Spontaneous summation or numerosity-selective coding?
Spontaneous summation or numerosity-selective coding?
复制标题
自发求和还是数量选择性编码？
DOI：
10.3389/fnhum.2013.00886
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发表时间：
2013
影响因子：
2.9
通讯作者：
Verguts T
中科院分区：
文献类型：
--
作者：
Chen Q;Verguts T
数字认知中的一个关键争论涉及数字表征的神经代码（例如，Nieder和Merten，2007年; Roggeman等人，2007年; Viswanathan和Nieder，2013年）。一种观点认为，个体神经元对个体数字的反应随着距离的增加而减弱（数字选择性编码或标记线编码）。另一种更隐式的表示数字的方法是求和编码。在这里，单个神经元的放电随着数量的增加而单调地变强或变弱。然后可以从合并的细胞活性中解码该数字。
这两种编码类型的计算性能进行了研究。在最近的一项建模研究中，在没有数字相关训练的情况下，从视觉显示中提取了求和代码，而不是数量选择性代码（Stoianov和Zorzi，2012）。此外，求和码在这样的模型中作为数位选择码的先驱（Dehaene和Changeux，1993; Verguts和Fias，2004）。此外，每种编码类型都具有明显的优点;求和编码更适合于较小-较大（即，幅度）处理，数量选择性编码对于相同-不同的数字区分更有效（Verguts，2007）。
在许多论文中，Nieder和同事证明了猕猴的数量选择性编码（例如，Nieder等人，2002; Nieder和米勒，2004）。然而，数字总是与任务相关的;换句话说，动物接受了数字训练（例如，Nieder等人，2002年）。计算建模工作共同预测，总和编码是主要的和基础的数量选择性编码，在没有数字相关的培训，只有总和编码将被观察到。Roitman et al.（2007）表明，在一项单单位记录研究中只观察到求和编码，其中数字与解决任务无关。然而，数字与计算试验抵消时的奖励相关，因此可能仍然是在训练期间发生的数字相关学习。
为了确定自然数字编码系统（即，Viswanathan和Nieder（2013）在一项没有数字相关性（因此没有数字学习）的任务中记录了两只猴子的腹侧顶内区[VIP，顶内沟（IPS）]和前额叶皮层（PFC）的细胞。他们发现，两个大脑区域的神经元对给定的数字（例如，一个神经元对1的响应最大，另一个神经元对2的响应最大，以此类推）。他们将数据解释为数字选择性编码。他们还发现，这些神经元最常偏好的数字是数字1和5，而相对较小的一组神经元被归类为中间数字2，3和4。然而，考虑到求和编码的计算首要性，我们考虑了作者对求和编码神经元进行采样的可能性。在这里，我们表明，数据与求和编码是一致的，求和编码可以解释数据的微妙和无法解释的方面。
A key debate in numerical cognition concerns the neural code for number representation (e.g., Nieder and Merten, 2007; Roggeman et al., 2007; Viswanathan and Nieder, 2013). One idea is that individual neurons are tuned to individual numbers, with decreasing response to numbers with increasing distance (numerosity-selective coding or labeled-line coding). An alternative, more implicit way of representing number is by summation coding. Here, individual neurons fire either monotonically stronger or weaker to increasing number. The number can then be decoded from the pooled cell activity.
The computational properties of both coding types have been studied. A summation code but not a numerosity-selective code was extracted without number-related training from a visual display in a recent modeling study (Stoianov and Zorzi, 2012). Also, the summation code serves as a precursor for a numerosity-selective code in such models (Dehaene and Changeux, 1993; Verguts and Fias, 2004). Furthermore, each coding type has distinct advantages; summation coding is more suited for smaller-larger (i.e., magnitude) processing, numerosity-selective coding is more efficient for same-different number discrimination (Verguts, 2007).
In a number of papers, Nieder and colleagues demonstrated numerosity-selective coding in macaque monkeys (e.g., Nieder et al., 2002; Nieder and Miller, 2004). However, number was always relevant for the task; in other words, animals were trained on number (e.g., Nieder et al., 2002). The computational modeling work jointly predicts that summation coding is primary and foundational to numerosity-selective coding, and that in the absence of number-relevant training, only summation coding would be observed. Consistently, Roitman et al. (2007) showed that only summation coding was observed in a single-unit recording study in which number was not relevant for solving the task. However, number was relevant for computing the reward at trial offset, so it may still be that number-relevant learning took place during training.
To determine the natural numerical coding system (i.e., without number learning), Viswanathan and Nieder (2013) recorded cells from ventral intraparietal area [VIP, in intraparietal sulcus (IPS)] and from prefrontal cortex (PFC) in two monkeys in a task without number relevance (and hence number learning). They found that neurons in both brain areas responded maximally to a given number (e.g., one neuron responded maximally to 1, another neuron maximally to 2, and so on). They interpret their data as suggesting numerosity-selective coding. They also found that the most frequently preferred numbers for these neurons were numbers 1 and 5, whereas a relatively small set of neurons were classified as tuned to intermediate numbers 2, 3, and 4. However, given the computational primacy of summation coding, we consider the possibility that the authors instead sampled summation coding neurons. Here, we show that the data are consistent with summation coding, and that summation coding can account for subtle and unexplained aspects of the data.
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影响因子：
3.2
作者：
DEHAENE, S;CHANGEUX, JP
通讯作者：
CHANGEUX, JP
影响因子：
3
作者：
Pearson J;Roitman JD;Brannon EM;Platt ML;Raghavachari S
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影响因子：
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作者：
Roggeman, Chantal;Verguts, Tom;Fias, Wim
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影响因子：
3.2
作者：
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通讯作者：
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影响因子：
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作者：
Roitman, Jamie D.;Brannon, Elizabeth M.;Platt, Michael L.
通讯作者：
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