Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain.
Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain.
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DOI：
10.1371/journal.pone.0124787
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Gurney KN
中科院分区：
文献类型：
--
作者：
Caballero JA;Lepora NF;Gurney KN
大脑中决策的计算理论通常假设感官“证据”是积累起来的，支持许多假设，第一个达到阈值的累积器会触发有利于其相关假设的决定。然而，证据通常被认为是一个连续的过程，其起源有点抽象，与神经信号（动作电位或“尖峰”）没有直接联系，而神经信号最终必须形成大脑决策的基础。在这里，我们介绍了一个新的变种，著名的多假设序贯概率比检验（MSPRT）的决策，其证据观察组成的神经信号的基本单位-脉冲间隔（ISI）-这是基于一种新形式的似然函数。我们把这种机制的S-MSPRT，并显示其精确的形式为一系列现实的ISI分布与积极的支持。以这种方式，我们表明，在尖峰的水平，不应期实际上可以促进更短的决策时间，并且该机制对假设的数据分布的不良选择是鲁棒的。我们发现，S-MSPRT性能相关的Kullback-Leibler分歧（KLD）或ISI分布之间的信息增益，通过它，我们能够在行为水平上的神经信号连接到心理物理学观察。因此，我们发现决策所需的平均信息是恒定的，从而提供了一个希克定律（决策时间与选择数量相关）的解释。此外，平均决策时间的s-MSPRT显示幂律依赖于KLD提供一个帐户的Piéron定律（有关反应时间刺激强度）。这些结果显示了一个研究计划的基础，在该计划中，尖峰序列分析可以作为预测多选择任务中行为的基础。
Computational theories of decision making in the brain usually assume that sensory 'evidence' is accumulated supporting a number of hypotheses, and that the first accumulator to reach threshold triggers a decision in favour of its associated hypothesis. However, the evidence is often assumed to occur as a continuous process whose origins are somewhat abstract, with no direct link to the neural signals - action potentials or 'spikes' - that must ultimately form the substrate for decision making in the brain. Here we introduce a new variant of the well-known multi-hypothesis sequential probability ratio test (MSPRT) for decision making whose evidence observations consist of the basic unit of neural signalling - the inter-spike interval (ISI) - and which is based on a new form of the likelihood function. We dub this mechanism s-MSPRT and show its precise form for a range of realistic ISI distributions with positive support. In this way we show that, at the level of spikes, the refractory period may actually facilitate shorter decision times, and that the mechanism is robust against poor choice of the hypothesized data distribution. We show that s-MSPRT performance is related to the Kullback-Leibler divergence (KLD) or information gain between ISI distributions, through which we are able to link neural signalling to psychophysical observation at the behavioural level. Thus, we find the mean information needed for a decision is constant, thereby offering an account of Hick's law (relating decision time to the number of choices). Further, the mean decision time of s-MSPRT shows a power law dependence on the KLD offering an account of Piéron's law (relating reaction time to stimulus intensity). These results show the foundations for a research programme in which spike train analysis can be made the basis for predictions about behavior in multi-alternative choice tasks.
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影响因子：
16.2
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