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Attractor Dynamics with Synaptic Depression
C. C. Alan Fung · K. Y. Michael Wong · He Wang · Si Wu

Tue Dec 07 09:55 AM -- 10:00 AM (PST) @ Regency Ballroom

Neuronal connection weights exhibit short-term depression (STD). The present study investigates the impact of STD on the dynamics of a continuous attractor neural network (CANN) and its potential roles in neural information processing. We find that the network with STD can generate both static and traveling bumps, and STD enhances the performance of the network in tracking external inputs. In particular, we find that STD endows the network with slow-decaying plateau behaviors, namely, the network being initially stimulated to an active state will decay to silence very slowly in the time scale of STD rather than that of neural signaling. We argue that this provides a mechanism for neural systems to hold short-term memory easily and shut off persistent activities naturally.

Author Information

C. C. Alan Fung (The Hong Kong University of Science and Technology)
K. Y. Michael Wong (Department of Physics, Hong Kong University of Science and Technology)
He Wang (Tsinghua University)
Si Wu (Beijing Normal University)

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