As Moore’s law slows down and memory-intensive tasks get prevalent, digital computing becomes increasingly capacity- and power-limited. In order to meet the requirement for increased computing capacity and efficiency in the post-Moore era, emerging computing architectures, such as in-memory computing and neuromorphic computing architectures based on memristors, have been extensively pursued and become an important candidate for new-generation non-von Neumann computers. Here, we report development of highly compact artificial neurons and synapses, especially that capture important neuronal and synaptic dynamics, as well as the construction of hardware systems based on such artificial elements. These devices and systems are of great significance for developing neuromorphic hardware with augmented information processing and learning capabilities. We report an optoelectronic synapse that is based on α-In2Se3 and has controllable temporal dynamics under electrical and optical stimuli. Tight coupling between ferroelectric and optoelectronic processes in the synapse can be used to realize heterosynaptic plasticity, with relaxation timescales that are tunable via light intensity or back-gate voltage. We use the synapses to create a multimode reservoir computing system with adjustable nonlinear transformation and multisensory fusion, which is demonstrated using a multimode handwritten digit recognition task and a QR code recognition task. We also realize a multiscale reservoir computing system via the tunable relaxation timescale of the α-In2Se3 synapse, which is tested using a temporal signal prediction task [1]. Moreover, we propose a highly efficient neuromorphic physiological signal processing system based on VO2 memristors [2]. The volatile and positive/negative symmetric threshold switching characteristics of VO2 memristors are leveraged to construct a sparse-spiking yet high-fidelity asynchronous spike encoder for physiological signals. Besides, the dynamical behavior of VO2 memristors is utilized in compact Leaky Integrate and Fire and Adaptive-LIF neurons, which are incorporated into a decision-making Long short-term memory Spiking Neural Network. The system demonstrates superior computing capabilities, needing only small-sized LSNNs to attain high accuracies of 95.83% and 99.79% in arrhythmia classification and epileptic seizure detection, respectively. This work highlights the potential of memristors in constructing efficient neuromorphic physiological signal processing systems and promoting next-generation human-machine interfaces.