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Experimental data have revealed that neuronal connection efficacy exhibitstwo forms of short-term plasticity, namely, short-term depression (STD) andshort-term facilitation (STF). They have time constants residing between fastneural signaling and rapid learning, and may serve as substrates for neuralsystems manipulating temporal information on relevant time scales. The presentstudy investigates the impact of STD and STF on the dynamics of continuousattractor neural networks (CANNs) and their potential roles in neuralinformation processing. We find that STD endows the network with slow-decayingplateau behaviors-the network that is initially being stimulated to an activestate decays to a silent state very slowly on the time scale of STD rather thanon the time scale of neural signaling. This provides a mechanism for neuralsystems to hold sensory memory easily and shut off persistent activitiesgracefully. With STF, we find that the network can hold a memory trace ofexternal inputs in the facilitated neuronal interactions, which provides a wayto stabilize the network response to noisy inputs, leading to improved accuracyin population decoding. Furthermore, we find that STD increases the mobility ofthe network states. The increased mobility enhances the tracking performance ofthe network in response to time-varying stimuli, leading to anticipative neuralresponses. In general, we find that STD and STP tend to have opposite effectson network dynamics and complementary computational advantages, suggesting thatthe brain may employ a strategy of weighting them differentially depending onthe computational purpose.

Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility