Attractor Neural Networks with Hypercolumns
نویسندگان
چکیده
We investigate attractor neural networks with a modular structure, where a local winner-takes-all rule acts within the modules (called hypercolumns). We make a signal-to-noise analysis of storage capacity and noise tolerance, and compare the results with those from simulations. Introducing local winner-takes-all dynamics improves storage capacity and noise tolerance, while the optimal size of the hypercolumns depends on network size and noise level.
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تاریخ انتشار 2002