Dynamical Synapses Enhance Neural Information Processing: Mobility, Memory and Decoding

Neuronal connection efficacy exhibits two forms of short-term plasticity, namely, short-term depression (STD) and short-term facilitation (STF). They have time constants residing between fast neural signaling and slow learning, and may serve as substrates for neural systems manipulating temporal information in relevant time scales. The present study investigates the impact of STD and STF on the dynamics of continuous attractor neural networks (CANNs) and their potential roles in neural information processing. We find that STD increases the mobility of the network states. The increased mobility enhances the tracking performance of the network in response to time-varying stimuli, leading to anticipative neural responses. Furthermore, we find that STD endows the network with slow-decaying plateau behaviors, namely, the network being initially stimulated to an active state decays to silence very slowly in the time scale of STD rather than that of neural signaling. This provides a mechanism for neural systems to hold short-term memory easily and shut off persistent activities naturally. With STF, we find that the network can hold a memory trace of external inputs in the facilitated neuronal interactions, which provides a way to stabilize the network response to noisy inputs, leading to improved population decoding performances. In general, we find that STD and STP tend to have opposite effects on network dynamics and complementary computational advantages, suggesting that the brain may employ a strategy of weighting them differentially for serving different computational purposes.