivers capable of tuning their
response functions for maintaining optimal detection performance in scenarios
with time-varying received signals. The proposed receiver architectures are
based on ligand-receptor interactions, with adaptivity achieved by modifying
the sigmoidal-shaped ligand-receptor response curve in response to fluctuations
in received signal statistics. The performance of these adaptive receivers is
evaluated across a range of MC scenarios, including those with stochastic
background interference, inter-symbol interference (ISI), and degrading
enzymes, which involve time-varying scaling or shifting of received signals.
Numerical results demonstrate the significant improvement in detection
performance provided by adaptive receivers in dynamic MC scenarios.
