Biology-derived synaptic dynamics and optimized system architecture for neuromorphic hardware

Neuromorphic circuits try to replicate aspects of the information processing in neural tissue. Historically, this has often meant some kind of long-term learning function which slowly adjusts the weight of a synapse to achieve a certain target network function. Recently, short-term dynamics at the synapse have also gained significant attention due to their role in dynamic and temporal information processing. However, only very few neuromorphic circuits have incorporated short term dynamics, with still fewer of these implementations being biologically realistic. We derive a circuit for biologically relevant short term dynamics, showing its accuracy with respect to biological measurements. Since this circuit significantly increases the overall complexity of the synapse, a direct integration in the synapse would be prohibitive. Thus, in addition to the short term dynamics, we also present a novel configurable topology for the neurons and synapses on chip which achieves a compact and flexible overall design while still augmenting all synapses with the new short term dynamics.