Stochastic Resonance Governs Memory Consolidation Accuracy in a Neural Network Model.
Stochastic Resonance Governs Memory Consolidation Accuracy in a Neural Network Model.
复制标题
随机共振控制神经网络模型中的记忆巩固准确性。
DOI：
10.1109/embc48229.2022.9871808
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Dorval,AlanD
中科院分区：
文献类型：
--
作者：
Caston,RoseM;Wilson,MatthewG;Comeaux,PhillipD;Dorval,AlanD
记忆的形成和回忆是一个容易出错的多步骤神经过程。我们提出了一个存储节点从有色TIC-TAC-TOE板接收输入的计算模型。当模型中引入不同的噪声水平时，我们会在合并和重新合并过程中报告内存错误。该模型基于Hebbian可塑性，并尝试存储板上X或O的颜色和位置。模拟神经元的记忆节点使用集成与点火模型，通过缩放突触权重来表示板信息的正确或不正确存储。我们探索了基线激发频率--我们认为它类似于存储记忆的噪音--是如何影响正确和错误记忆的创建的。我们发现，较高的发电率与较少的准确记忆有关。有趣的是，正确存储内存的理想噪声量是非零值。这种现象被称为随机共振，其中随机噪声增强了处理能力。我们还检查了我们的模型在出错之前可以重新激活记忆的次数。我们发现反应呈指数衰减，低的激发频率会产生更稳定的记忆。尽管我们的模型只包含两个记忆节点，但它为基于tic-tac-toe板的独特视觉输入检查记忆存储的合并和检索提供了基础。进一步的工作可能会包含不同的输入、更多的节点和更高的网络复杂性。临床相关性--该模型能够研究人类大脑皮层在信息处理过程中如何利用和利用噪音。
The formation and recollection of memories is a multi-step neural process subject to errors. We propose a computational model of memory nodes receiving input from a colored tic-tac-toe board. We report memory errors during consolidation and reconsolidation when different noise levels are introduced into the model. The model is based on Hebbian plasticity and attempts to store the color and position of an X or O from the board. Memory nodes simulating neurons use an integrate-and-fire model to represent the correct or incorrect storage of the board information by scaling synaptic weights. We explored how baseline firing rate, which we considered analogous to noise in storing memory, impacted the creation of correct and incorrect memories. We found that a higher firing rate was associated with fewer accurate memories. Interestingly, the ideal amount of noise for correct memory storage was nonzero. This phenomenon is known as stochastic resonance, wherein random noise enhances processing. We also examined how many times our model could reactivate a memory before making an error. We found an exponentially decaying response, with a low firing rate yielding more stable memories. Even though our model incorporates only two memory nodes, it provides a basis for examining the consolidation and retrieval of memory storage based on the unique visual input of a tic-tac-toe board. Further work may incorporate different inputs, more nodes, and increased network complexity. Clinical Relevance- This model enables investigation of how the human cortex may utilize and exploit noise during information processing.