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

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

2011-04-02

arxiv.org/abs/1104.0305v3

Neural Comput. 24 (2012) 1147-1185

Biology

Quantitative Biology

Neurons and Cognition

40 pages, 17 figures

Scientific paper

10.1162/NECO_a_00269

Experimental data have revealed that 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 rapid learning, and may serve as substrates for neural systems manipulating temporal information on 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 endows the network with slow-decaying plateau behaviors-the network that is initially being stimulated to an active state decays to a silent state very slowly on the time scale of STD rather than on the time scale of neural signaling. This provides a mechanism for neural systems to hold sensory memory easily and shut off persistent activities gracefully. 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 accuracy in population decoding. Furthermore, 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. 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 depending on the computational purpose.

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