About this Research Topic
Computational studies have explored the impact of STP on network dynamics, and found that STP can generate very rich intrinsic dynamical behaviors, including damped oscillations, state hopping with transient population spikes, traveling fronts and pulses, spiral waves, rotating bump states, robust self-organized critical activities and so on. These studies also strongly suggest that STP can play many important roles in neural computation. For instances, STD may provide a dynamic control mechanism that allows equal fractional changes on rapidly and slowly firing afferents to produce post-synaptic responses, realizing Weber’s law; STD may provide a mechanism to close down network activity naturally, achieving iconic sensory memory; and STF may provide a mechanism for implementing work-memory not relying on persistent neural firing.
From the computational point of view, the time scale of STP resides between fast neural signaling (in the order of milliseconds) and rapid learning (in the order of minutes or above), which is the time scale of many important temporal processes occurring in our daily lives, such as motion control and working memory. Thus, STP may serve as a substrate for neural systems manipulating temporal information on the relevant time scales.
This Research Topic aims to present the recent progress in understanding the roles of STP in neural information processing. It includes, but no exclusively, the studies on investigating various computational roles of STP, the modeling studies on exploring new dynamical behaviors generated by STP, and the experimental works which help us to understand the functional roles of STP.
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