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Bounded Ornstein-Uhlenbeck models for two-choice time controlled tasks

机译:用于二选时间控制任务的有界Ornstein-Uhlenbeck模型

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The Ornstein-Uhlenbeck (O-U) model has been successfully applied to describe the response accuracy and response time in 2-alternative choice tasks. This paper analyses properties and performance of variants of the O-U model with absorbing and reflecting boundary conditions that limit the range of possible values of the integration variable. The paper focuses on choice tasks with pre-determined response time. The type of boundary and the growth/decay parameter of the O-U model jointly determine how the choice is influenced by the sensory input presented at different times throughout the trial. It is shown that the O-U models with two types of boundary are closely related and can achieve the same performance under certain parameter values. The value of the growth/decay parameter that maximizes the accuracy of the model has been identified. It is shown that when the boundaries are introduced, the O-U model may achieve higher accuracy than the diffusion model. This suggests that given the limited range of the firing rates of integrator neurons, the neural decision circuits could achieve higher accuracy employing leaky rather than linear integration in certain tasks. We also propose experiments that could distinguish between different models of choice in tasks with pre-determined response time.
机译:Ornstein-Uhlenbeck(O-U)模型已成功应用于描述2种选择任务中的响应精度和响应时间。本文通过吸收和反映限制积分变量可能值范围的边界条件来分析O-U模型变体的性能和性能。本文重点介绍具有预定响应时间的选择任务。 O-U模型的边界类型和增长/衰减参数共同决定了整个试验过程中在不同时间呈现的感觉输入如何影响选择。结果表明,具有两种边界类型的O-U模型密切相关,并且在某些参数值下可以实现相同的性能。已经确定了使模型的准确性最大化的增长/衰减参数的值。结果表明,引入边界后,O-U模型比扩散模型可以获得更高的精度。这表明,鉴于积分神经元激发速率的范围有限,在某些任务中,神经决策电路可以采用泄漏而不是线性积分来实现更高的精度。我们还提出了可以区分具有预定响应时间的任务的不同选择模型的实验。

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