Developing multifunctional and diversified artificial neural systems to integrate multimodal plasticity, memory, and supervised learning functions is an important task toward the emulation of neuromorphic computation. Here, we present a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The artificial synapse is composed of an optoelectronic transistor based on graphene/MoS₂ heterostructure and an integrated triboelectric nanogenerator. By controlling the charge transfer/exchange in the heterostructure with triboelectric potential, the optoelectronic synaptic behaviors can be readily modulated, including postsynaptic photocurrents, persistent photoconductivity, and photosensitivity. The photonic synaptic plasticity is elaborately investigated under the synergistic effect of mechanical displacement and the light pulses embodying different spatiotemporal information. Furthermore, artificial neural networks are simulated to demonstrate the improved image recognition accuracy up to 92% assisted with mechanical plasticization. The mechano-photonic artificial synapse is highly promising for implementing mixed-modal interaction, emulating complex biological nervous system, and promoting the development of interactive artificial intelligence.

Low power electronics endowed with artificial intelligence and biological afferent characters are beneficial to neuromorphic sensory network. Highly distributed synaptic sensory neurons are more readily driven by portable, distributed, and ubiquitous power sources. Here, we report a contact-electrification-activated artificial afferent at femtojoule energy. Upon the contact-electrification effect, the induced triboelectric signals activate the ion-gel-gated MoS₂ postsynaptic transistor, endowing the artificial afferent with the adaptive capacity to carry out spatiotemporal recognition/sensation on external stimuli (e.g., displacements, pressures and touch patterns). The decay time of the synaptic device is in the range of sensory memory stage. The energy dissipation of the artificial afferents is significantly reduced to 11.9 fJ per spike. Furthermore, the artificial afferents are demonstrated to be capable of recognizing the spatiotemporal information of touch patterns. This work is of great significance for the construction of next-generation neuromorphic sensory network, self-powered biomimetic electronics and intelligent interactive equipment.