Neuromorphic computing, inspired by the functionality of biological brains, requires novel materials and architectures capable of mimicking neuronal and synaptic dynamics. A 3D nanonetwork composed of tetrapodal ZnO (tZnO) structures, coated with exfoliated graphene (EG) and molybdenum disulfide (MoS₂) is presented. This hybrid nanonetwork is infiltrated with the liquid battery electrolyte LP30 (EC:DMC with LiPF₆) to enable ionic transport. By introducing multiple needle electrodes into the network and applying periodic rectangular voltage pulses, the system adepts to time patterns via ion intercalation and path formation.

The analogy to the human brain lies in the behavior of the network during operation. The existing pathways act as artificial neurons, while the interfaces between nodes function as synapses that strengthen or grow due to Li-ion intercalation in the MoS₂ layer. During the training phase, spiking events reminiscent of neuronal firing occur. These spikes arise from overlapping conductive paths and the dynamic interplay between local resistances caused by embedded microbatteries. This self-organizing behavior leads to energy minimization effects and highlights the network’s potential for adaptive information processing.

The results demonstrate that the combination of tZnO’s unique 3D structure, EG’s high conductivity, and MoS₂’s intercalation capabilities enables the realization of a neuromorphic material platform. The spiking dynamics and emergent pathways closely resemble signal processing in biological neural networks, making this system a promising candidate for next-generation brain-inspired computing applications.