The analog channel conductance modulation of electrolyte-gated transistors (EGTs) is a desirable property for the emulation of synaptic weight modulation and thus gives them great potential in neuromorphic computing systems. In this work, an all-solid-state electrochemical EGT was introduced with a low channel conductance (~120 nS) using amorphous Nb₂O₅ and Li-doped SiO₂ (Li_xSiO₂) as the channel and gate electrolyte materials, respectively. By adjusting the applied gate voltage pulse parameters, the reversable and nonvolatile modulation of channel conductance were achieved, which was ascribed to reversible intercalation/deintercalation of Li⁺ ions into/from the Nb₂O₅ lattice. Essential functionalities of synapses, such as the short-term plasticity (STP), long-term plasticity (LTP), and transformation from STP to LTP, were simulated successfully by conductive channel modulation of the EGTs. Based on these characteristics, a simple associative learning circuit was designed by parallel a resistor between the gate and the source terminals. The Pavlovian dog classical conditioning behavior was simulated based on associative learning circuit, where the resistor represented the unconditioned synapse and shared the gate voltage with EGT according to the proportion of its resistance, and the resistance between gate and source for negative feedback regulation of synaptic weights. These results demonstrate the potential of EGT for artificial synaptic devices and provide an insight into hardware implementation of neuromorphic computing systems.