The biological brain has set a golden standard in computational efficiency, both due to its massive parallelism, and its ability to perform in-memory computing within the same ionic substrate. Ion-gated channels determine synaptic strength which is known to be a mechanism for memory storage, and the very same ions that pass through these gates encode data in the form of spikes. Communication, computation, and storage all occur within the same local medium. The brain's ability to perform in-memory processing within a unified ionic mechanism has driven many researchers to apply ion-driven non-volatile memories to emulate learning rules at the device-level. [1-12] The operating principles of resistive random access memory (RRAM) draw parallel with biological synapses. From a physical standpoint, the top electrode (TE) corresponds to a pre-synaptic terminal, the insulator layer acts as the substrate through which neurotransmitters are released, and the bottom electrode (BE) Biologically plausible computing systems require fine-grain tuning of analog synaptic characteristics. In this study, lithium-doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state-dependent decay to be reliably achieved. As a result, this device offers multi-bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short-term memory and long-term memory are emulated across dynamical timescales. Spike-timing-dependent plasticity and paired-pulse facilitation are also demonstrated. These mechanisms are capable of self-pruning to generate efficient neural networks. Time-dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human's higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.