Title: Synaptic Dynamics Realize First-order Adaptive Learning and Weight Symmetry. Abstract: Gradient-based first-order adaptive optimization methods such as the Adam optimizer are prevalent in training artificial networks, achieving the state-of-the-art results.  This work attempts to answer the question whether it is viable for biological neural systems to adopt such optimization methods. To this end, we demonstrate a realization of the Adam optimizer using biologically-plausible  mechanisms in synapses. The proposed learning rule has clear biological correspondence, runs continuously in time, and achieves performance to comparable  Adam's. In addition, we present a new approach, inspired by the predisposition property of synapses observed in neuroscience, to circumvent the biological implausibility of  the weight transport problem in backpropagation (BP). With only local information and no separate training phases, this  method establishes and maintains weight symmetry in the forward and backward signaling paths, and is applicable to the proposed biologically plausible Adam learning rule.  The aforementioned mechanisms may shed light on the way in which biological synaptic dynamics facilitate learning.