Memory without feedback in a neural network.

Memory without feedback in a neural network.
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DOI:
10.1016/j.neuron.2008.12.012
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发表时间:
2009-02-26
期刊:
影响因子:
16.2
通讯作者:
Goldman, Mark S.
Goldman, Mark S.
中科院分区:
医学1区
文献类型:
--
作者:
Goldman, Mark S.

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短时间尺度上的记忆存储被认为是由神经元活动维持的,这些神经元活动在记忆刺激被移除后仍然存在。虽然以前的工作表明,正反馈是必要的,以保持持续的活动,在这里,它证明了如何神经元的反应,而不是保持一个纯粹的前馈机制,其中活动是通过连续的网络状态链。这种前馈形式的存储器存储中出现的结构前馈网络和经常性的网络,但功能在前馈方式。这些网络可以被调整为它们输入的完美积分器,或者再现在一些工作记忆任务中观察到的时变放电模式,但不容易被基于反馈的吸引子模型再现。这项工作说明了一种新的机制,保持短期记忆,其中前馈和反馈过程相互作用,以管理网络行为。
Memory storage on short time scales is thought to be maintained by neuronal activity that persists after the remembered stimulus is removed. Although previous work suggested that positive feedback is necessary to maintain persistent activity, here it is demonstrated how neuronal responses can instead be maintained by a purely feedforward mechanism in which activity is passed sequentially through a chain of network states. This feedforward form of memory storage is shown to occur both in architecturally feedforward networks and in recurrent networks that nevertheless function in a feedforward manner. The networks can be tuned to be perfect integrators of their inputs, or to reproduce the time-varying firing patterns observed during some working memory tasks but not easily reproduced by feedback-based attractor models. This work illustrates a new mechanism for maintaining short-term memory in which both feedforward and feedback processes interact to govern network behavior.
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