Center for Theoretical Neuroscience, Department of Neuroscience, Columbia University Medical Center, New York, New York 10032, USA.
J Neurosci. 2013 Feb 27;33(9):3844-56. doi: 10.1523/JNEUROSCI.2753-12.2013.
Intelligent behavior requires integrating several sources of information in a meaningful fashion-be it context with stimulus or shape with color and size. This requires the underlying neural mechanism to respond in a different manner to similar inputs (discrimination), while maintaining a consistent response for noisy variations of the same input (generalization). We show that neurons that mix information sources via random connectivity can form an easy to read representation of input combinations. Using analytical and numerical tools, we show that the coding level or sparseness of these neurons' activity controls a trade-off between generalization and discrimination, with the optimal level depending on the task at hand. In all realistic situations that we analyzed, the optimal fraction of inputs to which a neuron responds is close to 0.1. Finally, we predict a relation between a measurable property of the neural representation and task performance.
智能行为需要以有意义的方式整合多种信息来源——无论是刺激与上下文的关系，还是颜色、大小与形状的关系。这要求潜在的神经机制以不同的方式对相似的输入做出反应（辨别），同时对相同输入的噪声变化保持一致的反应（泛化）。我们表明，通过随机连接混合信息源的神经元可以形成易于读取的输入组合表示。使用分析和数值工具，我们表明，这些神经元活动的编码水平或稀疏性控制着泛化和辨别之间的权衡，最优水平取决于手头的任务。在我们分析的所有现实情况下，神经元响应的输入的最优分数接近 0.1。最后，我们预测了神经表示的一个可测量性质与任务表现之间的关系。
