Raffone Antonino, van Leeuwen Cees
Department of Psychology, University of Sunderland, Sunderland SR6 0DD, United Kingdom.
Chaos. 2003 Sep;13(3):1090-104. doi: 10.1063/1.1602211.
Associative memory dynamics in neural networks are generally based on attractors. Retrieval based on fixed-point attractors works if only one memory pattern is retrieved at the time, but cannot enable the simultaneous retrieval of more than one pattern. Stable phase-locking of periodic oscillations or limit cycle attractors leads to incorrect feature bindings if the simultaneously retrieved patterns share some of their features. We investigate retrieval dynamics of multiple active patterns in a network of chaotic model neurons. Several memory patterns are kept simultaneously active and separated from each other by a dynamic itinerant synchronization between neurons. Neurons representing shared features alternate their synchronization between patterns, thus multiplexing their binding relationships. Our model includes a mechanism for self-organized readout or decoding of memory pattern coherence in terms of short-term potentiation and short-term depression of synaptic weights.
神经网络中的联想记忆动力学通常基于吸引子。基于定点吸引子的检索在每次仅检索一个记忆模式时有效，但无法同时检索多个模式。如果同时检索的模式共享某些特征，周期性振荡或极限环吸引子的稳定锁相会导致错误的特征绑定。我们研究了混沌模型神经元网络中多个活跃模式的检索动力学。通过神经元之间的动态巡回同步，几个记忆模式同时保持活跃并相互分离。代表共享特征的神经元在模式之间交替其同步，从而复用它们的绑定关系。我们的模型包括一种机制，用于根据突触权重的短期增强和短期抑制对记忆模式相干性进行自组织读出或解码。
