Neurobiology and Anatomy, University of Texas Medical School at Houston, Houston, TX, United States.
Applied Physics, Rice University, Houston, TX, United States.
Elife. 2021 Mar 18;10:e63751. doi: 10.7554/eLife.63751.
Multiple brain regions are able to learn and express temporal sequences, and this functionality is an essential component of learning and memory. We propose a substrate for such representations via a network model that learns and recalls discrete sequences of variable order and duration. The model consists of a network of spiking neurons placed in a modular microcolumn based architecture. Learning is performed via a biophysically realistic learning rule that depends on synaptic 'eligibility traces'. Before training, the network contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences. This model provides a possible framework for biologically plausible sequence learning and memory, in agreement with recent experimental results.
多个大脑区域能够学习和表达时间序列，而这种功能是学习和记忆的重要组成部分。我们通过一个网络模型提出了这样的表示的基础，该模型可以学习和回忆具有不同顺序和持续时间的离散序列。该模型由一个基于模块化微柱的网络组成，其中包含放置的尖峰神经元。学习是通过一种依赖于突触“资格迹”的生物物理上逼真的学习规则来完成的。在训练之前，网络中没有任何特定序列的记忆。在训练之后，只需呈现该序列的第一个元素，网络就足以回忆起整个序列的学习表示。该模型的扩展版本还展示了成功学习和回忆非马尔可夫序列的能力。该模型为具有生物学意义的序列学习和记忆提供了一个可能的框架，与最近的实验结果一致。
