Neural dynamics implement a flexible decision bound with a fixed firing rate for choice: a model-based hypothesis.
Neural dynamics implement a flexible decision bound with a fixed firing rate for choice: a model-based hypothesis.
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神经动力学实现了灵活的决策界限，具有固定的放电率可供选择：基于模型的假设
DOI：
10.3389/fnins.2014.00318
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发表时间：
2014
影响因子：
4.3
通讯作者：
Blohm G
中科院分区：
文献类型：
--
作者：
Standage D;Wang DH;Blohm G
当条件有利于速度时，决策更快，更不准确，当条件有利于准确时，决策更慢，更准确。这种速度-精度权衡（SAT）可以用有界积分的原理来解释，其中噪声证据被积分直到达到一个界限。更高的边界通过增加积分时间来减少噪声的影响，从而支持更高的精度（对于速度而言，反之亦然）。这些计算被假设为通过选择性决策方案的神经种群之间的反馈抑制来实现，每个神经种群对应于网络状态空间中的吸引子。由于决策相关的神经活动通常在承诺选择时达到固定的速率，因此假设神经实现的界限是固定的，并且SAT由整合证据的群体的共同输入支持。根据这一假设，更强的共同输入减少了基线放电率和用于制定选择的阈值率之间的差异。在二选一决策任务的模拟中，我们使用了一个简化版本的基于生物药理学的网络模型（Wong和Wang，2006）来表明，一个共同的输入可以控制SAT，但阈值基线差异的变化是副现象。相反，SAT是由网络动态变化控制的。较强的公共输入降低了模型的有效积分时间常数，并改变了吸引子景观的形状，因此初始状态处于更容易出错的位置。因此，更强的公共输入会减少决策时间并降低准确性。动态的变化还使得在理想的观察者可以根据网络活动做出决定的时间的速度条件下的发射率更高。这个速率和基线速率之间的差异实际上在速度条件下比在准确性条件下更大，这表明该界限不是由发射速率本身实现的。
Decisions are faster and less accurate when conditions favor speed, and are slower and more accurate when they favor accuracy. This speed-accuracy trade-off (SAT) can be explained by the principles of bounded integration, where noisy evidence is integrated until it reaches a bound. Higher bounds reduce the impact of noise by increasing integration times, supporting higher accuracy (vice versa for speed). These computations are hypothesized to be implemented by feedback inhibition between neural populations selective for the decision alternatives, each of which corresponds to an attractor in the space of network states. Since decision-correlated neural activity typically reaches a fixed rate at the time of commitment to a choice, it has been hypothesized that the neural implementation of the bound is fixed, and that the SAT is supported by a common input to the populations integrating evidence. According to this hypothesis, a stronger common input reduces the difference between a baseline firing rate and a threshold rate for enacting a choice. In simulations of a two-choice decision task, we use a reduced version of a biophysically-based network model (Wong and Wang, 2006) to show that a common input can control the SAT, but that changes to the threshold-baseline difference are epiphenomenal. Rather, the SAT is controlled by changes to network dynamics. A stronger common input decreases the model's effective time constant of integration and changes the shape of the attractor landscape, so the initial state is in a more error-prone position. Thus, a stronger common input reduces decision time and lowers accuracy. The change in dynamics also renders firing rates higher under speed conditions at the time that an ideal observer can make a decision from network activity. The difference between this rate and the baseline rate is actually greater under speed conditions than accuracy conditions, suggesting that the bound is not implemented by firing rates per se.
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