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首页> 外文期刊>Journal of Computational Neuroscience >Accuracy and response-time distributions for decision-making: linear perfect integrators versus nonlinear attractor-based neural circuits
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Accuracy and response-time distributions for decision-making: linear perfect integrators versus nonlinear attractor-based neural circuits

机译：决策的精度和响应时间分布：线性完美积分与基于非线性吸引子的神经电路

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摘要

Animals choose actions based on imperfect, ambiguous data. "Noise" inherent in neural processing adds further variability to this already-noisy input signal. Mathematical analysis has suggested that the optimal apparatus (in terms of the speed/accuracy trade-off) for reaching decisions about such noisy inputs is perfect accumulation of the inputs by a temporal integrator. Thus, most highly cited models of neural circuitry underlying decision-making have been instantiations of a perfect integrator. Here, in accordance with a growing mathematical and empirical literature, we describe circumstances in which perfect integration is rendered suboptimal. In particular we highlight the impact of three biological constraints: (1) significant noise arising within the decision-making circuitry itself; (2) bounding of integration by maximal neural firing rates; and (3) time limitations on making a decision. Under conditions (1) and (2), an attractor system with stable attractor states can easily best an integrator when accuracy is more important than speed. Moreover, under conditions in which such stable attractor networks do not best the perfect integrator, a system with unstable initial states can do so if readout of the system's final state is imperfect. Ubiquitously, an attractor system with a nonselective time-dependent input current is both more accurate and more robust to imprecise tuning of parameters than an integrator with such input. Given that neural responses that switch stochastically between discrete states can "masquerade" as integration in single-neuron and trial-averaged data, our results suggest that such networks should be considered as plausible alternatives to the integrator model.
机译：动物根据不完善的，模棱两可的数据选择行动。神经处理中固有的“噪声”为这个已经嘈杂的输入信号增加了更多的可变性。数学分析表明，用于做出有关此类嘈杂输入的决策的最佳设备（就速度/精度的权衡而言）是时间积分器对输入的完美累加。因此，在决策过程中被引用最多的神经电路模型是完美集成器的实例。在这里，根据不断增长的数学和经验文献，我们描述了使完美积分次优的情况。我们特别强调了三个生物学限制的影响：（1）决策电路本身内部产生了很大的噪声； （2）以最大的神经激发速率进行整合的边界； （3）做出决定的时间限制。在条件（1）和（2）下，当精度比速度更重要时，具有稳定吸引子状态的吸引子系统很容易成为最佳积分器。而且，在这种稳定的吸引子网络不能最好地完成理想的积分器的条件下，如果系统最终状态的读出不完善，则具有不稳定初始状态的系统也可以这样做。与具有这种输入的积分器相比，具有非选择性的与时间有关的输入电流的吸引器系统无处不在，对于参数的不精确调整而言，其精度更高，更鲁棒。鉴于在离散状态之间随机切换的神经反应可以“伪装”为单个神经元和试验平均数据中的积分，因此我们的结果表明，此类网络应被视为积分器模型的合理替代品。

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