Mejias Jorge F, Torres Joaquín J
Department of Electromagnetism and Matter Physics and Institute Carlos I for Theoretical and Computational Physics, University of Granada, E-18071 Granada, Spain.
Neural Comput. 2009 Mar;21(3):851-71. doi: 10.1162/neco.2008.02-08-719.
In this work, we study, analytically and employing Monte Carlo simulations, the influence of the competition between several activity-dependent synaptic processes, such as short-term synaptic facilitation and depression, on the maximum memory storage capacity in a neural network. In contrast to the case of synaptic depression, which drastically reduces the capacity of the network to store and retrieve "static" activity patterns, synaptic facilitation enhances the storage capacity in different contexts. In particular, we found optimal values of the relevant synaptic parameters (such as the neurotransmitter release probability or the characteristic facilitation time constant) for which the storage capacity can be maximal and similar to the one obtained with static synapses, that is, without activity-dependent processes. We conclude that depressing synapses with a certain level of facilitation allow recovering the good retrieval properties of networks with static synapses while maintaining the nonlinear characteristics of dynamic synapses, convenient for information processing and coding.
在这项工作中，我们通过分析和蒙特卡洛模拟，研究了几种与活动相关的突触过程（如短期突触易化和抑制）之间的竞争对神经网络最大记忆存储容量的影响。与突触抑制的情况相反，突触抑制会大幅降低网络存储和检索“静态”活动模式的能力，而突触易化在不同情况下会提高存储容量。特别是，我们发现了相关突触参数（如神经递质释放概率或特征易化时间常数）的最优值，在这些最优值下，存储容量可以达到最大，并且与静态突触（即没有与活动相关过程的突触）所获得的存储容量相似。我们得出结论，具有一定易化水平的抑制性突触能够恢复具有静态突触的网络的良好检索特性，同时保持动态突触的非线性特征，这便于信息处理和编码。
