Neurobiological models of two-choice decision making can be reduced to a one-dimensional nonlinear diffusion equation.
Neurobiological models of two-choice decision making can be reduced to a one-dimensional nonlinear diffusion equation.
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DOI：
10.1371/journal.pcbi.1000046
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发表时间：
2008-03-28
影响因子：
4.3
通讯作者：
Ledberg A
中科院分区：
文献类型：
--
作者：
Roxin A;Ledberg A
许多两种选择任务中的响应行为可以通过所谓的顺序采样模型得到很好的描述。在这些模型中，两种替代方案中每一种的证据都会随着时间的推移而积累，直到达到阈值，此时才会做出响应。在神经生理学层面，当猴子从事两种选择任务时记录的单个神经元数据可以通过赢家通吃的网络模型得到很好的描述，其中两种选择以不同神经元群体的放电率来表示。在这里，我们表明这种非线性网络模型通常可以简化为一维非线性扩散方程，它与标准的行为顺序采样模型在功能上相似。这种减少使得性能和反应时间在功能上依赖于原始系统中的外部输入，而与系统细节无关。更重要的是，非线性扩散方程可以通过改变这些外部输入来为来自二项选择决策任务的行为数据提供极好的拟合。这表明在各种实验条件下行为会发生变化，例如刺激一致性或反应期限的变化是由大脑中假定决策电路的传入输入的内部调制驱动的。对于某些模型系统，可以分析推导非线性扩散方程，从而将原始系统参数映射到扩散方程系数上。在这里，我们用三个模型系统来说明这一点，包括耦合速率方程和尖峰神经元网络。大脑在理性行为的科学理论中占据着中心地位。例如，大脑活动被认为与二选一知觉辨别任务中观察到的决策行为存在因果关系。尽管人们对这些任务期间的大脑活动和反应行为了解很多，但两者之间的关系尚不完全清楚。特别是，如何将大脑的高维动态活动与反应行为（例如表现和反应时间）的低维描述联系起来？我们解决这个问题的方法是将现有的大脑活动神经生物学模型与现有的反应行为模型联系起来。在本文中，我们在两项选择任务期间的标准赢家通吃大脑活动模型与一系列称为扩散模型的一维行为模型之间建立了正式联系。我们的分析表明，一方面神经生物学模型中神经群体的外部输入与另一方面一维模型中的反应时间和性能之间存在普遍的功能依赖性。重要的是，我们表明，可以仅通过这些外部输入的变化来预测实验测量的性能和反应时间。
The response behaviors in many two-alternative choice tasks are well described by so-called sequential sampling models. In these models, the evidence for each one of the two alternatives accumulates over time until it reaches a threshold, at which point a response is made. At the neurophysiological level, single neuron data recorded while monkeys are engaged in two-alternative choice tasks are well described by winner-take-all network models in which the two choices are represented in the firing rates of separate populations of neurons. Here, we show that such nonlinear network models can generally be reduced to a one-dimensional nonlinear diffusion equation, which bears functional resemblance to standard sequential sampling models of behavior. This reduction gives the functional dependence of performance and reaction-times on external inputs in the original system, irrespective of the system details. What is more, the nonlinear diffusion equation can provide excellent fits to behavioral data from two-choice decision making tasks by varying these external inputs. This suggests that changes in behavior under various experimental conditions, e.g. changes in stimulus coherence or response deadline, are driven by internal modulation of afferent inputs to putative decision making circuits in the brain. For certain model systems one can analytically derive the nonlinear diffusion equation, thereby mapping the original system parameters onto the diffusion equation coefficients. Here, we illustrate this with three model systems including coupled rate equations and a network of spiking neurons. The brain holds a central position in scientific theories of rational behavior. For example, brain activity is thought to stand in a causal relation to the decision making behavior observed in two-choice perceptual discrimination tasks. Although a lot is known about both the brain activity and the response behavior during these tasks, the relationships between the two are not fully understood. In particular, how can one relate the high-dimensional dynamic activity of the brain to the low-dimensional descriptions of response behavior such as performance and reaction-times? Our approach to this question is to relate existing neurobiological models of brain activity to existing models of response behavior. In this paper we establish a formal link between standard, winner-take-all models of brain activity during two-choice tasks and a family of one-dimensional behavioral models known as diffusion models. Our analysis demonstrates a universal functional dependence between the external inputs to the neural populations in the neurobiological model on the one hand, and reaction times and performance in the one-dimensional model on the other. Importantly, we show that experimentally measured performance and reaction-times can be predicted through changes in these external inputs alone.
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影响因子：
25
作者：
Lo, Chung-Chuan;Wang, Xiao-Jing
通讯作者：
Wang, Xiao-Jing
影响因子：
1.9
作者：
BRITTEN, KH;SHADLEN, MN;MOVSHON, JA
通讯作者：
MOVSHON, JA
DOI：
10.1126/science.ns-16.394.99
发表时间：
1890-08-22
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Jastrow, J
通讯作者：
Jastrow, J
影响因子：
5.3
作者：
Cisek, Paul
通讯作者：
Cisek, Paul
影响因子：
2.5
作者：
Horwitz, GD;Newsome, WT
通讯作者：
Newsome, WT