A recurrent network model of somatosensory parametric working memory in the prefrontal cortex
A recurrent network model of somatosensory parametric working memory in the prefrontal cortex
复制标题
DOI：
10.1093/cercor/bhg101
复制
发表时间：
2003-11-01
期刊：
影响因子：
3.7
通讯作者：
Wang, XJ
中科院分区：
文献类型：
--
作者：
Miller, P;Brody, CD;Wang, XJ
参数工作记忆网络以持续神经活动的形式存储模拟刺激的信息，该持续神经活动被单调地调谐到刺激。在完全相同的外部条件下（在瞬态刺激被撤回的延迟期间），具有连续范围的放电率的持续放电模式家族必须都是可实现的。如何通过神经机制实现这一点仍然是一个悬而未决的问题。在这里，我们提出了一个经常性的皮层网络模型的不规则尖峰神经元，旨在模拟行为猴子的体感工作记忆实验。我们的模型再现了所观察到的积极和消极的单调的持续活动，和异质调谐曲线的记忆活动。我们表明，微调数学上对应于一个精确的对齐的尖点在网络的分叉图。此外，我们表明，微调网络可以整合刺激输入超过几秒钟。假设这样的时间整合发生在神经群体下游的一个紧张性持久的神经群体，我们的模型是能够解释的缓慢斜升和斜降行为的神经元观察到的前额叶皮层。
A parametric working memory network stores the information of an analog stimulus in the form of persistent neural activity that is monotonically tuned to the stimulus. The family of persistent firing patterns with a continuous range of firing rates must all be realizable under exactly the same external conditions (during the delay when the transient stimulus is withdrawn). How this can be accomplished by neural mechanisms remains an unresolved question. Here we present a recurrent cortical network model of irregularly spiking neurons that was designed to simulate a somatosensory working memory experiment with behaving monkeys. Our model reproduces the observed positively and negatively monotonic persistent activity, and heterogeneous tuning curves of memory activity. We show that fine-tuning mathematically corresponds to a precise alignment of cusps in the bifurcation diagram of the network. Moreover, we show that the fine-tuned network can integrate stimulus inputs over several seconds. Assuming that such time integration occurs in neural populations downstream from a tonically persistent neural population, our model is able to account for the slow ramping-up and ramping-down behaviors of neurons observed in prefrontal cortex.