Drift in Neural Population Activity Causes Working Memory to Deteriorate Over Time.
Drift in Neural Population Activity Causes Working Memory to Deteriorate Over Time.
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DOI：
10.1523/jneurosci.3440-17.2018
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发表时间：
2018-05-23
期刊：
影响因子：
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通讯作者：
Bays PM
中科院分区：
文献类型：
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作者：
Schneegans S;Bays PM
短期记忆被认为是在神经群中以持续尖峰活动的形式维持的。随着记忆项目数量的增加，回忆精度的降低可以通过限制总峰值活动来解释，从而导致对每个单独项目的表示做出贡献的峰值减少。较长的记忆间隔同样会降低回忆的准确性，但不清楚人口活动的什么变化会产生这种影响。一种可能性是，峰值活动随着时间的推移而减弱，因此相同的机制可以同时解释集合大小和留存时间的影响。或者，性能下降可能是由于编码值随时间的漂移引起的，而不会减少总体峰值活动。男性和女性参与者都执行了一项可变延迟提示的回忆任务，其中包括跳眼反应，这提供了回忆延迟的精确测量。基于决策的峰值集成模型，如果集合大小和保留时间的影响都是由峰值活动的减少引起的，我们可以预测不同条件下召回精度和反应延迟之间存在固定的关系。相比之下，漂移假说预测随着延迟的增加，延迟没有系统的变化。我们的结果表明，随着设置大小的增加，延迟会增加，并且随着每个设置大小的延迟更长，响应精度会降低，但是随着延迟持续时间的增加，延迟不会系统地增加。这些结果是通过一个基于有限神经资源的模型定量再现的，在这个模型中，工作记忆是漂移的，而不是随着时间的推移而衰减。几秒钟内的快速退化是短期记忆的一个显著特征，但是是什么机制导致了这种内部表征的退化？本文通过引入延迟效应的可能机制，扩展了一个成功的工作记忆群体编码模型。我们表明，神经信号随时间的衰减预示着记忆检索所需的时间将随着延迟而增加，而存储值的随机漂移预示着延迟对检索时间没有影响。在一个多项目记忆任务中，我们用眼动反应测试了这些预测，发现漂移是记忆衰退的一个关键机制。这些结果为工作记忆的动态尖峰基础提供了证据，与最近提出的活动-沉默存储形成对比。
Short-term memories are thought to be maintained in the form of sustained spiking activity in neural populations. Decreases in recall precision observed with increasing number of memorized items can be accounted for by a limit on total spiking activity, resulting in fewer spikes contributing to the representation of each individual item. Longer retention intervals likewise reduce recall precision, but it is unknown what changes in population activity produce this effect. One possibility is that spiking activity becomes attenuated over time, such that the same mechanism accounts for both effects of set size and retention duration. Alternatively, reduced performance may be caused by drift in the encoded value over time, without a decrease in overall spiking activity. Human participants of either sex performed a variable-delay cued recall task with a saccadic response, providing a precise measure of recall latency. Based on a spike integration model of decision making, if the effects of set size and retention duration are both caused by decreased spiking activity, we would predict a fixed relationship between recall precision and response latency across conditions. In contrast, the drift hypothesis predicts no systematic changes in latency with increasing delays. Our results show both an increase in latency with set size, and a decrease in response precision with longer delays within each set size, but no systematic increase in latency for increasing delay durations. These results were quantitatively reproduced by a model based on a limited neural resource in which working memories drift rather than decay with time. SIGNIFICANCE STATEMENT Rapid deterioration over seconds is a defining feature of short-term memory, but what mechanism drives this degradation of internal representations? Here, we extend a successful population coding model of working memory by introducing possible mechanisms of delay effects. We show that a decay in neural signal over time predicts that the time required for memory retrieval will increase with delay, whereas a random drift in the stored value predicts no effect of delay on retrieval time. Testing these predictions in a multi-item memory task with an eye movement response, we identified drift as a key mechanism of memory decline. These results provide evidence for a dynamic spiking basis for working memory, in contrast to recent proposals of activity-silent storage.