Very-large-scale mimetic optogenetic synapses for physical reservoir computing

The scaling law of deep learning, which governs the relationship between model size and performance, has led to critical concerns regarding efficiency and sustainability. To address these challenges, this study presents a computational approach using self-organized submillimeter-long tungsten disulfide nanotube cluster as a 3D very-large-scale physical reservoir. The reservoir, with its 0D van der Waals interfaces on the order of 108, or 1.0×1010 mm-3, matches the synaptic quantity and density of the fruit fly’s brain. The reservoir demonstrates the capability to perform a wide range of tasks from monomodal challenges to multimodal endeavors such as speech-to-image and medical image generation. The photosensitive mimetic synaptic connections in the very large scale reservoir emulate the optogenetic modulation of neuron circuits in in-vivo biological systems. By integrating the principles of the scaling law, multimodal task capabilities, and mimetic optogenetic mechanisms, this research paves a path toward advanced computing architectures tailored for next-generation energy-efficient artificial intelligence. Han et al. present a very-large-scale 3D computing reservoir self-assembled from 1D WS2 nanotubes, with dense mimetic optogenetic synapses matching a fruit fly’s brain. It efficiently handles monomodal and multimodal tasks, featuring optogenetics-inspired, light-tunable computing dynamics.