PDF(Pubmed)
Abstract：
The property of mixed selectivity has been discussed at a computational level and offers a strategy to maximize computational power by adding versatility to the functional role of each neuron. Here, we offer a biologically grounded implementational-level mechanistic explanation for mixed selectivity in neural circuits. We define pure, linear, and nonlinear mixed selectivity and discuss how these response properties can be obtained in simple neural circuits. Neurons that respond to multiple, statistically independent variables display mixed selectivity. If their activity can be expressed as a weighted sum, then they exhibit linear mixed selectivity; otherwise, they exhibit nonlinear mixed selectivity. Neural representations based on diverse nonlinear mixed selectivity are high dimensional; hence, they confer enormous flexibility to a simple downstream readout neural circuit. However, a simple neural circuit cannot possibly encode all possible mixtures of variables simultaneously, as this would require a combinatorially large number of mixed selectivity neurons. Gating mechanisms like oscillations and neuromodulation can solve this problem by dynamically selecting which variables are mixed and transmitted to the readout.
摘要：
混合选择性的性质已经在计算层面上进行了讨论，并提供了一种策略，通过增加每个神经元的功能作用的多功能性来最大化计算能力。这里,我们为神经回路中的混合选择性提供了生物学上的实施层面的机制解释。我们定义纯粹，线性,和非线性混合选择性，并讨论如何在简单的神经电路中获得这些响应特性。对多个响应的神经元，统计独立变量显示混合选择性。如果他们的活动可以用加权和来表示，然后它们表现出线性混合选择性；否则，它们表现出非线性混合选择性。基于不同非线性混合选择性的神经表示是高维的；因此，它们为简单的下游读出神经回路赋予了巨大的灵活性。然而,一个简单的神经回路不可能同时编码所有可能的变量混合，因为这将需要组合大量的混合选择性神经元。像振荡和神经调节这样的门控机制可以通过动态地选择哪些变量被混合并传输到读出器来解决这个问题。
