SIMPLE-MODELS FOR READING NEURONAL POPULATION CODES
SIMPLE-MODELS FOR READING NEURONAL POPULATION CODES
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DOI：
10.1073/pnas.90.22.10749
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发表时间：
1993-11-15
影响因子：
11.1
通讯作者：
SOMPOLINSKY, H
中科院分区：
文献类型：
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作者：
SEUNG, HS;SOMPOLINSKY, H
在许多神经系统中，感觉信息分布在整个神经元群体中。我们研究简单的神经网络模型来提取这些信息。网络的输入是对定向刺激进行调谐的感觉神经元群体的随机反应。在心理物理任务中的每个网络模型的性能进行了比较，与最佳的最大似然过程。作为二维方向估计的模型，我们考虑一个线性网络，它计算人口向量。它的性能取决于人口调谐曲线的宽度，是最大的宽度，这与背景活动的水平。虽然对于窄调谐神经元的人口矢量的性能是显着劣于最大似然估计，两者之间的差异是小的调谐时是广泛的。对于方向歧视，我们考虑两种模型：一个感知器与完全自适应权重和网络通过添加自适应第二层的人口向量网络。我们计算这些网络在向特定方向进行穷举训练后的错误率。通过对所有可能的方向进行测试，可以计算出训练转移到新刺激的程度。研究发现，对于阈值线性网络，感知学习的传递是非单调的。虽然性能恶化远离训练刺激，它再次在一个中间的角度达到峰值。这种非单调性为这些模型提供了一个重要的心理物理测试。
In many neural systems, sensory information is distributed throughout a population of neurons. We study simple neural network models for extracting this information. The inputs to the networks are the stochastic responses of a population of sensory neurons tuned to directional stimuli. The performance of each network model in psychophysical tasks is compared with that of the optimal maximum likelihood procedure. As a model of direction estimation in two dimensions, we consider a linear network that computes a population vector. Its performance depends on the width of the population tuning curves and is maximal for width, which increases with the level of background activity. Although for narrowly tuned neurons the performance of the population vector is significantly inferior to that of maximum likelihood estimation, the difference between the two is small when the tuning is broad. For direction discrimination, we consider two models: a perceptron with fully adaptive weights and a network made by adding an adaptive second layer to the population vector network. We calculate the error rates of these networks after exhaustive training to a particular direction. By testing on the full range of possible directions, the extent of transfer of training to novel stimuli can be calculated. It is found that for threshold linear networks the transfer of perceptual learning is nonmonotonic. Although performance deteriorates away from the training stimulus, it peaks again at an intermediate angle. This nonmonotonicity provides an important psychophysical test of these models.