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Quantitative investigation of memory recall performance of a computational microcircuit model of the hippocampus

机译:数量研究海马计算微电路模型的记忆召回性能

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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.
机译:内存,编码,存储和维护信息随着时间的推移来影响未来的动作,在我们的生活中非常重要。失去了它,它具有很大的成本。因此,解读导致改善改善的生物物理机制应该是最重要的。在这项研究中,我们开始寻求改善哺乳动物海马的生物启发微电路模型的召回性能,该召回负责储存和回忆短期声明回忆的大脑区域。该模型包括兴奋性和抑制细胞。细胞属性紧随其目前从实验神经科学中已知的。将细胞'烧制定时到由具有高度常规爆破活动的两种不同的神经元群定时,其紧密地耦合到槽,另一个到θ的峰值。提供给兴奋单元上下文和定时信息的兴奋输入,用于检索先前存储的内存模式。对兴奋性细胞的抑制作用为非特定的全局阈值机器,在召回期间去除杂散的活动。为了系统地评估模型的召回性能,针对储存的模式,图案重叠,网络尺寸和有源细胞每种图案,我们选择性地调节馈电和反馈兴奋性和抑制靶向特异性兴奋性和抑制细胞的抑制途径。在测试的不同模型变化(调制途径)的情况下,“1”召回质量在所有条件下都是优异的。 '模型2'召回是最糟糕的。表示存储器模式的“活动单元”的数量是在改善模型的召回性能方面,无论存储模式的数量和它们之间的重叠重叠,确定因素。作为“每种图案的活动细胞”减少,模型的内存容量增加,存储模式之间的干扰效应减小,并且调用质量得到改善。

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