From distributed resources to limited slots in multiple-item working memory: a spiking network model with normalization.
From distributed resources to limited slots in multiple-item working memory: a spiking network model with normalization.
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从分布式资源到多项目工作记忆中的有限槽：标准化的尖峰网络模型
DOI：
10.1523/jneurosci.0735-12.2012
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发表时间：
2012-08-15
期刊：
影响因子：
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通讯作者：
Wang DH
中科院分区：
文献类型：
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作者：
Wei Z;Wang XJ;Wang DH
近期的行为研究催生了两种关于有限工作记忆容量的对比模型：一种是“离散槽位”模型，其中记忆项目存储在有限数量的槽位中；另一种是“共享资源”模型，其中项目的神经表征分布在有限的资源池中。为了阐明潜在的神经过程，我们研究了一个模拟特征的工作记忆连续网络模型。我们的模型网络从根本上以共享资源机制运行，线索阵列中的刺激由分布式神经群体编码。另一方面，网络动态和性能也与离散槽位模型一致，因为多个对象由不同的局部群体持续活动模式（凸起吸引子）维持。我们确定了循环电路动态的两种现象，它们导致了有限的工作记忆容量。随着工作记忆负载的增加，局部持续活动凸起可能会消失（因此相应项目的记忆丢失），或者与附近的另一个凸起合并（因此合并项目的记忆表征分辨率变得模糊）。我们确定了这两种现象对循环突触兴奋的强度和调节以及网络归一化的特定依赖性：总体群体活动对于集合大小和延迟持续时间是不变的；因此，恒定的神经资源由记忆项目共享并动态分配给它们。我们证明该模型重现了离散槽位和共享资源模型所预测的显著观察结果，并对合并现象提出了可检验的预测。
Recent behavioral studies have given rise to two contrasting models for limited working memory capacity: a “discrete-slot” model in which memory items are stored in a limited number of slots, and a “shared-resource” model in which the neural representation of items is distributed across a limited pool of resources. To elucidate the underlying neural processes, we investigated a continuous network model for working memory of an analog feature. Our model network fundamentally operates with a shared resource mechanism, and stimuli in cue arrays are encoded by a distributed neural population. On the other hand, the network dynamics and performance are also consistent with the discrete-slot model, because multiple objects are maintained by distinct localized population persistent activity patterns (bump attractors). We identified two phenomena of recurrent circuit dynamics that give rise to limited working memory capacity. As the working memory load increases, a localized persistent activity bump may either fade out (so the memory of the corresponding item is lost) or merge with another nearby bump (hence the resolution of mnemonic representation for the merged items becomes blurred). We identified specific dependences of these two phenomena on the strength and tuning of recurrent synaptic excitation, as well as network normalization: the overall population activity is invariant to set size and delay duration; therefore, a constant neural resource is shared by and dynamically allocated to the memorized items. We demonstrate that the model reproduces salient observations predicted by both discrete-slot and shared-resource models, and propose testable predictions of the merging phenomenon.