Department of Computational Biology (CB), School of Computer Science and Communication (CSC), Royal Institute of Technology (KTH) , Stockholm , Sweden.
Network. 2013;24(4):129-50. doi: 10.3109/0954898X.2013.859323.
An important research topic in neuroscience is the study of mechanisms underlying memory and the estimation of the information capacity of the biological system. In this report we investigate the performance of a modular attractor network with recurrent connections similar to the cortical long-range connections extending in the horizontal direction. We considered a single learning rule, the BCPNN, which implements a kind of Hebbian learning and we trained the network with sparse random patterns. The storage capacity was measured experimentally for networks of size between 500 and 46 K units with a constant activity level, gradually diluting the connectivity. We show that the storage capacity of the modular network with patchy connectivity is comparable with the theoretical values estimated for simple associative memories and furthermore we introduce a new technique to prune the connectivity, which enhances the storage capacity up to the asymptotic value.
神经科学中的一个重要研究课题是研究记忆的机制和估计生物系统的信息容量。在本报告中，我们研究了具有类似于在水平方向上延伸的皮质长程连接的递归连接的模块化吸引子网络的性能。我们考虑了单个学习规则，即 BCPNN，它实现了一种赫布学习，并且我们使用稀疏随机模式对网络进行了训练。我们通过逐渐稀释连接，用大小在 500 到 46K 个单元之间的网络来实验性地测量存储容量，保持恒定的活动水平。我们表明，具有块状连接的模块化网络的存储容量与为简单联想记忆体估计的理论值相当，此外，我们还引入了一种新的技术来修剪连接，从而将存储容量提高到渐近值。
