Memory, the process of encoding, storing, and maintaining information over time to influence future actions, is very important in our lives. Losing it, it comes with a great cost. Deciphering the biophysical mechanisms leading to recall improvement should thus be of outmost importance. In this study, we embarked on the quest to improve computationally the recall performance of a bio-inspired microcircuit model of the mammalian hippocampus, a brain region responsible for the storage and recall of short-term declarative memories. The model consisted of excitatory and inhibitory cells. The cell properties followed closely what is currently known from the experimental neurosciences. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. An excitatory input provided to excitatory cells context and timing information for retrieval of previously stored memory patterns. Inhibition to excitatory cells acted as a non-specific global threshold machine that removed spurious activity during recall. To systematically evaluate the model’s recall performance against stored patterns, pattern overlap, network size, and active cells per pattern, we selectively modulated feedforward and feedback excitatory and inhibitory pathways targeting specific excitatory and inhibitory cells. Of the different model variations (modulated pathways) tested, ‘model 1’ recall quality was excellent across all conditions. ‘Model 2’ recall was the worst. The number of ‘active cells’ representing a memory pattern was the determining factor in improving the model’s recall performance regardless of the number of stored patterns and overlap between them. As ‘active cells per pattern’ decreased, the model’s memory capacity increased, interference effects between stored patterns decreased, and recall quality improved.

中文翻译：

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海马计算微电路模型的记忆回忆性能的定量研究

内存是随时间推移对信息进行编码，存储和维护以影响未来行动的过程，在我们的生活中非常重要。丢失它会带来巨大的成本。因此，破解导致召回改善的生物物理机制应该是最重要的。在这项研究中，我们着手提高计算机海马生物启发的微电路模型的召回性能，该模型是负责短期陈述性记忆的存储和召回的大脑区域。该模型由兴奋性和抑制性细胞组成。细胞特性与实验神经科学目前所知道的密切相关。细胞的发射被定时到由两个表现出高度规律的爆发活动的不同神经元群体所加速的theta振荡，一个与波谷紧密耦合，另一个与theta峰值紧密耦合。提供给兴奋性细胞上下文和定时信息的兴奋性输入，用于检索先前存储的内存模式。对兴奋性细胞的抑制作用是一种非特异性的全局阈值机制，可消除召回过程中的虚假活动。为了系统地针对存储的模式，模式重叠，网络大小和每个模式的活动细胞来评估模型的召回性能，我们选择性地调节了针对特定兴奋性和抑制性细胞的前馈和反馈性兴奋性和抑制性途径。在测试的不同模型变体（调节路径）中，“模型1”的召回质量在所有条件下均非常出色。“模型2”召回事件最糟糕。代表存储模式的“活动单元”的数量是提高模型召回性能的决定因素，而与存储模式的数量及其之间的重叠无关。随着“每个模式的活动单元数”减少，模型的内存容量增加，存储的模式之间的干扰效应降低，召回质量提高。