Neuromorphic engineering, aiming at emulating neuro-biological architectures in efficient ways, has been widely studied both on com- ponent and VLSI system level. The design space of neuromorphic neuron, the basic unit to conduct signal processing and transmission in nervous system, has been widely explored while that of synapse, the specialized functional unit connecting neurons, is less investigated.

In this thesis, a current-based phenomenological synapse model with power-efficient structures, consisting of efficient synaptic learn- ing algorithms and multi-compartment synapses, has been proposed. A vertical insight is given into the design space of spike-based learn- ing rules in regards to design complexity and biological fidelity. Due to various biological conducting mechanisms, the receptors, namely AMPA, NMDA and GABAa, demonstrate different kinetics in re- sponse to stimulus. The designed circuit offers distinctive features of receptors as well as the joint synaptic function. A better compu- tation ability is demonstrated through a cross-correlation detection experiment with a recurrent network of synapse clusters. The analog multi-compartment synapse structure is able to detect and amplify the temporal synchrony embedded in the synaptic noise. The maximum amplification level is 2 times larger than that of single-receptor con- figurations. The final design implemented in UMC65nm technology consumes 1.92, 3.36, 1.11 and 35.22pJ per spike event of energy for AMPA, NMDA, GABAa receptors and the advanced learning circuit, respectively.