This thesis aims to develop a hardware platform using intercalation based lithium niobite memristors to implement scalable neuromorphic architectures with high energy-efficiency and co-localized processing and main memory, thus overcoming the memory wall. Brain-inspired neuromorphic computing is a crucial field in addressing the increased need for collection, analysis and decision making from high volumes of dynamic unstructured data generated globally, at low power consumption. Memristive devices have emerged a key enabling technology for developing such large scale neuromorphic computing platforms. Lithium niobite is an adaptive suboxide which has shown promise in developing volatile and non-volatile memristors for highly scalable and low-power neuromorphic circuitry. The culmination of this work has demonstrated a memristive technology that implements all major functions of a neural network: analog training, linear resistive changes, temporal tuning, varied temporal response and adaptive activation, with each efficiently implemented in hardware.