Neural information processing with dynamical synapses

Si Wu, K. Y. Michael Wong, Misha Tsodyks

Abstract: Experimental data have consistently revealed that the neuronal connection weight, which models the efficacy of firing of a pre-synaptic neuron in modulating the state of the post-synaptic neuron, varies on short time scales, ranging from tens to thousands of milliseconds. This is called short-term plasticity (STP). Two types of STP, with opposite effects on the connection efficacy, have been observed in experiments, which are known as short-term depression (STD) and short-term facilitation (STF). Computational studies have explored the impact of STP on single neuron and network dynamics, and found that STP can generate very rich intrinsic dynamical behaviors, including adaptation, temporal filtering, damped oscillation, state hopping with transient population spike, traveling front and pulse, spiral wave, rotating bump state, robust self-organized critical activity and so on. These studies also strongly suggest that STP may play many important roles in neural computation. For instances, STD may generate 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 generate a mechanism to close down network activity naturally, achieving iconic sensory memory; STD may provide a mechanism for memory searching by destabilizing attractor states; and STF may provide a mechanism for implementing work memory without recruiting neural firing. From the computational point of ...