Artificial intelligence (AI) algorithms are currently executed using silicon-based hardware, resulting in excessively high energy demand for data centers. Edge computing AI for healthcare, robotics, and autonomous vehicles presents even stricter constraints on power and latency, which are currently unmet by incumbent computing architectures. Efficient computation can be derived from the key properties of biological neurons, including memory-logic colocation, asynchronous parallelism, and spike-triggered computation. Here, we draw inspiration from the biological cerebellum to demonstrate an asymmetric-contact-gated MoS2 memtransistor that exhibits bias-polarity-dependent excitatory/inhibitory short-term plasticity. A memtransistor-based neural network realizes a changing interplay of excitatory/inhibitory responses, emulating the emergent synaptic differentiation of the cerebellum, enabling rapid identification of novel events. When applied to electrocardiogram data, arrhythmias are detected on the time scale of a single heartbeat with 10,000-fold fewer operations than existing silicon-based approaches. In this manner, cerebellum-inspired neuromorphic hardware provides a pathway to low-computation, high-speed novelty detection for edge intelligence.