The ion concentrations in body fluids modulate synaptic dynamics, which in turn modify perceptual capabilities and promote adaptive responses to environmental challenges. Therefore, to realize ion-concentration-modulated nanofluidic synapses is of great significance for the development of intelligent devices with environmental adaptability. We propose an ion-concentration-modulated nanofluidic memristor featuring asymmetric nanochannels based on graphene oxide (GO)/MXene composites. The Ti-O-C bonds formed between GO and MXene significantly enhance the structural stability of the thin film in water (>40 days); these results build a strong foundation for the development of nanofluidic memristors with long-term stability. At low K⁺ ion concentrations (10^-6 M), the device exhibits typical biological synaptic plasticity behaviors with strong temporal correlation, which disappear at high K⁺ ion concentrations (10^-2 M). Such an ion-concentration-modulated memristive mechanism can be attributed to the cation-π interactions between potassium ions and the material, whose concentration-dependent changes regulate surface charge and cation selectivity in the nanochannels, resulting in distinct electrical behaviors. Moreover, the dynamic neural regulation function during the predation process is demonstrated in the ion-concentration-modulated nanofluidic memristor-based neuromorphic system. This work offers a new strategy for the development of advanced functional neuromorphic devices by introducing ion concentration sensitivity for environment-adaptive dynamic perception.

Spiking neural network (SNN), widely known as the third-generation neural network, has been frequently investigated due to its excellent spatiotemporal information processing capability, high biological plausibility, and low energy consumption characteristics. Analogous to the working mechanism of human brain, the SNN system transmits information through the spiking action of neurons. Therefore, artificial neurons are critical building blocks for constructing SNN in hardware. Memristors are drawing growing attention due to low consumption, high speed, and nonlinearity characteristics, which are recently introduced to mimic the functions of biological neurons. Researchers have proposed multifarious memristive materials including organic materials, inorganic materials, or even two-dimensional materials. Taking advantage of the unique electrical behavior of these materials, several neuron models are successfully implemented, such as Hodgkin–Huxley model, leaky integrate-and-fire model and integrate-and-fire model. In this review, the recent reports of artificial neurons based on memristive devices are discussed. In addition, we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices. Finally, the future challenges and outlooks of memristor-based artificial neurons are discussed, and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.