循环神经网络中的上下文相关表示
Context-dependent representation in recurrent neural networks
摘要：
为了评估非线性随机神经网络的短期记忆性能，我们引入了一种度量来量化神经表示对过去上下文的依赖性。我们研究这一措施的数值和理论上使用随机神经网络的平均场论，显示存在一个最佳水平的突触权重异质性。我们进一步调查的网络拓扑结构的影响，特别是相互突触连接的对称性，这种措施的上下文依赖性，揭示了考虑非线性和连接结构之间的相互作用的重要性。
英文摘要：
In order to assess the short-term memory performance of non-linear random neural networks, we introduce a measure to quantify the dependence of a neural representation upon the past context. We study this measure both numerically and theoretically using the mean-field theory for random neural networks, showing the existence of an optimal level of synaptic weights heterogeneity. We further investigate the influence of the network topology, in particular the symmetry of reciprocal synaptic connections, on this measure of context dependence, revealing the importance of considering the interplay between non-linearities and connectivity structure.
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