Department of Mathematics, Imperial College London, London, United Kingdom.
PLoS One. 2013;8(1):e50276. doi: 10.1371/journal.pone.0050276. Epub 2013 Jan 22.
Short-term memory in the brain cannot in general be explained the way long-term memory can--as a gradual modification of synaptic weights--since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.
大脑中的短期记忆通常不能像长期记忆那样——通过逐渐改变突触权重来解释,因为短期记忆发生得太快了。然而,基于某种形式的细胞双稳态的理论,似乎无法解释嘈杂神经元能够以稳健的方式集体存储信息的事实。我们展示了一个足够聚集的简单模型神经元网络如何能够立即被诱导进入能够在短时间(几秒钟)内保留信息的亚稳态。该机制对不同的网络拓扑结构和神经模型都具有鲁棒性。对于大脑来说,这可能是一种可行的感觉和/或短期记忆手段,而无需进行突触学习。神经生物学和心理学所描述的相关现象,如突触输入的局部同步和遗忘雪崩的幂律统计,自然从这个机制中出现,我们建议在更具生物性的环境中进行可能的实验来测试其可行性。