论文标题
兴奋性和抑制性突触的最佳学习
Optimal Learning with Excitatory and Inhibitory synapses
论文作者
论文摘要
表征体重结构与输入/输出统计之间的关系对于理解神经回路的计算能力至关重要。在这项工作中，我研究了使用统计力学的方法在存在相关性的情况下，在存在相关性的情况下在模拟信号之间存储关联的问题。我根据随机输入和输出过程的功率谱系来表征典型的学习表现。我表明，对于抑制性重量的任何兴奋性，最佳突触重量构型的容量为0.5，并且具有有限的无声突触的特殊突触分布。我进一步提供了典型的学习绩效和单一情况下的主要成分分析之间的联系。这些结果可能会阐明脑电路的突触分布，例如小脑结构，这些结构被认为参与处理时间依赖的信号并进行在线预测。
Characterizing the relation between weight structure and input/output statistics is fundamental for understanding the computational capabilities of neural circuits. In this work, I study the problem of storing associations between analog signals in the presence of correlations, using methods from statistical mechanics. I characterize the typical learning performance in terms of the power spectrum of random input and output processes. I show that optimal synaptic weight configurations reach a capacity of 0.5 for any fraction of excitatory to inhibitory weights and have a peculiar synaptic distribution with a finite fraction of silent synapses. I further provide a link between typical learning performance and principal components analysis in single cases. These results may shed light on the synaptic profile of brain circuits, such as cerebellar structures, that are thought to engage in processing time-dependent signals and performing on-line prediction.
