Noise tolerance of attractor and feedforward memory models.
Noise tolerance of attractor and feedforward memory models.
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DOI：
10.1162/neco_a_00234
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发表时间：
2012-02
影响因子：
2.9
通讯作者：
Goldman MS
中科院分区：
文献类型：
--
作者：
Lim S;Goldman MS
在短期记忆网络中，瞬时刺激由在刺激抵消后持续很长时间的神经活动模式表示。在这里，我们比较了两个突出的记忆网络，基于反馈的吸引子网络和前馈网络，在高斯噪声的存在下，在传达信息的简要介绍刺激的幅度的性能。使用Fisher信息作为记忆性能的度量，我们发现，网络架构的最佳形式强烈依赖于网络中的非线性形式的假设。对于纯线性网络，我们发现前馈网络优于吸引子网络，因为当信号离开网络时，噪声不断从前馈网络中去除;因此，前馈网络可以放大它们接收到的信号，比噪声随着时间的推移积累得更快。相比之下，吸引子网络必须在信号衰减机制中运行，以避免噪声的积累。然而，如果信号的放大是有限的动态范围内的神经元的反应，或者如果噪声被重置在信号到达时，最近的实验表明，我们发现，吸引子网络可以优于前馈的。在一个简单的模型中，神经元有一个有限的动态范围，我们发现，最佳吸引子网络是健忘的，如果没有机制，降低噪声与信号的到来，但nonforgettful（完美的积分器）在一个强大的复位机制的存在。此外，我们发现，前馈和吸引子网络的最大Fisher信息表现出幂律衰减作为时间的函数，并与神经元的数量呈线性关系。这些结果突出了突出的因素，导致权衡的内存性能的网络与不同的架构和约束，并建议条件下，吸引或前馈网络可能是最适合存储有关以前的刺激信息。
In short-term memory networks, transient stimuli are represented by patterns of neural activity that persist long after stimulus offset. Here, we compare the performance of two prominent classes of memory networks, feedback-based attractor networks and feedforward networks, in conveying information about the amplitude of a briefly presented stimulus in the presence of gaussian noise. Using Fisher information as a metric of memory performance, we find that the optimal form of network architecture depends strongly on assumptions about the forms of nonlinearities in the network. For purely linear networks, we find that feedforward networks outperform attractor networks because noise is continually removed from feedforward networks when signals exit the network; as a result, feedforward networks can amplify signals they receive faster than noise accumulates over time. By contrast, attractor networks must operate in a signal-attenuating regime to avoid the buildup of noise. However, if the amplification of signals is limited by a finite dynamic range of neuronal responses or if noise is reset at the time of signal arrival, as suggested by recent experiments, we find that attractor networks can out-perform feedforward ones. Under a simple model in which neurons have a finite dynamic range, we find that the optimal attractor networks are forgetful if there is no mechanism for noise reduction with signal arrival but nonforgetful (perfect integrators) in the presence of a strong reset mechanism. Furthermore, we find that the maximal Fisher information for the feedforward and attractor networks exhibits power law decay as a function of time and scales linearly with the number of neurons. These results highlight prominent factors that lead to trade-offs in the memory performance of networks with different architectures and constraints, and suggest conditions under which attractor or feedforward networks may be best suited to storing information about previous stimuli.