The sparseness of mixed selectivity neurons controls the generalization-discrimination trade-off.
The sparseness of mixed selectivity neurons controls the generalization-discrimination trade-off.
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DOI：
10.1523/jneurosci.2753-12.2013
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发表时间：
2013-02-27
期刊：
影响因子：
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通讯作者：
Fusi S
中科院分区：
文献类型：
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作者：
Barak O;Rigotti M;Fusi S
智能行为需要以有意义的方式整合多种信息源——无论是情境与刺激，还是形状与颜色及大小。这要求潜在的神经机制对相似的输入以不同的方式做出反应（区分），同时对同一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能够形成一种易于解读的输入组合表征。利用分析和数值工具，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区分之间的权衡，最优水平取决于手头的任务。在我们分析的所有实际情况中，神经元做出反应的输入的最优比例接近0.1。最后，我们预测了神经表征的一个可测量属性与任务表现之间的关系。
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.