Amit D J, Bernacchia A, Yakovlev V
Dipartimento di Fisica, Istituto di Fisica (INFM), Università di Roma La Sapienza, Piazzale A Moro 1, 00185, Roma, Italy.
Cereb Cortex. 2003 May;13(5):435-43. doi: 10.1093/cercor/13.5.435.
In a psychophysics experiment, monkeys were shown a sequence of two to eight images, randomly chosen out of a set of 16, each image followed by a delay interval, the last image in the sequence being a repetition of any (one) of the images shown in the sequence. The monkeys learned to recognize the repetition of an image. The performance level was studied as a function of the number of images separating cue (image that will be repeated) from match for different sequence lengths, as well as at fixed cue-match separation versus length of sequence. These experimental results are interpreted as features of multi-item working memory in the framework of a recurrent neural network. It is shown that a model network can sustain multi-item working memory. Fluctuations due to the finite size of the network, together with a single extra ingredient, related to expectation of reward, account for the dependence of the performance on the cue-position, as well as for the dependence of performance on sequence length for fixed cue-match separation.
在一项心理物理学实验中，向猴子展示一系列由16张图片中随机选取的2至8张图片，每张图片后都有一个延迟间隔，序列中的最后一张图片是序列中所展示的任何（一张）图片的重复。猴子学会了识别图片的重复。研究了不同序列长度下，将提示（即将重复的图片）与匹配图片分隔开的图片数量对性能水平的影响，以及在固定提示 - 匹配间隔下性能与序列长度的关系。这些实验结果在循环神经网络的框架内被解释为多项目工作记忆的特征。结果表明，一个模型网络可以维持多项目工作记忆。由于网络规模有限产生的波动，再加上与奖励期望相关的一个额外因素，解释了性能对提示位置的依赖性，以及在固定提示 - 匹配间隔下性能对序列长度的依赖性。
