TY - JOUR
T1 - Short-term facilitation may stabilize parametric working memory trace
AU - Itskov, Vladimir
AU - Hansel, David
AU - Tsodyks, Misha
N1 - Publisher Copyright:
© 2011Itskov, Hansel and Tsodyks.
PY - 2011/10/24
Y1 - 2011/10/24
N2 - Networks with continuous set of attractors are considered to be a paradigmatic model for parametric working memory (WM), but require fine tuning of connections and are thus structurally unstable. Here we analyzed the network with ring attractor, where connections are not perfectly tuned and the activity state therefore drifts in the absence of the stabilizing stimulus. We derive an analytical expression for the drift dynamics and conclude that the network cannot function as WM for a period of several seconds, a typical delay time in monkey memory experiments. We propose that short-term synaptic facilitation in recurrent connections significantly improves the robustness of the model by slowing down the drift of activity bump. Extending the calculation of the drift velocity to network with synaptic facilitation, we conclude that facilitation can slow down the drift by a large factor, rendering the network suitable as a model of WM.
AB - Networks with continuous set of attractors are considered to be a paradigmatic model for parametric working memory (WM), but require fine tuning of connections and are thus structurally unstable. Here we analyzed the network with ring attractor, where connections are not perfectly tuned and the activity state therefore drifts in the absence of the stabilizing stimulus. We derive an analytical expression for the drift dynamics and conclude that the network cannot function as WM for a period of several seconds, a typical delay time in monkey memory experiments. We propose that short-term synaptic facilitation in recurrent connections significantly improves the robustness of the model by slowing down the drift of activity bump. Extending the calculation of the drift velocity to network with synaptic facilitation, we conclude that facilitation can slow down the drift by a large factor, rendering the network suitable as a model of WM.
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U2 - 10.3389/fncom.2011.00040
DO - 10.3389/fncom.2011.00040
M3 - Article
C2 - 22028690
AN - SCOPUS:84877977442
SN - 1662-5188
VL - 5
JO - Frontiers in Computational Neuroscience
JF - Frontiers in Computational Neuroscience
M1 - 40
ER -