Matsumoto Narihisa, Okada Masato
Graduate School of Science and Engineering, Saitama University, Saitama 338-8570, Japan.
Neural Netw. 2004 Sep;17(7):917-24. doi: 10.1016/j.neunet.2004.03.003.
Recent biological experimental findings have shown that synaptic plasticity depends on the relative timing of pre- and post-synaptic spikes and this is called spike-timing-dependent plasticity (STDP). Many authors have claimed that a precise balance between long-term potentiation (LTP) and long-term depression (LTD) of STDP is crucial in the storage of spatio-temporal patterns. Some authors have numerically investigated the impact of an imbalance between LTP and LTD on the network properties. However, the mathematical mechanism remains unknown. We analytically show that an associative memory network has the robust retrieval properties of spatio-temporal patterns, and these properties make the network less vulnerable to any deviation from a precise balance between LTP and LTD when the network contains a finite number of neurons.
最近的生物学实验结果表明,突触可塑性取决于突触前和突触后尖峰的相对时间,这被称为尖峰时间依赖性可塑性(STDP)。许多作者声称,STDP的长时程增强(LTP)和长时程抑制(LTD)之间的精确平衡对于时空模式的存储至关重要。一些作者已经通过数值研究了LTP和LTD之间的失衡对网络特性的影响。然而,其数学机制仍然未知。我们通过分析表明,一个联想记忆网络具有时空模式的强大检索特性,并且当网络包含有限数量的神经元时,这些特性使网络在面对LTP和LTD之间精确平衡的任何偏差时更不容易受到影响。