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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\u2019 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\u2019s 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, \u2018model 1\u2019 recall quality was excellent across all conditions. \u2018Model 2\u2019 recall was the worst. The number of \u2018active cells\u2019 representing a memory pattern was the determining factor in improving the model\u2019s recall performance regardless of the number of stored patterns and overlap between them. As \u2018active cells per pattern\u2019 decreased, the model\u2019s memory capacity increased, interference effects between stored patterns decreased, and recall quality improved.<\/jats:p>","DOI":"10.1186\/s40708-021-00131-7","type":"journal-article","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T11:03:36Z","timestamp":1620471816000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Quantitative investigation of memory recall performance of a computational microcircuit model of the hippocampus"],"prefix":"10.1186","volume":"8","author":[{"given":"Nikolaos","family":"Andreakos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigang","family":"Yue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9005-0260","authenticated-orcid":false,"given":"Vassilis","family":"Cutsuridis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,8]]},"reference":[{"issue":"2","key":"131_CR1","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1038\/nrn726","volume":"3","author":"S Corkin","year":"2002","unstructured":"Corkin S (2002) What\u2019s new with the amnesic patient HM? 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