Ubiquitous Internet of Things (IoT) platforms have exponentially increased the relay of an abundance of data between memory and processor in current computing architectures, ultimately limiting throughput and advancing towards the von Neumann bottleneck. Brain-inspired computing using neuromorphic devices presents an encouraging alternative framework capable of power-efficient real-time inference, self-learning, and decision-making. This work focuses specifically on ion-gated, three-terminal transistors that rely on electrochemically driven ion intercalation between an electronically insulating electrolyte and a conducting channel. While other nonvolatile memories (NVMs) can be used in neuromorphic devices, electrochemical random-access memory (ECRAM) advantageously enables the programming of multi-states and their retention in the absence of applied power. This talk will present our recent work on anatase TiO2 as a channel material in a lithium-ion-gated, three-terminal transistor. Based on the literature, anatase TiO2 covers a wide channel conductance range and exhibits a linear response, allowing for numerous well-defined synaptic weights [1]. We explore a range of channel conductance values using both potentiostatic and galvanostatic pulsing and correlate material properties to electrical measurements. These results provide a benchmark and roadmap for lithium-ion-based neuromorphic transistors capable of multi-states.[1] Y. Li, E.J. Fuller, S. Asapu, S. Agarwal, T. Kurita, J.J. Yang, and A.A. Talin, ACS Appl. Mater. Interfaces 11, 38982 (2019).