Valiant Leslie G
Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
Biol Cybern. 2006 Sep;95(3):205-11. doi: 10.1007/s00422-006-0079-3. Epub 2006 Jun 9.
We show how a general quantitative theory of neural computation can be used to explain two recent experimental findings in neuroscience. The first of these findings is that in human medial temporal lobe there exist neurons that correspond to identifiable concepts, such as a particular actress. Further, even when such concepts are preselected by the experimenter, such neurons can be found with paradoxical ease, after examining relatively few neurons. We offer a quantitative computational explanation of this phenomenon, where apparently none existed before. Second, for the locust olfactory system estimates of the four parameters of neuron numbers, synapse numbers, synapse strengths, and the numbers of neurons that represent an odor are now available. We show here that these numbers are related as predicted by the general theory. More generally, we identify two useful regimes for neural computation with distinct ranges of these quantitative parameters.
我们展示了一种通用的神经计算定量理论如何能够用于解释神经科学领域最近的两项实验发现。其中第一项发现是，在人类内侧颞叶中存在对应于可识别概念的神经元，比如某一位特定的女演员。此外，即使这些概念是由实验者预先选定的，在检查相对较少数量的神经元之后，也能极其容易地找到此类神经元。我们针对这一此前似乎不存在解释的现象给出了定量计算解释。其次，对于蝗虫嗅觉系统，目前已有关于神经元数量、突触数量、突触强度以及表征一种气味的神经元数量这四个参数的估计值。我们在此表明，这些数值正如通用理论所预测的那样相互关联。更一般地说，我们确定了神经计算的两种有用模式，它们具有这些定量参数的不同范围。
