使用具有中度强抑制作用的动态神经场来跟踪种群密度。
Tracking population densities using dynamic neural fields with moderately strong inhibition.
机构信息
Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS, Canada, B3H 1W5,
出版信息
Cogn Neurodyn. 2008 Sep;2(3):171-7. doi: 10.1007/s11571-008-9046-0. Epub 2008 Apr 17.
Abstract
We discuss the ability of dynamic neural fields to track noisy population codes in an online fashion when signals are constantly applied to the recurrent network. To report on the quantitative performance of such networks we perform population decoding of the 'orientation' embedded in the noisy signal and determine which inhibition strength in the network provides the best decoding performance. We also study the performance of decoding on time-varying signals. Simulations of the system show good performance even in the very noisy case and also show that noise is beneficial to decoding time-varying signals.
摘要
我们讨论了动态神经场在信号不断施加于递归网络时以在线方式跟踪噪声群体代码的能力。为了报告此类网络的定量性能，我们对噪声信号中嵌入的“方向”进行了群体解码，并确定了网络中哪种抑制强度提供了最佳解码性能。我们还研究了对时变信号的解码性能。即使在非常嘈杂的情况下，系统的模拟也表现出良好的性能，并且还表明噪声有利于解码时变信号。
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