Quantitative Investigation Of Memory Recall Performance Of A Computational Microcircuit Model Of The Hippocampus

Andreakos, Nikolas, Yue, Shigang and Cutsuridis, Vassilis (2021) Quantitative Investigation Of Memory Recall Performance Of A Computational Microcircuit Model Of The Hippocampus. Brain Informatics, 8 (9). ISSN 2198-4018

Full content URL: https://doi.org/10.1186/s40708-021-00131-7

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Quantitative Investigation Of Memory Recall Performance Of A Computational Microcircuit Model Of The Hippocampus
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Abstract

Memory, the process of encoding, storing, and maintaining information over time in order 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.

Keywords:Computer Model, Dendrite, Inhibition, Excitation, Bistratified cell, Medial Septum
Subjects:B Subjects allied to Medicine > B140 Neuroscience
Divisions:College of Science > School of Computer Science
ID Code:44717
Deposited On:30 Apr 2021 10:06

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